A Genetic Algorithm-Based Decision Support System for Allocating International Apparel Demand

Transcription

1 A Genetic Algorithm-Based Decision Support System for Allocating International Apparel Demand Shiue-Shiun Li 1, Rong-Chang Chen *, Chih-Chiang Lin 2 1 Graduate Institute of Business Administration * Department of Logistics Engineering and Management 2 Graduate Institute of Computer Science and Information Technology National Taichung Institute of Technology No 129, Sec. 3, Sanmin Rd., Taichung, Taiwan, ROC * Abstract: - It has become more and more important and difficult to minimize makespan in the global competitive markets. The purpose of this paper is to develop a decision support system to assist managers in making decisions for minimal makespan. There are too many and complex factors for senior managers to make suitable decisions. By traditional methods to allocate orders for minimum makespan, it often makes unfit decisions. Thus, we used genetic algorithm for analyzing complex data. After calculating by genetic algorithm and first-in-first-out (FIFO), the result shows that allotting orders by genetic algorithm could cause better outcomes. Key-Words: - Minimum makespan, Decision support system, Global logistics, Garment industry, Genetic algorithm 1 Introduction The on-time delivery is very important know-how for each kind of industries today because many transnational corporations always ask their supplier to deliver the goods with few times. Sometimes consumers make purchase requests suddenly and the orders are called rush orders. Rush orders often cause extra cost if suppliers want to deliver goods in the short time. By developing of information technology and sciences, senior managers have to face problems of getting lower operating cost for keeping competitive predominance in markets over the whole world. It is very hard to arrange each order requested by consumers into suitable factories that manufacturers have to produce and convey productions in a short period. In this paper, we tried to build a global decision support system to help senior managers and decision makers to make decisions for the lowest total delivery time. In the past methods to make decisions, lots of managers and scholars made decisions based on their experiences and reference materials including balance sheet, cash flow chart, and so on. However, it has become more and more difficult to make suitable decisions by these methods in recent years. By some researches [1-4], it shows an important message that fulfilling demands from consumers could help industry to get competitive advantages. For raising competitive capability, many companies make efforts on the global logistics management (GLM). To reduce operating cost is necessary for corporations because profits are limited in the global competitive environment, especially the corporations with lower entry barriers. Thus, some scholars [5, 6] had proposed that corporations could get resources from some developing countries and underdeveloped countries over the world for reducing cost. Sales office Fabric Factory Apparel Factory Supplier for secondary material of garment Fig. 1 The global deployment of a famous garment corporation in Taiwan Take a famous garment company in Taiwan for example; we can see deployment within the whole world as Fig. 1 shows. For finding cheaper labors and material, the locations of operating offices and manufacturing factories are in different locations.

2 Arranging orders into suitable factories for the minimum total makespan is so difficult that senior managers need a useful tool to help them to make decisions. Building factories near to places providing raw material may reduce material cost, but it also increase other costs including transportation cost because it often takes long time to transport goods from a country to other country. There is an important job for satisfying consumers by productions and delivery date. Perry et al. [7] proposed that reducing delivery time and quick responses would make corporations reduce cost. Making correct and good decisions could help companies to reduce cost and enhance competitive advantage. Thus, it is necessary to find a method to make decision under industry operating strategies. Through traditional methods to make decisions, senior managers and decision makers do their job subjectively. In addition, it would cause additional cost if they made an unsuitable decision because making decisions by personal experiences and easy tools is not enough to deal with complex factors. There are many uncertain and blurred elements around the environment and it happens all the time. To avoid wrong decisions, Decision Support System (DSS) was created to solve this situation [8]. Based on electronic systems and information technology, DSS is a significant role in the transnational industry [9, 10]. In this study, we extended our previous study [11] which was to minimize the total cost, and developed a decision support system for a garment corporation in Taiwan to find better allocating of orders under minimum cost. After the interview with senior managers, we know that the industry needs urgently an electronic tool to assist managers to make decisions. For getting minimum total makespan, we used Genetic Algorithm (GA) [12-20] to be the system s analytic tools because GA could solve problems with complex and excess factors. It is a better algorithm than some other algorithms for finding the optimal solution. In real situations, senior managers and decision makers don t have too much time to consult information so we used Gantt charts to present results. Through visual results, senior managers and decision makers could make decisions as fast as possible. The rest of this paper is organized as follows. The importance of delivery time for manufacturing in Taiwan is introduced in next section. And we would like to introduce slightly about genetic algorithm in section 3. Section 4 describes the approach of this study. Then, experimental results will be shown in visual graphs in Section 5. By summarizing the findings, conclusions are presented in Sections 6. 2 The Importance of Delivery Time for Manufacturing in Taiwan The manufacture in Taiwan is always famous in the whole world. In past periods, it not only earned lots of foreign exchange but also provided many chances to get jobs before the middle of 1980s. After then, manufacture has been impacted by foreign industry, and benefits have become fewer and fewer. To resist the influences, manufacture had done some changes. Two most important changes are outward industry and delivering goods in the few time. Manufacture moves factories to other countries for getting cheaper materiel and supplies [21]. There are many reasons explained why delivering goods with few time is important. First, if manufacturers could deliver goods as fast as possible, they won t shoulder too much inventory cost. It is necessary to reduce cost within limited benefits. Second, manufacturers deliver goods for long would cause many losses. Take science products for example, production life cycle is only 18 months, maybe fewer. Thus, when suppliers bring orders up, they hope they could get products in few days later. There are some interesting terms were created by the situation. For example, 102 means all of the products have to be delivered to consumers in two days. If a manufacturer could not make it, consumers would cancel orders and to find another manufacturers. The end reason is that if delivery time takes long, it also causes additional cost; even it is on time. Because some manufacturers tried to meet buyers engagements, they would change ways of delivery before the deadline. Garment industry, for example, often develops factories in Southeast Asia, and they are used to transport products by sea transport. Sometimes, there is a problem happened and they cannot deliver products on time. They would deliver products by air transport and it causes much cost than original transport ways. Thus, delivering products is not only satisfying demands of consumers but also reducing cost. The viewpoint is supported by literatures. Scholars [22] considered that to shorten delivery time could reduce cost of transportation. There are some researches which had provided methods to shorten delivery time. Chan [23] proposed effect of kanban size for improving fill rate and reducing makespan in just-in-time manufacturing systems. Wagner and Smits [24] built a model for the stochastic economic lot scheduling

