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Consideration of triple bottom line objectives for sustainability in the optimization of vehicle routing and loading operations: a systematic literature review

Abstract

The current global interest in improving the use of ever-scarcer natural resources calls for the re-alignment of supply chain operations to include not only economic factors, but environmental and social factors as well. Two of the most important supply chain activities that logistics managers have to deal with are the planning and improvement of the packing and distribution of products. Although the optimization of these two activities has been thoroughly studied by means of Vehicle Routing Problems and Packing Problems, their analysis is often done separately and, in most cases, they consider only the economic decisions. Independent optimization of these two operations may overlook the structural dependencies between them, resulting in impractical solutions; while the consideration of only the economic criteria can overlook the environmental and social impacts of distribution activities, in the scope of sustainable supply chains. With the objective of improving distribution logistics, the aim of this review is to provide an overview of recent optimization developments for integrating packing and routing problems, in order to propose a simple classification scheme for re-aligning the optimization criteria and operational constraints, taking into consideration the issues of sustainability.

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Fig. 1

Abbreviations

ACO:

Ant Colony Optimization

APH:

Author Proposed Heuristic

B&B:

Branch and Bound

B&C:

Branch and Cut

B&P:

Branch and Price

BCA:

Bee Colony Algorithm

BPP:

Bin Packing Problem

BS:

Beam Search

CCP:

Chance Constrained Programming

CG:

Column Generation

CLP:

Container loading problem

CP:

Cutting Problem

DP:

Dynamic Programming

EA:

Evolutionary Algorithm

ELS:

Evolutionary Local Search

FFA:

Firefly Algorithm

GA:

Genetic Algorithm

GDS:

Goal Driven Search

GHG:

Green House Gas

GLS:

Guided Local Search

GRASP:

Greedy Randomized Adaptive Search Procedure

GSCM:

Green Supply Chain Management

GVRP:

Green VRP

HE:

Heterogeneous items

HO:

Homogeneous items

ILS:

Iterated Local Search

IRP:

Inventory Routing Problem

KP:

Knapsack Problem

LIFO:

Last In–First Out

LNS:

Large Neighborhood Search

LP:

Linear Programming

MA:

Memetic Algorithm

MDVRP:

Multi-Depot Vehicle Routing Problem

MIP:

Mixed Integer Programming

MPNS:

Multiple Phase Neighborhood Search

NLP:

Non-Linear Programming

PLP:

Pallet Loading Problem

PP:

Packing Problem

PR:

Path Relinking

PSO:

Particle Swarm Optimization

SA:

Simulated Annealing

SC:

Supply chain

SCM:

Supply Chain Management

SP:

Stochastic Programming

SPP:

Strip Packing Problem

SS:

Scattered Search

SSCM:

Sustainable Supply Chain Management

TBL:

Triple-Bottom-Line

TRS:

Tree Search

TS:

Tabu Search

TSP:

Traveling Salesman Problem

VNS:

Variable Neighborhood Search

VRP:

Vehicle Routing Problem

VRPLC:

Vehicle Routing Problem with Loading Constraints

References

  1. Ahi, P., & Searcy, C. (2013). A comparative literature analysis of definitions for green and sustainable supply chain management. Journal of Cleaner Production, 52, 329–341. https://doi.org/10.1016/j.jclepro.2013.02.018.

    Google Scholar 

  2. Ahi, P., & Searcy, C. (2015). Assessing sustainability in the supply chain: A triple bottom line approach. Applied Mathematical Modelling, 39(10), 2882–2896. https://doi.org/10.1016/j.apm.2014.10.055.

    Google Scholar 

  3. Ahn, S., Yoon, K., & Park, J. (2015). A best-first branch and bound algorithm for the pallet-loading problem. International Journal of Production Research, 53(3), 835–849.

    Google Scholar 

  4. Alinaghian, M., & Naderipour, M. (2016). A novel comprehensive macroscopic model for time-dependent vehicle routing problem with multi-alternative graph to reduce fuel consumption: A case study. Computers and Industrial Engineering, 99, 210–222. https://doi.org/10.1016/j.cie.2016.07.029.

    Google Scholar 

  5. Allen, S. D., Burke, E. K., & Kendall, G. (2011). A hybrid placement strategy for the three-dimensional strip packing problem. European Journal of Operational Research, 209(3), 219–227. https://doi.org/10.1016/j.ejor.2010.09.023.

    Google Scholar 

  6. Alonso, M. T., Alvarez-Valdes, R., Iori, M., Parreno, F., & Tamarit, J. M. (2017). Mathematical models for multicontainer loading problems. Omega-International Journal of Management Science, 66, 106–117. https://doi.org/10.1016/j.omega.2016.02.002.

    Google Scholar 

  7. Alozn, A. E., Al Naimi, M. S., & Asad, O. Y. (2014). Single forward and reverse supply chain. In P. Golinska (Ed.), Logistics operations, supply chain management and sustainability (pp. 229–239). Cham: Springer. https://doi.org/10.1007/978-3-319-07287-6_15

  8. Andersson, H., Hoff, A., Christiansen, M., Hasle, G., & Løkketangen, A. (2010). Industrial aspects and literature survey: Combined inventory management and routing. Computers & Operations Research, 37(9), 1515–1536. https://doi.org/10.1016/j.cor.2009.11.009.

    Google Scholar 

  9. Araya, I., Guerrero, K., & Nuñez, E. (2017). VCS: A new heuristic function for selecting boxes in the single container loading problem. Computers & Operations Research, 82, 27–35. https://doi.org/10.1016/j.cor.2017.01.002.

    Google Scholar 

  10. Araya, I., & Riff, M.-C. (2014). A beam search approach to the container loading problem. Computers & Operations Research, 43, 100–107. https://doi.org/10.1016/j.cor.2013.09.003.

    Google Scholar 

  11. Ashby, A., Leat, M., & Hudson-Smith, M. (2012). Making connections: a review of supply chain management and sustainability literature. Supply Chain Management: An International Journal, 17(5), 497–516. https://doi.org/10.1108/13598541211258573.

    Google Scholar 

  12. Baker, B. M., & Carreto, C. A. C. (2003). A visual interactive approach to vehicle routing. Computers & Operations Research, 30(3), 321–337. https://doi.org/10.1016/S0305-0548(01)00099-5.

    Google Scholar 

  13. Baldi, M. M., Crainic, T. G., Perboli, G., & Tadei, R. (2012). The generalized bin packing problem. Transportation Research Part E: Logistics and Transportation Review, 48(6), 1205–1220. https://doi.org/10.1016/j.tre.2012.06.005.

    Google Scholar 

  14. Baldi, M. M., Crainic, T. G., Perboli, G., & Tadei, R. (2014). Branch-and-price and beam search algorithms for the Variable Cost and Size Bin Packing Problem with optional items. Annals of Operations Research, 222(1), 125–141.

    Google Scholar 

  15. Baldi, M. M., Perboli, G., & Tadei, R. (2012). The three-dimensional knapsack problem with balancing constraints. Applied Mathematics and Computation, 218(19), 9802–9818. https://doi.org/10.1016/j.amc.2012.03.052.

    Google Scholar 

  16. Baños, R., Ortega, J., Gil, C., Fernández, A., & de Toro, F. (2013). A Simulated Annealing-based parallel multi-objective approach to vehicle routing problems with time windows. Expert Systems with Applications, 40(5), 1696–1707. https://doi.org/10.1016/j.eswa.2012.09.012.

    Google Scholar 

  17. Batista-Galván, M., Riera-Ledesma, J., & Salazar-González, J. J. (2013). The traveling purchaser problem, with multiple stacks and deliveries: A branch-and-cut approach. Computers & Operations Research, 40(8), 2103–2115. https://doi.org/10.1016/j.cor.2013.02.007.

    Google Scholar 

  18. Battarra, M., Erdogan, G., Laporte, G., & Vigo, D. (2010). The traveling salesman problem with pickups, deliveries, and handling costs. Transportation Science, 44(3), 383–399.

    Google Scholar 

  19. Bektaş, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45(8), 1232–1250. https://doi.org/10.1016/j.trb.2011.02.004.

    Google Scholar 

  20. Belloso, J., Juan, A. A., & Faulin, J. (2017). An iterative biased-randomized heuristic for the fleet size and mix vehicle-routing problem with backhauls. International Transactions in Operational Research, 1–13. https://doi.org/10.1111/itor.12379.

  21. Belloso, J., Juan, A. A., Martinez, E., & Faulin, J. (2017). A biased-randomized metaheuristic for the vehicle routing problem with clustered and mixed backhauls. Networks, 69(3), 241–255. https://doi.org/10.1002/net.

    Google Scholar 

  22. Bhinge, R., Moser, R., Moser, E., Lanza, G., & Dornfeld, D. (2015). Sustainability optimization for global supply chain decision-making. Procedia CIRP, 26, 323–328. https://doi.org/10.1016/j.procir.2014.07.105.

    Google Scholar 

  23. Bin, W., Hong, C., & Zhi-yong, C. (2013). Artificial bee colony algorithm for two-dimensional loading capacitated vehicle routing problem. In 2013 International Conference on Management Science and Engineering (Icmse) (pp. 406–412).

  24. Birgin, E. G., Martinez, J. M., Mascarenhas, W. F., & Ronconi, D. P. (2006). Method of sentinels for packing items within arbitrary convex regions. Journal of the Operational Research Society, 57(6), 735–746.

    Google Scholar 

  25. Birgin, E. G., Martinez, J. M., & Ronconi, D. P. (2005). Optimizing the packing of cylinders into a rectangular container: A nonlinear approach. European Journal of Operational Research, 160(1), 19–33.

