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The Effect of Worker Learning on Scheduling Jobs in a Hybrid Flow Shop: A Bi-Objective Approach

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Abstract

This paper studies learning effect as a resource utilization technique that can model improvement in worker’s ability as a result of repeating similar tasks. By considering learning of workers while performing setup times, a schedule can be determined to place jobs that share similar tools and fixtures next to each other. The purpose of this paper is to schedule a set of jobs in a hybrid flow shop (HFS) environment with learning effect while minimizing two objectives that are in conflict: namely maximum completion time (makespan) and total tardiness. Minimizing makespan is desirable from an internal efficiency viewpoint, but may result in individual jobs being scheduled past their due date, causing customer dissatisfaction and penalty costs. A bi-objective mixed integer programming model is developed, and the complexity of the developed bi-objective model is compared against the bi-criteria one through numerical examples. The effect of worker learning on the structure of assigned jobs to machines and their sequences is analyzed. Two solution methods based on the hybrid water flow like algorithm and non-dominated sorting and ranking concepts are proposed to solve the problem. The quality of the approximated sets of Pareto solutions is evaluated using several performance criteria. The results show that the proposed algorithms with learning effect perform well in reducing setup times and eliminate the need for setups itself through proper scheduling.

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Acknowledgments

The authors thank the editor and reviewers fortheir comments, which helped in improving the quality of the paper.

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Authors and Affiliations

  1. Industrial Engineering and Management, Faculty of Technology, University of Oulu, Oulu, Finland

    Farzad Pargar, Osmo Kauppila & Jaakko Kujala

  2. Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran

    Mostafa Zandieh

Authors
  1. Farzad Pargar
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  2. Mostafa Zandieh
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  3. Osmo Kauppila
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  4. Jaakko Kujala
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Corresponding author

Correspondence to Farzad Pargar.

Additional information

Farzad Pargar obtained his doctoral degree in industrial engineering and management from the University of Oulu, Finland. He is currently working as a postdoctoral researcher in the same unit. His work focuses on optimization modeling and management of complex real-world systems using quantitative techniques. The topic of his doctoral thesis is related to resource optimization techniques in maintenance and production planning. His research interests include scheduling, optimization, project management, maintenance & production planning, supply chain management, system dynamics and agent-based simulation. He has published several journal and conference papers and acted as reviewer of many prestigious journals. He is an editorial board member of International Journal of Data Analysis Techniques and Strategies and acted as guest editor for applied Computational Intelligence and Soft Computing Journal.

Mostafa Zandieh accomplished his BSc in industrial engineering at Amirkabir University of Technology, Tehran, Iran, and MSc in industrial engineering at Sharif University of Technology, Iran. He obtained his PhD in industrial engineering from Amirkabir University of Technology, Iran. Currently, he is an Associate Professor at Industrial Management Department, Shahid Beheshti University, Iran. His research interests are production planning and scheduling, financial engineering, quality engineering, applied operations research, simulation and artificial intelligence techniques in the areas of manufacturing systems design.

Osmo Kauppila received his doctoral degree in industrial engineering and management from the University of Oulu, Finland. He currently holds a position of University Lecturer of Process and Quality Management at this institution. His research interests and teaching responsibilities span across a large variety of quantitative methods that support decision making. Some particular topics of interest include design of experiments, linear programming, regression analysis and machine learning, measurement system analysis, discrete event simulation and Bayesian methods. Lately he has worked on the application of operations research and statistical process control methods in railway environments. Despite having the main focus of his work on teaching and industry collaboration, Kauppila has authored or co-authored over twenty journal articles, conference papers and other scientific publications. He is a Distinguished Reviewer of the Project Management Journal and a certified Six Sigma Black Belt.

Jaakko Kujala is a professor in project and quality management. He currently works at the research unit of industrial engineering and management, University of Oulu, Finland. He has over ten years’ experience in international system industry before his career in the academia. He has initiated and served as a director for several large research projects with the participation of leading project-based firms, such as Nokia, Kone, Wärtsilä, Metso, and Outotec. His publications include more than 150 academic papers, book chapters and books. His current research interests include simulation and modelling in the context of complex systems, governance of complex inter-organizational project networks and value creation in the context of project business.

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Pargar, F., Zandieh, M., Kauppila, O. et al. The Effect of Worker Learning on Scheduling Jobs in a Hybrid Flow Shop: A Bi-Objective Approach. J. Syst. Sci. Syst. Eng. 27, 265–291 (2018). https://doi.org/10.1007/s11518-018-5361-0

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