Genetic search and the dynamic facility layout problem
Computers and Operations Research - Special issue: heuristic, genetic and tabu search
Production Scheduling and Genetic Algorithms
Information Management in Computer Integrated Manufacturing: A Comprehensive Guide to State-of-the-Art CIM Solutions
Development of a Rapid-Response Supply Chain at Caterpillar
Operations Research
Decomposition heuristic to minimize total cost in a multi-level supply chain network
Computers and Industrial Engineering
Integrated multistage logistics network design by using hybrid evolutionary algorithm
Computers and Industrial Engineering
Review article: A review of soft computing applications in supply chain management
Applied Soft Computing
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A new model and its solution procedure for the commodity distribution system consisting of distribution centers and consumer points are discussed. Demand is assumed to be a random variable that obeys a known, stationary probability distribution. An integrated optimization model is built where both the order-up-to-R policy, which is one of the typical inventory policies for periodic review models, and the transportation problem are considered simultaneously. The assignment of consumer points to distribution centers is not fixed. The problem is to determine the target inventory and the transportation quantity in order to minimize the expectation of the sum of inventory related costs and transportation costs. Simulation and linear programming are used to calculate the expected costs, and a random local search method is developed in order to determine the optimum target inventory. A genetic algorithm is also tested and compared with the proposed random local search method. The model and effectiveness of the proposed solution procedure are clarified by computational experiments.