A Flexible Integrated Forward/ Reverse Logistics Model with Random Path-based Memetic Algorithm

Document Type : Research Paper


1 Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran

2 Department of Industrial Engineering, University of Tehran, Iran

3 Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran


Due to business and environmental issues, the efficient design of an integrated forward/reverse logistics network has recently attracted more attention from researchers. The significance of transportation cost and customer satisfaction spurs an interest in developing a flexible network design model with different delivery paths. This paper proposes a flexible mixed-integer programming model to deal with such issues. The model integrates the network design decisions in both forward and backward logistics networks, and also applies three kinds of delivering modes (normal delivery, direct shipment, and direct delivery) which enrich the model to be able to deliver the products to customers by distribution-skipping the mid-process strategy in order to deliver products in more flexible paths to customer zones. To tackle with such an NP-hard problem, a memetic algorithm (MA) with random path-based direct representation and combinatorial local search methods is developed. Numerical experiments are conducted to demonstrate the significance and applicability of the model as well as the efficiency and accuracy of the proposed solution approach.


Main Subjects

Article Title [فارسی]

الگوریتم ممتیک بر پایة مسیرهای تصادفی در یک مدل انعطاف‌پذیر و یکپارچة رو به جلو/ بازگشتی

Authors [فارسی]

  • احسان یادگاری 1
  • حسام الدین نجمی 2
  • مرتضی قمی اویلی 3
  • مصطفی زندیه 1
1 دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران
2 گروه مهندسی صنایع، دانشگاه تهران، ایران
3 دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران
Abstract [فارسی]

طراحی کارایی شبکة لجستیک یکپارچة رو به جلو/ بازگشتی به دلایل سودآوری اقتصادی و الزامات زیست­محیطی توجه بسیاری از محققان را در دهة گذشته جلب کرده است. از طرفی، ضرورت مباحثی چون «رضایت مشتریان» و «هزینه­های حمل و نقل» به اهمیت پرداختن به شبکه­های لجستیکی انعطاف­پذیر با مسیرهای جایگزین برای حمل کالاها افزوده است. این مطالعه به طراحی شبکة زنجیرة تأمین انعطاف‌پذیر می‌پردازد که طی آن ضرورت‌های یادشده در مدلسازی شبکه منعکس می‌شود. این مدل ضمن ترکیب یکپارچة لجستیک مستقیم و معکوس با معرفی سه مسیر مختلف در رساندن کالا به مشتریان سعی دارد با استراتژی انتخاب مسیرهای کوتاه‌تر ضمن کاهش هزینه‌های حمل و نقل، رضایت مشتریان را از طریق تحویل سریع­تر کالاها افزایش دهد. از آنجا که این مسئله مسئلة NP-hard شناخته می‌شود، الگوریتم ممتیک بر پایة مسیرهای تصادفی به همراه یک روش جستجوی همسایگی ترکیبی برای حل آن پیشنهاد شده است. نمونه­هایی از مسائل کوچک تا بزرگ برای نشان دادن اهمیت و کاربرد مدل و دقت و کارایی روش حل بررسی شده است. 

Keywords [فارسی]

