An Integrated Production-Distribution-Routing Problem under an Unforeseen Circumstance within a Competitive Framework

Document Type : Research Paper


1 Department of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Qom, Iran

2 bDepartment of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Qom, Iran.

3 Department of Industrial Engineering, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, Iran



Nowadays, enhancing the products' quality and gaining market share are the primary purposes of any company in a competitive market. So, applying a proper management approach could help companies to make optimal decisions. One of the efficient approaches is supply chain management that can manage the flow of final products and services continually. The present study develops a supply chain with integrating production and distribution activities and a multi-period routing problem. Also, in this problem, a Stackelberg competition occurs between the suppliers under normal and critical situations (in which procurement costs of materials are increased and the suppliers encounter the shortage). Therefore, some parameters are considered uncertain, and a two-stage stochastic optimization model is constructed. The model is also multi-objective to reduce cost, lost sales, and defective products. The GAMS software is used for solving a case study for the medicine industry. Due to the NP-hardness, we consider Non-Dominated Sorting Genetic Algorithms II (NSGA_II), Multiple Objective Particle Swarm Optimization (MOPSO), and a hybrid algorithm for the large-sized instances. Subsequently, the performances of the proposed algorithms are considered. The obtained results reveal that the hybrid algorithm has a better function for solving the model in medium and large-sized instances.
The GAMS software is used for solving a case study for medicine industry. Due to the NP-hardness, we consider Non-Dominated Sorting Genetic Algorithms II (NSGA_II), Multiple Objective Particle Swarm Optimization (MOPSO), and a hybrid algorithm for the large-sized instances. Subsequently, the performances of the proposed algorithms are considered. The obtained results reveal that the hybrid algorithm has a better function for solving the model in medium and large-sized instances.


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