    Google Scholar 

  26. Bischoff, E. E. (2006). Three-dimensional packing of items with limited load bearing strength. European Journal of Operational Research, 168(3), 952–966. https://doi.org/10.1016/j.ejor.2004.04.037.

    Google Scholar 

  27. Bischoff, E. E., & Ratcliff, M. S. W. (1995). Issues in the development of approaches to container loading. Omega, 23(4), 377–390. https://doi.org/10.1016/0305-0483(95)00015-G.

    Google Scholar 

  28. Black, W. R. (1996). Sustainable transportation: a US perspective. Journal of Transport Geography, 4(3), 151–159. https://doi.org/10.1016/0966-6923(96)00020-8.

    Google Scholar 

  29. Bortfeldt, A. (2012). A hybrid algorithm for the capacitated vehicle routing problem with three-dimensional loading constraints. Computers & Operations Research, 39(9), 2248–2257. https://doi.org/10.1016/j.cor.2011.11.008.

    Google Scholar 

  30. Bortfeldt, A., & Gehring, H. (2001). A hybrid genetic algorithm for the container loading problem. European Journal of Operational Research, 131(1), 143–161. https://doi.org/10.1016/S0377-2217(00)00055-2.

    Google Scholar 

  31. Bortfeldt, A., Hahn, T., Männel, D., & Mönch, L. (2015). Hybrid algorithms for the vehicle routing problem with clustered backhauls and 3D loading constraints. European Journal of Operational Research, 243(1), 82–96. https://doi.org/10.1016/j.ejor.2014.12.001.

    Google Scholar 

  32. Bortfeldt, A., & Homberger, J. (2013). Packing first, routing second—a heuristic for the vehicle routing and loading problem. Computers & Operations Research, 40(3), 873–885. https://doi.org/10.1016/j.cor.2012.09.005.

    Google Scholar 

  33. Bortfeldt, A., & Jungmann, S. (2012). A tree search algorithm for solving the multi-dimensional strip packing problem with guillotine cutting constraint. Annals of Operations Research, 196(1), 53–71. https://doi.org/10.1007/s10479-012-1084-7.

    Google Scholar 

  34. Bortfeldt, A., & Mack, D. (2007). A heuristic for the three-dimensional strip packing problem. European Journal of Operational Research, 183(3), 1267–1279. https://doi.org/10.1016/j.ejor.2005.07.031.

    Google Scholar 

  35. Bortfeldt, A., & Wäscher, G. (2013). Constraints in container loading—A state-of-the-art review. European Journal of Operational Research, 229(1), 1–20. https://doi.org/10.1016/j.ejor.2012.12.006.

    Google Scholar 

  36. Braekers, K., Caris, A., & Janssens, G. K. (2013). Integrated planning of loaded and empty container movements. OR Spectrum, 35(2), 457–478.

    Google Scholar 

  37. Braekers, K., Caris, A., & Janssens, G. K. (2014). Bi-objective optimization of drayage operations in the service area of intermodal terminals. Transportation Research Part E: Logistics and Transportation Review, 65, 50–69. https://doi.org/10.1016/j.tre.2013.12.012.

    Google Scholar 

  38. Brunetta, L., & Grégoire, P. (2005). A general purpose algorithm for three-dimensional packing. Informs Journal on Computing, 17(3), 328–338.

    Google Scholar 

  39. Caceres-Cruz, J., Arias, P., Guimarans, D., Riera, D., & Juan, A. A. (2014). Rich vehicle routing problem. ACM Computing Surveys, 47(2), 1–28. https://doi.org/10.1145/2666003.

    Google Scholar 

  40. Carrabs, F., Cerulli, R., & Sciomachen, A. (2014). Environmental Sustainable Fleet Planning in B2C e-Commerce Urban Distribution Networks. In Smart city: How to create public and economic value with high technology in urban space (pp. 183–192).

  41. Carrabs, F., Cerulli, R., & Speranza, M. G. (2013). A branch-and-bound algorithm for the double travelling salesman problem with two stacks. Networks, 61(1), 58–75.

    Google Scholar 

  42. Ceschia, S., & Schaerf, A. (2013). Local search for a multi-drop multi-container loading problem. Journal of Heuristics, 19(2), 275–294.

    Google Scholar 

  43. Ceschia, S., Schaerf, A., Stützle, T., & Stuzle, T. (2013). Local search techniques for a routing-packing problem. Computers & Industrial Engineering, 66(4), 1138–1149. https://doi.org/10.1016/j.cie.2013.07.025.

    Google Scholar 

  44. Ceselli, A., Righini, G., & Salani, M. (2009). A column generation algorithm for a rich vehicle-routing problem. Transportation Science, 43(1), 56–69.

    Google Scholar 

  45. Che, C. H., Huang, W., Lim, A., & Zhu, W. (2011). The multiple container loading cost minimization problem. European Journal of Operational Research, 214(3), 501–511. https://doi.org/10.1016/j.ejor.2011.04.017.

    Google Scholar 

  46. Cheang, B., Gao, X., Lim, A., Qin, H., & Zhu, W. (2012). Multiple pickup and delivery traveling salesman problem with last-in-first-out loading and distance constraints. European Journal of Operational Research, 223(1), 60–75. https://doi.org/10.1016/j.ejor.2012.06.019.

    Google Scholar 

  47. Cherkesly, M., Desaulniers, G., & Laporte, G. (2015). A population-based metaheuristic for the pickup and delivery problem with time windows and LIFO loading. Computers & Operations Research, 62, 23–35. https://doi.org/10.1016/j.cor.2015.04.002.

    Google Scholar 

  48. Cinar, D., Gakis, K., & Pardalos, P. M. (2016). A 2-phase constructive algorithm for cumulative vehicle routing problems with limited duration. Expert Systems with Applications, 56, 48–58. https://doi.org/10.1016/j.eswa.2016.02.046.

    Google Scholar 

  49. Clarivate Analytics. (2017). Web of science fact book. Clarivate analytics. Retrieved from http://images.info.science.thomsonreuters.biz/Web/ThomsonReutersScience/%7Bd6b7faae-3cc2-4186-8985-a6ecc8cce1ee%7D_Crv_WoS_Upsell_Factbook_A4_FA_LR_edits.pdf

  50. Cochran, J. K., & Ramanujam, B. (2006). Carrier-mode logistics optimization of inbound supply chains for electronics manufacturing. International Journal of Production Economics, 103(2), 826–840. https://doi.org/10.1016/j.ijpe.2006.01.005.

    Google Scholar 

  51. Cordeau, J.-F., Dell’Amico, M., Falavigna, S., & Iori, M. (2015). A rolling horizon algorithm for auto-carrier transportation. Transportation Research Part B-Methodological, 76, 68–80. https://doi.org/10.1016/j.trb.2015.02.009.

    Google Scholar 

  52. Côté, J.-F., Gendreau, M., & Potvin, J.-Y. (2012). Large neighborhood search for the pickup and delivery traveling salesman problem with multiple stacks. Networks, 60(1), 19–30. https://doi.org/10.1002/net.20448.

    Google Scholar 

  53. Côté, J.-F., Guastaroba, G., & Speranza, M. G. (2017). The value of integrating loading and routing. European Journal of Operational Research, 257(1), 89–105. https://doi.org/10.1016/j.ejor.2016.06.072.

    Google Scholar 

  54. Currie, R. H., & Salhi, S. (2003). Exact and heuristic methods for a full-load, multi-terminal, vehicle scheduling problem with backhauling and time windows. Journal of the Operational Research Society, 54(4), 390–400.

    Google Scholar 

  55. da Graça Costa, M., & Captivo, M. E. (2016). Weight distribution in container loading: a case study. International Transactions in Operational Research, 23(1–2), 239–263. https://doi.org/10.1111/itor.12145.

    Google Scholar 

  56. da Silveira, J. L. M., Xavier, E. C., & Miyazawa, F. K. (2013). A note on a two dimensional knapsack problem with unloading constraints. Rairo-Theoretical Informatics and Applications, 47(4), 315–324.

    Google Scholar 

  57. da Silveira, J. L. M., Xavier, E. C., & Miyazawa, F. K. (2014). Two-dimensional strip packing with unloading constraints. Discrete Applied Mathematics, 164, 512–521. https://doi.org/10.1016/j.dam.2013.08.019.

    Google Scholar 

  58. Dahmani, N., Clautiaux, F., Krichen, S., & Talbi, E.-G. (2014). Self-adaptive metaheuristics for solving a multi-objective 2-dimensional vector packing problem. Applied Soft Computing, 16, 124–136. https://doi.org/10.1016/j.asoc.2013.12.006.

    Google Scholar 

  59. Davies, A. P., & Bischoff, E. E. (1999). Weight distribution considerations in container loading. European Journal of Operational Research, 114(3), 509–527. https://doi.org/10.1016/S0377-2217(98)00139-8.

    Google Scholar 

  60. de Almeida, A., & Figueiredo, M. B. (2010). A particular approach for the Three-dimensional Packing Problem with additional constraints. Computers & Operations Research, 37(11), 1968–1976. https://doi.org/10.1016/j.cor.2010.01.010.

    Google Scholar 

  61. de Araújo, O. C. B., & Armentano, V. A. (2007). A multi-start random constructive heuristic for the container loading problem. Pesquisa Operacional, 27(2), 311–331.

