Advertisement

Identifying ambient service location problems and its application using a humanized computing model

  • You-Shyang ChenEmail author
  • Heng-Hsing ChuEmail author
  • Arun Kumar Sangaiah
Original Research
  • 76 Downloads

Abstract

The purpose of this study is to identify a humanized computing model for solving ambient service location problems with a developed heuristic algorithm. In real situations, space constraints may result in a practical facility location planning problem in an urban environment. Because of space constraints, large warehouse facilities may not be allocated for a single location; and they need to be partitioned and placed at different locations with synergy and collaboration patterns to fulfill orders for many retail facilities. Therefore, properly locating different types of warehouse facilities to make logistics and supply efficient is important. This study considers the multiple-distinct facilities service location problems (MDFSLP) that are constrained in four attributes of single sourcing, p-median, p-dispersion, and hierarchical location. Although past research has discussed the same types of warehouse location problems, the MDFSLP has sparsely been explored and reviewed. Thus, this study proposes a fast-iterated local search (FSILS) algorithm to solve the problem efficiently. The results show that the FSILS algorithm contributes to the better efficiency of computational time than other listed similar approaches and are of high importance in a realistic location environment to promote industry development on business strategies.

Keywords

Ambient service location Warehouse location Multiple-distinct facilities service Single sourcing Iterated local search Environments/spaces planning 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this article.

