Solving integrated process planning and scheduling problem with constructive meta-heuristics

For product manufacturing, process planning is to select a series of manufacturing processes according to the product design specification, and scheduling is to allocate manufacturing resources such as machines and tools to these processes. It is a common problem that the process plan and the schedule are not able to cope with the changes in real time manufacturing. Integrated process planning and scheduling (IPPS) is to conduct the process planning and scheduling functions concurrently, with the aim to improve the dynamic responsiveness of the production schedule. This paper investigates the formulation and implementation of constructive meta-heuristics for solving IPPS problems. To begin with, a model representation is established to express IPPS problems with AND/OR graphs. With this model representation, a generic framework is proposed for implementing constructive meta-heuristics in the solution model. The generic framework provides a common procedure for the constructive meta-heuristics, which encapsulates the calculation of the search frontier and state transitions, and provides two interfaces for accommodating different constructive search algorithms. Ant colony optimization (ACO), a commonly-used algorithm which possesses all typical characteristics of constructive meta-heuristics, is adopted as a representative example for illustrating the implementation. Experiments and tests are conducted to validate the proposed system. The single objective minimizing the makespan is set for evaluating the performance of the proposed system. Experimental results of the benchmark problems have shown the effectiveness and high performance of the proposed approach based on the integration of the generic framework and ACO strategy. (C) 2016 Elsevier Inc. All rights reserved.