The buffer allocation problem in production lines: Formulations, solution methods, and instances

Weiss, Sophie; Schwarz, Justus Arne; Stolletz, Raik

doi:10.1080/24725854.2018.1442031

Cited by 62 publications

(38 citation statements)

References 156 publications

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“…The above assumptions are in agreement with those stated in previous simulation studies (e.g. El-Rayah 1979;Powell 1994;Sabuncuoglu et al 2006) as well as empirical findings (Weiss et al 2018).…”

Section: Model Descriptionsupporting

confidence: 92%

“…Most of the studies on parallel merging assembly lines (also known as fork-join or split-merge) have focused on line balancing (see for example, Akpınar and Bayhan 2011; Barron 2015;Purnomo et al 2013;Sönmez et al 2017). For a comprehensive literature review of merging line balancing methods, see Battaïa and Dolgui (2013), Sivasankaran and Shahabudeen (2014) and Weiss et al (2018). Other studies, however, focused on merging lines with uneven buffer capacities.…”

Section: Literature Reviewmentioning

confidence: 99%

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Performance of merging lines with uneven buffer capacity allocation: the effects of unreliability under different inventory-related costs

Shaaban

Romero-Silva

2020

Cent Eur J Oper Res

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This simulation study investigates whether machine efficiency, mean time to failure (MTTF) and mean time to repair (MTTR) significantly affect the performance of uneven buffer capacity allocation patterns for merging lines. Also studied is the trade-off between increasing throughput via bigger buffers and their associated inventory-related costs, since previous studies have shown that higher overall buffer capacity and higher average inventory content result in higher throughput. Results suggest that an ascending buffer allocation pattern (concentrating buffer capacity towards the end of the line) produces higher throughput in shorter, more unreliable lines; whereas the balanced pattern shows better performance in longer, more reliable lines. Increasing average buffer capacity per station and/or having higher average buffer content was found to be more cost-effective in lines with lower machine inefficiency, shorter MTTF and MTTR, and longer lines. Results differed between reliable and unreliable lines since reliable lines were particularly penalised by buffer capacity investiment/maintenance costs due to a relatively low increase in throughput resulting from the addition of extra buffer capacity.

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Section: Model Descriptionsupporting

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Performance of merging lines with uneven buffer capacity allocation: the effects of unreliability under different inventory-related costs

Shaaban

Romero-Silva

2020

Cent Eur J Oper Res

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“…In the third variant, the goal is to minimize the average system WIP subject to total buffer size and average throughput constraints. A more recent review of the BAP can be found in Weiss et al (2018). According to Tempelmeier (2003), who raised important practical considerations in the optimization of flow production systems, planners normally treat average throughput as a datum and therefore usually consider the primal BAP variant.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies

Liberopoulos

2019

Flex Serv Manuf J

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We compare the optimal buffer allocation of a manufacturing flow line operating under three different production control policies: installation buffer (IB), echelon buffer (EB), and CONWIP (CW). IB is the conventional policy where each machine may store the parts that it produces only in its immediate downstream buffer if the next machine is occupied. EB is a more flexible policy where each machine may store the parts that it produces in any of its downstream buffers. CW is a special case of EB where the capacities of all buffers, except the last one, are zero. The optimization problem that we consider is to maximize the average gross profit (AGP) minus the average cost (AC), subject to a minimum average throughput constraint. AGP is defined as the average throughput of the line weighted by the gross marginal profit (selling price minus production cost per part), and AC is the sum of the average WIP plus total buffer capacity plus transfer rate of parts to remote buffers, weighted by the inventory holding cost rate, the cost of storage space, and the marginal cost of transferring parts to remote buffers, respectively. Numerical results show that the optimal EB policy generally outperforms the optimal IB and CW policies. They also show that as the production rates of the machines decrease, the relative advantage in performance of the EB policy over the other two policies increases. When the cost of transferring parts to remote buffers increases, the dominance of the EB policy over the IB policy decreases while the dominance of the EB policy over CW increases.

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“…Whether the allocation result is reasonable directly affects the realization of the reliability index and the design cycle and life cycle cost of the product, and the result directly influences a product's quality and the robustness of the system. e reliability allocation of a production system must consider the influences of the buffers, the polymorphism of the state of buffers, and the large number of components of the production system, so its reliability allocation algorithm is complex and difficult to use [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Research on the Reliability Allocation Method for a Production System Based on Availability

Wang

et al. 2020

Mathematical Problems in Engineering

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This paper involves a production system, which is composed of units (workstations) and buffers. The buffer is used to store semifinished and finished products in the production process, to reduce the impacts of bad equipment in the production system on the entire system performance. Considering the characteristics of the large number of components and the state of the buffers in the production system, this paper considers the influences of buffer states on upstream and downstream units. When using the availability as the allocation index and combining it with Markov theory, the production unit (workstation) and upstream and downstream buffers are regarded as an equivalent unit (workstation) with multiple output states. We establish the relationship between the availability of each equivalent unit (workstation) and the production system availability and determine a scaling factor for the availability of the equivalent unit to account for the system availability. The expected availability goal of the production system is allocated to each equivalent unit (workstation) by the scaling factor; then, the availability of each equivalent unit (workstation) is assigned to each unit. Finally, the Plant Simulation software is used to simulate and analyze the production system to verify the correctness of the allocation method and realize the reliability allocation from a complex production system to a unit.

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The buffer allocation problem in production lines: Formulations, solution methods, and instances

Cited by 62 publications

References 156 publications

Performance of merging lines with uneven buffer capacity allocation: the effects of unreliability under different inventory-related costs

Performance of merging lines with uneven buffer capacity allocation: the effects of unreliability under different inventory-related costs

Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies

Research on the Reliability Allocation Method for a Production System Based on Availability

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