    Google Scholar 

  62. de Queiroz, T. A., Hokama, P. H. D. B., Schouery, R. C. S., & Miyazawa, F. K. (2017). Two-dimensional Disjunctively Constrained Knapsack Problem: Heuristic and exact approaches. Computers & Industrial Engineering, 105, 313–328. https://doi.org/10.1016/j.cie.2017.01.015.

    Google Scholar 

  63. de Queiroz, T. A., & Miyazawa, F. K. (2013). Two-dimensional strip packing problem with load balancing, load bearing and multi-drop constraints. International Journal of Production Economics, 145(2), 511–530. https://doi.org/10.1016/j.ijpe.2013.04.032.

    Google Scholar 

  64. Demir, E., Bektaş, T., & Laporte, G. (2014). A review of recent research on green road freight transportation. European Journal of Operational Research, 237(3), 775–793. https://doi.org/10.1016/j.ejor.2013.12.033.

    Google Scholar 

  65. Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan & A. Bryman (Eds.), The SAGE handbook of organizational research methods (pp. 671–689). London: SAGE Publications Ltd.

    Google Scholar 

  66. Dereli, T., & Das, G. S. (2010). A hybrid simulated annealing algorithm for solving multi-objective container-loading problems. Applied Artificial Intelligence, 24(5), 463–486.

    Google Scholar 

  67. Dereli, T., & Das, G. S. (2011). A hybrid “bee(s) algorithm” for solving container loading problems. Applied Soft Computing, 11(2), 2854–2862. https://doi.org/10.1016/j.asoc.2010.11.017.

    Google Scholar 

  68. Derigs, U., & Pullmann, M. (2014). Solving multitrip vehicle routing under order incompatibilities: A VRP arising in supply chain management. Networks, 64(1), 29–39.

    Google Scholar 

  69. Doerner, K. F., Fuellerer, G., Hartl, R. F., Gronalt, M., & Iori, M. (2007). Metaheuristics for the vehicle routing problem with loading constraints. Networks, 49(4), 294–307. https://doi.org/10.1002/net.20179.

    Google Scholar 

  70. Domingo, B. M., Ponnambalam, S. G., & Kanagaraj, G. (2013). A Differential Evolution Based Algorithm for Single Container Loading Problem. In Proceedings of the 2013 Ieee Symposium on Differential Evolution (Sde).

  71. Dominguez, O., Guimarans, D., Juan, A. A., & de la Nuez, I. (2016). A biased-randomised large neighbourhood search for the two-dimensional vehicle routing problem with Backhauls. European Journal of Operational Research, 255(2), 442–462. https://doi.org/10.1016/j.ejor.2016.05.002.

    Google Scholar 

  72. Dominguez, O., Juan, A. A., Barrios, B., Faulin, J., & Agustin, A. (2016). Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet. Annals of Operations Research, 236(2), 383–404. https://doi.org/10.1007/s10479-014-1551-4.

    Google Scholar 

  73. Dominguez, O., Juan, A. A., de la Nuez, I., & Ouelhadj, D. (2016). An ILS-biased randomization algorithm for the two-dimensional loading HFVRP with sequential loading and items rotation. Journal of the Operational Research Society, 67, 37–53. https://doi.org/10.1057/jors.2015.48.

    Google Scholar 

  74. Dominguez, O., Juan, A. A., & Faulin, J. (2014). A biased-randomized algorithm for the two-dimensional vehicle routing problem with and without item rotations. International Transactions in Operational Research, 21(3), 375–398. https://doi.org/10.1111/itor.12070.

    Google Scholar 

  75. Duhamel, C., Lacomme, P., Quilliot, A., & Toussaint, H. (2011). A multi-start evolutionary local search for the two-dimensional loading capacitated vehicle routing problem. Computers & Operations Research, 38(3), 617–640. https://doi.org/10.1016/j.cor.2010.08.017.

    Google Scholar 

  76. Egeblad, J., Garavelli, C., Lisi, S., & Pisinger, D. (2010). Heuristics for container loading of furniture. European Journal of Operational Research, 200(3), 881–892. https://doi.org/10.1016/j.ejor.2009.01.048.

    Google Scholar 

  77. Egeblad, J., & Pisinger, D. (2009). Heuristic approaches for the two- and three-dimensional knapsack packing problem. Computers & Operations Research, 36(4), 1026–1049. https://doi.org/10.1016/j.cor.2007.12.004.

    Google Scholar 

  78. Eglese, R., & Bektaş, T. (2014). Green vehicle routing. In P. Toth & D. Vigo (Eds.), Vehicle routing: Problems, methods, and applications (2nd ed., pp. 437–458). MOS-SIAM Series on Optimization. https://doi.org/10.1007/978-3-319-17175-3

  79. Ehmke, J. F., Campbell, A. M., & Thomas, B. W. (2016). Vehicle routing to minimize time-dependent emissions in urban areas. European Journal of Operational Research, 251(2), 478–494. https://doi.org/10.1016/j.ejor.2015.11.034.

    Google Scholar 

  80. Eley, M. (2002). Solving container loading problems by block arrangement. European Journal of Operational Research, 141(2), 393–409. https://doi.org/10.1016/S0377-2217(02)00133-9.

    Google Scholar 

  81. Eley, M. (2003). A bottleneck assignment approach to the multiple container loading problem. OR Spectrum, 25(1), 45–60.

    Google Scholar 

  82. Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: an optimization-oriented review. Omega, 54, 11–32. https://doi.org/10.1016/j.omega.2015.01.006.

    Google Scholar 

  83. European-Commission. (2006). Road transport policy: Open roads across Europe. Brussels. Retrieved from http://ec.europa.eu/transport/road/doc/road_transport_policy_en.pdf

  84. Fanslau, T., & Bortfeldt, A. (2010). A tree search algorithm for solving the container loading problem. Informs Journal on Computing, 22(2), 222–235.

    Google Scholar 

  85. Faulin, J., Juan, A., Lera, F., & Grasman, S. (2011). Solving the capacitated vehicle routing problem with environmental criteria based on real estimations in road transportation: A case study. Procedia—Social and Behavioral Sciences, 20, 323–334. https://doi.org/10.1016/j.sbspro.2011.08.038.

    Google Scholar 

  86. Felipe, A., Ortuno, M. T., & Tirado, G. (2009a). New neighborhood structures for the Double Traveling Salesman Problem with Multiple Stacks. Top, 17(1), 190–213. https://doi.org/10.1007/s11750-009-0080-9.

    Google Scholar 

  87. Felipe, A., Ortuno, M. T., & Tirado, G. (2009b). The double traveling salesman problem with multiple stacks: A variable neighborhood search approach. Computers & Operations Research, 36(11), 2983–2993. https://doi.org/10.1016/j.cor.2009.01.015.

  88. Fernández, A., Gil, C., Baños, R., & Montoya, M. G. (2013). A parallel multi-objective algorithm for two-dimensional bin packing with rotations and load balancing. Expert Systems with Applications, 40(13), 5169–5180. https://doi.org/10.1016/j.eswa.2013.03.015.

    Google Scholar 

  89. Fréville, A. (2004). The multidimensional 0–1 knapsack problem: An overview. European Journal of Operational Research, 155(1), 1–21. https://doi.org/10.1016/S0377-2217(03)00274-1.

    Google Scholar 

  90. Fuellerer, G., Doerner, K. F., Hartl, R. F., & Iori, M. (2009). Ant colony optimization for the two-dimensional loading vehicle routing problem. Computers & Operations Research, 36(3), 655–673. https://doi.org/10.1016/j.cor.2007.10.021.

    Google Scholar 

  91. Fuellerer, G., Doerner, K. F., Hartl, R. F., & Iori, M. (2010). Metaheuristics for vehicle routing problems with three-dimensional loading constraints. European Journal of Operational Research, 201(3), 751–759.

    Google Scholar 

  92. Gendreau, M., Iori, M., Laporte, G., & Martello, S. (2006). A tabu search algorithm for a routing and container loading problem. Transportation Science, 40(3), 342–350. https://doi.org/10.1287/trsc.1050.0145.

    Google Scholar 

  93. Gendreau, M., Iori, M., Laporte, G., & Martello, S. (2008). A Tabu search heuristic for the vehicle routing problem with two-dimensional loading constraints. Networks, 51(1), 4–18.

    Google Scholar 

  94. Gendreau, M., Potvin, J.-Y., Bräysy, O., Hasle, G., & Løkketangen, A. (2008). Metaheuristics for the vehicle routing problem and its extensions: A categorized bibliography. In B. L. Golden, S. Raghavan, & E. A. Wasil (Eds.), The vehicle routing problem: Latest advances and new challenges (pp. 143–170). New York: Springer. https://doi.org/10.1007/978-0-387-77778-8_7

  95. Goeke, D., & Schneider, M. (2015). Routing a mixed fleet of electric and conventional vehicles. European Journal of Operational Research, 245(1), 81–99. https://doi.org/10.1016/j.ejor.2015.01.049.

    Google Scholar 

  96. Gonçalves, R. F., & de Queiroz, T. A. (2014). The knapsack problem with three practical constraints. In 2014 International conference on computational science (Vol. 29, pp. 2192–2200). https://doi.org/10.1016/j.procs.2014.05.204.

  97. Gonzalez-Barbosa, J. J., Delgado-Orta, J. F., Cruz-Reyes, L., Fraire-Huacuja, H. J., & Ramirez-Saldivar, A. (2010). Comparative analysis of hybrid techniques for an ant colony system algorithm applied to solve a real-world transportation problem. Soft Computing for Recognition Based on Biometrics, 312, 365–385.