3 problem (SELSP) and used Local Search heuristic to find optimal solutions to this model. In this case of study, the corporation is a transnational industry and influencing factors of decisions are complex. Thus, we used genetic algorithm to analyze the factors and find optimal solutions. We would introduce the approach of this paper in the next section. 3 Genetic Algorithm John Holland began his work on genetic algorithms at the beginning of the 60s from the University of Michigan. A first accomplishment was the publication of Adaptation in Natural and Artificial System in 1975 [19]. He had two goals: to improve the comprehension of natural adaptation process, and to design artificial systems with properties similar to natural systems [25]. The essential idea of genetic algorithm is as follow: the genetic pool of a given population probably contains the solution, or a better solution, to a given adaptive problem. This solution is not "active" because the combination of chromosomes on which it depends is separated between several subjects. Only the association of different chromosomes can lead to the solution. In one word, no subject has such a genome, but during reproduction and crossover, new genetic combinations occur and, finally, a subject can inherit a "good gene" from both parents. Encoding, defining fitness function, and setting operation parameters Random population (Chromosomes) Calculating the fitness values The flowchart of genetic algorithm was described as figure 2 shows. The method is especially effective because it not only considered the role of mutation (mutations improve very seldom the algorithms) but also utilized genetic recombination, (crossover): these recombinations, the crossover of fractional solutions greatly improve the capability of the algorithm to approach, and eventually find, the optimum. We can then see that the principle of genetic algorithms is simple as follow: 1) Encoding of the problem based on binary or real number string. 2) Random generation of a population. This one includes a genetic pool representing a group of possible solutions. 3) Calculating of a fitness value for each subject. It will directly depend on the distance to the optimum. 4) Selection of the subjects that will mate according to their share in the population global fitness. 5) Genomes crossover and mutations. 6) And then start again from point 3. Genetic algorithm is very different from some classical optimization algorithms. 1) Use of the encoding of the parameters, not the parameters themselves. 2) Work on a population of points, not a unique one. It is easier to find global optimal value than some algorithms. 3) Use the only values of the function to optimize, not their derived function or other auxiliary knowledge. 4) Use probabilistic transition function not determinist ones. GA has been successfully applied to many fields, for example, Chen [26] has provided a personal model to detect credit card fraud. Because real data distributed disorderly and confused, it is very difficult to use traditional method to choose suitable support vectors. Thus, genetic algorithm was employed to select support vectors.. Selecting pool No Mating Pool Crossover Mutation Yes Decode, output 4 Approach In the paper, we tried to build a decision support system to allocate orders for minimal makespan. We would like to separate the approach into 3 parts as follows in this section. 4.1 The major influencing factors For finding minimums of delivery time, there are some important influencing factors which have to be discussed. These factors include delivery time, cost Fig. 2 The flowchart of GA