References

  1. Abo-Elnaga Y, El-Sobky B, Al-Naser L (2017) An active-set trust-region algorithm for solving warehouse location problem. J Taibah Univ Sci 11(2):353–358.  https://doi.org/10.1016/j.jtusci.2016.04.003 CrossRefGoogle Scholar
  2. Alavidoost MH, Tarimoradi M, Zarandi MF (2015) Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks. J Intell Manuf.  https://doi.org/10.1007/s10845-015-1130-9 CrossRefGoogle Scholar
  3. Albareda-Sambola M, Hinojosa Y, Puerto J (2015) The reliable p-median problem with at-facility service. Eur J Oper Res 245(3):656—666.  https://doi.org/10.1016/j.ejor.2015.03.049 MathSciNetzbMATHCrossRefGoogle Scholar
  4. Aliakbarian N, Dehghanian F, Salari M (2015) A bi-level programming model for protection of hierarchical facilities. Comput Oper Res 64:210—224.  https://doi.org/10.1016/j.cor.2015.05.016 zbMATHCrossRefGoogle Scholar
  5. An Y, Zeng B, Zhang Y, Zhao L (2014) Reliable p-median facility location problem: two-stage robust models and algorithms. Transp Res Part B 64:54—72.  https://doi.org/10.1016/j.trb.2014.02.005 CrossRefGoogle Scholar
  6. Arabani AB, Farahani RZ (2012) Facility location dynamics: an overview of classifications and applications. Comput Ind Eng 62(1):408—420.  https://doi.org/10.1016/j.cie.2011.09.018 CrossRefGoogle Scholar
  7. Bautista J, Pereira J (2007) A GRASP algorithm to solve the unicost set covering problem. Comput Oper Res 34(10):162—3173.  https://doi.org/10.1016/j.cor.2005.11.026 MathSciNetzbMATHCrossRefGoogle Scholar
  8. Blanco V, Puerto J, Ben-Ali SEH (2016) Continuous multifacility ordered median location problems. Eur J Oper Res 250(1):56—64.  https://doi.org/10.1016/j.ejor.2015.10.065 MathSciNetzbMATHCrossRefGoogle Scholar
  9. Brimberg J, Drezner Z (2013) A new heuristic for solving the p-median problem in the plane. Comput Oper Res 40(1):427—437.  https://doi.org/10.1016/j.cor.2012.07.012 MathSciNetzbMATHCrossRefGoogle Scholar
  10. Cao B, Uebe G (1993) An algorithm for solving capacitated multicommodity p-median transportation problems. J Oper Res Soc.  https://doi.org/10.2307/2584196 zbMATHCrossRefGoogle Scholar
  11. Casas-Ramírez MS, Camacho-Vallejo JF (2017) Solving the p-median bilevel problem with order through a hybrid heuristic. Appl Soft Comput 60:73—86.  https://doi.org/10.1016/j.asoc.2017.06.026 CrossRefGoogle Scholar
  12. Chen D, Zhou S, Xie Y, Li X (2015) Optimal facility location model based on genetic simulated annealing algorithm for siting urban refueling stations. Math Prob Eng.  https://doi.org/10.1155/2015/981370 CrossRefGoogle Scholar
  13. Chen Y, Zhao Q, Wang L, Dessouky M (2016) The regional cooperation-based warehouse location problem for relief supplies. Comput Ind Eng 102:259—267.  https://doi.org/10.1016/j.cie.2016.10.021 CrossRefGoogle Scholar
  14. Chen H, Wang X, Liu Z, Zhao R (2017) Impact of risk levels on optimal selling to heterogeneous retailers under dual uncertainties. J Ambient Intell Humaniz Comput 8(5):727—745.  https://doi.org/10.1007/s12652-017-0481-9 CrossRefGoogle Scholar
  15. Cokelez S, Burns JR (1989) Distribution systems-warehouse location and capacity. Omega 17(1):45—51.  https://doi.org/10.1016/0305-0483(89)90019-4 CrossRefGoogle Scholar
  16. Costantino N, Pellegrino R (2010) Choosing between single and multiple sourcing based on supplier default risk: a real options approach. J Purch Supply Manag 16(1):27—40.  https://doi.org/10.1016/j.pursup.2009.08.001 CrossRefGoogle Scholar
  17. Drezner Z, Scott C, Song JS (2003) The central warehouse location problem revisited. IMA J Manag Math 14(4):321—336.  https://doi.org/10.1093/imaman/14.4.321 MathSciNetzbMATHCrossRefGoogle Scholar
  18. Drezner Z, Brimberg J, Mladenović N, Salhi S (2015) New heuristic algorithms for solving the planar p-median problem. Comput Oper Res 62:296—304.  https://doi.org/10.1016/j.cor.2014.05.010 MathSciNetzbMATHCrossRefGoogle Scholar
  19. Elmaghraby WJ (2000) Supply contract competition and sourcing policies. Manuf Ser Oper Manag 2(4):350—371.  https://doi.org/10.1287/msom.2.4.350.12340 CrossRefGoogle Scholar
  20. Farahani RZ, Hekmatfar M, Fahimnia B, Kazemzadeh N (2014) Hierarchical facility location problem: models, classifications, techniques, and applications. Comput Ind Eng 68:104—117.  https://doi.org/10.1016/j.cie.2013.12.005 CrossRefGoogle Scholar
  21. Feo TA, Venkatraman K, Bard JF (1991) A GRASP for a difficult single machine scheduling problem. Comput Oper Res 18(8):635—643.  https://doi.org/10.1016/0305-0548(91)90001-8 zbMATHCrossRefGoogle Scholar
