<![CDATA[ The objective of this research was to develop an intelligent decision support system for optimization of horticulture supply chain model using genetic algorithms. The case study was conducted at PT. Saung Mirwan, Megamendung-Bogor, a major producer of packed fresh vegetable and fresh-cut vegetable. The output of this research is an Intelligent Decision Support System of Supply Chain Management for Horticulture Agro industry (IDSS-SCM). IDSS-SCM consists of eight models: Products Demand Forecast, Vegetables Supply Forecast, Planting Schedule, Aggregate Planning, Material Requirements Planning I, Material Requirements Planning II, Inventory Management, and Transportation Route. Based on the most recent data collected, IDSS-SCM predicts that product demand will increase and it then gives optimum recommendations to the user such as plant schedule, material requirements planning, inventory, human resource allocation, and distribution route to fulfil the demand. The unique feature of this research was that a genetic algorithm (GA) with Partially Matched Crossover (PMX) operator was used to find the shortest distribution route as well as to optimize human resource allocation problem. The experiment results indicate that the GA developed in this research can solve a complex agroindustrial supply chain design problem faster and more efficiently. Keywords: intelligent decision support system, genetic algorithms, supply chain management, agroindustry, partially matched crossover.]]>
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