حل مساله مسیریابی تولید رقابتی با استفاده از بهینه سازی گروه ذرات

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه مهندسی صنایع دانشکده مهندسی، دانشگاه بوعلی سینا، همدان

2 دانشیار گروه مهندسی صنایع دانشکده مهندسی، دانشگاه بوعلی سینا، همدان

چکیده

مساله مسیریابی تولید به تلفیق دو مساله مسیریابی خودرو و برنامه ریزی تولید می‌پردازد. عموما در مسئله فوق فرض بر این است که نوعی انحصار در محیط وجود دارد و توجهی به تاثیر رقبا در این مسائل در نظر گرفته نشده است. پر واضح است که در دنیای واقعی دیگر نمی‌توان به داشتن بازار انحصاری امید بست. در فضای رقابتی مشتریان متناسب با قیمت و کیفیت تامین‌کننده را انتخاب می‌کنند. بنابراین در این مقاله به عنوان تعریفی از کیفیت، تامین سریع نیاز مشتری و در دستری بودن الزام فضای رقابتی تبیین شده است و به همین جهت، مساله مسیریابی تولید رقابتی با فرض دانستن زودترین و دیرترین زمان تامین تقاضا توسط رقیب مدل‌بندی شده است. به این ترتیب در صورت تاخیر در تامین تقاضای مشتری به نسبت میزان تاخیر سهمی از بازار از دست می‌رود. همچنین مدل‌سازی انجام شده به وسیله نرم افزار گمز حل شده است. علاوه بر این به دلیل استفاده موفق الگوریتم بهینه‌سازی گروه ذرات در حل مسائل بهینه‌سازی، در اینجا نیز الگوریتم بهینه‌سازی گروه ذرات برای حل مساله مسیریابی تولید در ابعاد بزرگ توسعه داده می‌شود. برای بررسی عملکرد الگوریتم ارائه شده جواب‌های حاصل در ابعاد کوچک با جواب‌های حاصل از حل با نرم افزار گمز مقایسه شده است.
 

کلیدواژه‌ها


عنوان مقاله [English]

Particle Swarm Optimization to Solve Competitive Production Routing Problem

نویسندگان [English]

  • Farzaneh Adabi 1
  • Javad Behnamian 2
چکیده [English]

The production routing problem (PRP) integrates vehicle routing and production planning problems. Generally, in PRPs, the impact of competitors has not been considered. Clearly, in the real world, it is no longer possible to have a monopoly market. In competitive environment, customers choose a supplier based on price and quality. So in this article as a definition of quality, providing quick access to customer needs and availability are determined as the requirements of a competitive environment. Therefore, the production routing problem has been modeled with knowing the earliest and latest time of competitor demand meeting. In this way, In case of delay in supplying customers demand, the market share is lost relative to the amount of delay. The problem is modeled and it has been solved by the GAMS software. Since particle swarm optimization has been successfully applied to a variety of problems, here, to solve the problem for the large-sized instances a particle swarm optimization algorithm is also presented. To evaluate the performance of the proposed algorithm, the results with small-sized instances were compared with solutions of GAMS.

کلیدواژه‌ها [English]

  • Production Routing Problem
  • Competitive Environment
  • Particle Swarm Optimization
Adulyasak, Y., Cordeau, J. F., & Jans, R. (2012). Optimization-based adaptive large neighborhood search for the production routing problem. Transportation Science, 48(1), 20-45.

Adulyasak, Y., Cordeau, J. F., & Jans, R. (2013). Formulations and branch-and-cut algorithms for multivehicle production and inventory routing problems. INFORMS Journal on Computing, 26(1), 103-120.

Adulyasak, Y., Cordeau, J. F., & Jans, R. (2015a). Benders decomposition for production routing under demand uncertainty. Operations Research, 63(4), 851-867.

Adulyasak, Y., Cordeau, J. F., & Jans, R. (2015b). The production routing problem: A review of formulations and solution algorithms. Computers & Operations Research, 55, 141-152.

Archetti, C., Bertazzi, L., Paletta, G., & Speranza, M. G. (2011). Analysis of the maximum level policy in a production-distribution system. Computers & Operations Research, 38(12), 1731-1746.

Armentano, V. A., Shiguemoto, A. L., & Løkketangen, A. (2011). Tabu search with path relinking for an integrated production–distribution problem. Computers & Operations Research, 38(8), 1199-1209.

Bard, J. F., & Nananukul, N. (2009a). The integrated production–inventory–distribution–routing problem. Journal of Scheduling, 12(3), 257.

Bard, J. F., & Nananukul, N. (2009b). Heuristics for a multiperiod inventory routing problem with production decisions. Computers & Industrial Engineering, 57(3), 713-723.

Bard, J. F., & Nananukul, N. (2010). A branch-and-price algorithm for an integrated production and inventory routing problem. Computers & Operations Research, 37(12), 2202-2217.

Boudia, M., & Prins, C. (2009). A memetic algorithm with dynamic population management for an integrated production–distribution problem. European Journal of Operational Research, 195(3), 703-715.

Boudia, M., Louly, M. A. O., & Prins, C. (2007). A reactive GRASP and path relinking for a combined production–distribution problem. Computers & Operations Research, 34(11), 3402-3419.

Brahimi, N., & Aouam, T. (2012). Integrated and decoupled models for the production routing problem with backlogging. In The Second International Conference on Industrial Engineering and Manufacturing (ICIEM 2012).