  22. Guastaroba G, Speranza MG (2014) A heuristic for BILP problems: the single source capacitated facility location problem. Eur J Oper Res 238(2):438—450.  https://doi.org/10.1016/j.ejor.2014.04.007 MathSciNetzbMATHCrossRefGoogle Scholar
  23. Gueye S, Menezes MB (2015) General asymptotic and submodular results for the median problem with unreliable facilities. Oper Res Lett 43(5):519—521.  https://doi.org/10.1016/j.orl.2015.07.005 MathSciNetzbMATHCrossRefGoogle Scholar
  24. Hamidi MR, Gholamian MR, Shahanaghi K, Yavari A (2017) Reliable warehouse location-network design problem under intentional disruption. Comput Ind Eng 113:123—134.  https://doi.org/10.1016/j.cie.2017.09.012 CrossRefGoogle Scholar
  25. Hinojosa Y, Puerto J, Fernández FR (2000) A multiperiod two-echelon multicommodity capacitated plant location problem. Eur J Oper Res 123(2):271—291.  https://doi.org/10.1016/S0377-2217(99)00256-8 MathSciNetzbMATHCrossRefGoogle Scholar
  26. Hsieh LF, Tsai L (2006) The optimum design of a warehouse system on order picking efficiency. Int J Adv Manuf Technol 28(5—6):626—637.  https://doi.org/10.1007/s00170-004-2404-0 CrossRefGoogle Scholar
  27. Huang HC, Li R (2008) A k-product uncapacitated facility location problem. Eur J Oper Res 185(2):552—562.  https://doi.org/10.1016/j.ejor.2007.01.010 MathSciNetzbMATHCrossRefGoogle Scholar
  28. Huang X, Yin C, Dadras S, Cheng Y, Bai L. Computing (2018) Adaptive rapid defect identification in ECPT based on K-means and automatic segmentation algorithm. J Ambient Intell Hum.  https://doi.org/10.1007/s12652-017-0671-5 CrossRefGoogle Scholar
  29. IBM Inc (2018) IBM ILOG CPLEX optimization studio getting started with CPLEX. https://www.ibm.com/support/knowledgecenter/SSSA5P_12.7.1/ilog.odms.studio.help/pdf/gscplex.pdf. Accessed 1 Feb 2018
  30. Jayaraman V, Pirkul H (2001) Planning and coordination of production and distribution facilities for multiple commodities. Eur J Oper Res 133(2):394—408.  https://doi.org/10.1016/S0377-2217(00)00033-3 zbMATHCrossRefGoogle Scholar
  31. Kalcsics J (2011) The multi-facility median problem with pos/neg weights on general graphs. Comput Oper Res 38(3):674—682.  https://doi.org/10.1016/j.cor.2010.08.002 MathSciNetzbMATHCrossRefGoogle Scholar
  32. Kalcsics J, Nickel S, Pozo MA, Puerto J, Rodríguez-Chía AM (2014) The multicriteria p-facility median location problem on networks. Eur J Oper Res 235(3):484—493.  https://doi.org/10.1016/j.ejor.2014.01.003 MathSciNetzbMATHCrossRefGoogle Scholar
  33. Kalfakakou R, Katsavounis S, Tsouros K (2003) Minimum number of warehouses for storing simultaneously compatible products. Int J Prod Econ 81:559—564.  https://doi.org/10.1016/S0925-5273(02)00368-7 CrossRefGoogle Scholar
  34. Kallrath J (2005) Solving planning and design problems in the process industry using mixed integer and global optimization. Ann Oper Res 140(1):339—373.  https://doi.org/10.1007/s10479-005-3976-2 MathSciNetzbMATHCrossRefGoogle Scholar
  35. Ke H, Liu J (2017) Dual-channel supply chain competition with channel preference and sales effort under uncertain environment. J Ambient Intell Humaniz Comput 8(5):781—795.  https://doi.org/10.1007/s12652-017-0502-8 CrossRefGoogle Scholar
  36. Köchel P, Thiem S (2011) Search for good policies in a single-warehouse, multi-retailer system by particle swarm optimisation. Int J Prod Econ 133(1):319—325.  https://doi.org/10.1016/j.ijpe.2010.03.021 CrossRefGoogle Scholar
  37. Lee WI, Shih BY, Chen CY (2012) A hybrid artificial intelligence sales-forecasting system in the convenience store industry. Hum Factors Ergon Manuf Ser Ind 22(3):188—196.  https://doi.org/10.1002/hfm.20272 CrossRefGoogle Scholar
  38. Lin CKY (2009) Stochastic single-source capacitated facility location model with service level requirements. Int J Prod Econ 117(2):439—451.  https://doi.org/10.1016/j.ijpe.2008.11.009 CrossRefGoogle Scholar
  39. Lin G, Guan J (2018) A hybrid binary particle swarm optimization for the obnoxious p-median problem. Inf Sci 425:1—17.  https://doi.org/10.1016/j.ins.2017.10.020 MathSciNetCrossRefGoogle Scholar
  40. LINDO Systems (2006) Optimization modeling with LINGO—sixth edition. ISBN: 1-893355-00-4. https://www.lindo.com/downloads/LINGO_text/TOC.pdf. Accessed 1 Feb 2018
