4.7 Article

Simultaneous allocation of buffer capacities and service times in unreliable production lines

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Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2023.2168310

Keywords

Buffer and service time allocation; finite perturbation analysis; genetic algorithm; production lines; production rate

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This paper proposes a simulation-based optimization approach for designing unreliable production lines to maximize production rate. The method combines a Genetic Algorithm (GA) with Finite Perturbation Analysis (FPA) to simultaneously allocate service times and buffer capacities. The main focus is on investigating the persistence or absence of buffer and service rate allocation patterns.
Simultaneous allocation of service times and buffer capacities in manufacturing systems in a random environment is a NP-hard combinatorial optimisation problem. This paper presents a sophisticated simulation-based optimisation approach for the design of unreliable production lines to maximise the production rate. The proposed method allows for a global search using a Genetic Algorithm (GA), which is coupled with Finite Perturbation Analysis (FPA) as a local search technique. Traditional techniques based on perturbation analysis optimise decision variables of the same nature (e.g. service time only, buffer capacity only), whereas the proposed technique simultaneously provides an allocation of service times and buffer capacities. One of the main focuses of this paper is the investigation of the persistence or absence of the buffer and service rate allocation patterns which are among the most essential insights that come from designing production lines. The results show the superiority of the combined GA-FPA approach regarding GA and FPA in terms of solution quality and convergence behaviour. Moreover, considering instances ranging from 3 to 100 machines, our numerical experiments are in line with the literature for small instances (as similar allocation patterns are identified in our work), but important differences are highlighted for medium/large instances.

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