Multi-objective modeling of production and pollution routing problem with time window: A self-learning particle swarm optimization approach

Computers and Industrial Engineering

Volumn 99, Issue , 2016, Pages 29-40

  Kumar, Ravi Shankar ^a   Kondapaneni, Karthik ^a   Dixit, Vijaya ^b   Goswami, A ^a   Thakur, L S ^c   Tiwari, M K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

^b NATIONAL INSTITUTE OF INDUSTRIAL ENGINEERING NITIE   (India)

^c UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Meta heuristics; Multi objective optimization; Multi vehicle; Pollution; Production routing; Time window

Indexed keywords

CARBON FOOTPRINT; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL EFFICIENCY; FLEET OPERATIONS; GENETIC ALGORITHMS; OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); PLANTS (BOTANY); POLLUTION; SUPPLY CHAINS; VEHICLE ROUTING; VEHICLES;

META HEURISTICS; MULTI-OBJECTIVE MODELING; MULTI-VEHICLES; NON DOMINATED SORTING GENETIC ALGORITHM II (NSGA II); PARTICLE SWARM OPTIMIZATION APPROACHES; SUPPLY CHAIN PLANNING; TIME WINDOWS; VEHICLE ROUTING PROBLEM;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84978394054     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2015.07.003     Document Type: Article

References (42)

