Stochastic assembly line balancing problems involving robots and reliability restriction

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2022-7-1

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Şahin, Muhammet Ceyhan

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When considering assembly processes in the manufacturing ecosystem, the task times may vary from cycle to cycle, especially in assembly lines where manual operations are abundant. Line stops, defective products, and off-line tasks caused by the uncertainty in assembly processes can be highly costly for companies. Stochastic assembly line balancing problems (SALBPs) consider the task processing times as random variables to deal with uncertainty in real-life assembly operations. The difficulties faced due to uncertainties in assembly processes can be alleviated by using advanced technological solutions. Manufacturing companies replace human workers with robots in their assembly processes to keep up with the Industry 4.0 technological revolution and get the upper hand over competitors. A popular approach in the manufacturing industry is to design an assembly line with human-robot collaboration. In the first part of this thesis, we investigate a robotic stochastic assembly line balancing problem (RSALBP), with the motivation to observe the effects of robots on the cycle time in stochastic assembly lines where human workers and robots operate in different workstations. In the literature, robotic assembly line balancing is only studied with deterministic task times. However, assembly line balancing contains stochastic processes in real life. We assume that the processing time of each task follows a normal distribution whose parameters depend on the type of the operator performing the task, with robots having much less (possibly zero) variation in task times than human workers. It is assumed that human workers are fully capable while robots can perform a subset of the tasks. We study type-II RSALBP, which aims to minimize the cycle time for an assembly line with stochastic task times, given a fixed number of workstations and robots, and fixed confidence levels for the workstations. This problem is NP-hard and includes non-linearity. We propose a mixed-integer second-order cone programming formulation and a constraint programming formulation to solve the problem. Instances from the literature are used to test the effectiveness of the proposed formulations. Additionally, the effects of robots on cycle times are evaluated by conducting a computational study with a comprehensive experimental design. In the second part of this thesis, we consider a type-II stochastic assembly line balancing problem with a reliability restriction (type-II SALBP-R) where all the operators are identical. Given a fixed cycle time, the reliability of an assembly line is defined as the probability that the workload of none of the workstations exceeds the cycle time. In type-II SALBP-R, we aim to minimize the cycle time for an assembly line with stochastic task times, given a fixed number of workstations and a fixed lower bound on reliability. This problem has been investigated in only a few studies in the literature. We propose the first matheuristic for the problem and compare its performance with an existing heuristic from the literature.

Subject Keywords

Assembly lines, Robotic assembly line balancing, Stochastic assembly line balancing, Industry 4.0, Human-robot collaboration, Reliability

URI

https://hdl.handle.net/11511/98115

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

M. C. Şahin, “Stochastic assembly line balancing problems involving robots and reliability restriction,” M.S. - Master of Science, Middle East Technical University, 2022.