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research-article

A multi-objective co-evolutionary algorithm for energy and cost-oriented mixed-model assembly line balancing with multi-skilled workers

Authors:

Liping ZhangAuthors Info & Claims

Volume 236, Issue C

https://doi.org/10.1016/j.eswa.2023.121221

Published: 01 February 2024 Publication History

Abstract

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.

Highlights

•

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.

References

[1]

Abidin Çil Z., Kizilay D., Constraint programming model for multi-manned assembly line balancing problem, Computers & Operations Research 124 (2020).

[2]

Akagi F., Osaki H., Kikuchi S., A method for assembly line balancing with more than one worker in each station, International Journal of Production Research 21 (5) (1983) 755–770.

[3]

Andreu-Casas E., García-Villoria A., Pastor R., Multi-manned assembly line balancing problem with dependent task times: a heuristic based on solving a partition problem with constraints, European Journal of Operational Research 302 (1) (2022) 96–116.

[4]

Battini D., Delorme X., Dolgui A., Persona A., Sgarbossa F., Ergonomics in assembly line balancing based on energy expenditure: a multi-objective model, International Journal of Production Research 54 (3) (2016) 824–845.

[5]

Belkharroubi L., Yahyaoui K., Solving the energy-efficient robotic mixed-model assembly line balancing problem using a memory-based cuckoo search algorithm, Engineering Applications of Artificial Intelligence 114 (2022).

[6]

Boysen N., Schulze P., Scholl A., Assembly line balancing: What happened in the last fifteen years?, European Journal of Operational Research 301 (3) (2022) 797–814.

[7]

Chen Y.-Y., A hybrid algorithm for allocating tasks, operators, and workstations in multi-manned assembly lines, Journal of Manufacturing Systems 42 (2017) 196–209.

[8]

Chen Y.-Y., Cheng C.-Y., Li J.-Y., Resource-constrained assembly line balancing problems with multi-manned workstations, Journal of Manufacturing Systems 48 (2018) 107–119.

[9]

Chutima P., Khotsaenlee A., Multi-objective parallel adjacent U-shaped assembly line balancing collaborated by robots and normal and disabled workers, Computers & Operations Research 143 (2022).

[10]

Fonseca C.M., Knowles J.D., Thiele L., Zitzler E., A tutorial on tbe performance assessment of stochastic multiobjective optimizers, TIK-report (2006).

[11]

Gong G., Deng Q., Gong X., Liu W., Ren Q., A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators, Journal of Cleaner Production 174 (2018) 560–576.

[12]

Li R., Gong W., Wang L., Lu C., Jiang S., Two-stage knowledge-driven evolutionary algorithm for distributed green flexible job shop scheduling with type-2 fuzzy processing time, Swarm and Evolutionary Computation 74 (2022).

[13]

Li Z., Janardhanan M.N., Ponnambalam S.G., Cost-oriented robotic assembly line balancing problem with setup times: multi-objective algorithms, Journal of Intelligent Manufacturing 32 (4) (2021) 989–1007.

[14]

Li Z., Janardhanan M.N., Tang Q., Multi-objective migrating bird optimization algorithm for cost-oriented assembly line balancing problem with collaborative robots, Neural Computing & Applications 33 (14, SI) (2021) 8575–8596.

[15]

Li Z., Tang Q., Zhang L., Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm, Journal of Cleaner Production 135 (2016) 508–522.

[16]

Liu R., Liu M., Chu F., Zheng F., Chu C., Eco-friendly multi-skilled worker assignment and assembly line balancing problem, Computers & Industrial Engineering 151 (2021).

[17]

Liu X., Yang X., Lei M., Optimisation of mixed-model assembly line balancing problem under uncertain demand, Journal of Manufacturing Systems 59 (2021) 214–227.

[18]

Lopes T.C., Pastre G.V., Michels A.S., Magatão L., Flexible multi-manned assembly line balancing problem: Model, heuristic procedure, and lower bounds for line length minimization, Omega 95 (2020).

[19]

Meng K., Tang Q., Cheng L., Zhang Z., Mixed-model assembly line balancing problem considering preventive maintenance scenarios: MILP model and cooperative co-evolutionary algorithm, Applied Soft Computing 127 (2022).

