خلاصة:
The competitive market and declined economy have increased the relevant importance of making supply chain network efficient. This has created many motivations to reduce the cost of services, and simultaneously, to increase the quality of them. The network as a tri-echelon one consists of Suppliers, Warehouses or Distribution Centers (DCs), and Retailer nodes. To bring the problem closer to reality, the majority of the parameters in this network consist of retailer demands, lead-time, warehouses holding and shipment costs, and also suppliers procuring and stocking costs all are assumed to be stochastic. The aim is to determine the optimum service level so that total cost could be minimized. Reaching to such issues passes through determining which suppliers nodes, and which DCs nodes in network should be active to satisfy the retailers' needs, the matter that is a network optimization problem per se. Proposed supply chain network for this paper is formulated as a mixed integer nonlinear programming, and to solve this complicated problem, since the literature for related benchmark is poor, three ones of GA-based algorithms called Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA), and Pareto Envelope-based Selection Algorithm (PESA-II) are applied and compared to validate the obtained results.
ملخص الجهاز:
Proposed supply chain network for this paper is formulated as a mixed integer nonlinear programming, and to solve this complicated problem, since the literature for related benchmark is poor, three ones of GA-based algorithms called Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA), and Pareto Envelope-based Selection Algorithm (PESA-II) are applied and compared to validate the obtained results.
Some other approaches in literature which are noticeable for SC problems could be taken into account as Moncayo-Martínez and Zhang (2011) that proposed an algorithm based on a Pareto AC2 optimization for minimizing both the SC current cost and the total lead-time for a 1- Reliable Facility Location Problem 2- Ant Colony family of commodities.
The proposed mathematical model of this work is hard to be resolved by common analytical or exact approaches, so three ones of MOGA are utilized to find Pareto Fronts; and since the literature for benchmarks to validate the obtained solutions is poor, these applied algorithm called NSGA-II4, NRGA5, and PESA-II6 are 1- Tabu Search 2- Multi-Objective Hybrid Particle Swarm Optimization 3- Mixed-Integer Nonlinear Programming 4- Non-Dominated Sorting Genetic Algorithm 5- Non-Dominated Ranking Genetic Algorithm 6- Pareto Envelope-Based Selection Algorithm compared together via six numbers of cited indexes.
Designing a distribution network in a supply chain system: Formulation and efficient solution procedure, European Journal of Operational Research, vol.
Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution.