An optimization algorithm for integrated remanufacturing production planning and scheduling system

Wen, Huabing; Hou, Shiwang; Liu, Zhaohua; Liu, Yongjiang

doi:10.1016/j.chaos.2017.10.012

Cited by 34 publications

(8 citation statements)

References 12 publications

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“…Therefore, metaheuristic algorithms are widely preferred today in real scheduling problems in industries from the 1970s up to now [1]. As one of the most popular optimization methods, Genetic Algorithm (GA) and its hybrid variation are one of the widely used algorithm for integrated planning and scheduling systems [2]. Its mechanism is inspired by natural selection.…”

Section: Introductionmentioning

confidence: 99%

An adaptive hybrid approach: Combining genetic algorithm and ant colony optimization for integrated process planning and scheduling

Uslu

Bulut

2020

ACI

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Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.

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Section: Introductionmentioning

confidence: 99%

An adaptive hybrid approach: Combining genetic algorithm and ant colony optimization for integrated process planning and scheduling

Uslu

Bulut

2020

ACI

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“…1 -the decrease in the standard is caused by the average length of the continuously welded rails which is shorter than the standard one; 2 -the decrease in the standard is caused by the insufficient thickness of the crashed stones on the sections; 3 -the decrease in the standard is caused by the absence of wheel grinding; 4 -the decrease in the standard is caused by the proximity of coal shipping areas;…”

Section: Discussionmentioning

confidence: 99%

The determination of the repairs of railway track considering the reliability and risk level

Grin

Kovalenko

2020

E3S Web Conf.

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The method of predictive calculations of the state of the rails as the main element of assessing the state of the track when planning major repairs of the track is given. With a standard service life of 600 to 700 million tons gross, it is necessary to predict the number of defective rails on the track when increasing the service life to 1100 million tons gross (considering local rail replacement during intermediate repairs). The polygon for assigning repairs is determined by the number of “Windows” that can be provided per week on the section. The projected increase in the work of the track causes the need to increase life cycle cost depreciation charges for the formation of the repair Fund for capital repairs by 30-35%.

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“…The production planning and scheduling problems can also be modeled as an optimization problem with different level of constraints [27]. There are commercial software/packages available to optimize problems using robust optimization methods such as genetic algorithm (GA).…”

Section: Simulation and Optimization In Textile Industrymentioning

confidence: 99%

An Optimization Tool for Production Planning: A Case Study in a Textile Industry

et al. 2021

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The textile industry is an important sector of the Brazilian economy, being considered the fifth largest textile industry in the world. To support further growth and development in this sector, this document proposes a process for production analysis through the use of Discrete Event Simulation (DES) and optimization through genetic algorithms. The focus is on production planning for weaving processes and optimization to help make decisions about batch sizing and production scheduling activities. In addition, the correlations between some current technological trends and their implications for the textile industry are also highlighted. Another important contribution of this study is to detail the use of the commercial software Tecnomatix Plant Simulation 13®, to simulate and optimize a production problem by applying genetic algorithms with real production data.

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An optimization algorithm for integrated remanufacturing production planning and scheduling system

Cited by 34 publications

References 12 publications

An adaptive hybrid approach: Combining genetic algorithm and ant colony optimization for integrated process planning and scheduling

An adaptive hybrid approach: Combining genetic algorithm and ant colony optimization for integrated process planning and scheduling

The determination of the repairs of railway track considering the reliability and risk level

An Optimization Tool for Production Planning: A Case Study in a Textile Industry

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