[20]

Michels A.S., Lopes T.C., Sikora C.G.S., Magatão L., A benders’ decomposition algorithm with combinatorial cuts for the multi-manned assembly line balancing problem, European Journal of Operational Research 278 (3) (2019) 796–808.

[21]

nez M.L.-I., Paquete L., Stützle T., Hybrid population-based algorithms for the bi-objective quadratic assignment problem, Journal of Mathematical Modelling & Algorithms 5 (1) (2006) 111–137.

[22]

Nilakantan J.M., Li Z., Tang Q., Nielsen P., Multi-objective co-operative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems, Journal of Cleaner Production 156 (2017) 124–136.

[23]

Niroomand S., Hybrid artificial electric field algorithm for assembly line balancing problem with equipment model selection possibility, Knowledge-Based Systems 219 (2021).

[24]

Pan Z., Lei D., Wang L., A bi-population evolutionary algorithm with feedback for energy-efficient fuzzy flexible job shop scheduling, IEEE Transactions on Systems, Man, and Cybernetics: Systems 52 (8) (2022) 5295–5307.

[25]

Pan Z., Lei D., Wang L., A knowledge-based two-population optimization algorithm for distributed energy-efficient parallel machines scheduling, IEEE Transactions on Cybernetics 52 (6) (2022) 5051–5063.

[26]

Pereira J., Ritt M., Óscar C., Vásquez P., A memetic algorithm for the cost-oriented robotic assembly line balancing problem, Computers & Operations Research 99 (2018) 249–261.

[27]

Rabbani M., Mousavi Z., Farrokhi-Asl H., Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem, Journal of Industrial and Production Engineering 33 (7) (2016) 472–484.

[28]

Rahman H.F., Janardhanan M.N., Ponnambalam S., Energy aware semi-automatic assembly line balancing problem considering ergonomic risk and uncertain processing time, Expert Systems with Applications 231 (2023).

[29]

Şahin M., Kellegöz T., A new mixed-integer linear programming formulation and particle swarm optimization based hybrid heuristic for the problem of resource investment and balancing of the assembly line with multi-manned workstations, Computers & Industrial Engineering 133 (2019) 107–120.

[30]

Salehi M., Maleki H.R., Niroomand S., Solving a new cost-oriented assembly line balancing problem by classical and hybrid meta-heuristic algorithms, Neural Computing & Applications 32 (12, SI) (2020) 8217–8243.

[31]

Scholl A., Fliedner M., Boysen N., Absalom: Balancing assembly lines with assignment restrictions, European Journal of Operational Research 200 (3) (2010) 688–701.

[32]

Sun B., Wang L., Peng Z., Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing, Computers & Industrial Engineering 146 (2020).

[33]

Tan K., Yang Y., Goh C., A distributed cooperative coevolutionary algorithm for multiobjective optimization, IEEE Transactions on Evolutionary Computation 10 (5) (2006) 527–549.

[34]

Tian Y., Zhang T., Xiao J., Zhang X., Jin Y., A coevolutionary framework for constrained multiobjective optimization problems, IEEE Transactions on Evolutionary Computation 25 (1) (2021) 102–116.

[35]

Wu T., Zhang Z., Zeng Y., Zhang Y., Mixed-integer programming model and hybrid local search genetic algorithm for human–robot collaborative disassembly line balancing problem, International Journal of Production Research (2023) 1–25.

[36]

Wu T., Zhang Z., Zhang Y., Zeng Y., Modelling and optimisation of two-sided disassembly line balancing problem with human–robot interaction constraints, Expert Systems with Applications 230 (2023).

[37]

Yagmahan B., Mixed-model assembly line balancing using a multi-objective ant colony optimization approach, Expert Systems with Applications 38 (10) (2011) 12453–12461.

[38]

Yang H., Lee J.-H., Lee S.H., Lee S.G., Kim H.R., Kim H.-J., A multi-manned assembly line worker assignment and balancing problem with positional constraints, IEEE Robotics and Automation Letters 7 (3) (2022) 7786–7793.

