Two-sided disassembly line balancing problem with sequence-dependent setup time: A constraint programming model and artificial bee colony algorithm - Çil, Zeynel Abidin; Kizilay, Damla; Li, Zixiang; Öztop, Hande (Expert systems with applications, 2022)
•TDLBP-SDST is handled for the first time in the literature.•The proposed CP model for the TDLBP obtains the optimal results.•The MILP models and CP approach are proposed to solve the TDLBP-SDST.•Effective four metaheuristic approaches are also developed for the TDLBP-SDST.•TDLBP-SDST aims to minimize mated workstations and workstations, respectively. The large-size products can allow workers to perform tasks on both sides of the line. Hence, a two-sided disassembly line is preferred to ensure several advantages, such as a shorter line. The two-sided disassembly line balancing problem (TDLBP) is relatively new in the literature. This study considers the two-sided disassembly line balancing problem with sequence-dependent setup time (TDLBP-SDST) to reflect the real practice better, as sequence-dependent setup times may exist between tasks in many real-life applications. To the authors’ best knowledge, sequence-dependent setup time has not been considered for the TDLBP in the current literature. The proposed problem creates a more complicated problem. Therefore, proposing effective solution techniques is more critical for obtaining better results. This study proposes two new mixed-integer linear programming models and a novel constraint programming (CP) model to define and solve the TDLBP-SDST. A genetic algorithm, an artificial bee colony algorithm, and the improved versions of these two algorithms are also developed to solve the large-size problems due to the NP-hardness of the TDLBP-SDST. Furthermore, a novel CP model is proposed for the standard TDLBP without considering sequence-dependent setup times. Initially, we compare the performance of the proposed CP model to those of the previous state-of-the-art methods in the literature for the TDLBP without sequence-dependent setup time. The computational results show that the proposed CP model outperforms all the other solution methods and reports the best-known results for all existing benchmark instances for the TDLBP. Then, we present the computational results of the proposed models and algorithms for the TDLBP-SDST. Computational study on a comprehensive set of generated instances indicates that the proposed solution methods effectively solve the TDLBP-SDST.