    Google Scholar 

  98. Gonzalez, Y., Miranda, G., & Leon, C. (2016). Multi-objective multi-level filling evolutionary algorithm for the 3D cutting stock problem. In Knowledge-based and intelligent information & engineering systems: Proceedings of the 20th international conference Kes-2016 (Vol. 96, pp. 364–373). https://doi.org/10.1016/j.procs.2016.08.148.

  99. Guimarans, D., Dominguez, O., Juan, A. A., & Martinez, E. (2016). A multi-start simheuristic for the stochastic two-dimensional vehicle routing problem. In 2016 Winter simulation conference (Wsc) (pp. 2326–2334).

  100. Gutierrez-Jarpa, G., Marianov, V., & Obreque, C. (2009). A single vehicle routing problem with fixed delivery and optional collections. IIE Transactions, 41(12), 1067–1079.

    Google Scholar 

  101. Halvorsen-Weare, E. E., & Savelsbergh, M. W. P. (2016). The bi-objective mixed capacitated general routing problem with different route balance criteria. European Journal of Operational Research, 251(2), 451–465. https://doi.org/10.1016/j.ejor.2015.11.024.

    Google Scholar 

  102. He, K., & Huang, W. (2010a). A caving degree based flake arrangement approach for the container loading problem. Computers & Industrial Engineering, 59(2), 344–351. https://doi.org/10.1016/j.cie.2010.05.007.

  103. He, K., & Huang, W. (2010b). A quasi-human algorithm for solving the three-dimensional rectangular packing problem. Science China-Information Sciences, 53(12), 2389–2398. https://doi.org/10.1007/s11432-010-4112-8.

    Google Scholar 

  104. He, K., & Huang, W. (2011). An efficient placement heuristic for three-dimensional rectangular packing. Computers & Operations Research, 38(1), 227–233. https://doi.org/10.1016/j.cor.2010.04.015.

    Google Scholar 

  105. Hokama, P., Miyazawa, F. K., & Xavier, E. C. (2016). A branch-and-cut approach for the vehicle routing problem with loading constraints. Expert Systems with Applications, 47, 1–13. https://doi.org/10.1016/j.eswa.2015.10.013.

    Google Scholar 

  106. Hsu, C.-I., Hung, S.-F., & Li, H.-C. (2007). Vehicle routing problem with time-windows for perishable food delivery. Journal of Food Engineering, 80(2), 465–475. https://doi.org/10.1016/j.jfoodeng.2006.05.029.

    Google Scholar 

  107. Hu, N.-Z., Li, H.-L., & Tsai, J.-F. (2012). Solving packing problems by a distributed global optimization algorithm. Mathematical Problems in Engineering, 931092.

  108. Hu, Z.-H., Zhao, Y., Tao, S., & Sheng, Z.-H. (2015). Finished-vehicle transporter routing problem solved by loading pattern discovery. Annals of Operations Research, 234(1), 37–56. https://doi.org/10.1007/s10479-014-1777-1.

    Google Scholar 

  109. Huang, W., & He, K. (2009a). A caving degree approach for the single container loading problem. European Journal of Operational Research, 196(1), 93–101. https://doi.org/10.1016/j.ejor.2008.02.024.

    Google Scholar 

  110. Huang, W., & He, K. (2009b). A new heuristic algorithm for cuboids packing with no orientation constraints. Computers & Operations Research, 36(2), 425–432. https://doi.org/10.1016/j.cor.2007.09.008.

    Google Scholar 

  111. Huang, Y.-H., Hwang, F. J., & Lu, H.-C. (2016). An effective placement method for the single container loading problem. Computers & Industrial Engineering, 97, 212–221. https://doi.org/10.1016/j.cie.2016.05.008.

    Google Scholar 

  112. International Standard Organization. (2010). Guidance on social responsibility—ISO 26000:2010 (Vol. 2010).

  113. Iori, M., & Martello, S. (2010). Routing problems with loading constraints. Top, 18(1), 4–27.

    Google Scholar 

  114. Iori, M., & Riera-Ledesma, J. (2015). Exact algorithms for the double vehicle routing problem with multiple stacks. Computers & Operations Research, 63, 83–101. https://doi.org/10.1016/j.cor.2015.04.016.

    Google Scholar 

  115. Iori, M., Salazar-González, J.-J., & Vigo, D. (2007). An exact approach for the vehicle routing problem with two-dimensional loading constraints. Transportation Science, 41(2), 253–264. https://doi.org/10.1287/trsc.1060.0165.

    Google Scholar 

  116. IPCC. (2014). Climate Change 2014: Synthesis Report. Contribution of working groups I, II and III to the Fifth assessment report of the intergovernmental panel on climate change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. Geneva, Switzerland.

  117. Irnich, S., Toth, P., & Vigo, D. (2014). The family of vehicle routing problems. In P. Toth & D. Vigo (Eds.), Vehicle routing: Problems, methods, and applications (2nd ed., pp. 1–33). MOS-SIAM Series on Optimization. https://doi.org/10.1137/1.9781611973594.ch1.

  118. Islam, D. M. Z., Fabian Meier, J., Aditjandra, P. T., Zunder, T. H., & Pace, G. (2013). Logistics and supply chain management. Research in Transportation Economics, 41(1), 3–16. https://doi.org/10.1016/j.retrec.2012.10.006.

    Google Scholar 

  119. Iwasawa, H., Hu, Y., Hashimoto, H., Imahori, S., & Yagiura, M. (2016). A heuristic algorithm for the container loading problem with complex loading constraints. Journal of Advanced Mechanical Design Systems and Manufacturing, 10(3), 1–12. https://doi.org/10.1299/jamdsm.2016jamdsm0041.

    Google Scholar 

  120. Jamrus, T., & Chien, C.-F. (2016). Extended priority-based hybrid genetic algorithm for the less-than-container loading problem. Computers & Industrial Engineering, 96, 227–236. https://doi.org/10.1016/j.cie.2016.03.030.

    Google Scholar 

  121. Jin, Z., Ito, T., & Ohno, K. (2003). The three-dimensional bin packing problem and its practical algorithm. JSME International Journal Series C-Mechanical Systems Machine Elements and Manufacturing, 46(1), 60–66.

    Google Scholar 

  122. Jozefowiez, N., Semet, T., & Talbi, E.-G. (2008). From single-objective to multi-objective vehicle routing problems: Motivations, case studies, and methods. In B. L. Golden, S. Raghavan, & E. A. Wasil (Eds.), The vehicle routing problem: Latest advances and new challenges (pp. 445–471). New York: Springer. https://doi.org/10.1007/978-0-387-77778-8_20.

  123. Juan, A. A., Faulin, J., Grasman, S., Riera, D., Marull, J., & Mendez, C. (2011). Using safety stocks and simulation to solve the vehicle routing problem with stochastic demands. Transportation Research Part C: Emerging Technologies, 19(5), 751–765. https://doi.org/10.1016/j.trc.2010.09.007.

    Google Scholar 

  124. Junqueira, L., & Morabito, R. (2015). Heuristic algorithms for a three-dimensional loading capacitated vehicle routing problem in a carrier. Computers & Industrial Engineering, 88, 110–130. https://doi.org/10.1016/j.cie.2015.06.005.

    Google Scholar 

  125. Junqueira, L., Morabito, R., & Yamashita, D. S. (2012a). MIP-based approaches for the container loading problem with multi-drop constraints. Annals of Operations Research, 199(1), 51–75.

    Google Scholar 

  126. Junqueira, L., Morabito, R., & Yamashita, D. S. (2012b). Three-dimensional container loading models with cargo stability and load bearing constraints. Computers & Operations Research, 39(1), 74–85. https://doi.org/10.1016/j.cor.2010.07.017.

    Google Scholar 

  127. Junqueira, L., Oliveira, J. F., Carravilla, M. A., & Morabito, R. (2013). An optimization model for the vehicle routing problem with practical three-dimensional loading constraints. International Transactions in Operational Research, 20(5), 645–666.

    Google Scholar 

  128. Kang, K., Moon, I., & Wang, H. (2012). A hybrid genetic algorithm with a new packing strategy for the three-dimensional bin packing problem. Applied Mathematics and Computation, 219(3), 1287–1299. https://doi.org/10.1016/j.amc.2012.07.036.

    Google Scholar 

  129. Karoonsoontawong, A., & Heebkhoksung, K. (2015). A modified wall-building-based compound approach for the knapsack container loading problem. Maejo International Journal of Science and Technology, 9(1), 93–107.

    Google Scholar 

  130. Khebbache-Hadji, S., Prins, C., Yalaoui, A., & Reghioui, M. (2013). Heuristics and memetic algorithm for the two-dimensional loading capacitated vehicle routing problem with time windows. Central European Journal of Operations Research, 21(2), 307–336. https://doi.org/10.1007/s10100-011-0204-9.

    Google Scholar 

  131. Kramer, R., Subramanian, A., Vidal, T., & Cabral, L. dos A. F. (2015). A matheuristic approach for the Pollution-Routing Problem. European Journal of Operational Research, 243(2), 523–539. https://doi.org/10.1016/j.ejor.2014.12.009.

    Google Scholar 

  132. Kritikos, M. N., & Ioannou, G. (2010). The balanced cargo vehicle routing problem with time windows. International Journal of Production Economics, 123(1), 42–51. https://doi.org/10.1016/J.IJPE.2009.07.006.

    Google Scholar 

  133. Kritikos, M. N., & Ioannou, G. (2013). The heterogeneous fleet vehicle routing problem with overloads and time windows. International Journal of Production Economics, 144(1), 68–75. https://doi.org/10.1016/j.ijpe.2013.01.020.

