Multi-objective inventory routing with uncertain demand using population-based metaheuristics

Integrated Computer-Aided Engineering

Volumn 23, Issue 3, 2016, Pages 205-220

  Yang, Zhiwei ^a   Emmerich, Michael ^a   Bäck, Thomas ^a   Kok, Joost ^a  

^a LEIDEN UNIVERSITY   (Netherlands)

Author keywords

cooperative swarm algorithm; indicator based evolutionary algorithms; Inventory routing; multi objective optimization; robust optimization

Indexed keywords

ALGORITHMS; APPROXIMATION ALGORITHMS; ARTIFICIAL INTELLIGENCE; COSTS; ECONOMIC AND SOCIAL EFFECTS; EVOLUTIONARY ALGORITHMS; OPTIMIZATION; PARETO PRINCIPLE; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

ALGORITHMIC METHODS; COOPERATIVE SWARM; EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION; INVENTORY ROUTING; INVENTORY ROUTING PROBLEMS; OPTIMAL TRADE-OFF SURFACES; POISSON RANDOM VARIABLES; ROBUST OPTIMIZATION;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84977571937     PISSN: 10692509     EISSN: 18758835     Source Type: Journal    
DOI: 10.3233/ICA-160515     Document Type: Article

References (59)

1. Y. Adulyasak, J.-F. Cordeau and R. Jans, Formulations and branch-And-cut algorithms for multivehicle production and inventory routing problems, INFORMS Journal on Computing 26(1) (2013), 103-120
2. E.-H. Aghezzaf, B. Raa and H. Van Landeghem, Modeling inventory routing problems in supply chains of high consumption products, European Journal of Operational Research 169(3) (2005), 1048-1063
3. S. Anily and A. Federgruen, One warehouse multiple retailer systems with vehicle routing costs, Management Science 36(1) (1990), 92-114
4. C. Archetti, L. Bertazzi, A. Hertz and M.G. Speranza, A hybrid heuristic for an inventory routing problem, INFORMS Journal on Computing 24(1) (2012), 101-116
5. C. Archetti, L. Bertazzi, G. Laporte and M.G. Speranza, A branch-And-cut algorithm for a vendor-managed inventory routing problem, Transportation Science 41(3) (2007), 382-391
6. D. Barnes-Schuster and Y. Bassok, Direct shipping and the dynamic single-depot/multi-retailer inventory system, European Journal of Operational Research 101(3) (1997), 509-518
7. W.J. Bell, L.M. Dalberto, M.L. Fisher, A.J. Greenfield, R. Jaikumar, P. Kedia, R.G. Mack and P.J. Prutzman, Improving the distribution of industrial gases with an on-line computerized routing and scheduling optimizer, Interfaces 13(6) (1983), 4-23
8. L. Bertazzi, G. Paletta and M.G. Speranza, Deterministic order-up-To level policies in an inventory routing problem, Transportation Science 36(1) (2002), 119-132
9. N. Beume, B. Naujoks and M. Emmerich, SMS-EMOA: Multiobjective selection based on dominated hypervolume, European Journal of Operational Research 181(3) (2007), 1653-1669
10. I. Boulkaibet, L. Mthembu, F. De Lima Neto and T. Marwala, Finite element model updating using fish school search and volitive particle swarm optimization, Integrated Computer-Aided Engineering 22(4) (2015), 361-376
11. K. Bringmann and T. Friedrich, Approximating the least hypervolume contributor: Np-hard in general, but fast in practice. In Evolutionary Multi-Criterion Optimization, Springer, 2009, pp. 6-20
12. L.D. Burns, R.W. Hall, D.E. Blumenfeld and C.F. Daganzo, Distribution strategies that minimize transportation and inventory costs, Operations Research 33(3) (1985), 469-490
13. A. Campbell, L. Clarke, A. Kleywegt and M. Savelsbergh. The inventory routing problem. In Fleet management and logistics, Springer, 1998, pp. 95-113
14. B.R. Campomanes-Álvareza, O. Cordln and S. Damasa, Evolutionary multi-objective optimization for mesh simplification of 3d open models, Integrated Computer-Aided Engineering 20(4) (2013), 375-390
15. M.W. Carter, J.M. Farvolden, G. Laporte and J. Xu, Solving an integrated logistics problem arising in grocery distribution, INFOR 34(4) (1996), 290
16. J. Cheng, G. Zhang, F. Caraffini and F. Neri, Multicriteria adaptive differential evolution for global numerical optimization, Integrated Computer-Aided Engineering 22(2) (2015), 103-107
