The stochastic U-line balancing problem: A heuristic procedure |
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| Affiliation: | 1. Freie Universität Berlin, School of Business and Economics, Advanced Business Analytics Group, 14195 Berlin, Germany;2. The University of Iowa, Tippie College of Business, Department of Management Sciences, Iowa City, IA 52242-1000, United States;3. The University of Tulsa, Collins College of Business, Operations Management Group, Tulsa, OK 74104-9700, United States;1. School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China;2. Key Laboratory of Fiber Optic Sensing Technology and Information Processing (Wuhan University of Technology), Ministry of Education, Wuhan 430070, China;3. Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK;1. Production Engineering Department, Federal University of Santa Catarina (UFSC), Campus Universitário Trindade, CEP 88040-970, Florianópolis, SC, Brazil;2. Mechanical Engineering Department, Federal University of Santa Catarina (UFSC), Campus Universitário Trindade, CEP 88040-970, Florianópolis, SC, Brazil;1. Rehabilitation Bioengineering Group, Department of Management Studies, Indian Institute of Technology Madras,Chennai 600036, Tamil Nadu, India;2. Rehabilitation Bioengineering Group, Department of Engineering Design, Indian Institute of Technology Madras,Chennai 600036, Tamil Nadu, India |
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| Abstract: | Many heuristics have been proposed for the assembly line balancing problem due to its computational complexity and difficulty in identifying an optimal solution. Still, the basic line balancing model fails to consider a number of realistic elements. The implementation of a Just-In-Time manufacturing system generally entails the replacement of traditional straight assembly lines with U-shaped lines. An important issue in the U-line balancing problem is the consideration of task time variability due to human factors or various disruptions. In this paper, we consider the stochastic U-line balancing problem. A hybrid heuristic is presented consisting of an initial feasible solution module and a solution improvement module. To gain insight into its performance, we analyze the heuristic under different scenarios of task time variability. Computational results clearly demonstrate the efficiency and robustness of our algorithm. |
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