تصمیم گیری های اختصاصی و محلی برای شبکه زنجیره تامین چند پله ای؛ رویکرد تکاملی چند هدفه
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|912||2013||12 صفحه PDF||سفارش دهید||9090 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 2, 1 February 2013, Pages 551–562
This paper aims at multi-objective optimization of single-product for four-echelon supply chain architecture consisting of suppliers, production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to DCs, from DCs to CZs so as to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met. To optimize these two objectives simultaneously, four-echelon network model is mathematically represented considering the associated constraints, capacity, production and shipment costs and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. This evolutionary based algorithm incorporates non-dominated sorting algorithm into particle swarm optimization so as to allow this heuristic to optimize two objective functions simultaneously. This can be used as decision support system for location of facilities, allocation of demand points and monitoring of material flow for four-echelon supply chain network.
Supply chain (SC) is an integrated system of facilities and activities that synchronizes inter-related business functions of material procurement, material transformation to intermediates and final products and distribution of these products to customers. Supply chain management is a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses, and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system-wide costs while satisfying service level requirements (Simchi-Levi, Kaminsky, & Simchi-Levi, 2000). Above definition reveals that there are many independent entities in a supply chain each of which tries to maximize its own inherent objective functions in business transactions. This is a complicated problem as too many factors are involved and needs more than one objective to be satisfied simultaneously. Such a problem is called multi-objective optimization problem and has many Pareto solutions. The final decision is made taking the total balance over all criteria into account. This balancing over criteria is called trade-off. Since today, the success measures for the companies are thought as lower costs, shorter production time, shorter lead time, less stock, larger product range, more reliable delivery time, better customer services, higher quality, and providing the efficient coordination between demand, supply and production, the trade-off between cost investment and service levels may change over time. Hence the supply chain performance needs to be evaluated continuously and supply chain managers should make timely and right decisions (Shen, 2007). The key issues in supply chain management can broadly be divided into three main categories: (i) supply chain design (ii) supply chain planning and (iii) supply chain control. In the supply chain design phase, strategic decisions, such as facility location decisions and technology selection decisions play major roles. It is very important to design an efficient supply chain to facilitate the movements of goods. These strategic decisions lead to costly, time consuming investment as the facilities located today, are expected to remain in operation for long time. Environmental changes during the facility’s lifetime can drastically alter the appeal of a particular site, turning today’s optimal location into tomorrow’s investment blunder. Determining the best locations for new facilities is thus an important strategic challenge (Owen & Daskin, 1998). Once the supply chain configuration is determined, the focus shifts to decisions at the tactical and operational levels, such as inventory management decisions on raw materials, intermediate products, and end products and distribution decisions within the supply chain (Chopra & Meindl, 2005). In traditional supply chain management, the focus of the designs of supply chain network is usually on single objective, minimum cost or maximum profit. But the design, planning and scheduling projects are usually involving trade-offs among different incompatible goals such as fair profit distribution among all members, customer service levels, fill rates, safe inventory levels, volume flexibility, etc. (Chen & Lee, 2004). Hence real supply chains are to be optimized simultaneously considering more than one objective. Many of the problems that occur in supply chain optimization are combinatorial in nature and picking a set of optimal solutions in the case of multi-objective formulations requires a algorithm that can efficiently search the entire objective space using small amounts of computation time. Literature shows that evolutionary algorithms perform well in this respect and give good optimal results when applied to many combinatorial problems. This work proposes the utility of non-dominated sorting particle swarm optimization algorithm for simultaneous optimization of two objectives, minimizing total supply chain cost and maximizing fill rate for a four-echelon supply chain architecture so as to arrive at an efficient supply chain design and optimal transportation/shipment plan which can be used as decision support system.
نتیجه گیری انگلیسی
In this paper, an analytical model is formulated for the location and allocation of facilities of four-echelon supply chain network for the optimal facility location and capacity allocation decisions. Fixed location and variable material cost, production, inventory and transportation costs are considered while making strategic decisions. Two objective functions of minimizing total SC cost and maximizing fill rate are considered. A heuristic based hybrid MOPSO is used as optimizer. This algorithm is mainly aimed at characterizing the Pareto Optimal front by computing well-distributed non-dominated solutions. These solutions represent trade-off solutions which facilitate decision makers to develop management policies under a changing environment. Further this can be used as decision support system for strategic supply chain design and monitoring of material flow. Above explained model and algorithm further can consider safety stock costs, risk related and reliability costs. Also the model can optimize more than two conflicting objectives simultaneously.