17. T.W. Chien, A. Balakrishnan and R.T. Wong, An integrated inventory allocation and vehicle routing problem, Transportation Science 23(2) (1989), 67-76
18. G. Clarke and J.Wright, Scheduling of vehicles from a central depot to a number of delivery points, Operations Research 12(4) (1964), 568-581
19. L.C. Coelho, J.-F. Cordeau and G. Laporte, Thirty years of inventory routing, Transportation Science 48(1) (2013), 1-19
20. L.C. Coelho and G. Laporte, A branch-And-cut algorithm for the multi-product multi-vehicle inventory-routing problem, International Journal of Production Research 51(23-24) (2013), 7156-7169
21. L.C. Coelho and G. Laporte, The exact solution of several classes of inventory-routing problems, Computers and Operations Research 40(2) (2013), 558-565
22. C.A. Coello and M.S. Lechuga, Mopso: A proposal for multiple objective particle swarm optimization. In Evolutionary Computation, 2002. CEC?02. Proceedings of the 2002 Congress on, volume 2, IEEE, 2002, pp. 1051-1056
23. K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 6(2) (2002), 182-197
24. M. Dorigo and M. Birattari, Ant colony optimization. In Encyclopedia of machine learning, Springer, 2010, pp. 36-39
25. M. Dror and M. Ball, Inventory routing: Reduction from an annual to short period problem. Université de Montréal, Centre de recherche sur les transports, 1984
26. M. Emmerich, N. Beume and B. Naujoks, An emo algorithm using the hypervolume measure as selection criterion. In Evolutionary Multi-Criterion Optimization, Springer, 2005, pp. 62-76
27. M.T. Emmerich and C.M. Fonseca, Computing hypervolume contributions in low dimensions: Asymptotically optimal algorithm and complexity results. In Evolutionary Multi-Criterion Optimization, Springer, 2011, pp. 121-135
28. E. Forcael, V. González, F. Orozco, S. Vargas, A. Pantoja and P. Moscoso, Ant colony optimization model for tsunamis evacuation routes, Computer-Aided Civil and Infrastructure Engineering 29(10) (2014), 723-737
29. G. Gallego and D. Simchi-Levi, On the effectiveness of direct shipping strategy for the one-warehouse multi-retailer rsystems, Management Science 36(2) (1990), 240-243
30. L.M. Gambardella, E. Taillard and G. Agazzi, Macs-Vrptw: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows. Technical Report. Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale, 1999
31. M.J. Geiger and M. Sevaux, The biobjective inventory routing problem-problem solution and decision support. In Network Optimization, Springer, 2011, pp. 365-378
32. A.P. Guerreiro, C.M. Fonseca and M.T. Emmerich, A fast dimension-sweep algorithm for the hypervolume indicator in four dimensions. In CCCG, 2012, pp. 77-82
33. S.-H. Huang and P.-C. Lin, A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty, Transportation Research Part E: Logistics and Transportation Review 46(5) (2010), 598-611
34. S. Huber, M.J. Geiger and M. Sevaux, Interactive referencepoint-based guided local search for the bi-objective inventory routing problem. arXiv preprint arXiv: 1405.5643, 2014
35. I. Hupkens and M. Emmerich, Logarithmic-Time updates in smsemoa and hypervolume-based archiving. In EVOLVE-A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation IV, Springer, 2013, pp. 155-169
36. L. Jia, Y. Wang and L. Fan, Multiobjective bilevel optimization for production-distribution planning problems using hybrid genetic algorithm, Integrated Computer-Aided Engineering 21(1) (2014), 77-90
37. T. Kimura and T. Ikeguchi, An optimum strategy for dynamic and stochastic packet routing problems by chaotic neurodynamics, Integrated Computer-Aided Engineering 14(4) (2007), 307-322
38. A.J. Kleywegt, V.S. Nori and M.W. Savelsbergh, The stochastic inventory routing problem with direct deliveries, Transportation Science 36(1) (2002), 94-118
