ارائه مدل دو‌هدفه مکان‌یابی-مسیریابی برای محصولات فاسد‌شدنی با در نظر‌ گرفتن ماندگاری محصولات

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی صنایع، دانشگاه کردستان

2 دانشگاه کردستان

چکیده

در این پژوهش، یک مدل دوهدفه برای مسئله مکان‌یابی-مسیریابی محصولات فاسدشدنی همراه با پنجره زمانی نرم ارائه شده است که هدف اول آن کمینه کردن هزینه‌های موجود و هدف دوم حداکثر کردن عمر ماندگاری محصولات می‌باشد. به منظور اعتبار‌‌سنجی مدل ارائه شده، تعدادی مسئله نمونه به‌صورت تصادفی و به کمک داده‌های مقالات معتبر تولید گردیده است. مسئله در محیط نرم‌افزار بهینه‌سازی GAMS کد شده و با روش محدودیت اپسیلون بهبود یافته حل شده است. به دلیل NP-hard بودن مسئله، یک الگوریتم NSGAII برای حل مسئله در ابعاد بزرگ پیشنهاد شده است. نتایج محاسباتی، بیانگر کارایی الگوریتم پیشنهادی است.

کلیدواژه‌ها


عنوان مقاله [English]

A Bi-objective Location-routing Model for Perishable Products Considering Shelf-life of Products

نویسندگان [English]

  • alireza eydi 1
  • Azadeh Sepahi 2
  • Amir Khaleghi 2
1 Department of Industrial Engineering, University of Kurdistan
2 university of kurdistan
چکیده [English]

In this paper, we present a bi-objective mathematical model of location-routing problem with soft time windows for perishable products. The first objective is to minimize costs and the second objective is to maximize products shelf-life. In order to validate the proposed model, some random numerical examples produced by using previous papers. The problem is coded in the GAMS environment and solved using Augmented Epsilon-Constraint method. Because of NP-hardness of location-routing problem, the NSGA-II meta-heuristic method has been proposed to solve large scale problems in a reasonable time. Computational results proves good performance of the proposed algorithm.

کلیدواژه‌ها [English]

