J. Basic. Appl. Sci. Res., 2(8)8085-8090, 2012 © 2012, TextRoad Publication ISSN 2090-4304 Journal of Basic and Applied Scientific Research www.textroad.com * Corresponding author. B. Razmi Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Email address: behrouz.razmi@yahoo.com (B. Razmi). Using Compromise Programming to Solve a New Multi-Objective Model for Industrial Clusters B. Razmi 1 , M.B. Aryanezhad 2 , R. Tavakkoli-Moghaddam 3 , S.J. Sajadi 2 and R. Soltani 2 1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2 Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran 3 Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran ABSTRACT Tremendous progress in production and changing the customers’ tastes in recent years, make a competitive environment, in which production units can no longer survive with just resorting to their own ability and on hand technology. To enhance the competitive power of these industries, they need to cooperate with each other as a cluster and avoid individual activities as far as possible. Two industrial units can cooperate in different fields. Therefore, determining the best amount and combination of these fields regarding all members of the cluster that lead to the maximum benefit and promote other gains (e.g., increasing in the volume and quality level of products) is a challenging issue. On the other hand, each individual unit has a contribution in achieving the goal of the cluster. Knowing the degree of their contribution is also critical for strategic plans of the cluster. To the best of our knowledge, no model exists in the literature to answer the previously mentioned requirements. Determining multiple and directional objectives and deciding about the type and combinations of cooperation fields between members of a cluster is a crucial task. The problem is formulated as a multi-objective mixed- integer programming (MIP) model that determines the decision variables regarding the considered objectives. Modeling and solving the problem to cope with these issues are the main contribution of this paper. Finally, the associated results are illustrated and discussed. KEYWORDS: Industrial cluster; compromise programming; Multi-objective model; 1. INTRODUCTION Growing competence in today’s environment and globalizing industries, markets and technologies necessitate the industrial units to be centralized and operate in industrial clusters [1]. Being member of a cluster has some privileges for industrial units in the world competences [2-5]. Innovation expenditures can decrease substantially when industries and institutions operate in the framework of a cluster [6]. In addition, they can acquire complementary resources, knowledge and financial funds [7-10]. Through such a relationship, the competitive position of the members of the cluster is promoted in the world [11]. A cluster is defined as a set of companies that are centralized in a geographical area and encountered with common opportunities and threats. The above-mentioned companies present a set of products, which are related or complementary. Within a cluster framework, they can exchange and share their specialties. In general, the cooperation fields are classified in three main categories that are horizontal cooperation (e.g., cooperation in supply chain fields), vertical cooperation (e.g., common production) and orthogonal cooperation (e.g., exchanging production resources) [12]. Other fields can be cooperated in sale, procurement of raw materials, exchanging technical knowledge, workforce and experts, cooperation in production, after sale services, repair and maintenance, education and development of human resources, supplying spare parts and essentials, advertisements, marketing, joint investments, research and scientific cooperation, research and development and so forth [13- 22]. Clusters can grow powerfully only when their industrial units cooperate intelligently and aim at a common goal that is promoting the competitive position of all members of a cluster. Although each member of the cluster competes with others; however, all members simultaneously take advantage of the total profit of the competition. The goal of the clusters is to make each member capable of gaining the advantage of comprehensive competition. To intensify the competitiveness in the cluster, institutions are required to set their organizations in the direction of other members of the cluster so that they can appear in the world level competitions [25]. improvement of the competitive power is the main incentive of industrial units to join the clusters [23]. One of the most challenging issues in a cluster confronted with is to set up a suitable scientific decision- making system to measure how degree a member’s performance is in the direction of the strategic plans of the cluster [24]. Changing the prevalent and traditional approach in operations of cluster’s members into scientific approaches is the main requirements of development process [13, 25] and the success of clusters as an important factor in economical development of countries merely depends on the scientific planning [26]. 8085