Brahimi, N., & Aouam, T. (2016). Multi-item production routing problem with backordering: a MILP approach. International Journal of Production Research, 54(4), 1076-1093.

Bräysy, O., & Gendreau, M. (2005). Vehicle routing problem with time windows, Part I: Route construction and local search algorithms. Transportation science, 39(1), 104-118.

Chandra, P. (1993). A dynamic distribution model with warehouse and customer replenishment requirements. Journal of the Operational Research Society, 44(7), 681-692.

Chandra, P., & Fisher, M. L. (1994). Coordination of production and distribution planning. European Journal of Operational Research, 72(3), 503-517.

Cordeau JF, Desaulniers G, Desrosiers J, Solomon MM, Soumis F, (2002). The VRP with time windows. In: Toth P, Vigo D, editors. The vehicle routing problem, SIAM Monographs on Discrete Mathematics and Applications, Vol. 9, Philadelphia, PA;. 157–194.

Eberhart, R. C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence. Elsevier. San Francisco, CA: Morgan Kaufmann.

Fumero, F., & Vercellis, C. (1999). Synchronized development of production, inventory, and distribution schedules. Transportation science, 33(3), 330-340.

Kulkarni, R. V., & Bhave, P. R. (1985). Integer programming formulations of vehicle routing problems. European Journal of Operational Research, 20(1), 58-67.

Kumar, R. S., Kondapaneni, K., Dixit, V., Goswami, A., Thakur, L. S., & Tiwari, M. K. (2016). Multi-objective modeling of production and pollution routing problem with time window: A self-learning particle swarm optimization approach. Computers & Industrial Engineering, 99, 29-40.

Kuo, R. J., Wibowo, B. S., & Zulvia, F. E. (2016). Application of a fuzzy ant colony system to solve the dynamic vehicle routing problem with uncertain service time. Applied Mathematical Modelling, 40(23-24), 9990-10001.

Lahyani, R., Khemakhem, M., & Semet, F. (2015). Rich vehicle routing problems: From a taxonomy to a definition. European Journal of Operational Research, 241(1), 1-14.

Lai, D. S., Demirag, O. C., & Leung, J. M. (2016). A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph. Transportation Research Part E: Logistics and Transportation Review, 86, 32-52.

Lei, L., Wang, Q., & Fan, C. (2006). Optimal business policies for a supplier–transporter–buyer channel with a price-sensitive demand. Journal of the Operational Research Society, 57(3), 281-289.

Lenstra, J. K., & Kan, A. H. G. (1981). Complexity of vehicle routing and scheduling problems. Networks, 11(2), 221-227.

Moin, N. H., & Yuliana, T. (2015). Three-phase methodology incorporating scatter search for integrated production, inventory, and distribution routing problem. Mathematical Problems in Engineering, 2015.

Montoya-Torres, J. R., Franco, J. L., Isaza, S. N., Jiménez, H. F., & Herazo-Padilla, N. (2015). A literature review on the vehicle routing problem with multiple depots. Computers & Industrial Engineering, 79, 115-129.

Norouzi, N., Tavakkoli-Moghaddam, R., Ghazanfari, M., Alinaghian, M., & Salamatbakhsh, A. (2012). A new multi-objective competitive open vehicle routing problem solved by particle swarm optimization. Networks and Spatial Economics, 12(4), 609-633.

Nourmohammadzadeh, A., & Hartmann, S. (2016, December). The fuel-efficient platooning of heavy duty vehicles by mathematical programming and genetic algorithm. In International Conference on Theory and Practice of Natural Computing (pp. 46-57). Springer, Cham.

Osaba, E., Carballedo, R., Yang, X. S., & Diaz, F. (2016). An evolutionary discrete firefly algorithm with novel operators for solving the vehicle routing problem with time windows. In Nature-Inspired Computation in Engineering , Springer, Cham. 21-41

Qureshi, A. G., Taniguchi, E., & Yamada, T. (2009). An exact solution approach for vehicle routing and scheduling problems with soft time windows. Transportation Research Part E: Logistics and Transportation Review, 45(6), 960-977.

Repoussis, P. P., Tarantilis, C. D., Bräysy, O., & Ioannou, G. (2010). A hybrid evolution strategy for the open vehicle routing problem. Computers & Operations Research, 37(3), 443-455.

Ruokokoski, M., Solyali, O. G. U. Z., Cordeau, J. F., Jans, R., & Süral, H. (2010). Efficient formulations and a branch-and-cut algorithm for a production-routing problem. GERAD Technical Report G-2010-66.

Shiguemoto, A. L., & Armentano, V. A. (2010). A tabu search procedure for coordinating production, inventory and distribution routing problems. International Transactions in Operational Research, 17(2), 179-195.

Solyalı, O., & Süral, H. (2009). A relaxation based solution approach for the inventory control and vehicle routing problem in vendor managed systems. In Modeling, computation and Optimization (pp. 171-189).

Tavakkoli-Moghaddam, R., Safaei, N., & Shariat, M. A. (2005). A multi-criteria vehicle routing problem with soft time windows by simulated annealing. Journal of Industrial Engineering-Int 1.1 28-36.

Tavakkoli-Moghaddam, R., Saremi, A. R., & Ziaee, M. S. (2006). A memetic algorithm for a vehicle routing problem with backhauls. Applied Mathematics and Computation, 181(2), 1049-1060.

Zachariadis, E. E., & Kiranoudis, C. T. (2010). A strategy for reducing the computational complexity of local search-based methods for the vehicle routing problem. Computers & operations research, 37(12), 2089-2105.