  • زنجیرة تأمین یکپارچه
  • طراحی شبکة لجستیکی
  • کدگذاری بر پایة مسیرهای تصادفی
  • الگوریتم ممتیک
Aras, N.; Aksen, D. & Gönül Tanuğur, A. (2008). "Locating collection centers for incentive-dependent returns under a pick-up policy with capacitated vehicles". European Journal of Operational Research, 191(3), 1223-1240 .
Biehl, M.; Prater, E. & Realff, M .J. (2007). "Assessing performance and uncertainty in developing carpet reverse logistics systems". Computers & Operations Research, 34(2), 443-463 .
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 .
Chaabane, A.; Ramudhin, A. & Paquet, M. (2012). "Design of sustainable supply chains under the emission trading scheme". International Journal of Production Economics, 135(1), 37-49 .
Dengiz, B.; Altiparmak, F. & Smith, A. E. (1997). "Local search genetic algorithm for optimal design of reliable networks". Evolutionary Computation, IEEE Transactions on, 1(3), 179-188 .
Devika, K.; Jafarian, A. & Nourbakhsh, V. (2014). "Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques". European Journal of Operational Research, 235(3), 594-615 .
Dullaert, W.; Bräysy, O.; Goetschalckx ,M.; Raa, B. & Center, A. (2007). "Supply chain (re) design: Support for managerial and policy decisions". European Journal of Transport and Infrastructure Research, 7(2), 73-92 .
El-Sayed, M.; Afia, N. & El-Kharbotly, A. (2010). "A stochastic model for forward–reverse logistics network design under risk". Computers & Industrial Engineering, 58(3), 423-431 .
Elhedhli, S. & Merrick, R. (2012). "Green supply chain network design to reduce carbon emissions". Transportation Research Part D: Transport and Environment, 17(5), 370-379 .
ElMekkawy, T. & Liu, S. (2009). "A new memetic algorithm for optimizing the partitioning problem of tandem AGV systems". International Journal of Production Economics, 118(2), 508-520 .
Fleischmann, M.; Beullens, P.; BLOEMHOF‐RUWAARD, J. M. & WASSENHOVE, L. N. (2001). "The impact of product recovery on logistics network design". Production and operations management, 10(2), 156-173 .
Fonseca, M. C.; García-Sánchez, Á.; Ortega-Mier, M.; & Saldanha-da-Gama, F. (2010). "A stochastic bi-objective location model for strategic reverse logistics". Top, 18(1), 158-184 .
Gen, M. & Cheng, R. (1999). Genetic algorithms and engineering optimization (Vol. 7): Wiley-interscience.
Gen, M.; Cheng, R. & Lin, L. (2008). Network models and optimization: Multiobjective genetic algorithm approach: Springer.
Govindan, K.; Soleimani, H. & Kannan, D. (2014). "Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626.
Hamid, A. (2014). "A Study on the Relationship between Consumer Attitude, Perceived Value and Green Products". Iranian Journal of Management Studies, 7(2), 315-328 .
Jayaraman, V.; Guide Jr, V. & Srivastava, R. (1999). "A closed-loop logistics model for remanufacturing". Journal of the operational research society, 49, 507-508.
Jayaraman, V. & Pirkul, H. (2001). "Planning and coordination of production and distribution facilities for multiple commodities". European Journal of Operational Research, 133(2), 394-408 .
Kannan, D.; Diabat, A.; Alrefaei, M.; Govindan, K. & Yong, G. (2012). "A carbon footprint based reverse logistics network design model". Resources, Conservation and Recycling, 67, 75-79 .
Kannan, G.; Sasikumar, P. & Devika, K. (2010). "A genetic algorithm approach for solving a closed loop supply chain model: Acase of battery recycling". Applied Mathematical Modelling, 34(3), 655-670 .
Kerr, W. & Ryan, C. (2001). "Eco-efficiency gains from remanufacturing: A case study of photocopier remanufacturing at Fuji Xerox Australia". Journal of cleaner production, 9(1), 7, 81-85.
Kim, S.-S.; Smith, A. E. & Lee, J.-H. (2007). "A memetic algorithm for channel assignment in wireless FDMA systems". Computers & operations research, 34(6), 1842-1856 .
Koh, S.-G.; Hwang, H.; Sohn, K.-I. & Ko, C.-S. (2002). "An optimal ordering andrecovery policy for reusable items". Computers & Industrial Engineering, 43(1), 59-73 .
Krikke, H.; van Harten, A. & Schuur, P. (1999). "Business case Oce: reverse logistic network re-design for copiers". OR-Spektrum, 21(3), 381-409 .
Lee, D. -H. & Dong, M. (2008). "A heuristic approach to logistics network design for end-of-lease computer products recovery". Transportation Research Part E: Logistics and Transportation Review, 44(3), 455-474 .
Listeş, O. & Dekker, R. (2005). "A stochastic approach to a case study for product recovery network design". European Journal of Operational Research, 160(1), 268-287 .
Mansourfar, G. (2013). "Econometrics and Metaheuristic Optimization
