Document Type : Original Article

Authors

1 Department of Industrial Engineering, Arak University, Arak, Iran.

2 Department of Industrial Engineering and Future Studies, Faculty of Engineering, University of Isfahan, Isfahan, Iran.

3 Department of Industrial Engineering, Kermanshah University of Technology, Kermanshah, Iran.

Abstract

Purpose: The study aims to present a mathematical model for reducing system costs by adequately locating the required warehouses and routing vehicles that carry the products from the warehouses within a time window.
Methodology: Concerning the specific nature of the location-routing problem, the consumption of fuel and the depreciation of vehicles are directly affected by the distance covered. The model proposed in this research seeks to minimize the undue length of the distance that vehicles have to travel. Moreover, to approximate the model to real-world conditions as much as possible, the 'time window' concept is employed to determine the maximum allowable time for the distribution of goods.
Findings: Three metaheuristic algorithms, including NSGA-II, PAES, and MOICA, are used to solve the proposed model. Several problems of different sizes are introduced and solved to evaluate the efficiency of the solutions. Then, the results are compared regarding the SM, MID, and QM criteria. The comparative results suggest the superiority of the MOICA algorithm for big-size problems.
Originality/Value: Setting a time window to reduce the distance travelled by the vehicles gets the model close to real-world conditions. It also makes it possible to estimate the costs more accurately. 

Keywords

Adeleke, O. O., Idoko, S., Kolo, S. S., Anwar, A. R., Sijuwola, O. O., & Akinola, O. (2019). Web-based advanced traveller information system for minna metropolis, Nigeria. Arid Zone journal of engineering, technology and environment15(4), 1026-1037.
Bányai, T., Tamás, P., Illés, B., Stankevičiūtė, Ž., & Bányai, Á. (2019). Optimization of municipal waste collection routing: Impact of industry 4.0 technologies on environmental awareness and sustainability. International journal of environmental research and public health16(4), 634. https://doi.org/10.3390/ijerph16040634
Basso, R., Kulcsár, B., & Sanchez-Diaz, I. (2021). Electric vehicle routing problem with machine learning for energy prediction. Transportation research part B: methodological145, 24-55.
Christofides, N., & Eilon, S. (1969). An algorithm for the vehicle-dispatching problem. Journal of the operational research society20(3), 309-318.
Cuthbertson, R. W. (1998). The Logic of logistics: theory, algorithms and applications for logistics management. Journal of the operational research society, 49(9), 1016-1017. https://doi.org/10.1057/palgrave.jors.2600034
Ji, Y., Du, J., Han, X., Wu, X., Huang, R., Wang, S., & Liu, Z. (2020). A mixed integer robust programming model for two-echelon inventory routing problem of perishable products. Physica a: statistical mechanics and its applications548, 124481. https://doi.org/10.1016/j.physa.2020.124481
Li, P., Lan., H., & Saldanha-Da-Gama, F. (2019). A bi-objective capacitated location-routing problem for multiple perishable commodities. IEEE access7, 136729-136742.
Min, H., Jayaraman, V., & Srivastava, R. (1998). Combined location-routing problems: a synthesis and future research directions. European journal of operational research108(1), 1-15.
Rafie-Majd, Z., Pasandideh, S. H. R., & Naderi, B. (2018). Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Computers & chemical engineering109, 9-22.
Salhi, S., & Rand, G. K. (1989). The effect of ignoring routes when locating depots. European journal of operational research39(2), 150-156.
Shahabi-Shahmiri, R., Asian, S., Tavakkoli-Moghaddam, R., Mousavi, S. M., & Rajabzadeh, M. (2021). A routing and scheduling problem for cross-docking networks with perishable products, heterogeneous vehicles and split delivery. Computers & industrial engineering157, 107299. https://doi.org/10.1016/j.cie.2021.107299
Tavakkoli-Moghaddam, R., Rabbani, M., Saremi, A., & Safaei, N. (2005). Solving the backhaul vehicle routing problem by genetic algorithms. 35th international conference on computers and industrial engineering (pp. 1905-1910). Istanbul Technical University, Istanbul, Turkey. https://www.researchgate.net/profile/Reza-Tavakkoli-
Moghaddam/publication/255636808_SOLVING_THE_BACKHAUL_VEHICLE_ROUTING_PROBLEM_BY
_GENETIC_ALGORITHMS/links/54a53b9f0cf267bdb90817fc/SOLVING-THE-BACKHAUL-
VEHICLE-ROUTING-PROBLEM-BY-GENETIC-ALGORITHMS.pdf
Tsang, Y. P., Wu, C. H., Lam, H. Y., Choy, K. L., & Ho, G. T. (2021). Integrating internet of things and multi-temperature delivery planning for perishable food E-commerce logistics: a model and application. International journal of production research59(5), 1534-1556.
Wang, Y., Yuan, Y., Guan, X., Xu, M., Wang, L., Wang, H., & Liu, Y. (2020). Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation. Journal of cleaner production258, 120590. https://doi.org/10.1016/j.jclepro.2020.120590
Webb, M. H. J. (1968). Cost functions in the location of depots for multiple-delivery journeys. Journal of the operational research society19(3), 311-320.
Zhao, Z., Li, X., & Zhou, X. (2020). Optimization of transportation routing problem for fresh food in time-varying road network: Considering both food safety reliability and temperature control. PloS one15(7), e0235950.