Integrated supplier selection and order allocation incorporating customer flexibility

Supplier selection and order allocation are significant decisions for a manufacturer to ensure stable material flows in a highly competitive supply chain, in particular when customers are willing to accept products with less desirable product attributes. Hence, this study develops efficient methodologies to solve optimally the integrated supplier selection and order allocation problem incorporating customer flexibility for a manufacturer producing multiple products over a multi-period planning horizon. In this research, a new fuzzy multi-attribute approach is proposed to evaluate customer flexibility which is characterized through range and response. The approach calculates the product’s general utility value. This value is used by a bi-variant function which is developed to determine the retail price for the product. A new mixed integer program model describing the behavior of the basic problem is firstly developed. This basic model is the first to jointly determine: 1) type and quantity of the product variants to be offered; 2) the suppliers to be selected and orders to be allocated; and 3) inventory levels of product variants and raw materials/components. The objective is to maximize the manufacturer’s total profit subject to various operating constraints. This basic problem constitutes a very complex combinatorial optimization problem that is Nondeterministic Polynomial (NP)-hard. To tackle this challenge, two new optimization algorithms, i.e., an improved genetic approach called king GA (KGA) and an innovative hybrid algorithm called (CP-SA) _I which combines the techniques of constraint programming and simulated annealing are developed to locate optimal solutions. Extensive computational experiments demonstrate the effectiveness of these algorithms and also show clearly that (CP-SA) _I outperforms KGA in terms of both solution quality and computational cost. To examine the influence of subcontracting as one widespread practice in modern production management, this study also develops a modified mathematical model. It shares some similarity with the basic model but brings additional complexity by taking into consideration subcontractors for inter-mediate components and machine capacity. Since (CP-SA) _I outperforms KGA, it is employed and modified to solve the modified problem. Hence, this study presents a new hybrid algorithm called (CP-SA) _II, to locate optimal solutions. This study also establishes a new parallel (CP-SA) _II algorithm to enhance the performance of (CP-SA) _II. This parallel algorithm is implemented on a distributed computing platform based on the contemporary Graphic Processing Unit (GPU) using the Compute Unified Device Architecture (CUDA) programming model. Extensive numerical experiments conducted clearly demonstrate that the parallel (CP-SA) _II algorithm and its serial counterpart are efficient and robust optimization tools for formulating integrated supplier selection and order allocation decisions. Sensitivity analysis is employed to study the effects of the critical parameters on the performance of these algorithms. Finally, the convergence behavior of the proposed parallel (CP-SA) _II algorithm is studied theoretically. The results prove that the search process eventually converges to the global optimum if the overall best solution is maintained over time.published_or_final_versionIndustrial and Manufacturing Systems EngineeringDoctoralDoctor of Philosoph