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Journal of Intelligent Manufacturing

, Volume 30, Issue 1, pp 163–184 | Cite as

An intelligent truck scheduling and transportation planning optimization model for product portfolio in a cross-dock

  • H. Khorshidian
  • M. Akbarpour ShiraziEmail author
  • S. M. T. Fatemi Ghomi
Article

Abstract

Selecting an effective category of products and their distribution are a challenge in distribution centers separated in two successive stages. First, the optimal number of the products and their participation will be selected. Then, an appropriate planning for distributing and transporting the selected products is determined. Hence, this paper develops a bi-objective mathematical model to integrate truck scheduling and transportation planning in a cross-docking system in a forward/reverse logistics network. For effective products category selection, a hybrid intelligent product portfolio optimization model is proposed. To solve the bi-objective model, a hybrid of the improved version of the augmented e-constraint method (AUGMECON2) and TOPSIS is designed and utilized. Moreover, a real industrial case is provided to justify the performance and applicability of the model and the solution approach.

Keywords

Forward/reverse cross-dock Product portfolio Truck scheduling Transportation planning AUGMECON2 

References

  1. Agustina, D., Lee, C., & Piplani, R. (2014). Vehicle scheduling and routing at a cross docking center for food supply chains. International Journal of Production Economics, 152, 29–41.Google Scholar
  2. Alumur, S. A., Nickel, S., Saldanha-da-Gama, F., & Verter, V. (2012). Multi-period reverse logistics network design. European Journal of Operational Research, 220(1), 67–78.Google Scholar
  3. Bodie, Z., Kane, A., & Marcus, A. J. (2009). Investments (8th ed.). Irwin, NY: McGraw Hill.Google Scholar
  4. Buijs, P., Vis, I. F. A., & Carlo, H. J. (2014). Synchronization in cross-docking networks: A research classification and framework. European Journal of Operational Research, 239, 593–608.Google Scholar
  5. Chopra, S. (2003). Designing the distribution network in a supply chain. Transportation Research, 5, 124–140.Google Scholar
  6. Cardoso, S., Barbosa-Póvoa, A., & Relvas, S. (2013). Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty. European Journal of Operational Research, 226, 436–451.Google Scholar
  7. Cóccola, M., Me’ndez, C. A., & Dondo, R. G. (2015). A branch-and-price approach to evaluate the role of cross-docking operations in consolidated supply chains. Computer Chemical Engineering, 80, 15–29.Google Scholar
  8. Deb, K. (2001). Multiobjective optimization using evolutionary algorithms. Chichester: Wiley.Google Scholar
  9. Dondo, R., & Cerdá, J. (2015). The heterogeneous vehicle routing and truck scheduling problem in a multi-door cross-dock system. Computers and Chemical Engineering, 76, 42–62.Google Scholar
  10. Fazel Zarandi, M. H., Khorshidian, H., & Akbarpour Shirazi, M. (2014). A constraint programming model for the scheduling of JIT cross-docking systems with pre-emption. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-013-0860-9.
  11. Gaohao, L., & Noble, J. S. (2012). An integrated model for cross dock operations including staging. International Journal of Production Research, 50, 2451–2464.Google Scholar
  12. Gokgoz, F., & Atmaca, E. M. (2012). Financial optimization in the Turkish electricity market: Markowitz’s mean-variance approach. Renewable and Sustainable Energy Reviews, 16, 357–368.Google Scholar
  13. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading: Addison-Wesley.Google Scholar
  14. Gorchels, L. (2000). The product manager’s handbook: The complete product management resource (2nd ed.). New York: McGraw-Hill.Google Scholar
  15. Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333–347.Google Scholar
  16. Hadavandi, E., Shavandi, H., Ghanbari, A., & Abbasian, S. (2012). Developing a hybrid artificial intelligence model for outpatient visit forecasting in hospitals. Applied Soft Computing, 12, 700–711.Google Scholar
  17. Heidari, F., Zegordi, S. H., & Tavakkoli-Moghaddam, R. (2015). Modeling truck scheduling problem at a cross-dock facility through a bi-objective bi-level optimization approach. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-015-1160-3.