4 of transportation, tariff duties and quotas, demands of consumers, competitive environments, and etc. Delivery time and cost of transportation would impact on fill rate deeply. Different kinds of industry have own limits. For example, garment industry cannot accept higher cost and it often get much time than science products manufacture. Thus, senior managers could allot orders in the countries with cheaper labors and materials. Demands of consumers are also important to make decisions for minimum total makespan. In order to satisfy consumers, manufacturers usually arrange orders into different factories if consumers have brought requests up. For example, the quality of products is the most important factor for IT products suppliers. Thus, manufacturers would put orders into the countries with higher technicality for instance Japan or Taiwan. 4.2 The process of DSS As Fig. 3 shows, the process of DSS can be separated into four major parts. Each part will be introduced briefly as follows. Delivery date Competition environment Production technology Outdie factors Capacity employ Stage 1 Supply chain strategy Stage 2 Weeding unsuitable factories Stage 3 Conformed factories Internal restriction Demands of consumers Quotas and Rewards Capacity of techniques internal restriction. The system measures conditions of companies could accept orders or not, and it allots each order into suitable areas roughly. After stage 1, the system would think about some factors of factories. In this phase, demands of consumers, production technology, and other outside factors would influence results of choosing suitable factories. Some unfit factories will be eliminated from each area. In the third stage, the system would check automatically capacity of every feasible factory. Through this stage, it could avoid wasting remnant capacity. In the end of process, DSS would arrange orders to suitable factories after estimating other requests and factors of cost. 4.3 The structure of decisions-making The concept of DSS was presented by Scott Morton [27] and the system was called Management Decision Systems. A decision support system (DSS) is an interactive computer-based system to help decision makers use communications technologies, data, documents, knowledge and/or models to identify and solve problems, complete decision process tasks, and make decisions. Also, decision support systems refer to a science field of research that involves designing and studying decision support systems in their context of use. Generally, decision support systems are a class of computerized information system that supports decision-making activities. The steps of decision support systems for making decisions include three major parts as figure 4 shows as follow: Input Data Analyzing Tools Present Results Fig. 4 The process of decisions-making Factors of cost Stage 4 The most applicable factory Fig. 3 The flowchart of DSS Other requests In the first stage, the system would confirm company strategies, especially delivery date and In this study, input data is so complex that managers should used useful and suitable tools for decision making. We used genetic algorithms for analyzing tools and we expected to find global optimal values. In the end of DSS, we used visual graphs to present the best solutions. Managers could make decisions quickly for minimal makespan through graphs.

5 5 Results and Discussion In this paper, we developed the global decision support system by using Microsoft Visual C++ and the operation system was Windows P. We used two-point crossover and two-point mutation; population size was 100; crossover rate was 0.8; mutation rate was 0.05; and generation was 100. Because of complex types of real data, we wanted to present the status of data as true as possible, and then, enumerating encoding was employed rather than in binary. The objective function is to minimize total makespan. Table 1 Work calendar Sun Mon Tue Wed Thu Fri Sat The work calendar shows that the work days of the month. In addition, the number below each grid is the daily capacity. There are differences about the work calendar in each factories, it also depends on the policy of the countries. Because of these factors, the system we built has to be with high flexibility to deal with complex problems. For satisfying the demands of consumers and other influencing factors, each order can be allotted into different factories. Of course, amounts of each order are different. Thus, we built an electronic system and used GA for analyzing complex data. In this paper, we used first-in-first-out (FIFO) [28] to arrange orders manually and used GA to analyze orders first. First-in-first-out is a common method for manufacturer to plan schedules. This method thought managers make decisions by orders sequence. The early order has priority to be allotted. Both of these two methods would process the same data. Table 2 Calculated results Start Date End Date FIFO GA After calculating by two methods, we could see the dates showing in the Table 2. The end date calculated by FIFO is later than the date calculated by GA. Thus, GA could create better results for handling complex and excess factors. The results were presented by Gantt charts as figure 5 shows. Users could adjust order by own self, or the system would allocate orders automatically based on fitness function. In this study, the expected goal is to find minimum makespan. This system could compute holidays and some special days because capacities would not be the same everyday. The colorful labels mean orders. When users drag the label, this system would show important dates about the order. Senior managers and decision makers could make producing decisions quickly by this system for operating goals. Fig. 5 The interface of DSS 6 Conclusion We have built a decision support system for decision makers of a garment corporation with a global deployment, and this system can help decision makers to make decisions of order allocation effectively. This system allocates orders based on the minimum makespan. The global DSS consist of three major parts, including input databases and parameters, the analytical tools, and the presentation mechanism. The input data was gathered by Enterprise Resource Planning (ERP) system of a real garment industry. We used Genetic Algorithms (GA) to be an analyzing tool because of many factors in making decision. GA is a useful tool in solving complex factors. Finally, tables and Gantt charts the reference materials of decision makers. This system is emphasized to be easy and quick to use. Also, it is suitable for global garment industry in order allocation. In the future, we could combine more factors, for instance, extra flight brings lots of additional cost. With more factors, the system could simulate a real situation of global garment industry.

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