Integrated production planning and scheduling for a mixed batch job-shop based on alternant iterative genetic algorithm

Yan, Hong‐Sen; Wan, Xiao-Qin; Xiong, Fuli

doi:10.1057/jors.2014.88

Cited by 24 publications

(11 citation statements)

References 32 publications

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“…The genetic algorithm was first introduced by Holland [27] and currently enjoys wide use as a solution procedure for generating optimal or near-optimal solutions. Examples of its use in manufacturing and remanufacturing fields can be found in Kesen and Güngör [28], Zhang et al [29], Yildirim and Mouzon [30] and Yan et al [31]. Also, Liu and Yi [32] developed a genetic algorithm to evaluate the performance of a green supply chain.…”

Section: Solution Proceduresmentioning

confidence: 99%

Effect of Inspection Policies and Residual Value of Collected Used Products: A Mathematical Model and Genetic Algorithm for a Closed-Loop Green Manufacturing System

Song

2017

Sustainability

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Abstract:In the green manufacturing system that pursues the reuse of used products, the residual value of collected used products (CUP) hugely affects a variety of managerial decisions to construct profitable and environmental remanufacturing plans. This paper deals with a closed-loop green manufacturing system for companies which perform both manufacturing with raw materials and remanufacturing with collected used products (CUP). The amount of CUP is assumed as a function of buy-back cost while the quality level of CUP, which means the residual value, follows a known distribution. In addition, the remanufacturing cost can differ according to the quality of the CUP. Moreover, nowadays companies are subject to existing environment-related laws such as Extended Producer Responsibility (EPR). Therefore, a company should collect more used products than its obligatory take-back quota or face fines from the government for not meeting its quota. Through the development of mathematical models, two kinds of inspection policies are examined to validate the efficiency of two different operation processes. To find a managerial solution, a genetic algorithm is proposed and tested with numerical examples.

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Section: Solution Proceduresmentioning

confidence: 99%

Effect of Inspection Policies and Residual Value of Collected Used Products: A Mathematical Model and Genetic Algorithm for a Closed-Loop Green Manufacturing System

Song

2017

Sustainability

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“…Bushuev, (2014) proposed a new convex optimization approach for solving the APP problem. Yan et al, (2014) modeled an integrated optimization production planning and scheduling problem through a non-linear mixed integer programming formulation. Yan et al, (2014) developed an iterative genetic algorithm to solve the problem.…”

Section: Literature Of Past Workmentioning

confidence: 99%

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

Khalili‐Damghani

Shahrokh

Pakgohar

2017

Int. J. Prod. Manag. Eng.

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In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.

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“…It is easy to implement and gives a high probability for the best individual; these aspects are the main strengths [15]. Another advantage of this approach is that it provides no bias with unlimited spread [16]. However, the difficulty is encountered when a significant difference appears in the fitness values [14,17,18].…”

Section: Introductionmentioning

confidence: 99%

Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator

Hussain

Shad

2019

Complex Intell. Syst.

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As an intelligent search optimization technique, genetic algorithm (GA) is an important approach for non-deterministic polynomial (NP-hard) and complex nature optimization problems. GA has some internal weakness such as premature convergence and low computation efficiency, etc. Improving the performance of GA is a vital topic for complex nature optimization problems. The selection operator is a crucial strategy in GA, because it has a vital role in exploring the new areas of the search space and converges the algorithm, as well. The fitness proportional selection scheme has essence exploitation and the linear rank selection is influenced by exploration. In this article, we proposed a new selection scheme which is the optimal combination of exploration and exploitation. This eliminates the fitness scaling issue and adjusts the selection pressure throughout the selection phase. The χ 2 goodness-of-fit test is used to measure the average accuracy, i.e., mean difference between the actual and expected number of offspring. A comparison of the performance of the proposed scheme along with some conventional selection procedures was made using TSPLIB instances. The application of this new operator gives much more effective results regarding the average and standard deviation values. In addition, a two-tailed t test is established and its values showed the significantly improved performance by the proposed scheme. Thus, the new operator is suitable and comparable to established selection for the problems related to traveling salesman problem using GA.

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Integrated production planning and scheduling for a mixed batch job-shop based on alternant iterative genetic algorithm

Cited by 24 publications

References 32 publications

Effect of Inspection Policies and Residual Value of Collected Used Products: A Mathematical Model and Genetic Algorithm for a Closed-Loop Green Manufacturing System

Effect of Inspection Policies and Residual Value of Collected Used Products: A Mathematical Model and Genetic Algorithm for a Closed-Loop Green Manufacturing System

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator

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