EMOGA: A Hybrid Genetic Algorithm With Extremal Optimization Core for Multiobjective Disassembly Line Balancing

Abstract

In a world where products get obsolescent ever more quickly, discarded devices produce million tons of electronic waste. Improving how end-of-life products are dismantled helps reduce this waste, as resources are conserved and fed back into the supply chain, thereby promoting reuse and recycling. This paper presents the Extremal MultiObjective Genetic Algorithm (EMOGA), a hybrid nature-inspired optimization technique for a multiobjective version of the Disassembly Line Balancing Problem (DLBP). The aim is to minimize the number of workstations, and to maximize profit and disassembly depth, when dismounting products in disassembly lines. EMOGA is a Pareto-based genetic algorithm (GA) hybridized with a module based on extremal optimization (EO), which uses a tailored mutation operator and a continuous relaxation-based seeding technique. The experiments involved the disassembly of a hammer drill and a microwave oven. Performance evaluation was carried out by comparing EMOGA to various efficient algorithms. The results showed that EMOGA is faster or gets closer to the Pareto front, or both, in all comparisons.

TeMA: A Tensorial Memetic Algorithm for Many-Objective Parallel Disassembly Sequence Planning in Product Refurbishment

“…In accordance with the relative importance of each objective, the decision maker chose solution S = [( 1, 2 ), ( 3, 5, 7 , 15 ), ( 4 , 6 ), ( 8 ), ( 11, 13 , 9 ), ( 14, 16 , 10 ), ( 17 , 12 ), ( 19, 20 , 21 ), ( 22,18 )], whose fitness is g(S) = (−2, −6.63, 72.92, 88.89, −3.99). This solution was chosen because it takes the best values of the secondary objectives.…”

“…Hybrid techniques have also been proposed. For instance, in [8] a GA is hybridized with extremal optimization. Scatter search is used with Petri nets in [9], and with dual objective program in [10].…”

An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

“…Liu and Wang [21] developed a new mathematical model for sequence-dependent disassembly lines and proposed a multi-objective algorithm to solve the sequence-dependent DLBP. Pistolesi et al [22] presented a hybrid genetic algorithm to solve the DLBP with considerations of the number of workstations, profit, and disassembly depth.…”

Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

“…Due to the DLBP's NP-hard nature (McGovern and Gupta 2007a) and the inclusion of interval task times, robot paralleling configuration and simultaneously considered multiple objectives listed above, the solving difficulty for our problem will increase geometrically with the increase of the problem scale. In recent years, various metaheuristic algorithms have been widely used in solving DLBP because of their excellent solution performance, such as genetic algorithms (McGovern and Gupta 2007b;Aydemir-Karadag and Turkbey 2013;Jiang et al 2016;Kalayci, Polat, and Gupta 2016;Pistolesi et al 2018;Fang et al 2019;Wang, Li, and Gao 2019), ant colony algorithms (Agrawal and Tiwari 2008;Ding et al 2010;Mete et al 2018), artificial bee colony algorithms (Kalayci and Gupta 2013a;Kalayci et al 2015;Gao et al 2018), simulated annealing algorithms (Kalayci and Gupta 2013b;Wang, Li, and Gao 2019), artificial fish swarm algorithms (Zhang et al 2017), and firefly algorithms (Zhu, Zhang, and Wang 2018). Most of these studies deal with multi-objective DLBPs, but optimise each objective step by step by using the lexicographic method, or get a single objective by using the weighted method.…”

“…However, objectives generally conflicting with each other, thus these approaches cannot guarantee the equilibrium of the optimisation of all objectives. In contrast, Pareto-based metaheuristic algorithms (Ding et al 2010; Aydemir-Karadag and Turkbey 2013; Kalayci et al 2015;Jiang et al 2016;Zhang et al 2017;Gao et al 2018;Pistolesi et al 2018;Zhu, Zhang, and Wang 2018;Fang et al 2019;Wang, Li, and Gao 2019) overcome the disadvantage as they provide a uniform set of Pareto-optimal solutions per run, with no weights or orders to specify. Therefore, it is appropriate to design metaheuristic algorithms based on Pareto solution set to solve multi-objective DLBPs.…”