Joint optimization of production planning and supplier selection incorporating customer flexibility: an improved genetic approach

Efficient and effective production planning and supplier selection are important decisions for manufacturing industries in a highly competitive supply chain, in particular, when customers are willing to accept products with less desirable product attributes (e.g., color, material) for economic reasons. Yet these two decision making problems have traditionally been studied separately due to their inherent complexity. This paper attempts to solve optimally the challenging joint optimization problem of production planning and supplier selection, considering customer flexibility for a manufacturer producing multiple products to satisfy customers' demands. This integrated problem has been formulated as a new mixed integer programming model. The objective is to maximize the manufacturer's total profit subject to various operating constraints of the supply chain. Due to the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, an improved genetic approach is proposed to locate near-optimal solutions. This approach differs from a canonical genetic algorithm in three aspects, i.e., a new selection method to reduce the chance of premature convergence and two problem-specific repair heuristics to guarantee the feasibility of the solutions. The computational results of applying the proposed approach to solve a set of randomly generated test problems clearly demonstrate its excellent performance.