Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, jobcluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities Communicated by V. Loia. and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.

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Wafer fabrications provide the semiconductor backend turnkey service (SBTS) that help their customers to handle the outsourcing business of the three backend processing stages including circuit probing testing, integrated circuit assembly, and final testing. When the process of wafer fabrication is completed, the SBTS provider needs to select appropriate outsourcing firms and to allocate the semi-finished orders to them. In the process of selecting suitable outsourcing firms, the SBTS providers consider the constraints of limited capacity, production cost, due dates, and the processing capabilities of each outsourcing firm. The planning problem for SBTS is very complicated because of requirement variations of orders and capability variations of outsourcing firms. In this paper, a mixed integer programming model for SBTS with the total cost minimization criterion is presented. A case is also provided to demonstrate its applicability of the IP model in real practice.