[39]

Yuan Y., Xu H., Multiobjective flexible job shop scheduling using memetic algorithms, IEEE Transactions on Automation Science and Engineering 12 (1) (2015) 336–353.

[40]

Zangaro F., Minner S., Battini D., The multi-manned joint assembly line balancing and feeding problem, International Journal of Production Research 61 (16) (2023) 5543–5565.

[41]

Zhang Z., Tang Q., Chica M., Multi-manned assembly line balancing with time and space constraints: A MILP model and memetic ant colony system, Computers & Industrial Engineering 150 (2020).

[42]

Zhang Z., Tang Q., Chica M., A robust MILP and gene expression programming based on heuristic rules for mixed-model multi-manned assembly line balancing, Applied Soft Computing 109 (2021).

[43]

Zhang Z., Tang Q., Chica M., Li Z., Reinforcement learning-based multiobjective evolutionary algorithm for mixed-model multimanned assembly line balancing under uncertain demand, IEEE Transactions on Cybernetics (2023) 1–14.

[44]

Zhang Z., Tang Q., Han D., Li Z., Multi-manned assembly line balancing with sequence-dependent set-up times using an enhanced migrating birds optimization algorithm, Engineering Optimization 55 (7) (2023) 1243–1262.

[45]

Zhang Z., Tang Q., Han D., Qian X., An enhanced multi-objective JAYA algorithm for U-shaped assembly line balancing considering preventive maintenance scenarios, International Journal of Production Research 59 (20) (2021) 6146–6165.

[46]

Zhang Z., Tang Q., Li Z., Han D., An efficient migrating birds optimization algorithm with idle time reduction for type-i multi-manned assembly line balancing problem, Journal of Systems Engineering and Electronics 32 (2) (2021) 286–296.

[47]

Zhang Z., Tang Q., Li Z., Zhang L., Modelling and optimisation of energy-efficient U-shaped robotic assembly line balancing problems, International Journal of Production Research 57 (17) (2019) 5520–5537.

[48]

Zhang Z., Tang Q., Zhang L., Mathematical model and grey wolf optimization for low-carbon and low-noise U-shaped robotic assembly line balancing problem, Journal of Cleaner Production 215 (2019) 744–756.

[49]

Zhang B., Xu L., An improved flower pollination algorithm for solving a type-II U-shaped assembly line balancing problem with energy consideration, Assembly Automation 40 (6) (2020) 847–856.

[50]

Zhang B., Xu L., Zhang J., A multi-objective cellular genetic algorithm for energy-oriented balancing and sequencing problem of mixed-model assembly line, Journal of Cleaner Production 244 (2020).

[51]

Zhao W., Alam S., Abbass H.A., MOCCA-II: A multi-objective co-operative co-evolutionary algorithm, Applied Soft Computing 23 (2014) 407–416.

Digital Library

[52]

Zhao F., He X., Wang L., A two-stage cooperative evolutionary algorithm with problem-specific knowledge for energy-efficient scheduling of no-wait flow-shop problem, IEEE Transactions on Cybernetics 51 (11) (2021) 5291–5303.

[53]

Zhou B.-H., Shen C.-Y., Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration, Journal of Cleaner Production 192 (2018) 293–305.

[54]

Zitzler E., Deb K., Thiele L., Comparison of multiobjective evolutionary algorithms: Empirical results, Evolutionary Computation 8 (2) (2000) 173–195.

Digital Library

[55]

Zitzler E., Laumanns M., Thiele L., SPEA2: Improving the strength pareto evolutionary algorithm, Technical Report Gloriastrasse (2001).

[56]

Zitzler E., Thiele L., Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation 3 (4) (1999) 257–271.

Digital Library

Cited By

Zhang BChe AWang Y(2024)Grid-based artificial bee colony algorithm for multi-objective job shop scheduling with manual loading and unloading tasksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123011245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123011

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A multi-objective co-evolutionary algorithm for energy and cost-oriented mixed-model assembly line balancing with multi-skilled workers

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 236, Issue C

Feb 2024

1583 pages

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2024

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Cited By

Zhang BChe AWang Y(2024)Grid-based artificial bee colony algorithm for multi-objective job shop scheduling with manual loading and unloading tasksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123011245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123011

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