A Problem Specific Genetic Algorithm for Disassembly Planning and Scheduling Considering Process Plan Flexibility and Parallel Operations

Increased awareness of resource scarcity and man-made pollution has driven consumers and manufacturers to reflect ways how to deal with end-of-life products and exploit their remaining value. The options of repair, remanufacturing or recycling each require at least partial disassembly of the structure with the variety of feasible process plans and large number of emerging parts and sub-assemblies generally making for a challenging optimization problem. Its complexity is further accentuated by considering divergent process flows which result from multiple parts or sub-assemblies that are released in the course of disassembly. In a previous study, it was shown that exact solution using an and/or graph based mixed integer linear program (MILP) was only practical for smaller problem instances. Consequently, a meta-heuristic approach is now taken to enable solution of large size problems. This study presents a genetic algorithm (GA) along with a problem specific representation to address both the scheduling and process planning aspect while allowing for parallel execution of certain disassembly tasks. Performance analysis with artificial test data shows that the proposed GA is capable of producing good quality solutions in reasonable time and bridging the gap regarding application to large scale problems as compared to the existing MILP formulation.