A State-of-the-Art Review on Meta-heuristics Application in Remanufacturing

Ansari, Zulfiquar N.; Daxini, Sachin D.

doi:10.1007/s11831-021-09580-z

Cited by 10 publications

(8 citation statements)

References 145 publications

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“…The optimal solution is obtained using the dominancy concept [22]. For a comprehensive survey of the methods used to solve the MOPs problems, the reader can consult [23,1,25]. In this paper, the non-dominated sorting genetic algorithm (NSGA-II) is used to solve the MOP at hand.…”

Section: Basic Conceptmentioning

confidence: 99%

Three‐time scale multi‐objective optimal PID control with filter design

Aafaque¹,

Boker

Sardahi

2023

Asian Journal of Control

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This paper presents the design of a multi-objective PID (proportional, integral, and derivative) controller in three-time scales for a system with load disturbances and sensor noise. The key to this design method is to divide the problem into three-time scales by following a singular perturbation approach, which allows for the optimization of fewer parameters in each time scale instead of all the three PID gains at once, hence less computation and design effort. The optimization objectives are the minimization of the overshoot and peak time, and the maximization of the closed-loop system's ability to reject noise and load disturbances. The impact of the integral action and filter on the optimal results is investigated by tuning the singular perturbation parameters. The obtained results show that the performance of the closed-loop system recovers the optimal solution, which is based on the fast subsystem, as the singular perturbation parameters get sufficiently small.

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Section: Basic Conceptmentioning

confidence: 99%

Three‐time scale multi‐objective optimal PID control with filter design

Aafaque¹,

Boker

Sardahi

2023

Asian Journal of Control

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“…For example, in an analysis of 160 scientific journal publications, Suzanne et al [14] state that although from an academic perspective RPS have attracted a lot of interest, but only weak links with industrial applications can be perceived. In addition, Ansari and Daxini [2], in an analysis of 123 scientific journal publications, point out that in real cases, companies are faced with manufacturing multiple products, further complicating remanufacturing production planning. Therefore, they call for future research to optimize RPS in a way, that manufacturers are involved in remanufacturing multiple products and the system consists of a number of uncertainties, such as uncertain quality, time, return, and demand [2].…”

Section: Motivation Of This Researchmentioning

confidence: 99%

“…In addition, Ansari and Daxini [2], in an analysis of 123 scientific journal publications, point out that in real cases, companies are faced with manufacturing multiple products, further complicating remanufacturing production planning. Therefore, they call for future research to optimize RPS in a way, that manufacturers are involved in remanufacturing multiple products and the system consists of a number of uncertainties, such as uncertain quality, time, return, and demand [2]. In addition to the research needs from academia, those challenges presented in [1] can be observed in a remanufacturing plant for gas engines.…”

Section: Motivation Of This Researchmentioning

confidence: 99%

“…The main objective of this work is to develop an approach to optimize RPS, taking into account the uncertain quality of the returned products as well as the diversity of variants. Subordinately, this work attempts to remedy the above-mentioned research gaps of Suzanne et al [14] and Ansari and Daxini [2] by developing a procedure with stronger connections to industrial applications and by incorporating uncertainties from reality into the optimization.…”

Section: Motivation Of This Researchmentioning

confidence: 99%

“…1 In order to follow the call of Ansari and Daxini [2] and to optimize RPS in such a way that different product variance and a system with different uncertainties are taken into account, the population within the agent is defined with different influencing factors, such as series, product variants, production times. The DES that the agents run through acts with the uncertainties of the reject rate, which varies randomly per series and station, as it was measured in reality.…”

Section: Simulation Model Of a Real Remanufacturing Applicationmentioning

confidence: 99%

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Optimization of a Remanufacturing Production Planning System with the Help of Artificial Intelligence

Gal

Gallina

Szaller³

et al. 2023

Lecture Notes in Mechanical Engineering

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Although production planning in remanufacturing systems has attracted great interest from the research community, only a couple of real industrial applications can be perceived. Additionally, in real cases, companies are faced with manufacturing multiple products, which further complicates remanufacturing production planning (RPP). Therefore, there is a need to optimise RPP where manufacturers are involved in remanufacturing multiple products. Also optimized systems should consist of a number of uncertainties, such as the uncertain quality of the returned products.Because of these uncertainties the manufacturers have to use new parts or components - with both higher environmental impacts, as well as costs. In the present paper a line balancing scheduler of a remanufacturing system is presented - focusing on the disassembly, machining and reassembly of parts. The objective of the paper is the reduction of usage of the energy and cost intensive new parts with production scheduling using a genetic algorithm (GA). The achievements are illustrated and presented with a real industrial use case from a gas engine producer. A discrete event simulation (DES) is used for evaluation purposes and the results from the scheduler are compared with benchmarks of the current production planning of the gas engine manufacturer.

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