    Google Scholar 

  134. Kucukoglu, I., Ene, S., Aksoy, A., & Ozturk, N. (2015). A memory structure adapted simulated annealing algorithm for a green vehicle routing problem. Environmental Science and Pollution Research, 22(5), 3279–3297.

    Google Scholar 

  135. Kuo, Y. (2010). Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Computers & Industrial Engineering, 59(1), 157–165. https://doi.org/10.1016/j.cie.2010.03.012.

    Google Scholar 

  136. Kuo, Y., & Wang, C.-C. (2012). A variable neighborhood search for the multi-depot vehicle routing problem with loading cost. Expert Systems with Applications, 39(8), 6949–6954. https://doi.org/10.1016/j.eswa.2012.01.024.

    Google Scholar 

  137. Lacomme, P., Toussaint, H., & Duhamel, C. (2013). A GRASP x ELS for the vehicle routing problem with basic three-dimensional loading constraints. Engineering Applications of Artificial Intelligence, 26(8), 1795–1810.

    Google Scholar 

  138. Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), 408–416. https://doi.org/10.1287/trsc.1090.0301.

    Google Scholar 

  139. Lee, C.-G., Epelman, M. A., White, C. C., & Bozer, Y. A. (2006). A shortest path approach to the multiple-vehicle routing problem with split pick-ups. Transportation Research Part B: Methodological, 40(4), 265–284. https://doi.org/10.1016/j.trb.2004.11.004.

    Google Scholar 

  140. Leung, S. C. H., Zhang, Z., Zhang, D., Hua, X., & Lim, M. K. (2013). A meta-heuristic algorithm for heterogeneous fleet vehicle routing problems with two-dimensional loading constraints. European Journal of Operational Research, 225(2), 199–210.

    Google Scholar 

  141. Leung, S. C. H., Zheng, J., Zhang, D., & Zhou, X. (2010). Simulated annealing for the vehicle routing problem with two-dimensional loading constraints. Flexible Services and Manufacturing Journal, 22(1–2), 61–82.

    Google Scholar 

  142. Leung, S. C. H., Zhou, X., Zhang, D., & Zheng, J. (2011). Extended guided tabu search and a new packing algorithm for the two-dimensional loading vehicle routing problem. Computers & Operations Research, 38(1), 205–215.

    Google Scholar 

  143. Li, H.-L., Tsai, J.-F., & Hu, N.-Z. (2003). A distributed global optimization method for packing problems. Journal of the Operational Research Society, 54(4), 419–425.

    Google Scholar 

  144. Li, H., Yuan, J., Lv, T., & Chang, X. (2016). The two-echelon time-constrained vehicle routing problem in linehaul-delivery systems considering carbon dioxide emissions. Transportation Research Part D-Transport and Environment, 49, 231–245. https://doi.org/10.1016/j.trd.2016.10.002.

    Google Scholar 

  145. Li, J., Lu, Q., & Fu, P. (2015). Carbon footprint management of road freight transport under the carbon emission trading mechanism. Mathematical Problems in Engineering, 13. https://doi.org/10.1155/2015/814527.

  146. Li, K., & Tian, H. (2016). A two-level self-adaptive variable neighborhood search algorithm for the prize-collecting vehicle routing problem. Applied Soft Computing, 43, 469–479. https://doi.org/10.1016/j.asoc.2016.02.040.

    Google Scholar 

  147. Li, X., & Zhang, K. (2015). A hybrid differential evolution algorithm for multiple container loading problem with heterogeneous containers. Computers & Industrial Engineering, 90, 305–313. https://doi.org/10.1016/j.cie.2015.10.007.

    Google Scholar 

  148. Lim, A., Ma, H., Qiu, C., & Zhu, W. (2013). The single container loading problem with axle weight constraints. International Journal of Production Economics, 144(1), 358–369. https://doi.org/10.1016/j.ijpe.2013.03.001.

    Google Scholar 

  149. Lin, C., Choy, K. L., Ho, G. T. S., Chung, S. H., & Lam, H. Y. (2014). Survey of green vehicle routing problem: Past and future trends. Expert Systems with Applications, 41(4), 1118–1138. https://doi.org/10.1016/j.eswa.2013.07.107.

    Google Scholar 

  150. Lin, J.-L., Chang, C.-H., & Yang, J.-Y. (2006). A study of optimal system for multiple-constraint multiple-container packing problems. Advances in Applied Articial Intelligence, Proceedings, 4031, 1200–1210.

    Google Scholar 

  151. Lin, J., Zhou, W., & Wolfson, O. (2016). Electric vehicle routing problem. Ninth International Conference on City Logistics, 12, 508–521. https://doi.org/10.1016/j.trpro.2016.02.007.

    Google Scholar 

  152. Lin, M.-H., Tsai, J.-F., & Chang, S.-C. (2017). A superior linearization method for signomial discrete functions in solving three-dimensional open-dimension rectangular packing problems. Engineering Optimization, 49(5), 746–761. https://doi.org/10.1080/0305215X.2016.1211403.

    Google Scholar 

  153. Liu, D. S., Tan, K. C., Huang, S. Y., Goh, C. X., & Ho, W. K. (2008). On solving multiobjective bin packing problems using evolutionary particle swarm optimization. European Journal of Operational Research, 190(2), 357–382.

    Google Scholar 

  154. Liu, J., Smith, A. E., & Qian, D. (2016). The vehicle loading problem with a heterogeneous transport fleet. Computers & Industrial Engineering, 97, 137–145. https://doi.org/10.1016/j.cie.2016.04.021.

    Google Scholar 

  155. Liu, J., Yue, Y., Dong, Z., Maple, C., & Keech, M. (2011). A novel hybrid tabu search approach to container loading. Computers & Operations Research, 38(4), 797–807. https://doi.org/10.1016/j.cor.2010.09.002.

    Google Scholar 

  156. Lodi, A., Martello, S., & Vigo, D. (2002). Recent advances on two-dimensional bin packing problems. Discrete Applied Mathematics, 123(1–3), 379–396. https://doi.org/10.1016/S0166-218X(01)00347-X.

    Google Scholar 

  157. Mack, D., & Bortfeldt, A. (2012). A heuristic for solving large bin packing problems in two and three dimensions. Central European Journal of Operations Research, 20(2), 337–354. https://doi.org/10.1007/s10100-010-0184-1.

    Google Scholar 

  158. Mahvash, B., Awasthi, A., & Chauhan, S. (2017). A column generation based heuristic for the capacitated vehicle routing problem with three-dimensional loading constraints. International Journal of Production Research, 55(6), 1730–1747. https://doi.org/10.1080/00207543.2016.1231940.

    Google Scholar 

  159. Malapert, A., Guéret, C., & Jussien, N. (2008). Two-dimensional pickup and delivery routing problem with loading constraints. In CPAIOR’08 1st Workshop on Bin Packing and Placement Constraints (BPPC’08) (pp. 1–6). Retrieved from http://www.emn.fr/jussien/publications/CIRRELT-2008-37.pdf.

  160. Männel, D., & Bortfeldt, A. (2016). A hybrid algorithm for the vehicle routing problem with pickup and delivery and three-dimensional loading constraints. European Journal of Operational Research, 254(3), 840–858. https://doi.org/10.1016/j.ejor.2016.04.016.

    Google Scholar 

  161. Marinakis, Y., Iordanidou, G.-R., & Marinaki, M. (2013). Particle swarm optimization for the vehicle routing problem with stochastic demands. Applied Soft Computing, 13(4), 1693–1704. https://doi.org/10.1016/j.asoc.2013.01.007.

    Google Scholar 

  162. Martins, G. H. A., & Dell, R. F. (2007). The minimum size instance of a Pallet Loading Problem equivalence class. European Journal of Operational Research, 179(1), 17–26. https://doi.org/10.1016/j.ejor.2006.03.009.

    Google Scholar 

  163. Martins, G. H. A., & Dell, R. F. (2008). Solving the pallet loading problem. European Journal of Operational Research, 184(2), 429–440. https://doi.org/10.1016/j.ejor.2006.11.012.

    Google Scholar 

  164. McGuigan, J. R., Moyer, C., & Harris, F. (2014). Managerial Economics (13th ed.). Stanford: CENGAGE Learning.

    Google Scholar 

  165. Mehrjerdi, Y. Z. (2014). A multiple objective stochastic approach to vehicle routing problem. International Journal of Advanced Manufacturing Technology, 74(5–8), 1149–1158.

    Google Scholar 

  166. Mendoza, J. E., Castanier, B., Guéret, C., Medaglia, A. L., & Velasco, N. (2010). A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands. Computers & Operations Research, 37(11), 1886–1898. https://doi.org/10.1016/j.cor.2009.06.015.

    Google Scholar 

  167. Miao, L., Ruan, Q., Woghiren, K., & Ruo, Q. (2012). A hybrid genetic algorithm for the vehicle routing problem with three-dimensional loading constraints. Rairo-Operations Research, 46(1), 63–82.

    Google Scholar 

  168. Molina, J. C., Eguia, I., Racero, J., & Guerrero, F. (2014). Multi-objective vehicle routing problem with cost and emission functions. Xi Congreso De Ingenieria Del Transporte (Cit 2014), 160, 254–263. https://doi.org/10.1016/j.sbspro.2014.12.137.

    Google Scholar 

  169. Montoya-Torres, J. R. (2015). Designing sustainable supply chains based on the triple bottom line approach. In Proceedings of the 2015 international conference on advanced logistics and transport (ICALT 2015) (pp. 1–6). Valenciennes, France: IEEE Publishing.

