Constraint Based Fast Simulated Annealing (CBFSA) Approach to Solve the Disassembly Scheduling Problem

摘要

Increasingly, globalization coupled with environmental requirements, have spearheaded new levels of requirements for product end-of-life, the last phase of product lifecycle management (PLM) especially, for product remanufacturing and recycling which involves product disassembly to retrieve the desired parts and subassemblies. Selection of optimal disassembly schedule is a major concern for remanufacturing and recycling industries as it directly affects the inventory of the manufacturing unit and influences the final product cost. This paper proposes the Constraint Based Fast Simulated Annealing (CBFSA) algorithm methodology to determine the ordering and disassembly schedule to minimize inventory level for products with general assembly product structure, i.e., the general structure with parts commonalities. The proposed CBFSA algorithm uses constraint based genetic operators integrated with the SA approach to make the algorithm more search exploratory (guarantee the optimal or near optimal solution) and converge efficiently (less time consuming). The proposed algorithm provides a greater likelihood of avoiding the local optima in comparison to the standard simulated annealing and genetic algorithms by exploring a population of points, rather than a single point in the solution space. The proposed methodology is validated based on a numerical case study for disassembly scheduling problem with parts commonality.