Approaches to International Portfolio Diversification". Iranian Journal of Management Studies, 6(6), 45-75 .
Min, H.; Jeung Ko. H. & Seong Ko. C. (2006). "A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns". Omega, 34(1), 56-69 .
Moscato, P. & Cotta, C. (2003). "A gentle introduction tomemetic algorithms". Handbook of metaheuristics (pp. 105-144): Springer.
Moscato, P. & Norman, M. G. (1992). "A memetic approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passingsystems". Parallel Computing and Transputer Applications, 1, 177-186.
Pishvaee, M.; Torabi, S. & Razmi, J. (2012). "Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty". Computers & Industrial Engineering, 62 (2), 624-632.
Pishvaee, M. S.; Farahani, R. Z. & Dullaert, W. (2010). "A memetic algorithm for bi-objective integrated forward/reverse logistics network design". Computers & operations research, 37(6), 1100-1112 .
Pishvaee, M. S.; Kianfar, K. & Karimi, B. (2010). "Reverse logistics network design using simulated annealing". The International Journal of Advanced Manufacturing Technology, 47(1-4), 269-281 .
Pishvaee, M. S. & Razmi, J. (2012). "Environmental supply chain network design using multi-objective fuzzy mathematical programming". Applied Mathematical Modelling, 36(8), 3433-3446 .
Pokharel, S. & Mutha, A. (2009). "Perspectives in reverse logistics: a review". Resources, Conservation and Recycling, 53(4), 175-182 .
Pouralikhani, Hamed; Najmi, Hesamaddin; Yadegari, Ehsan & Mohammadi, Emran (2013). "A Multi-Period Model for Managing Used Products in Green Supply Chain Management under Uncertainty". J. Basic Appl. Sci. Res., 3(2), 984-995 .
Safari, S.; Sardari, A. & Sabzian, H. (2012). "Designing a Mathematical Model for Allocating Budget to University Research and Educational Goals: A Case Study in Shahed University". Iranian Journal of Management Studies, 5(5), 88-113 .
Salema, M. I. G.; Barbosa-Povoa, A. P. & Novais, A. Q. (2007). "An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty". European Journal of Operational Research, 179(3), 1063-1077 .
Sasikumar, P. & Kannan, G. (2008a). "Issues in reverse supply chains, partI: end‐of‐life product recovery and inventory management – an overview". International Journal of Sustainable Engineering, 1(3), 154-172. doi: 10.1080/19397030802433860.
Sasikumar, P. & Kannan, G. (2008b). "Issues in reverse supply chains, part
II: reversedistribution issues – an overview". International Journal of Sustainable Engineering, 1(4), 234-249. doi: 10.1080/19397030802509974.
Sasikumar, P. & Kannan, G. (2009). "Issues in reverse supply chain, part III: classification and simple analysis". International Journal of Sustainable Engineering, 2(1), 2-27 .
Schultmann, F.; Zumkeller, M. & Rentz, O. (2006). "Modeling reverse logistic tasks within closed-loop supply chains: An example from the automotive industry". European Journal of Operational Research, 17(3) 1033-1050.
Soleimani, H.; Seyyed-Esfahani, M. & Kannan, G. (2014). "Incorporating risk measures in closed-loop supply chain network design". International Journal of Production Research, 52(6), 1843-1867 .
Srivastava, S. K. (2008). "Network design for reverse logistics". Omega, 36(4), 535-548. doi: http://dx.doi.org/10.1016/j.omega.2006.11.012.
Syarif, A.; Yun, Y., & Gen, M. (2002). "Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach". Computers & Industrial Engineering, 43(1), 299-314 .
Tavakkoli-Moghaddam, R.; Safaei, N. & Sassani, F. (2009). "A memetic algorithm for the flexible flow line scheduling problem with processor blocking". Computers & Operations Research, 36(2), 402-414 .
Van Der Laan, E.; Salomon, M.; Dekker, R. & Van Wassenhove, L. (1999). "Inventory control in hybrid systems with remanufacturing". Management science, 45(5), 733-747 .
Verstrepen, S.; Cruijssen, F.; de Brito, M. P. & Dullaert, W. (2007). "An exploratory analysis of reverse logisticsin Flanders". European Journal of Transport and Infrastructure Research, 7(4), 301-316.
Wang, F.; Lai, X. & Shi, N. (2011). "A multi-objective optimization for green supply chain network design". Decision Support Systems, 51(2), 262-269.
Wang, H.-F. & Hsu, H.-W. (2010). "A closed-loop logistic model with a spanning-tree based genetic algorithm". Computers & operations research, 37(2), 376-389.
Yao, M.-J. & Hsu, H.-W. (2009). "A new spanning tree-based genetic algorithm for the design of multi-stage supplychain networks with nonlinear transportation costs". Optimization and Engineering, 10(2), 219-237 .
Jandaghi. GH. (2011). "Application of qualitative research in management (why, when and how)". Iranian Journal of Management Studies, 3(3), 59-73 .