نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد، دانشگاه شهید بهشتی.

2 دانشیار، دانشگاه شهید بهشتی.

3 استاد، دانشگاه شهید بهشتی.

چکیده

با توسعه اقتصاد جهانی و گسترش بازاریابی الکترونیکی در بین کشورها، اینکه چگونه یک سیستم لجستیک به نحو کارآمد مدیریت شود، به یک موضوع کلیدی برای کاهش هزینه های شرکت ها تبدیل شده است، مخصوصاً شرکت های چند ملیتی که در فضای سخت رقابتی قرار دارند. یکی از زمینه های مناسب برای یکپارچه سازی در شبکه های لجستیک، طراحی یکپارچه شبکه لجستیک مستقیم و معکوس است که می تواند باعث جلوگیری از زیربهینگی ناشی از طراحی جدا از هم شبکه لجستیک مستقیم و معکوس شود. در این مقاله، یک مدل برنامه ریزی خطی عدد صحیح آمیخته برای طراحی شبکه یکپارچه لجستیک مستقیم و معکوس با هدف حداقل سازی هزینه ها ارائه شده است. با توجه به اینکه مدل ارائه شده به دسته NP-hard تعلق دارد، دو الگوریتم فراابتکاری الگوریتم ممتیک ) MA ) –و الگوریتم فرایند گروهی TPA برای حل مدل به کار گرفته شده است. الگوریتم ها از نظر بهترین مقدار تابع هدف و اولین زمان رسیدن به بهترین مقدار تابع هدف مقایسه شده اند. بر اساس نتایج به دست آمده، از نظر مقدار تابع هدف، الگوریتم ممتیک و از نظر زمان رسیدن به جواب، الگوریتم فرایند گروهی برتری داشتند

کلیدواژه‌ها

عنوان مقاله [English]

Application of meta-heuristic algorithms to the logistic integration network distribution model

نویسندگان [English]

  • Hamzeh Ghojavand 1
  • Mostafa Zandieh 2
  • Behrooz Dorri Nokarani 3

1 M.A., Shahid Beheshti University.

2 Associate Professor, Shahid Beheshti University.

3 Professor, Shahid Beheshti University.

چکیده [English]

With the development of the global economy and the spread of e-marketing across countries, how a logistics system is managed efficiently has become a key issue for cost-cutting companies, especially multinationals that are in a tough competitive environment. One of the most suitable areas for integration in logistics networks is the integrated design of direct and reverse logistics networks, which can prevent overlap from the design of separate direct and reverse logistics networks. In this paper, a mixed integer linear programming model for the design of a direct and reverse logistics integrated network with the aim of minimizing costs is presented. Given that the proposed model belongs to the NP-hard category, two algorithms of Memetic algorithm (MA) and TPA group process algorithm are employed to solve the model. The algorithms are compared in terms of the best value of the objective function and the first time to reach the best value of the objective function. Based on the results, the Memtec algorithm was superior in terms of the objective function value and the group process algorithm was superior in terms of time.

کلیدواژه‌ها [English]

  • Integrated design
  • logistics
  • MA algorithm
  • TPA algorithm
1. Aras, N, Aksen, D, Tanugur, AG (2008) “Locating collection centers for incentive- dependent returns under a pick-up policy with capacitated vehicles”, European Journal of Operational Research, 191:12, 23-40.
2. Du, F, Evans, GW (2008) “A bi-objective reverse logistics network analysis for post-sale service”, Computers & Operations Research, 35:26, 17–34.
3. Dullaert, W, Braysy, O, Goetschalckx, M, Raa, B (2007) “Supply chain (re)design: support for managerial and policy decisions”, European Journal of Transport and Infrastructure Research, 7:2, 73–91.
4. Gen, M, Altiparmak, F and Lin, L (2006) “A genetic algorithm for two-stage transportation problem using priority-based encoding” OR Spectrum, 28, 337–354.
5. Gen, M, Cheng, R and Lin, L (2008) Network models and optimization: multiobjective genetic algorithm approach. Springer, London, chapter 3.
6. Jayaraman, V and Pirkul, H (2001) “Planning and coordination of production and distribution facilities for multiple commodities” European Journal of Operational Research, 133, 394–408.
7. Jayaraman, V, Guige, VDR and Srivastava, R (1999) “A closed-loop logistics model for manufacturing”, Operational Research Society, 50, 497-508.
8. Ko, HJ and Evans, GW (2007) “A genetic-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs” Computers & Operations Research, 34, 346-366.
9. Lee, D and Dong, M (2008) “A heuristic approach to logistics network design for end-of- lease computer products recovery”, Transportation Research, 44:E, 455–474.
10. Li, Y and Chen, Y (2010) “An effective TPA-based algorithm for job-shop scheduling problem”, Expert Systems with Applications.
11. Listes, O and Dekker, R (2005) “A stochastic approach to a case study for product recovery network design”, European Journal of Operational Research, 160, 268–287.
12. Meade, L., Sarkis, J. and Presley, A. (2007). “The theory and practice of Reverse Logistics.” International Journal of Logistics systems and Management, Vol. 3, PP. 56-84.
13. Meepetchdee, Y and Shah, N (2007) “Logistical network design with robustness and complexity considerations”, International Journal of Physical Distribution & Logistics Management, 37:20, 1–22.
14. Melachrinoudis, E, Messac, A and Min, H (2005) “Consolidating a warehouse network: a physical programming approach”, International Journal of Production Economics, 97, 1-17.
15. Pishvaee, MS, Farahani, RZ and Dullert, W (2009) “A MA algorithm for bi-objective integrated forward/reverse logistics network design”, Computers and Operation Research, 37:6, 1100-1112.
16. Sabri, EH and Beamon, BM (2000) “A multi-objective approach to simultaneous strategic & operational planning in supply chain design”, Omega, 28:5, 581–598.
17. Thomas, DJ and Griffin, PM (1996) “Coordinated supply chain management”, European Journal of Operational Research, 94:1, 1–15.