39. R. Li, M. Emmerich, J. Eggermont and E.G. Bovenkamp, Mixed-integer optimization of coronary vessel image analysis using evolution strategies. In Proceedings of the 8th annual conference on Genetic and evolutionary computation, GECCO ?06, ACM, 2006, pp. 1645-1652
40. X. Li, W. Li, X. Cai and F. He, A hybrid optimization approach for sustainable process planning and scheduling, Integrated Computer-Aided Engineering 22(4) (2015), 311-326
41. K. Miettinen, Nonlinear multiobjective optimization, volume 12. Springer Science & Business Media, 1999
42. N.H. Moin, S. Salhi and N. Aziz, An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem, International Journal of Production Economics 133(1) (2011), 334-343
43. S. Mostaghim, J. Branke and H. Schmeck, Multi-objective particle swarm optimization on computer grids. In Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM, 2007, pp. 869-875
44. S. Mostaghim, J. Branke and H. Schmeck, Multi-objective particle swarm optimization on computer grids. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO ?07, New York, NY, USA, 2007. ACM, pp. 869-875
45. S. Mostaghim and J. Teich, Strategies for finding good local guides in multi-objective particle swarm optimization (mopso). In Swarm Intelligence Symposium, 2003. SIS?03. Proceedings of the 2003 IEEE, IEEE, 2003, pp. 26-33
46. R. Ribeiro and H. Ramalhinho-Lourenço, Inventory-routing model, for a multi-period problem with stochastic and deterministic demand. UPF Economics and Business Working Paper, 725, 2003
47. A. Rodríguez and J.A. Reggia, A distributed learning algorithm for particle systems, Integrated Computer-Aided Engineering 16(1) (2009), 1-20
48. S. Rokni and A.R. Fayek, A multi-criteria optimization framework for industrial shop scheduling using fuzzy set theory, Integrated Computer-Aided Engineering 17(3) (2010), 175-196
49. M. Savelsbergh and J.-H. Song, An optimization algorithm for the inventory routing problem with continuous moves, Computers& Operations Research 35(7) (2008), 2266-2282
50. H. Seada and K. Deb, U-nsga-iii: A unified evolutionary optimization procedure for single, multiple and many objectives: Proof-of-principle results. In Evolutionary Multi-Criterion Optimization, Springer, 2015, pp. 34-49
51. N. Siddique and H. Adeli, Computational intelligence: Synergies of fuzzy logic, neural networks and evolutionary computing. John Wiley & Sons, 2013
52. M.M. Solomon, Algorithms for the vehicle routing and scheduling problems with time window constraints, Operations research 35(2) (1987), 254-265
53. O. Solyali and H. Süral, A branch-And-cut algorithm using a strong formulation and an a priori tour-based heuristic for an inventory-routing problem, Transportation Science 45(3) (2011), 335-345
54. B. van Veen, M. Emmerich, Z. Yang, T. Bäck and J. Kok, Ant colony algorithms for the dynamic vehicle routing problem with time windows. In Natural and Artificial Computation in Engineering and Medical Applications, Springer, 2013, pp. 1-10
55. Z. Yang, M. Emmerich and T. Back, Ant based solver for dynamic vehicle routing problem with time windows and multiple priorities. In Evolutionary Computation (CEC), 2015 IEEE Congress on, IEEE, 2015, pp. 2813-2819
56. Z. Yang, M. Emmerich, T. Bäck and J. Kok, Multicriteria inventory routing by cooperative swarms and evolutionary algorithms. In Bioinspired Computation in Artificial Systems, Springer, 2015, pp. 127-137
57. Q.-H. Zhao, S. Chen and C.-X. Zang, Model and algorithm for inventory/routing decision in a three-echelon logistics system, European Journal of Operational Research 191(3) (2008), 623-635
58. Z. Zhu, J. Xiao, J.-Q. Li, F. Wang and Q. Zhang, Global path planning of wheeled robots using multi-objective memetic algorithms, Integrated Computer-Aided Engineering 22(4) (2015), 387-404
59. E. Zitzler, L. Thiele, M. Laumanns, C.M. Fonseca and V.G. Da Fonseca, Performance assessment of multiobjective optimizers: An analysis and review, Evolutionary Computation, IEEE Transactions on 7(2) (2003), 117-132