روش برنامه نویسی چند هدفه فازی برای انتخاب یک سیستم تولید انعطاف پذیر

Global competition in manufacturing environment has forced the firms to consider increasing the quality and responsiveness to customization, while decreasing costs. The evolution of flexible manufacturing systems offers great potential for increasing flexibility and changing the basis of competition by ensuring both cost effective and customized manufacturing at the same time. This paper presents a fuzzy multiple objective programming approach to facilitate decision making in the selection of a flexible manufacturing system (FMS). Fuzzy set theory is introduced in the model to incorporate the vague nature of future investments and the uncertainty of the production environment. Linguistic variables and triangular fuzzy numbers are used to quantify the vagueness inherent in decision parameters, e.g., increase in market response, improvement in quality, reduction in setup cost, and so forth. The model proposed in this paper determines the most appropriate FMS alternative through maximization of objectives such as reduction in labor cost, reduction in setup cost, reduction in work-in-process (WIP), increase in market response and improvement in quality, and minimization of capital and maintenance cost and floor space used. These objectives are assigned priorities indicating their importance levels using linguistic variables. A numerical example is presented to illustrate the application of the model developed in this paper.

Recently, the expanding competitiveness in manufacturing due to the globalization has forced the manufacturers to increase their product types and respond to the changes in demand very quickly. Flexible manufacturing systems (FMSs) provide the means to reach these objectives. Robots, CNC machines and automated material handling systems controlled by dedicated computers are the main components of an FMS. The main benefits of an FMS can be listed as increase in product types, enhancement in quality, and reduction in WIP and setup costs. Before investing in such advanced manufacturing technologies requiring substantial capital expenses, both the dollar-denominated consequences and those effects not readily reduced to monetary terms have to be considered. Within the past decade, a number of articles have been published for justification and selection of advanced manufacturing technologies. These studies can be classified in three main groups: economic analysis, strategic techniques, and analytical methods. Although major strategic benefits such as early entry to market, market leadership, innovation, improved flexibility and quality are extremely important for the growth and survival of the manufacturing firm, they are not readily expressed in cash flow terms [1]. Hence, economic analysis methods such as payback period and discounted cash flow (DCF) techniques are not suitable on their own for the justification of FMS since they disregard tactical and strategic benefits such as improvement in market response, increase in quality and product flexibility, etc. Strategic methods generally consider the main objectives of the firm and they do not offer a generalized solution procedure. Analytical methods attempt to take into consideration both the qualitative and quantitative benefits. However, the mathematical programming, which is one of the frequently used analytical methods, falls short of modeling the linguistic expressions corresponding to strategic and qualitative benefits. In this study, the FMS alternatives have been evaluated incorporating their strategic and economic benefits using a fuzzy multiple objective programming technique.

The mathematical programming is one of the analytical techniques previously used for the justification and the selection of advanced manufacturing systems. In this paper, fuzzy multiple objective programming approach is proposed as an alternative to the classical mathematical programming formulations in evaluating FMS alternatives. The decision-maker can take into account the vagueness inherent in FMS investments utilizing the fuzzy set theory. The strategic benefits of FMS alternatives that are considered solely in strategic justification approaches are incorporated into the model using linguistic variables, and the imprecise or uncertain quantitative data are efficiently represented employing triangular fuzzy numbers. Another contribution of the proposed approach is that one can distinguish between the importance of each objective by considering their respective fuzzy priorities. This is accomplished by assigning an importance degree to each objective utilizing linguistic variables, and then, integrating the objective's membership function and the membership function corresponding to its importance degree employing the composition operator. The fuzzy multiple objective programming approach presented in this paper for FMS selection also has advantages compared to the decision making approaches using fuzzy number ranking methods. First, fuzzy number ranking methods do not provide consistent results, i.e. the results obtained might differ according to the ranking method chosen for application purposes. Furthermore, when fuzzy multiple objective programming approach is employed, technological constraints can be incorporated into the formulation, which is an important property not present in fuzzy number ranking methods. Finally, the fuzzy multiple objective programming approach not only deals with data expressed by linguistic terms or triangular fuzzy numbers as in the FMS selection example provided in this paper, but it can also consider crisp data for precisely known values. It is also worth noting that the decision framework presented in this paper is equally applicable to diverse multiple objective decision making problems encountered in management science that incorporate vagueness.