  41. Lourenço HR, Martin OC, Stützle T (2010) Iterated local search: framework and applications. In: Handbook of metaheuristics. Springer, US, pp 363—397.  https://doi.org/10.1007/978-1-4419-1665-5_12 CrossRefGoogle Scholar
  42. Mac Queen J (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability 1(14):281—297. https://projecteuclid.org/ euclid.bsmsp/1200512992. Accessed 1 Feb 2018
  43. Meena PL, Sarmah SP, Sarkar A (2011) Sourcing decisions under risks of catastrophic event disruptions. Transp Res Part E 47(6):1058—1074.  https://doi.org/10.1016/j.tre.2011.03.003 CrossRefGoogle Scholar
  44. Michel L, Van Hentenryck P (2004) A simple tabu search for warehouse location. Eur J Oper Res 157(3):576—591.  https://doi.org/10.1016/S0377-2217(03)00247-9 MathSciNetzbMATHCrossRefGoogle Scholar
  45. Mladenović N, Brimberg J, Hansen P, Moreno-Pérez JA (2007) The p-median problem: a survey of metaheuristic approaches. Eur J Oper Res 179(3):927—939.  https://doi.org/10.1016/j.ejor.2005.05.034 MathSciNetzbMATHCrossRefGoogle Scholar
  46. Nezhad AM, Manzour H, Salhi S (2013) Lagrangian relaxation heuristics for the uncapacitated single-source multi-product facility location problem. Int J Prod Econ 145(2):713—723.  https://doi.org/10.1016/j.ijpe.2013.06.001 CrossRefGoogle Scholar
  47. Pisinger D (2006) Upper bounds and exact algorithms for p-dispersion problems. Comput Oper Res 33(5):1380—1398.  https://doi.org/10.1016/j.cor.2004.09.033 zbMATHCrossRefGoogle Scholar
  48. Rath S, Gutjahr WJ (2014) A math-heuristic for the warehouse location–routing problem in disaster relief. Comput Oper Res 42:25—39.  https://doi.org/10.1016/j.cor.2011.07.016 MathSciNetzbMATHCrossRefGoogle Scholar
  49. Ravi SS, Rosenkrantz DJ, Tayi GK (1994) Heuristic and special case algorithms for dispersion problems. Oper Res 42(2):299—310.  https://doi.org/10.1287/opre.42.2.299 zbMATHCrossRefGoogle Scholar
  50. Resende MG, Martí R, Gallego M, Duarte A (2010) GRASP and path relinking for the max–min diversity problem. Comput Oper Res 37(3):498—508.  https://doi.org/10.1016/j.cor.2008.05.011 MathSciNetzbMATHCrossRefGoogle Scholar
  51. Sayyady F, Fathi Y (2016) An integer programming approach for solving the p-dispersion problem. Eur J Oper Res 253(1):216—225.  https://doi.org/10.1016/j.ejor.2016.02.026 MathSciNetzbMATHCrossRefGoogle Scholar
  52. Sayyady F, Tutunchi GK, Fathi Y (2015) P-median and p-dispersion problems: a bi-criteria analysis. Comput Oper Res 61:46—55.  https://doi.org/10.1016/j.cor.2015.02.007 MathSciNetzbMATHCrossRefGoogle Scholar
  53. Sharma RRK, Agarwal P (2014) Approaches to solve MID_CPLP problem: theoretical framework and empirical investigation. Am J Oper Res 4(03):142—154.  https://doi.org/10.4236/ajor.2014.43014 CrossRefGoogle Scholar
  54. Sharma RRK, Tyagi P, Kumar V, Jha A (2015) Developing strong and hybrid formulation for the single stage single period multi commodity warehouse location problem: theoretical framework and empirical investigation. Am J Oper Res 5(3):112—128.  https://doi.org/10.4236/ajor.2015.53010 CrossRefGoogle Scholar
  55. Tang H, Cheng TCE, Ng CT (2009) Finite dominating sets for the multi-facility ordered median problem in networks and algorithmic applications. Comput Ind Eng 57(3):707—712.  https://doi.org/10.1016/j.cie.2009.01.015 CrossRefGoogle Scholar
  56. Wutthisirisart P, Sir MY, Noble JS (2015) The two-warehouse material location selection problem. Int J Prod Econ 170:780—789.  https://doi.org/10.1016/j.ijpe.2015.07.008 CrossRefGoogle Scholar
  57. Yaobao Z, Ping H, Shu Y (2013) An improved particle swarm optimization for the automobile spare part warehouse location problem. Math Prob Eng.  https://doi.org/10.1155/2013/726194 zbMATHCrossRefGoogle Scholar
  58. Yu H, Zeng AZ, Zhao L (2009) Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega 37(4):788—800.  https://doi.org/10.1016/j.omega.2008.05.006 CrossRefGoogle Scholar
  59. Zarrinpoor N, Fallahnezhad MS, Pishvaee MS (2017) Design of a reliable hierarchical location-allocation model under disruptions forhealth service networks: a two-stage robust approach. Comput Ind Eng 109:130—150.  https://doi.org/10.1016/j.cie.2017.04.036 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information ManagementHwa Hsia University of TechnologyNew Taipei CityTaiwan
  2. 2.Department of Information and Finance ManagementNational Taipei University of TechnologyTaipeiTaiwan
  3. 3.School of Computing Science and EngineeringVIT UniversityVelloreIndia

Personalised recommendations