بهینه سازی دو هدفه طراحی زنجیره تامین و عملیات توزیع با استفاده از الگوریتم مرتب سازی غیر تحت سلطه : مطالعه موردی

This paper considers simultaneous optimization of strategic design and distribution decisions for three-echelon supply chain architecture consisting of following three players; suppliers, production plants, and distribution centers (DCs). 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 distribution centers, so as to minimize the combined facility location, production, inventory, and shipment costs and maximize fill rate. To achieve this, three-echelon network model is mathematically represented and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization algorithm (MOHPSO). This heuristic incorporates non-dominated sorting (NDS) procedure to achieve bi-objective optimization of two conflicting objectives. The applicability of proposed optimization algorithm was then tested by applying it to standard test problems found in literature. On achieving comparable results, the approach was applied to actual data of a pump manufacturing industry. The results show that the proposed solution approach performs efficiently.

In today’s industrial environment, the rapid technological advancements, together with increased economic uncertainty and the globalization of economic activities have resulted in tough competition, and chaotic, demanding customers. There is a need to focus on revenue growth, asset utilization, cost reduction, short and reliable delivery time, increased customer satisfaction so as to balance customers’ demands with the need for profitable growth. Realizing that supply chain (SC) can be a strategic differentiator in this direction, market leaders keep refining their SCs so as to gain competitive advantage (Cohen & Roussel, 2005). 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 (SCM) 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 across the entire SC. (Simchi-Levi, Kaminsky, & Simchi-Levi, 2001). Thus SC consists of many independent organizations each of which tries to focus on its own inherent objectives in business for better profitability. Many of the interests of these organizations will be conflicting. Such a problem which tries to optimize many conflicting objectives simultaneously is called multi-objective optimization problem and has many optimal solutions. In such a situation, analyzing the system using traditional optimization techniques such as weighted objective method leads to subjective and sub-optimal results. The ideal situation is that the decision maker should be presented with a vector of optimal solutions. The final decision is made among them by taking the total balance over all criteria into account. This balancing over criteria is called trade-off. The trade-off level may change over time due to uncertainty and global competitiveness. Hence the SC performance needs to be evaluated continuously and SC managers should make timely and right decisions (Shen, 2007). Real SCs are to be optimized simultaneously considering more than one objective. This is because design, planning and scheduling projects are usually involving trade-offs among different conflicting goals such as customer service levels, fill rates, safe inventory levels, volume flexibility etc. (Chen & Lee, 2004). In this work a bi-objective mixed-integer non-linear programming model is formulated that accounts for major characteristics of SC, such as material cost, production cost, inventory cost, fill rate etc. Two conflicting objectives considered are, (1) Minimizing total SC operating cost of production, inventory, and distribution. (2) Maximizing fill rate. The problem is then solved using NDS based MOHPSO algorithm. The proposed approach is illustrated through a live case study of a pump manufacturing industry. This bi-objective model when solved results in Pareto-optimal curve that reveals the trade-off between the total SC operating costs and fill rate. The solution simultaneously predicts the optimal network design, facility location, SC operating cost, inventory control, and logistics management decisions. The cost values obtained are compared with actual industrial data. Rest of the paper is organized as follows. Section 2 deals with the work that is done previously in the related field. Section 3 explains modeling and mathematical formulation of design-distribution network considered. Section 4 deals with Hybrid MOPSO methodology adopted to solve bi-objective design-distribution problem. A systematic application of the proposed algorithm is demonstrated in Section 5. Section 6 includes results and discussions, which is followed by concluding Section 7 where in future work is outlined.

Since nowadays, the competition is not between the companies, but between the SCs that consist of many such companies, the basic priority for supply chain management should be designing the SC network properly, to gain competitive advantage. Many real world issues in SCN architecture are optimization problems and are combinatorial in nature. There are often a large number of decision variables, many of which can only take on discrete values. Owing to high complexity and large search space traditional operations research techniques fail to find solutions to such supply chain network optimization problems. Hence in this paper a new application of meta-heuristic based on swarm intelligence is demonstrated for simultaneous optimization of two conflicting objectives. For this, an analytical model is formulated for three-echelon SC 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 as it is important for a company to find the right trade-off between supply chain cost and customer service in real life situations. An intelligent MOHPSO is used as optimizer. Testing an algorithm’s performance on application problems is necessary to demonstrate the use of algorithm in practice. Hence the applicability and effectiveness of this algorithm is checked by applying it to a pump manufacturing company. The supplier variables to both the plants and plant distribution variables of the organization have been optimized for total SC cost reduction and fill rate maximization. The results indicate that the total SC cost of the industrial SC network could be reduced by 3.8% when demand satisfied is 100%. Similar cost reductions are possible for different fill rate values. Also, the optimization suggests how many pumps each plant must manufacture so as to achieve the maximum reduction in SC cost against the existing method of practice. These results clearly indicate that this optimizer can act as decision support system for location of facilities and distribution decisions in real three stage SC optimization with many players in each stage. Location decisions help to decide whether a facility needs to be located at a given site or if a facility is already located whether a facility needs to be operated or not when a demand scenario arises. Distribution decisions help to decide flow of products from sources to destination facilities. The model developed here aids in the design of efficient and effective supply chains, and in the evaluation of competing SC networks. Whenever demand changes optimizer can be fine tuned by changing very few parameters which will make the logistics manager’s task easier. Further what-if analysis of changing different key parameters will help to gain significant managerial insights about different trade-off situations with respect to total cost involved and fraction of demand satisfied. This will help to reduce the total cost incurred in producing the product especially when demand is less by closing few of the facilities which will increase the savings in monetary terms. This study could be further extended by considering other types of pumps and provide a holistic solution to the industry and also account more features like uncertainty, stock planning, lead time and credit days of the suppliers. By incorporating these details, the theoretical model could move closer to the actual model of the organization.