Throughput,flow times,and service level in an unreliable assembly system |
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| Institution: | 1. School of Economics & Management, Tongji University, Shanghai 200092, China;2. Laboratoire IBISC, Univ-Évry, Université Paris-Saclay, Évry 91025, France;3. School of Economics and Management, Fuzhou University, Fuzhou 350116, China;4. Glorious Sun School of Business & Management, Donghua University, Shanghai 200051, China;5. ESIEE Paris, Université Paris-Est, Noisy-le-Grand Cedex 93162, France;1. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;2. Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, British Columbia V6T 1Z2, Canada;3. Ningbo Logistics and Planning Research Institute & Ningbo Transport Development Research Center, Yinzhou, Ningbo, Zhejiang, China;1. School of Business, Sun Yat-sen University, Guangzhou, China;2. School of Public Health, Sun Yat-sen University, Guangzhou, China;3. Department of Logisitics & Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Hong Kong |
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| Abstract: | This paper considers an unreliable assembly network where different types of components are processed by two separate work centers before being merged at an assembly station. The operation complexity of the system is a result of finite inter-station buffers, uncertain service times, and random breakdowns that lead to blocking at the work centers and starvation at the assembly station. The objective of this study is to gain an understanding of the behavior of such systems so that we can find a way to maximize the system throughput while maintaining the required customer service level. By constructing appropriate Markov processes, we obtain the probability distribution of the production flow time and derive formulas for throughput, the loss probability of type-2 workpieces, and the mean flow time. We present expressions for average work-in-process (WIP) and study their monotone properties. Using the distribution of the flow time, a customer service level can be defined and computed. We then formulate a system optimization model that can be used to maximize the throughput while maintaining an acceptable service level. |
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