Vehicle Routing with Time Windows and Customer Selection for Perishable Goods

Document Type: Research Paper

Authors

1 Tehran University, Tehran, Iran

2 College of Engineering, University of Tehran, Tehran, Iran

Abstract

Delivering perishable products to customers as soon as possible and with the minimum cost has been always a challenge for producers and has been emphasized over recent years due to the global market becoming more competitive. In this paper a multi-objective mix integer non-linear programming model is proposed to maximize both profits of a distributer and the total freshness of the several products to be delivered to customers with respect to their demands and with consideration of different soft time windows for each customer, heterogeneous distribution fleet and customer selection option for the distributer. The proposed model is solved with TH method. The two genetic algorithm and simulated annealing algorithm are used to solve large-sized problems. Finally, their results are compared to each other when the optimization software becomes unable of solution representation.

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Main Subjects


Amorim, P., & Almada-Lobo, B. (2014). The impact of food perishability issues in the vehicle routing problem. Computers & Industrial Engineering, Vol. 67, pp. 223-233.

Amorim, P., Parragh, S. N., Sperandio, F., & Almada-Lobo, B. (2012). A rich vehicle routing problem dealing with perishable food: a case study. Top, pp. 1-20.

Chen, H.-K., Hsueh, C.-F., & Chang, M.-S. (2009). Production scheduling and vehicle routing with time windows for perishable food products. Computers & Operations Research, Vol. 36(7), pp. 2311-2319.

Cordeau, J.-F., Desaulniers, G., Desrosiers, J., Solomon, M. M., & Soumis, F. (2001). VRP with time windows. The vehicle routing problem, Vol. 9, pp. 157-193.

Doerner, K. F., Gronalt, M., Hartl, R. F., Kiechle, G., & Reimann, M. (2008). Exact and heuristic algorithms for the vehicle routing problem with multiple interdependent time windows. Computers & Operations Research, Vol. 35(9), pp. 3034-3048.

Eglese, R. (1990). Simulated annealing: a tool for operational research. European Journal of Operational Research, Vol. 46(3), pp. 271-281.

Glover, F., & Kochenberger, G. A. (2003). Handbook of metaheuristics: Springer.

Golden, B. L., Raghavan, S., & Wasil, E. A. (2008). The Vehicle Routing Problem: Latest Advances and New Challenges: latest advances and new challenges (Vol. 43): Springer.

Hsu, C.-I., Hung, S.-F., & Li, H.-C. (2007). Vehicle routing problem with time-windows for perishable food delivery. Journal of Food Engineering, Vol. 80(2), pp. 465-475.

Naso, D., Surico, M., Turchiano, B., & Kaymak, U. (2007). Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete. European Journal of Operational Research, Vol. 177(3), pp. 2069-2099.

Osvald, A., & Stirn, L. Z. (2008). A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food. Journal of Food Engineering, Vol. 85(2), pp. 285-295.

Rahimi, M., Baboli, A., & Rekik, Y. (2014). A bi-objective inventory routing problem by considering customer satisfaction level in context of perishable product. Paper presented at the Computational Intelligence in Production and Logistics Systems (CIPLS), 2014 IEEE Symposium on.

Tarantilis, C., & Kiranoudis, C. (2002). Distribution of fresh meat. Journal of Food Engineering, Vol. 51(1), pp. 85-91.

Torabi , S., & Hassini, E. (2009). Multi-site production planning integrating procurement and distribution plans in multi-echelon supply chains: an interactive fuzzy goal programming approach. International Journal of Production Research, Vol. 47(19), pp. 5475-5499.

Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, Vol. 159(2), pp. 193-214.

Toth, P., & Vigo, D. (2001). The vehicle routing problem: Siam.