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In recent years, the interests of disassembly line have increased owing to economic reasons and the increase of environmental awareness. Effective line can provide many advantages in terms of economic aspect and it facilitates competition the companies with others. This study contributes to the relevant literature by a branch, bound and remember algorithm for disassembly line balancing problem with AND/OR precedence. The proposed exact solution method employs the memory-based dominance rule to eliminate the reduplicated sub-problems by storing all the searched sub-problems and to utilise cyclic best-first search strategy to obtain high-quality complete solutions fast. In this paper, minimising the number of stations is taken as the performance measure. The proposed methodology is tested on a set of 260 instances and compared with the mathematical model using CPLEX solver and five well-known metaheuristics. Computational results show that the proposed method is capable of obtaining the optimal solutions for all the tested instances with less than 0.1 seconds on average. Additionally, comparative study demonstrates that the proposed method is the state-of-the-art algorithm and outperforms the CPLEX solver and metaheuristics in terms of both solution quality and search speed aspects. [ABSTRACT FROM AUTHOR]

Industries are increasingly looking for opportunities at utilizing collaborative robots in assembly lines to perform the tasks independently or assist the human workers due to the advancement of industry 4.0 technologies. Purchasing cost is one of the important factors to be considered by production managers, while designing or redesigning assembly line when collaborative robots are being utilized. Several objectives are to be optimized in an assembly line balancing problem and optimizing line efficiency along with purchasing cost sometimes results in conflicting situation. This paper presents the first study to tackle the cost-oriented assembly line balancing problem with collaborative robots, where several different types of collaborative robots with different purchasing costs are available and selected. A multi-objective mixed-integer programming model is developed to minimize the cycle time and the total collaborative robot purchasing cost. The multi-objective migrating bird optimization algorithm is developed to obtain a set of high-quality Pareto solutions. This algorithm utilizes the fast non-dominated sorting approach to update the population and develops a restart mechanism to select one solution in the permanent Pareto archive to replace the abandoned solution which remains unchanged for several iterations. The computational study validates that the utilization of the multi-objective model is reasonable and developed algorithm produces competing performance in comparison with multi-objective non-dominated sorting genetic algorithm II, multi-objective simulated annealing algorithm and two multi-objective artificial bee colony algorithms. [ABSTRACT FROM AUTHOR]

Exact methods have shown advanced and promising performance in solving the simple assembly line balancing problem, known as NP-hard. This research investigates the impact of various structural parameters on the performance of exact methods, including branching methods, search direction, method to achieve upper bounds, utilized lower bounds, utilized dominance rules and search strategy. In accordance with the structural parameter evaluation, utilized dominance rules and search strategy have shown the most important effect on the exact methods' performance. This research also improves and re-implements three well-known exact methods [i.e., SALOME, bounded dynamic programming (BDP) heuristic and branch, bound and remember (BBR) algorithm] using effective parameters. Computational study demonstrates that the utilization of high-performance structural parameters enhances the performance of exact methods by a significant margin. The re-implemented BBR method with proper parameters shows clear superiority over all the published exact methods and might be regarded as the state-of-the-art exact methodology. [ABSTRACT FROM AUTHOR]

• Profit-oriented U-shaped partial disassembly line balancing problem is studied. • Mixed-integer linear programming model is developed for the proposed problem. • The proposed model solves the small-size problems optimally. • The cuckoo search algorithm is developed and improved for the considered problem. • Experimental tests demonstrate the superiority of the proposed algorithm. Disassembly lines are utilized frequently to disassemble the end-of-life products completely or partially to retain the valuable components for remanufacturing or recycling. This research introduces and solves the profit-oriented U-shaped partial disassembly line balancing problem (PUPDLBP) for the first time. A 0–1 integer linear programming model is formulated to tackle the PUPDLBP with AND/OR precedence, which is capable of solving the small-size instances optimally. As the considered problem is NP-hard, a novel discrete cuckoo search (DCS) algorithm is implemented and improved to solve the considered problem. The proposed DCS employs a two-phase decoding procedure to handle the precedence constraint, and new population update and new method to select and replace the abandoned individuals to achieve the proper balance between exploitation and exploration. Case studies demonstrate that the U-shaped line might obtain the larger total profit than a straight line. The comparative study shows that the improvements enhance the performance of DCS by a significant margin. The proposed algorithm outperforms CPLEX solver when solving large-sized instances and produce competing performance in comparison with 11 other algorithms. [ABSTRACT FROM AUTHOR]

Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study. [ABSTRACT FROM AUTHOR]

The collaboration of human workers and robots draws increasing attention from the manufacturing enterprises to embrace the Industry 4.0 paradigm in a competitive way. Motivated by the requirements of collaboration between human workers and robots in assembly lines, this study investigates the mixed-model assembly line balancing (MMALB) problem with the collaboration between human workers and robots. A mixed-integer linear programming (MILP) model is formulated to tackle the small-size problems optimally to minimize the sum of cycle times of models. Also, bee algorithm (BA) and artificial bee colony (ABC) algorithm are implemented and improved to solve the large-size problems due to the NP-hardness of this problem. The proposed BA algorithm utilizes a new employed bee phase to accelerate the evolution of the swarm and new scout phase to escape from being trapped into local optima and produce a high-quality and diverse population. The developed ABC proposes a new onlooker phase to accelerate the evolution of the whole swarm by removing the poor-quality solutions, new scout phase to achieve high-quality solutions while preserving the diversity of the swarm, and local search to enhance exploitation capacity. Computational study on a set of generated instances indicates that the improvements enhance the BA and ABC algorithm by a significant margin, and the proposed BA and ABC algorithm achieve competing performance in comparison with nine other algorithms, including the late acceptance hill-climbing algorithm, simulated annealing algorithm, genetic algorithm, particle swarm optimization algorithm, discrete cuckoo search algorithm, the original bee algorithm, and three artificial bee colony algorithms. • Mixed-model assembly line balancing problem with physical human–robot collaboration is studied. • A novel mixed integer linear programming model is developed to formulate the problem. • The proposed MILP model can solve the small-size problems optimally. • Bees algorithms are developed and improved for the considered problem. • Computational tests demonstrate the superiority of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Energy-saving, one of the most significant strategies for green manufacturing, has become the focus of more and more scholars and enterprise managers. Hence, this work addresses the minimization of energy and cost requirements on mixed-model multi-manned assembly line balancing with multi-skilled workers. A new mixed-integer linear programming model is proposed to define the problem. Additionally, a novel multi-objective co-evolutionary algorithm is designed to achieve the trade-off between energy and cost requirements. This algorithm includes a two-layer solution representation to achieve full coverage of the solution space and a new decoding mechanism with idle time reduction. A collaborative initialization as the first stage of the algorithm is extended to get high-quality and great-diversity initial solutions. A self-learning evolution for each sub-population with four problem-specific evolutionary operators is developed to explore task or worker assignment sequences, and a dual-cooperation strategy is proposed to enhance the interaction between sub-populations. The final experiments, based on 269 instances, demonstrate that the improvement components are effective and the proposed algorithm is superior to seven latest multi-objective evolutionary algorithms from numerical, statistical and differential analyses. • Define the MALBP-MW by a MILP model to minimize the energy and cost requirements. • Design a new decoding with idle time reduction and a collaborative initialization. • Propose four problem-specific evolutionary operators for MOCEA. • Develop MOCEA with a self-learning selection and dual-cooperation. [ABSTRACT FROM AUTHOR]