| dc.description.abstract | Technological innovation and competition in agroindustry in today’s manufacturing economy have led to the improvements in supply chain management for agricultural products. Agroindustry is defined as an enterprise that transforms agricultural products (plant, marine and aquatic, livestock, and forestry) into industrial products in order to gain their added value. Each different step in the entire production process, from the farming of basic raw materials to delivery of final products to consumer, is viewed as link in the chain of agroindustrial systems. Agroindustrial Supply Chain Management (Agro-SCM), therefore, represents the management of the entire set of production, manufacturing/transformations, distribution and marketing activities by which a consumer is supplied with a desired product. The objective of this research is to develop an integrated decision support system consisting of a new multi-objective genetic algorithm and fuzzy logic for optimization of supply chain of bio-diesel industry, an important agroindustry in Indonesia. Supply chain improvements will reduce inventories, waste and costs, and thus increase efficiency within the bio-diesel industry and the market channel. The mathematical model for supply chain management developed in this research attempts to capture the dynamics of a single product being produced from coconut or palm oil. There are j coconut or palm oil farming (suppliers), k bio-diesel industry and l customer demands. Coconut or palm oil can be supplied by any of the j farming. This raw material can be shipped to any of the k bio-diesel industry where the product is made. Then they are shipped to customers based on demands. The multiple objectives used in this research are minimizing Total Supply Chain Cost (TSCC) and minimizing Expected Number of Deteriorated Product (ENDP). The second objective is essential for agroindustry. The variables to be optimized are the amount of coconut or palm oil from suppliers to plants, the amount of bio-diesel shipped from plants to customer zone and the inventories in the plants. The numerical result indicated that the genetic algorithm developed and fuzzy logic introduced in this work are robust and reliable as they can produce promising results. Keywords: Genetic Algorithm, Fuzzy Logic, Supply Chain Management, Multiobjective optimization | id |
