Disassembly sequence planning using a Simplified Teaching-Learning-Based Optimization algorithm

Disassembly Sequence Planning (DSP) is a challenging NP-hard combinatorial optimization problem. As a new and promising population-based evolutional algorithm, the Teaching–Learning-Based Optimization (TLBO) algorithm has been successfully applied to various research problems. However, TLBO is not capable or effective in DSP optimization problems with discrete solution spaces and complex disassembly precedence constraints. This paper presents a Simplified Teaching–Learning-Based Optimization (STLBO) algorithm for solving DSP problems effectively. The STLBO algorithm inherits the main idea of the teach-ing–learning-based evolutionary mechanism from the TLBO algorithm, while the realization method for the evolutionary mechanism and the adaptation methods for the algorithm parameters are different. Three new operators are developed and incorporated in the STLBO algorithm to ensure its applicability to DSP problems with complex disassembly precedence constraints: i.e., a Feasible Solution Generator (FSG) used to generate a feasible disassembly sequence, a Teaching Phase Operator (TPO) and a Learning Phase Operator (LPO) used to learn and evolve the solutions towards better ones by applying the method of precedence preservation crossover operation. Numerical experiments with case studies on waste product disassembly planning have been carried out to demonstrate the effectiveness of the designed operators and the results exhibited that the developed algorithm performs better than other relevant algorithms under a set of public benchmarks. Mass-customized productions, technology updating and shortening lifespan of products in modern societies have resulted in generation of enormous amount of waste products like Waste Electrical and Electronic Equipment (WEEE). Developing technical solutions for sustainable recovery of waste products becomes a global trend. End-of-life recovery options include part reuse, remanufacturing, material recycling, energy recovery and disposal. As shown in Fig. 1, disassembly, which is a systematic method for separating a product into its constituent components and subas-semblies [1], is a critical stage for end-of-life recovery. Finding an optimum or near optimum disassembly sequence is crucial to increasing the efficiency of the disassembly process. Disassembly Sequence Planning (DSP) determines the order in which components are removed from products aiming at minimizing the disassembly time or cost, while considering the disassembly direction, disassembly method, and other attributes of components. DSP has been proved as a NP-hard problem [2] and has been becoming an important but still a challenging research topic in recent years. In the previous research, heuristics and meta-heuristics were used to find near optimum or optimum solutions and generate cost-effective and feasible disassembly sequences. Heuristics include rule-based recursive method [3], graph-based heuristic approach …