Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines

Zandieh, M.; Joreir-Ahmadi, M. N.; Fadaei-Rafsanjani, A.

doi:10.1007/s00170-016-9744-4

Cited by 26 publications

(10 citation statements)

References 26 publications

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“…Cross-entropy method was used in [31] to solve the BAP for throughput and buffer size optimization in unreliable serial lines. [32] studied buffer and preventive maintenance periods allocation problems. An integrated simulation and meta-heuristic algorithm method (genetic and particle swarm) was used to solve the problem for throughput maximization and total buffer size as well as total number of defective units minimization.…”

Section: B the Multi-objective Bapmentioning

confidence: 99%

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

2022

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The current context of rising ecological awareness and high competitiveness, reveals a strong necessity to integrate the sustainability paradigm into the design of production systems. The buffer allocation problem is of particular interest since buffers absorb disruptions in the production line. However, despite the rich literature addressing the BAP, there are no studies that use a multi-objective framework to deal with energetic considerations. In this study, the energy-efficient buffer allocation problem (EE-BAP) is studied through a multi-objective resolution approach. The multi-objective problem is solved to optimize two conflicting objectives: maximizing production throughput and minimizing its energy consumption, under a total storage capacity available. The weighted sum and epsilon-constraint methods as well as the elitist non-dominated sorting genetic algorithm (NSGA-II) are adapted and implemented to solve the EE-BAP. The obtained solutions are analyzed and compared using different performance metrics. Numerical experiments show that epsilon-constraint outperforms the NSGA-II when considering comparable computational time. The Pareto solutions obtained are trade-offs between the two objectives, enabling decision making that balances productivity maximization with energy economics in the design of production lines.

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Section: B the Multi-objective Bapmentioning

confidence: 99%

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

2022

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“…With respect to the objective function, the majority of studies done in the literature deal with a single objective problem. Some papers have addressed a bi-objective problem formulation (Lee, Chen, and Shunder 2009;Amiri and Mohtashami 2011;Cruz et al 2012), and other studies involve three objectives (Gershwin and Schor 2000;Cruz, Van Woensel, and Smith 2010;Zandieh, Joreir-Ahmadi, and Fadaei-Rafsanjani 2017). This observation is also true for the job-scheduling field, where only a few studies have considered jointly more than two objective-function components (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Buffer allocation design for unreliable production lines using genetic algorithm and finite perturbation analysis

Kassoul

Cheikhrouhou

Zufferey

2021

International Journal of Production Research

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The buffer allocation problem in production lines is an NP-hard combinatorial optimisation problem. This paper proposes a new hybrid optimisation approach (using simulation) relying on genetic algorithm (GA) and finite perturbation analysis (FPA). Unlike the infinitesimal perturbation analysis, which deals with small (infinitesimal variation) perturbations for estimating gradients of the performance measure, FPA deals with larger (finite) or more lasting perturbations. It is an extension specifically dedicated to discrete decision variables and applicable to most discrete-event dynamic systems. The proposed method allows a global search using GA, with refinement in specific solution-space regions using FPA. The main objective is to maximise the average production rate of a production line with unreliable machines, by allocating the total buffer capacity in locations between machines. Extensive numerical experiments show that: (1) the proposed hybrid GA-FPA method clearly outperforms the state-of-the-art methods from the literature; (2) combining FPA and GA is beneficial when compared to employing GA or FPA independently.

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“…A production line where the processing time of all stations are similar is called a homogeneous production line. Conversely, if the process times are different, it is known as a heterogeneous line [25]. In some cases, literature ignores the possibility that failures may occur, or that failures are negligible, in which case the line is assumed to be reliable.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving the buffer allocation problem using simulation-based optimisation

Joubert

D²

2021

ORION

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In production lines, buffers function as a means to decouple stations, which reduce the effect that station failures and varying process times have on the complete line's throughput. However, adding larger buffers can be costly, for example, in the automotive industry where it results in increased working capital. This manuscript addresses the buffer allocation problem (BAP), seeking the smallest total buffer size while meeting a prescribed throughput by employing a simulation-based optimisation approach. A Tabu Search algorithm searches the solution space for the optimal buffer configuration while a discrete event simulation model evaluates each configuration, accounting for the machine (un)reliability. Since the multiple simulations add a sizeable computational burden, our approach introduces a novel neighbourhood search mechanism, which borrows from the Theory of Constrains. Solving test sets available in the literature suggest that this approach is 18 times faster than prior Adaptive Tabu Search approaches for small problems, and more than five times faster for medium-sized problems.

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Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines

Cited by 26 publications

References 26 publications

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

Buffer allocation design for unreliable production lines using genetic algorithm and finite perturbation analysis

Solving the buffer allocation problem using simulation-based optimisation

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