  170. Montoya-Torres, J. R., López Franco, J., Nieto Isaza, S., Felizzola Jiménez, H., & Herazo-Padilla, N. (2015). A literature review on the vehicle routing problem with multiple depots. Computers & Industrial Engineering, 79, 115–129. https://doi.org/10.1016/j.cie.2014.10.029.

    Google Scholar 

  171. Moura, A., & Bortfeldt, A. (2017). A two-stage packing problem procedure. International Transactions in Operational Research, 24(1–2), 43–58. https://doi.org/10.1111/itor.12251.

    Google Scholar 

  172. Moura, A., & Oliveira, J. F. (2009). An integrated approach to the vehicle routing and container loading problems. OR Spectrum, 31(4), 775–800.

    Google Scholar 

  173. Mu, Q., & Eglese, R. W. (2013). Disrupted capacitated vehicle routing problem with order release delay. Annals of Operations Research, 207(1), 201–216.

    Google Scholar 

  174. Newbert, S. L. (2007). Empirical research on the resource based view of the firm: An assessment and suggestions for future research. Strategic Management Journal, 28(2), 121–146. https://doi.org/10.1002/smj.573.

    Google Scholar 

  175. NHS Centre for Reviews and Dissemination. (2001). Undertaking systematic reviews of research on effectiveness: CRD’s guidance for carrying out or commissioning reviews. York.

  176. Norouzi, N., Sadegh-Amalnick, M., & Tavakkoli-Moghaddam, R. (2017). Modified particle swarm optimization in a time-dependent vehicle routing problem: minimizing fuel consumption. Optimization Letters, 11(1), 121–134. https://doi.org/10.1007/s11590-015-0996-y.

    Google Scholar 

  177. Nowak, M., Ergun, O., & White, C. C. (2009). An empirical study on the benefit of split loads with the pickup and delivery problem. European Journal of Operational Research, 198(3), 734–740. https://doi.org/10.1016/j.ejor.2008.09.041.

    Google Scholar 

  178. Okude, M., & Taniguchi, E. (2012). An approximation algorithm for vehicle routing problems with hierarchized traffic network. Seventh International Conference on City Logistics, 39, 369–386. https://doi.org/10.1016/j.sbspro.2012.03.115.

    Google Scholar 

  179. Omar, M. K., & Ramakrishnan, K. (2011). EPSO for solving non-oriented two-dimensional bin packing problem. In 2011 IEEE international conference on industrial engineering and engineering management (IEEM) (pp. 106–110).

  180. Oncan, T., Aksu, D. T., Sahin, G., & Sahin, M. (2011). A branch and cut algorithm for the multi-vehicle one-to-one pickup and delivery problem with split loads. In 2011 IEEE International conference on industrial engineering and engineering management (IEEM) (pp. 1864–1868).

  181. Parreno, F., Alvarez-Valdes, R., Oliveira, J. F., & Tamarit, J. M. (2010). A hybrid GRASP/VND algorithm for two- and three-dimensional bin packing. Annals of Operations Research, 179(1), 203–220. https://doi.org/10.1007/s10479-008-0449-4.

    Google Scholar 

  182. Pelikan, J., & Fabry, J. (2012). Heuristics for routes generation in pickup and delivery problem. Central European Journal of Operations Research, 20(3), 463–472.

    Google Scholar 

  183. Perboli, G., Gobbato, L., & Perfetti, F. (2014). Packing problems in transportation and supply chain: New problems and trends. In Transportation: Can we do more with less resources?—16th meeting of the Euro working group on transportation—Porto 2013 (Vol. 111, pp. 672–681). https://doi.org/10.1016/j.sbspro.2014.01.101.

  184. Perboli, G., Tadei, R., & Baldi, M. M. (2012). The stochastic generalized bin packing problem. Discrete Applied Mathematics, 160(7–8), 1291–1297. https://doi.org/10.1016/j.dam.2011.10.037.

    Google Scholar 

  185. Pérez-Bernabeu, E., Juan, A. A., Faulin, J., & Barrios, B. B. (2015). Horizontal cooperation in road transportation: A case illustrating savings in distances and greenhouse gas emissions. International Transactions in Operational Research, 22(3), 585–606. https://doi.org/10.1111/itor.12130.

    Google Scholar 

  186. Petersen, H. L., & Madsen, O. B. G. (2009). The double travelling salesman problem with multiple stacks—Formulation and heuristic solution approaches. European Journal of Operational Research, 198(1), 139–147. https://doi.org/10.1016/j.ejor.2008.08.009.

    Google Scholar 

  187. Piera, M. A., Zuniga, C., & Mujica, M. (2009). A pallet packing CPN optimization approach for distribution center. Automatika, 50(1–2), 29–38.

    Google Scholar 

  188. Pisinger, D. (2002). Heuristics for the container loading problem. European Journal of Operational Research, 141(2), 382–392. https://doi.org/10.1016/S0377-2217(02)00132-7.

    Google Scholar 

  189. Pollaris, H., Braekers, K., Caris, A., Janssens, G. K., & Limbourg, S. (2015). Vehicle routing problems with loading constraints: state-of-the-art and future directions. OR Spectrum, 37(2), 297–330. https://doi.org/10.1007/s00291-014-0386-3.

    Google Scholar 

  190. Pradenas, L., Oportus, B., & Parada, V. (2013). Mitigation of greenhouse gas emissions in vehicle routing problems with backhauling. Expert Systems with Applications, 40(8), 2985–2991. https://doi.org/10.1016/j.eswa.2012.12.014.

    Google Scholar 

  191. Ramos, A. G., Oliveira, J. F., Gonçalves, J. F., & Lopes, M. P. (2016). A container loading algorithm with static mechanical equilibrium stability constraints. Transportation Research Part B-Methodological, 91, 565–581. https://doi.org/10.1016/j.trb.2016.06.003.

    Google Scholar 

  192. Ramos, A. G., Oliveira, J. F., & Lopes, M. P. (2016). A physical packing sequence algorithm for the container loading problem with static mechanical equilibrium conditions. International Transactions in Operational Research, 23(1–2), 215–238. https://doi.org/10.1111/itor.12124.

    Google Scholar 

  193. Rardin, R. L. (1997). Optimization in operations research (1st ed.). Upper Saddle: Prentice Hall.

    Google Scholar 

  194. Ren, J., Tian, Y., & Sawaragi, T. (2011). A tree search method for the container loading problem with shipment priority. European Journal of Operational Research, 214(3), 526–535. https://doi.org/10.1016/j.ejor.2011.04.025.

    Google Scholar 

  195. Respen, J., & Zufferey, N. (2017). Metaheuristics for truck loading in the car production industry. International Transactions in Operational Research, 24(1–2), 277–301. https://doi.org/10.1111/itor.12306.

    Google Scholar 

  196. Richardson, B. C. (2005). Sustainable transport: Analysis frameworks. Journal of Transport Geography, 13(1), 29–39. https://doi.org/10.1016/j.jtrangeo.2004.11.005.

    Google Scholar 

  197. Riff, M. C., Bonnaire, X., & Neveu, B. (2009). A revision of recent approaches for two-dimensional strip-packing problems. Engineering Applications of Artificial Intelligence, 22(4–5), 833–837. https://doi.org/10.1016/j.engappai.2008.10.025.

    Google Scholar 

  198. Ruan, Q., Zhang, Z., Miao, L., & Shen, H. (2013). A hybrid approach for the vehicle routing problem with three-dimensional loading constraints. Computers & Operations Research, 40(6), 1579–1589. https://doi.org/10.1016/j.cor.2011.11.013.

    Google Scholar 

  199. Salam, M. A., & Khan, S. A. (2016). Simulation based decision support system for optimization: A case of Thai logistics service provider. Industrial Management & Data Systems, 116(2), 236–254. https://doi.org/10.1108/IMDS-05-2015-0192.

    Google Scholar 

  200. Schmid, V., Doerner, K. F., & Laporte, G. (2013). Rich routing problems arising in supply chain management. European Journal of Operational Research, 224(3), 435–448. https://doi.org/10.1016/j.ejor.2012.08.014.

    Google Scholar 

  201. Schneider, M., Stenger, A., & Hof, J. (2015). An adaptive VNS algorithm for vehicle routing problems with intermediate stops. OR Spectrum, 37(2), 353–387.

    Google Scholar 

  202. Schwarze, S. (2016). Pricing strategies for the site-dependent vehicle routing problem. OR Spectrum, 38(1), 137–173. https://doi.org/10.1007/s00291-015-0399-6.

    Google Scholar 

  203. Schwarze, S., & Voss, S. (2013). Improved load balancing and resource utilization for the Skill Vehicle Routing Problem. Optimization Letters, 7(8), 1805–1823.

    Google Scholar 

  204. Seuring, S. (2013). A review of modeling approaches for sustainable supply chain management. Decision Support Systems, 54(4), 1513–1520. https://doi.org/10.1016/j.dss.2012.05.053.

    Google Scholar 

  205. Seuring, S., Müller, M., & Westhaus, M. (2005). Conducting a literature review—The example of sustainability in supply chains. In H. Kotzab, S. Seuring, M. Müller, & G. Reiner (Eds.), Research Methodologies in Supply Chain Management (pp. 92–106). Heidelberg: Physica-Verlag.

    Google Scholar 

  206. Sheng, L., Hongxia, Z., Xisong, D., & Changjian, C. (2016). A heuristic algorithm for container loading of pallets with infill boxes. European Journal of Operational Research, 252(3), 728–736. https://doi.org/10.1016/j.ejor.2016.01.025.