  18. Hwang, C. L., Masud, A. (1979). Multiple objective decision making. In Methods and applications: a state of the art survey, Lecture notes in economics and mathematical systems (vol. 164). Berlin: Springer.Google Scholar
  19. Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Berlin: Springer.Google Scholar
  20. Kaul, A., & Rao, V. R. (1995). Research for product positioning and design decisions: An integrative review. International Journal of Research in Marketing, 12(4), 293–320.Google Scholar
  21. Keshtzari, M., Naderi, B., & Mehdizadeh, E. (2016). An improved mathematical model and a hybrid metaheuristic for truck scheduling in cross-dock problems. Computers and Industrial Engineering, 91, 197–204.Google Scholar
  22. Kilic, H. S., Cebeci, U., & Ayhan, M. B. (2015). Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey. Resource Conservation Recycling, 95, 120–132.Google Scholar
  23. Kheirkhah, A. S., & Rezaei, S. (2015). Using cross-docking operations in a reverse logistics network design: a new approach. Production Engineering Research and Development. doi: 10.1007/s11740-015-0646-3.
  24. Konar, A. (2005). Computational intelligence principles, techniques. Berlin: Springer.Google Scholar
  25. Konur, D., & Golias, M. (2013). Analysis of different approaches to cross-dock truck scheduling with truck arrival time uncertainty. Computers and Industrial Engineering, 65, 663–672.Google Scholar
  26. Kuo, Y. (2013). Optimizing truck sequencing and truck dock assignment in a cross docking system. Expert Systems with Applications, 40, 5532–41.Google Scholar
  27. Kwak, M., & Kim, H. (2015). Design for life-cycle profit with simultaneous consideration of initial manufacturing and end-of-life remanufacturing. Engineering Optimization, 47(1), 18–35.Google Scholar
  28. Ladier, A., & Alpan, G. (2014). Crossdock truck scheduling with time windows: Earliness, tardiness and storage policies. Journal of Intelligent Manufacturing,. doi: 10.1007/s10845-014-1014-4.Google Scholar
  29. Langenberg, K. U., Seifert, R. W., & Tancrez, J.-S. (2012). Aligning supply chain portfolios with product portfolios. International Journal of Production Economics, 135(1), 500–513.Google Scholar
  30. Larbi, R., Alpan, G., Baptiste, P., & Penz, B. (2011). Scheduling cross docking operations under full, partial and no information on inbound arrivals. Computers and Operations Research, 38, 889–900.Google Scholar
  31. Li, Y., Chu, X., Chen, D., Liu, Q., & Shen, J. (2014). An integrated module portfolio planning approach for complex products and systems. International Journal of Computer Integrated Manufacturing., 28(9), 988–998. doi: 10.1080/0951192X.2014.961551.Google Scholar
  32. Liao, T. W., Egbelu, P. J., & Chang, P. C. (2013). Simultaneous dock assignment and sequencing of inbound trucks under a fixed outbound truck schedule in multi-door cross docking operations. International Journal of Production Economics, 141(1), 212–229.Google Scholar
  33. Liao, C.-J., Lin, Y., & Shih, S. C. (2010). Vehicle routing with crossdocking in the supply chain. Expert Systems with Applications, 37, 6868–6873.Google Scholar
  34. Ma, H., Miao, Z., Lim, A., & Rodrigues, B. (2011). Cross docking distribution networks with setup cost and time window constraint. Omega, 39, 64–72.Google Scholar
  35. Mahaboob Sheriff, K. M., Gunasekaran, A., & Nachiappan, S. (2012). Reverse logistics network design: A review on strategic perspective. International Journal of Logistics Systems and Management, 12(2), 171–194.Google Scholar
  36. Maheut, J., Garcia-Sabater, J. P. (2012). A mixed-integer linear programming model for transportation planning in the full truck load strategy to supply products with unbalanced demand in the just in time context: a case study. Advances in Production Management Systems. International Conference, APMS 2012 Rhodes, Greece, September.Google Scholar
  37. Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.Google Scholar
  38. Mavrotas, G. (2009). Effective implementation of the \(\varepsilon \)-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213, 455–65.Google Scholar
  39. Mavrotas, G., & Florios, K. (2013). An improved version of the augmented \(\varepsilon \)-constraint method (AUGMECON2) for finding the exact Pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219, 9652–69.Google Scholar
  40. Miao, Z., Lim, A., & Ma, H. (2009). Truck dock assignment problem with operational time constraint within cross-docks. European Journal of Operational Research, 199, 105–115.Google Scholar
