Disassembly Sequencing Problem: Resolving the Complexity by Random Search Techniques
This chapter attempts to optimize the net profits from product or part recovery so as to minimize the disassembly cost in a real-world environment where knowledge about the quality of returned products as well as their constituent parts is vague. It explores the application potential of several state-of-the-art artificial intelligence techniques such as genetic algorithm, simulated annealing, tabu search, particle swarm optimization, and ant colony optimization on the proposed five algorithms on the proposed fuzzy disassembly optimization problem model. The chapter illustrates the various random search techniques used for optimization and the implementation procedure of these techniques on the problem concerned. It examines the implementation of several state-of-the-art random search techniques on the disassembly sequencing problem along with a consideration of ambiguities in the condition of the returned products. The chapter explains the mathematical model for the disassembly sequence optimization problem.