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Abstract: Based on credibility theory, this paper presents a new raw materials allocation (RMA) model in which the demands of products and the prices of surplus products are supposed to fuzzy variables, the involved restrictions mainly include the inventory of each raw material and the production capacity of company. Since the fuzzy variables in the RMA model are usually continuous variables and the exact analytical expression of objective function cannot be obtained, traditional optimization algorithms cannot be used to solve it. Therefore, we firstly apply an approximation approach (AA) to turn the original infinite-dimensional optimization problem into an approximating finite-dimensional one, then design a hybrid algorithm which combines neural network (NN) and particle swarm optimization (PSO) to solve the approximating model. A given numerical example illustrates that the designed algorithm works well. [Copyright &y& Elsevier]

Abstract: A great deal of research has been done on production planning and sourcing problems, most of which concern deterministic or stochastic demand and cost situations and single period systems. In this paper, we consider a new class of multi-period production planning and sourcing problem with credibility service levels, in which a manufacturer has a number of plants and subcontractors and has to meet the product demand according to the credibility service levels set by its customers. In the proposed problem, demands and costs are uncertain and assumed to be fuzzy variables with known possibility distributions. The objective of the problem is to minimize the total expected cost, including the expected value of the sum of the inventory holding and production cost in the planning horizon. Because the proposed problem is too complex to apply conventional optimization algorithms, we suggest an approximation approach (AA) to evaluate the objective function. After that, two algorithms are designed to solve the proposed production planning problem. The first is a PSO algorithm combining the AA, and the second is a hybrid PSO algorithm integrating the AA, neural network (NN) and PSO. Finally, one numerical example is provided to compare the effectiveness of the proposed two algorithms. [Copyright &y& Elsevier]