    Google Scholar 

  207. Sheng, L., Wei, T., Zhiyuan, X., & Xiwei, L. (2014). A tree search algorithm for the container loading problem. Computers & Industrial Engineering, 75, 20–30. https://doi.org/10.1016/j.cie.2014.05.024.

    Google Scholar 

  208. Shimizu, Y., Sakaguchi, T., & Yoo, J.-K. (2016). A hybrid method for solving multi-depot VRP with simultaneous pickup and delivery incorporated with Weber basis saving heuristic. Journal of Advanced Mechanical Design Systems and Manufacturing, 10(1), 1–13. https://doi.org/10.1299/jamdsm.2016jamdsm0004.

    Google Scholar 

  209. Sicilia, J. A., Quemada, C., Royo, B., & Escuin, D. (2016). An optimization algorithm for solving the rich vehicle routing problem based on Variable Neighborhood Search and Tabu Search metaheuristics. Journal of Computational and Applied Mathematics, 291, 468–477. https://doi.org/10.1016/j.cam.2015.03.050.

    Google Scholar 

  210. Sicilia, J. A., Royo, B., Larrode, E., & Fraile, A. (2014). A decision support system for a long-distance routing problem. based on the ant colony optimization metaheuristic. In Transportation: Can we do more with less resources?—16th Meeting of the Euro working group on transportation—Porto 2013 (Vol. 111, pp. 1035–1044). https://doi.org/10.1016/j.sbspro.2014.01.138.

  211. Silva, E., Oliveira, J. F., & Wäscher, G. (2016). The pallet loading problem: A review of solution methods and computational experiments. International Transactions in Operational Research, 23(1–2), 147–172. https://doi.org/10.1111/itor.12099.

    Google Scholar 

  212. Skorna, A. C. H., & Fleisch, E. (2012). Loss prevention in transportation to ensure product quality: Insights from the cargo insurance sector. IFIP Advances in Information and Communication Technology, 384 AICT, 148–156. https://doi.org/10.1007/978-3-642-33980-6_18.

    Google Scholar 

  213. Soysal, M., Bloemhof-Ruwaard, J. M., & Bektas, T. (2015). The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations. International Journal of Production Economics, 164, 366–378. https://doi.org/10.1016/j.ijpe.2014.11.016.

    Google Scholar 

  214. Tang, J., Guan, J., Yu, Y., & Chen, J. (2014). Beam search combined with MAX–MIN ant systems and benchmarking data tests for weighted vehicle routing problem. IEEE Transactions on Automation Science and Engineering, 11(4), 1097–1109. https://doi.org/10.1109/TASE.2013.2295092.

    Google Scholar 

  215. Tang, J., Ma, Y., Guan, J., & Yan, C. (2013). A Max–Min ant system for the split delivery weighted vehicle routing problem. Expert Systems with Applications, 40(18), 7468–7477. https://doi.org/10.1016/j.eswa.2013.06.068.

    Google Scholar 

  216. Tang, J., Zhang, J., & Pan, Z. (2010). A scatter search algorithm for solving vehicle routing problem with loading cost. Expert Systems with Applications, 37(6), 4073–4083. https://doi.org/10.1016/j.eswa.2009.11.027.

    Google Scholar 

  217. Tao, Y., & Wang, F. (2015). An effective tabu search approach with improved loading algorithms for the 3L-CVRP. Computers & Operations Research, 55, 127–140. https://doi.org/10.1016/j.cor.2013.10.017.

    Google Scholar 

  218. Tarantilis, C. D., Zachariadis, E. E., & Kiranoudis, C. T. (2009). A hybrid metaheuristic algorithm for the integrated vehicle routing and three-dimensional container-loading problem. IEEE Transactions on Intelligent Transportation Systems, 10(2), 255–271.

    Google Scholar 

  219. Tian, T., Zhu, W., Lim, A., & Wei, L. (2016). The multiple container loading problem with preference. European Journal of Operational Research, 248(1), 84–94. https://doi.org/10.1016/j.ejor.2015.07.002.

    Google Scholar 

  220. Tiwari, A., & Chang, P.-C. (2015). A block recombination approach to solve green vehicle routing problem. International Journal of Production Economics, 164, 379–387. https://doi.org/10.1016/j.ijpe.2014.11.003.

    Google Scholar 

  221. Todosijevic, R., Hanafi, S., Urosevic, D., Jarboui, B., & Gendron, B. (2017). A general variable neighborhood search for the swap-body vehicle routing problem. Computers & Operations Research, 78, 468–479. https://doi.org/10.1016/j.cor.2016.01.016.

    Google Scholar 

  222. Toffolo, T. A. M., Esprit, E., Wauters, T., & Berghe, G. Vanden. (2017). A two-dimensional heuristic decomposition approach to a three-dimensional multiple container loading problem. European Journal of Operational Research, 257(2), 526–538. https://doi.org/10.1016/j.ejor.2016.07.033.

    Google Scholar 

  223. Tol, R. S. J. (2005). The marginal damage costs of carbon dioxide emissions: An assessment of the uncertainties. Energy Policy, 33(16), 2064–2074. https://doi.org/10.1016/j.enpol.2004.04.002.

    Google Scholar 

  224. Tricoire, F., Doerner, K. F., Hartl, R. F., & Iori, M. (2011). Heuristic and exact algorithms for the multi-pile vehicle routing problem. OR Spectrum, 33(4), 931–959.

    Google Scholar 

  225. Trivella, A., & Pisinger, D. (2016). The load-balanced multi-dimensional bin-packing problem. Computers & Operations Research, 74, 152–164. https://doi.org/10.1016/j.cor.2016.04.020.

    Google Scholar 

  226. Tsai, J.-F., & Li, H. L. (2006). A global optimization method for packing problems. Engineering Optimization, 38(6), 687–700.

    Google Scholar 

  227. Tsai, J.-F., Wang, P.-C., & Lin, M.-H. (2015). A global optimization approach for solving three-dimensional open dimension rectangular packing problems. Optimization, 64(12), 2601–2618. https://doi.org/10.1080/02331934.2013.877906.

    Google Scholar 

  228. Tsao, Y.-C., & Lu, J.-C. (2012). A supply chain network design considering transportation cost discounts. Transportation Research Part E: Logistics and Transportation Review, 48(2), 401–414. https://doi.org/10.1016/j.tre.2011.10.004.

    Google Scholar 

  229. Tzur, M., & Drezner, E. (2011). A lookahead partitioning heuristic for a new assignment and scheduling problem in a distribution system. European Journal of Operational Research, 215(2), 325–336. https://doi.org/10.1016/j.ejor.2011.06.013.

    Google Scholar 

  230. Ubeda, S., Arcelus, F. J., & Faulin, J. (2011). Green logistics at Eroski: A case study. International Journal of Production Economics, 131(1), 44–51. https://doi.org/10.1016/j.ijpe.2010.04.041.

    Google Scholar 

  231. Vargas-Osorio, S., & Zuniga, C. (2016). A literature review on the pallet loading problem. Revista Digital Lampsakos, 15, 69–80. https://doi.org/10.21501/21454086.1790.

  232. Veenstra, M., Roodbergen, K. J., Vis, I. F. A., & Coelho, L. C. (2017). The pickup and delivery traveling salesman problem with handling costs. European Journal of Operational Research, 257(1), 118–132. https://doi.org/10.1016/j.ejor.2016.07.009.

    Google Scholar 

  233. Wang, L., Zhang, H., Xiong, Y., & Li, D. (2010). Ant colony optimization algorithm based on space division for container loading problem. In 2010 Chinese control and decision conference, Vols 1–5 (p. 3448–+). https://doi.org/10.1109/CCDC.2010.5498563.

  234. Wang, Y., Ma, X., Li, Z., Liu, Y., Xu, M., & Wang, Y. (2017). Profit distribution in collaborative multiple centers vehicle routing problem. Journal of Cleaner Production, 144, 203–219. https://doi.org/10.1016/j.jclepro.2017.01.001.

    Google Scholar 

  235. Wang, Z., Li, K. W., & Levy, J. K. (2008). A heuristic for the container loading problem: A tertiary-tree-based dynamic space decomposition approach. European Journal of Operational Research, 191(1), 86–99.

    Google Scholar 

  236. Wäscher, G., Haußner, H., & Schumann, H. (2007). An improved typology of cutting and packing problems. European Journal of Operational Research, 183(3), 1109–1130. https://doi.org/10.1016/j.ejor.2005.12.047.

    Google Scholar 

  237. Wei, L., & Lim, A. (2015). A bidirectional building approach for the 2D constrained guillotine knapsack packing problem. European Journal of Operational Research, 242(1), 63–71. https://doi.org/10.1016/j.ejor.2014.10.004.

    Google Scholar 

  238. Wei, L., Oon, W.-C., Zhu, W., & Lim, A. (2012). A reference length approach for the 3D strip packing problem. European Journal of Operational Research, 220(1), 37–47. https://doi.org/10.1016/j.ejor.2012.01.039.

    Google Scholar 

  239. Wei, L., Tian, T., Zhu, W., & Lim, A. (2014). A block-based layer building approach for the 2D guillotine strip packing problem. European Journal of Operational Research, 239(1), 58–69. https://doi.org/10.1016/j.ejor.2014.04.020.

    Google Scholar 

  240. Wei, L., Zhang, Z., & Lim, A. (2014). An adaptive variable neighborhood search for a heterogeneous fleet vehicle routing problem with three-dimensional loading constraints. IEEE Computational Intelligence Magazine, 9(4), 18–30.