1. Adulyasak, Y., Cordeau, J.-F., Jans, R., Formulations and branch-and-cut algorithms for multivehicle production and inventory routing problems. INFORMS Journal on Computing 24:1 (2014), 103–120.
2. Adulyasak, Y., Cordeau, J.-F., Jans, R., Optimization-based adaptive large neighborhood search for the production routing problem. Transportation Science 48:1 (2014), 20–45.
3. Adulyasak, Y., Cordeau, J.-F., Jans, R., The production routing problem: A review of formulations and solution algorithms. Computers & Operation Research 55 (2015), 141–152.
4. Amorim, P., Almada-Lobo, B., The impact of food perishability issues in the vehicle routing problem. Computers & Industrial Engineering 67 (2014), 223–233.
5. Andersson, H., Hoff, A., Christiansen, M., Hasle, G., Løkketangen, A., Industrial aspects and literature survey: Combined inventory management and routing. Computers & Operations Research 37:9 (2010), 1515–1536.
6. Archetti, C., Bertazzi, L., Laporte, G., Speranza, M.G., A branch-and-cut algorithm for a vendor-managed inventory-routing problem. Transportation Science 41:3 (2007), 382–391.
7. Archetti, C., Bertazzi, L., Paletta, G., Speranza, M.G., Analysis of the maximum level policy in a production-distribution system. Computers & Operations Research 38:12 (2011), 1731–1746.
8. Armentano, V.A., Shiguemoto, A.L., Løkketangen, A., Tabu search with path relinking for an integrated production–distribution problem. Computers & Operations Research 38:8 (2011), 1199–1209.
9. Baños, R., Ortega, J., Gil, C., Márquez, A.L., de Toro, F., A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows. Computers & Industrial Engineering 65:2 (2013), 286–296.
10. Bard, J.F., Nananukul, N., Heuristics for a multiperiod inventory routing problem with production decisions. Computers & Industrial Engineering 57:3 (2009), 713–723.
11. Bard, J.F., Nananukul, N., A branch-and-price algorithm for an integrated production and inventory routing problem. Computers & Operations Research 37:12 (2010), 2202–2217.
12. Barth, M., Boriboonsomsin, K., Real-world carbon dioxide impacts of traffic congestion. Journal of the Transportation Research Board 2058:1 (2008), 163–171.
13. Barth, M., Younglove, T., Scora, G., Development of a heavy-duty diesel modal emissions and fuel consumption model. 2005, California University, Berkeley.
14. Bektas, T., Laporte, G., The pollution-routing problem. Transportation Research Part B: Methodological 45:8 (2011), 1232–1250.
15. Boudia, M., Prins, C., A memetic algorithm with dynamic population management for an integrated production–distribution problem. European Journal of Operational Research 195:3 (2009), 703–715.
16. Braekers, K., Caris, A., Janssens, G.K., Bi-objective optimization of drayage operations in the service area of intermodal terminals. Transportation Research Part E: Logistics and Transportation Review 65 (2014), 50–69.
17. Chandra, P., Fisher, M.L., Coordination of production and distribution planning. European Journal of Operational Research 72:3 (1994), 503–517.
18. Chen, Z. L. (2004). Integrated production and distribution operations: Taxonomy, models and review. In Handbook of quantitative supply chain analysis: Modelling in the E-Business Era (pp. 711–746).
19. Chen, K., Ji, P., A mixed integer programming model for advanced planning and scheduling (APS). European Journal of Operational Research 181:1 (2007), 515–522.
20. Coello Coello, C.A., Lechuga, M.S., MOPSO: A proposal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), Vol. 2, 2002, IEEE, 1051–1056.
21. Deb, K., Multi-objective optimization using evolutionary algorithms. 2001, Wiley, Chichester, UK.
22. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6:2 (2002), 182–197.
23. Demir, E., Bektaş, T., Laporte, G., A comparative analysis of several vehicle emission models for road freight transportation. Transportation Research Part D: Transport and Environment 16:5 (2011), 347–357.
24. Demir, E., Bektaş, T., Laporte, G., An adaptive large neighborhood search heuristic for the pollution-routing problem. European Journal of Operational Research 223:2 (2012), 346–359.
25. Demir, E., Bektaş, T., Laporte, G., The bi-objective pollution-routing problem. European Journal of Operational Research 232:3 (2014), 464–478.
26. Eberhart, R., Kennedy, J., A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, IEEE, 39–43.
27. Franceschetti, A., Honhon, D., Van Woensel, T., Bektaş, T., Laporte, G., The time-dependent pollution-routing problem. Transportation Research Part B: Methodological 56 (2013), 265–293.
28. Fumero, F., Vercellis, C., Synchronized development of production, inventory and distribution schedules. Transportation Science 33:3 (1999), 330–340.
29. Ghoseiri, K., Ghannadpour, S.F., Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Applied Soft Computing 10:4 (2010), 1096–1107.
30. Goldberg, D.E., Genetic algorithms in search, optimization and machine learning. 1989, Addison-Wesley Longman Publishing Co., Inc, New York.
31. Govindan, K., Jafarian, A., Khodaverdi, R., Devika, K., Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics 152 (2014), 9–28.
32. Khanesar, M.A., Teshnehlab, M., Shoorehdeli, M.A., A novel binary particle swarm optimization. Mediterranean Conference on Control and Automation, Vol. 1, 2007, IEEE, Athens-Greece, 1–6.
33. Li, C., Yang, S., Nguyen, T.T., A self-learning particle swarm optimizer for global optimization problems. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 42:3 (2012), 627–646.
34. Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10:3 (2006), 281–295.
35. Melián-Batista, B., De Santiago, A., AngelBello, F., Alvarez, A., A bi-objective vehicle routing problem with time windows: A real case in Tenerife. Applied Soft Computing 17 (2014), 140–152.
36. Miettinen, K., Nonlinear multiobjective optimization. 1999, Springer Science & Business Media.
37. Solyalı, Q., Süral, H., A relaxation based solution approach for the inventory control and vehicle routing problem in vendor managed systems. Modeling Computation and Optimization, 2009, World Scientific, Singapore, 171–189.
38. Solyalı, O., Süral, H., 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.
39. Srinivas, N., Deb, K., Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2:3 (1995), 221–248.
40. Vicente, A. S. (2011). Laying the foundations for greener transport. TERM 2011: Transport indicators tracking progress towards environmental targets in Europe, EEA Report 7/2011. European Environment Agency, Copenhagen, Denmark.
41. Wright, L.A., Kemp, S., Williams, I., “Carbon footprinting”: Towards a universally accepted definition. Carbon Management 2:1 (2011), 61–72.
42. Zitzler, E., Deb, K., Thiele, L., Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8:2 (2000), 173–195.