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In recent years, the global resource shortage has become a serious issue. Recycling end-of-life (EOL) products is conducive to resource reuse and circular economy and can mitigate the resource shortage issue. The disassembly of EOL products is the first step for resource reuse. Disassembly activities need tools, and tool deterioration occurs inevitably during the disassembly process. This work studies the influence of tool deterioration on disassembly efficiency. A disassembly line balancing model with the goal of maximizing disassembly profits is established, in which tool selection and assignment is a critical part. A modified discrete migratory bird optimizer is proposed to solve optimization problems. The well-known IBM CPLEX optimizer is used to verify the correctness of the model. Six real-world products are used for disassembly experiments. The popular fruit fly optimization algorithm, whale optimization algorithm and salp swarm algorithm are used for search performance comparison. The results show that the discrete migratory bird optimizer outperforms all three other algorithms in all disassembly instances. [ABSTRACT FROM AUTHOR]

Community Supported Agriculture (CSA), which offers two outstanding advantages, high-quality food and localized production, has come to the fore. In CSA, the output of picking scheduling is the input of delivery scheduling. Hence, only by scheduling the picking stage and distribution stage in a coordinated way can we achieve fresh agricultural products at minimum cost. However, due to asymmetric information in the picking and distribution stage, the integrated scheduling of picking and distribution may lead to an asymmetric optimization problem, which is suitable for solving with an iterative algorithm. Based on this, this work studies an integrated scheduling problem of the picking and distribution of fresh agricultural products with the consideration of minimizing picking and distribution costs as well as maximizing the freshness of orders. First, a nonlinear mixed-integer programming model for the problem under consideration is constructed. Second, a multi-objective multi-population genetic algorithm with local search (MOPGA-LS) is designed. Finally, the algorithm is compared with three multi-objective optimization algorithms in the literature: the non-dominated sorted genetic algorithm-II (NSGA-Ⅱ), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the multi-objective evolutionary algorithm based on decomposition that is combined with the bee algorithm (MOEA/D-BA). The comparison results show the excellent performance of the designed algorithm. Thus, the reported model and algorithm can assist managers and engineers in making well-informed decisions in managing the farm operation. [ABSTRACT FROM AUTHOR]