PurposeA successful adaptive reuse process relies heavily on the strong performance of disassembly sequence planning (DSP), yet the studies in the field are limited to sequential disassembly planning (SDP). Since in sequential disassembly, one component or subassembly is removed with only one manipulator at a time, it can be a relatively inefficient and lengthy process for large or complex assemblies and cannot fully utilize the DSP benefits for adaptive reuse of buildings. This study aims to present a new hybrid method for the single-target selective DSP that supports both sequential and parallel approaches.Design/methodology/approachThis study uses asynchronous parallel selective disassembly planning (aPDP) method, one of the newest and most effective parallel approaches in the manufacturing industry, to develop a parallel approach toward DSP in adaptive reuse of buildings. In the proposed method, three objectives (i.e. disassembly sequence time, cost and environmental impacts) are optimized using the Non-dominated Sorting Genetic Algorithm (NSGA-II).FindingsThe proposed method can generate feasible sequential solutions for multi-objective DSP problems as the sequence disassembly planning for buildings (SDPB) method, and parallel solutions lead to 17.6–23.4% time reduction for understudy examples. Moreover, in disassembly planning problems with more complex relations, the parallel approach generates more effective and time-efficient sequences.Originality/valueThis study introduces the parallel approach for the first time in this field. In addition, it supports both sequential and parallel approaches as a novel strategy that enables the decision-makers to select the optimum approach (i.e. either the parallel or the sequential approach) for DSP. Moreover, a metaheuristic method (i.e. NSGA-II) is adopted as the optimization tool with robust results in the field in which those heuristic methods have only been employed in the past.
Energy Consumption Optimization of Selective Disassembly Planning Considering Product Embodied Energy during Manufacturing