  • Location-routing Problems / Perishable Goods / Multi-Objective Problems / Soft Time Window / NSGA-II Meta-heuristic Method
محمدی شاد، ع.ر.، فتاحی، پ.، (2012). یک روش فراابتکاری ترکیبی برای مسئله مکان‌یابی-مسیریابی وسیله نقلیه ظرفیت‌دار با پنجره‌های زمانی سخت. نشریه مهندسی صنایع، 46(2): صفحه 219-233.
Amorim, P., H.-O. Günther, and B. (2012). Almada-Lobo, Multi-objective integrated production and distribution planning of perishable products. International Journal of Production Economics, 138(1): p. 89-101.
Andreica, A. and C. Chira, (2015). Best-order crossover for permutation-based evolutionary algorithms. Applied Intelligence, 42(4): p. 751-776.
Chao, C., Zhihui, T., & Baozhen, Y. (2017). Optimization of two-stage location–routing–inventory problem with time-windows in food distribution network. Annals of Operations Research, 1-24.
Christopher, M. (2016). Logistics & Supply Chain Management. Pearson UK.
Cornuejols, G., Fisher, M. L., & Nemhauser, G. L. (1977). Exceptional paper—                                       Location of bank accounts to optimize float: An analytic study of exact and approximate algorithms. Management Science, 23(8), 789-810.
Daofang, C., Z. Jinfeng, and L. Danping, (2015 ). Cold chain logistics distribution network planning subjected to cost constraints. International Journal of Advanced Science and Technology. 75: p. 1-10.
Deb, K., Pratap, A.; Agarwal, S.; Meyarivan, T. A. M. T., (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2): p. 182-197.
Derens-Bertheau, E., et al., (2015). Cold chain of chilled food in France. International Journal of Refrigeration, 52: p. 161-167.
Drexl, M., Schneider, M., (2015). “A survey of variants and extensions of the location-routing problem”, European Journal of Operational Research, 241, 283–308.
Ghezavati, V. R., & Beigi, M. (2016). Solving a bi-objective mathematical model for location-routing problem with time windows in multi-echelon reverse logistics using metaheuristic procedure. Journal of Industrial Engineering International, 12(4), 469-483.
Govindan, K., A. Jafarian, and R. Khodaverdi, (2014). 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: p. 9-28.
Farrokhi-Asl, H., Tavakkoli-Moghaddam, R., Asgarian, B., & Sangari, E. (2017). Metaheuristics for a bi-objective location-routing-problem in waste collection management. Journal of Industrial and Production Engineering, 34(4), 239-252.
Han, J., C. Lee, and S. Park, (2013). A robust scenario approach for the vehicle routing problem with uncertain travel times. Transportation Science, 48(3): p. 373-390.
Hiassat, A., Diabat, A., & Rahwan, I. (2017). A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of Manufacturing Systems, 42, 93-103.
Hsu, C.-I. and S. Feng, (2003), Vehicle routing problem for distributing refrigerated food. Journal of the Eastern Asia Society for Transportation Studies, 5: p. 2261-2272.
Hsu, C.-I., S.-F. Hung, and H.-C. Li, (2007 ), Vehicle routing problem with time-windows for perishable food delivery. Journal of Food Engineering, 80(2): p. 465-475.
Hugos, M.H., (2011). Essentials of Supply Chain Management. Vol. 62.: John Wiley & Sons.
Karp, R. M. (1972). Reducibility among combinatorial problems. In Complexity of Computer Computations (pp. 85-103). Springer, Boston, MA.
Laporte, G. and Y. Nobert, (1981), An exact algorithm for minimizing routing and operating costs in depot location. European Journal of Operational Research. 6(2): p. 224-226.
Lenstra, J. K., & Kan, A. R. (1981). Complexity of vehicle routing and scheduling problems. Networks, 11(2), 221-227.
Li, K. (2016). A new discrete particle swarm optimization for location inventory routing problem in cold logistics. Revista de la Facultad de Ingeniería, 31(5).
Lopes, R. B., Ferreira, C., Santos, B. S., Barreto, S., (2013). “A taxonomical analysis, current methods and objectives on location-routing problems”, International Transportation Operational Research, 20, 795–822.
Min, H., V. Jayaraman, and R. Srivastava, (1998 ).Combined location-routing problems: A synthesis and future research directions. European Journal of Operational Research,. 108(1): p. 1-15.
Moghaddam, R.T., A.M. Zohrevand, and K. Rafiee, (2012). Solving a New Mathematical Model for a Periodic Vehicle Routing Problem by Particle Swarm Optimization. Transportation Research, 2(1): p. 77.
Nahmias, S., (1982 ), Perishable inventory theory: A review. Operations Research, 30(4): p. 680-708.
Nagy, G. and S. Salhi, (2007). Location-routing: Issues, models and methods. European Journal of Operational Research, 177(2): p. 649-672.
Nikbakhsh, E. and S. Zegordi, (2010). A heuristic algorithm and a lower bound for the two-echelon location-routing problem with soft time window constraints. Scientia Iranica. Transaction E, Industrial Engineering, 17(1): p. 36.
Osvald, A. and L.Z. Stirn, (2008 ). A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food. Journal of Food Engineering, 85(2): p. 285-295.
Perl, J., & Daskin, M. S. (1985). A warehouse location-routing problem. Transportation Research Part B: Methodological, 19(5), 381-396.
Prodhon, C., Prins, C., (2014). “A survey of recent research on location-routing problems”, European Journal of Operational Research, 238, 1–17.
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 Engineering, 109, 9-2
Song, B.D. and Y.D. Ko, (2016 ). A vehicle routing problem of both refrigerated-and general-type vehicles for perishable food products delivery. Journal of Food Engineering, 169: p. 61-71.
Tavakkoli-Moghaddam, R., & Raziei, Z. (2016). A new bi-objective location-routing-inventory problem with fuzzy demands. IFAC-Papers Online, 49(12), 1116-1121.
Tuzun, D. and L.I. Burke, (1999). A two-phase tabu search approach to the location routing problem. European Journal of Operational Research, 116(1): p. 87-99.
Vidović, M., Ratković, B., Bjelić, N., & Popović, D. (2016). A two-echelon location-routing model for designing recycling logistics networks with profit: MILP and heuristic approach. Expert Systems with Applications, 51, 34-48.
Vincent, F. Yu., Lin, S. W., Lee, W., & Ting, C. J. (2010). A simulated annealing                                heuristic for the capacitated location routing problem. Computers & Industrial Engineering, 58(2), 288-299.
Wang, S., Tao, F., & Shi, Y. (2018). Optimization of Location-Routing Problem for Cold Chain Logistics Considering Carbon Footprint. International Journal of Environmental Research and Public Health, 15(1), 86.
Wu, T.-H., C. Low, and J.-W. Bai, (2002). Heuristic solutions to multi-depot location-routing problems. Computers & Operations Research, 29(10): p. 1393-1415.
Zarandi, M. H. F., Hemmati, A., & Davari, S. (2011). The multi-depot capacitated location-routing problem with fuzzy travel times. Expert Systems with Applications, 38(8), 10075-10084.