    Google Scholar 

  241. Wei, L., Zhang, Z., Zhang, D., & Lim, A. (2015). A variable neighborhood search for the capacitated vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research, 243(3), 798–814. https://doi.org/10.1016/j.ejor.2014.12.048.

    Google Scholar 

  242. Wei, L., Zhu, W., & Lim, A. (2015). A goal-driven prototype column generation strategy for the multiple container loading cost minimization problem. European Journal of Operational Research, 241(1), 39–49. https://doi.org/10.1016/j.ejor.2014.08.015.

    Google Scholar 

  243. Wu, K. C., & Ting, C. J. (2007). A two-phase algorithm for the manufacturer’s pallet loading problem. 2007 IEEE International Conference on Industrial Engineering and Engineering Management, 1–4, 1574–1578. https://doi.org/10.1109/IEEM.2007.4419457.

    Google Scholar 

  244. Wu, W., Tian, Y., & Jind, T. (2016). A label based ant colony algorithm for heterogeneous vehicle routing with mixed backhaul. Applied Soft Computing, 47, 224–234. https://doi.org/10.1016/j.asoc.2016.05.011.

    Google Scholar 

  245. Wu, Y., Li, W., Goh, M., & de Souza, R. (2010). Three-dimensional bin packing problem with variable bin height. European Journal of Operational Research, 202(2), 347–355. https://doi.org/10.1016/j.ejor.2009.05.040.

    Google Scholar 

  246. Xiao, Y., & Konak, A. (2016). The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion. Transportation Research Part E-Logistics and Transportation Review, 88, 146–166. https://doi.org/10.1016/j.tre.2016.01.011.

    Google Scholar 

  247. Xiao, Y., Zhao, Q., Kaku, I., & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research, 39(7), 1419–1431. https://doi.org/10.1016/j.cor.2011.08.013.

    Google Scholar 

  248. Xu, H., Chen, Z.-L., Rajagopal, S., & Arunapuram, S. (2003). Solving a practical pickup and delivery problem. Transportation Science, 37(3), 347–364.

    Google Scholar 

  249. Yang, B., Hu, Z.-H., Wei, C., Li, S.-Q., Zhao, L., & Jia, S. (2015). Routing with time-windows for multiple environmental vehicle types. Computers & Industrial Engineering, 89, 150–161. https://doi.org/10.1016/j.cie.2015.02.001.

    Google Scholar 

  250. Yang, H., Yang, S., Xu, Y., Cao, E., Lai, M., & Dong, Z. (2015). Electric vehicle route optimization considering time-of-use electricity price by learnable partheno-genetic algorithm. IEEE Transactions on Smart Grid, 6(2), 657–666. https://doi.org/10.1109/TSG.2014.2382684.

    Google Scholar 

  251. Yeung, L. H. W., & Tang, W. K. S. (2005). A hybrid genetic approach for container loading in logistics industry. IEEE Transactions on Industrial Electronics, 52(2), 617–627. https://doi.org/10.1109/TIE.2005.844224.

    Google Scholar 

  252. Yi, J., Chen, X.-G., & Zhou, J. (2009). The pinwheel pattern and its application to the manufacturer’s pallet-loading problem. International Transactions in Operational Research, 16(6), 809–828.

    Google Scholar 

  253. Yin, P. Y., & Chuang, Y. L. (2016). Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking. Applied Mathematical Modelling, 40, 9302–9315. https://doi.org/10.1016/j.apm.2016.06.013.

    Google Scholar 

  254. Zachariadis, E. E., Tarantilis, C. D., & Kiranoudis, C. T. (2009). A Guided Tabu Search for the Vehicle Routing Problem with two-dimensional loading constraints. European Journal of Operational Research, 195(3), 729–743. https://doi.org/10.1016/j.ejor.2007.05.058.

    Google Scholar 

  255. Zachariadis, E. E., Tarantilis, C. D., & Kiranoudis, C. T. (2013a). Designing vehicle routes for a mix of different request types, under time windows and loading constraints. European Journal of Operational Research, 229(2), 303–317. https://doi.org/10.1016/j.ejor.2013.02.056.

    Google Scholar 

  256. Zachariadis, E. E., Tarantilis, C. D., & Kiranoudis, C. T. (2013b). Integrated distribution and loading planning via a compact metaheuristic algorithm. European Journal of Operational Research, 228(1), 56–71. https://doi.org/10.1016/j.ejor.2013.01.040.

    Google Scholar 

  257. Zachariadis, E. E., Tarantilis, C. D., & Kiranoudis, C. T. (2015). The load-dependent vehicle routing problem and its pick-up and delivery extension. Transportation Research Part B: Methodological, 71, 158–181. https://doi.org/10.1016/j.trb.2014.11.004.

    Google Scholar 

  258. Zachariadis, E. E., Tarantilis, C. D., & Kiranoudis, C. T. (2016). The vehicle routing problem with simultaneous pick-ups and deliveries and two-dimensional loading constraints. European Journal of Operational Research, 251(2), 369–386. https://doi.org/10.1016/j.ejor.2015.11.018.

    Google Scholar 

  259. Zhang, D., Cai, S., Ye, F., Si, Y.-W., & Nguyen, T. T. (2017). A hybrid algorithm for a vehicle routing problem with realistic constraints. Information Sciences, 394, 167–182. https://doi.org/10.1016/j.ins.2017.02.028.

    Google Scholar 

  260. Zhang, D., Peng, Y., & Leung, S. C. H. (2012). A heuristic block-loading algorithm based on multi-layer search for the container loading problem. Computers & Operations Research, 39(10), 2267–2276. https://doi.org/10.1016/j.cor.2011.10.019.

    Google Scholar 

  261. Zhang, J., Lam, W. H. K., & Chen, B. Y. (2016). On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows. European Journal of Operational Research, 249(1), 144–154. https://doi.org/10.1016/j.ejor.2015.08.050.

    Google Scholar 

  262. Zhang, Q., Shah, N., Wassick, J., Helling, R., & van Egerschot, P. (2014). Sustainable supply chain optimisation: An industrial case study. Computers & Industrial Engineering, 74, 68–83. https://doi.org/10.1016/j.cie.2014.05.002.

    Google Scholar 

  263. Zhang, R., Yun, W. Y., & Kopfer, H. (2010). Heuristic-based truck scheduling for inland container transportation. OR Spectrum, 32(3), 787–808.

    Google Scholar 

  264. Zhang, Y., & Chen, X. D. (2014). An optimization model for the vehicle routing problem in multi-product frozen food delivery. Journal of Applied Research and Technology, 12(2), 239–250. https://doi.org/10.1016/S1665-6423(14)72340-5.

    Google Scholar 

  265. Zhang, Z., Wei, L., & Lim, A. (2015). An evolutionary local search for the capacitated vehicle routing problem minimizing fuel consumption under three-dimensional loading constraints. Transportation Research Part B-Methodological, 82, 20–35. https://doi.org/10.1016/j.trb.2015.10.001.

    Google Scholar 

  266. Zhao, X., Bennell, J. A., Bektaş, T., & Dowsland, K. (2016). A comparative review of 3D container loading algorithms. International Transactions in Operational Research, 23(1–2), 287–320. https://doi.org/10.1111/itor.12094.

    Google Scholar 

  267. Zheng, J.-N., Chien, C.-F., & Gen, M. (2015). Multi-objective multi-population biased random-key genetic algorithm for the 3-D container loading problem. Computers & Industrial Engineering, 89, 80–87. https://doi.org/10.1016/j.cie.2014.07.012.

    Google Scholar 

  268. Zhu, W., Huang, W., & Lim, A. (2012). A prototype column generation strategy for the multiple container loading problem. European Journal of Operational Research, 223(1), 27–39. https://doi.org/10.1016/j.ejor.2012.05.039.

    Google Scholar 

  269. Zhu, W., & Lim, A. (2012). A new iterative-doubling Greedy–Lookahead algorithm for the single container loading problem. European Journal of Operational Research, 222(3), 408–417. https://doi.org/10.1016/j.ejor.2012.04.036.

    Google Scholar 

  270. Zhu, W., Oon, W.-C., Lim, A., & Weng, Y. (2012). The six elements to block-building approaches for the single container loading problem. Applied Intelligence, 37(3), 431–445.

    Google Scholar 

  271. Zhu, W., Qin, H., Lim, A., & Wang, L. (2012). A two-stage tabu search algorithm with enhanced packing heuristics for the 3L-CVRP and M3L-CVRP. Computers & Operations Research, 39(9), 2178–2195. https://doi.org/10.1016/j.cor.2011.11.001.

    Google Scholar 

  272. Zhu, W., Zhang, Z., Oon, W.-C., & Lim, A. (2012). Space defragmentation for packing problems. European Journal of Operational Research, 222(3), 452–463. https://doi.org/10.1016/j.ejor.2012.05.031.

    Google Scholar 

  273. Zuniga, C., Piera, M. A., & Narciso, M. (2011). Revisiting the pallet loading problem using a discrete event system approach to minimise logistic costs. International Journal of Production Research, 49(8), 2243–2264.

    Google Scholar 

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Vega-Mejía, C.A., Montoya-Torres, J.R. & Islam, S.M.N. Consideration of triple bottom line objectives for sustainability in the optimization of vehicle routing and loading operations: a systematic literature review. Ann Oper Res 273, 311–375 (2019). https://doi.org/10.1007/s10479-017-2723-9

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Keywords

  • Vehicle Routing Problem
  • Packing Problem
  • Loading constraints
  • Triple bottom line
  • Sustainable Supply Chain Management
  • Systematic literature review