A hierarchical DNA based approach for solving integrated process planning and scheduling problem
This research addresses an integrated process planning and scheduling problem where the objective is to improve system efficiency and enhance resource utilization, thereby ameliorating the production activities. The notion of simultaneous manufacturing of prismatic parts has been conceived in this work to improve the system output in both cost and time frame. The computational prowess of DNA algorithm in preliminary graph based combinatorial optimization has been utilized to resolve the complexities of the proposed Integrated Process Planning and Scheduling (IPPS) framework. Further, the inherently parallel search strategy and structured architecture used in DNA algorithm meets the implementation requirements of IPPS. Being first of its kind attempt, the proposed strategy has first been tested over standard test functions, thus establishing its superiority in computational optimization. Thereafter, its application to four integrated process planning and scheduling problems (three benchmarks and one simulated) with real sized data authenticate the robustness of the proposed solution methodology. Finally, the results obtained substantiate that it is a better alternative that should further be explored for various computer aided design and process planning applications.