  41. Mohtashami, A. (2015). A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation. Computers and Industrial Engineering, 90, 221–240.Google Scholar
  42. Mohtashami, A., Tavana, M., Santos-Arteaga, F., & Fallahian-Najafabadi, A. (2015). A novel multi-objective meta-heuristic model for solving cross-docking scheduling problems. Applied Soft Computing, 31, 30–47.Google Scholar
  43. Mokhtarinejad, M., Ahmadi, A., Karimi, B., & Rahmati, S. (2015). A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment. Applied Soft Computing, 34, 274–285.Google Scholar
  44. Mousavi, S. M., & Tavakkoli-Moghaddam, R. (2013). A hybrid simulated annealing algorithm for location and routing scheduling problems with cross-docking in the supply chain. Journal of Manufacturing Systems, 32(2), 335–347.Google Scholar
  45. Musa, R., Arnaout, J.-P., & Jung, H. (2010). Ant colony optimization algorithm to solve for the transportation problem of cross-docking network. Computers and Industrial Engineering, 59, 85–92.Google Scholar
  46. Orfi, N., Terpenny, J., & Asli, S. S. (2011). Harnessing product complexity: Step 1—Establishing product complexity dimensions and indicators. Engineering Economics, 56(1), 59–79.Google Scholar
  47. Pishvaee, M. S., Farahani, R. Z., & Dullaert, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computer and Operation Research, 37(6), 1100–1112.Google Scholar
  48. Pishvaee, M., & Razmi, J. (2011). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36, 3433–3446.Google Scholar
  49. Rahmanzadeh Tootkaleh, S., Fatemi Ghomi, S. M. T., & Sajadieh, M. S. (2016). Cross dock scheduling with fixed outbound trucks departure times under substitution condition. Computers and Industrial Engineering, 92, 50–56.Google Scholar
  50. Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modeling, 37, 328–344.Google Scholar
  51. Ross, A., & Jayaraman, V. (2008). An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design. Computers and Industrial Engineering, 55, 64–79.Google Scholar
  52. Rumelhart, D., & McClelland, J. (1986). Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1: foundations. Cambridge, MA: MIT Press.Google Scholar
  53. Sadeghi, A., Alem-Tabriz, A., & Zandieh, M. (2011). Product portfolio planning: A metaheuristic-based simulated annealing algorithm. International Journal of Production Research, 49(8), 2327–2350.Google Scholar
  54. Salvador, F., Forza, C., & Rungtusanatham, M. (2002). Modularity, product variety, production volume, and component sourcing: Theorizing beyond generic prescriptions. Journal of Operations Management, 20(5), 549–575.Google Scholar
  55. Shahrabi, J., Hadavandi, E., & Asadi, S. (2013). Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series. Knowledge-Based Systems, 43, 112–122.Google Scholar
  56. Song, Z., & Kusiak, A. (2009). Optimising product configurations with a data-mining approach. International Journal of Production Research, 47(7), 1733–1751.Google Scholar
  57. Shakeri, M., Low, M., Turner, S., & Lee, E. (2012). A robust two-phase heuristic algorithm for the truck scheduling problem in a resource-constrained crossdock. Computers and Operations Research, 39(11), 2564–2577.Google Scholar
  58. Shi, W., Liu, Z., Shang, J., & Cui, Y. (2013). Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain. European Journal of Operational Research, 229(3), 695–706.Google Scholar
  59. Tang, S.-L., & Yan, H. (2010). Pre-distribution vs. post-distribution for crossdocking with transshipments. Omega, 38, 192–202.Google Scholar
  60. Van Belle, J., Valckenaers, P., & Cattrysse, D. (2012). Cross-docking: State of the art. Omega, 40, 827–846.Google Scholar
  61. Wan, X., Evers, P. T., & Dresner, M. E. (2012). Too much of a good thing: The impact of product variety on operations and sales performance. Journal of Operations Management, 30(4), 316–324.Google Scholar
  62. Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in the behavior sciences. Ph.D. Thesis: Harvard University, Cambridge, MA, USA.Google Scholar
  63. Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. In IEEE transaction on system: Man, and cybernetics.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • H. Khorshidian
    • 1
  • M. Akbarpour Shirazi
    • 1
    Email author
  • S. M. T. Fatemi Ghomi
    • 1
  1. 1.Department of Industrial EngineeringAmirkabir University of TechnologyTehranIran

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