Multiple rules decision-based DE solution for the earliness-tardiness case of hybrid flow-shop scheduling problem

Han, Zhonghua; Shi, Haibo; Qiao, Feng; Yue, Lei

doi:10.1504/ijmic.2012.047118

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…Song [11] proposed an improved greedy genetical algorithm(IGA). The second hot objective is to minimize the sum of the earliness and tardiness costs, such as Han et al [12] proposed a differential evolution (DE) algorithm based on multiple decision rules. M'Hallah and Alhajraf [13] proposed an Ant colony systems.…”

Section: Literature Review a Single Objective Hfs Scheduling Problemsmentioning

confidence: 99%

A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

Song

2021

IEEE Access

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Efficient scheduling benefits productivity promotion, energy savings and the customer's satisfaction. In recent years, with a growing concern about the energy saving and environmental impact, energy oriented scheduling is going to be a hot issue for sustainable manufacturing. In this study, we investigate an energy-oriented scheduling problem deriving from the hybrid flow shop with unrelated parallel machine. First, we formulate the scheduling problem with a mixed integer linear programming (MILP) model, which considers two objectives including minimizing the completion time and energy consumption. Second, a hybrid multi-objective teaching-learning based optimization (HMOTLBO) algorithm based on decomposition is proposed. In the proposed HMOTLBO, a new solution presentation and five decoding rules are designed for mining the optimal solution. To reduce the standby energy consumption and turning on/off energy consumption, a greedy shifting algorithm is developed without changing the completion time of a scheduling. To improve the converge speed of the algorithm, a weight matching strategy is designed to avoid randomly matching weight vectors with students. To enhance the exploration and exploitation capacities of the algorithm, A teaching operator based on crossover and a self-learning operator based on a variable neighborhood search(VNS) are proposed. Finally, fourth different experiments are performed on 15 cases, the comparison result verified the effectiveness and the superiority of the proposed algorithm.INDEX TERMS Hybrid flow shop scheduling, teaching and learning based optimization, multi-objective, makespan, energy consumption.

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Section: Literature Review a Single Objective Hfs Scheduling Problemsmentioning

confidence: 99%

A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

Song

2021

IEEE Access

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“…As an important area of operation research, the production scheduling, has been attractive for a long time (Han et al, 2012(Han et al, , 2014Cao and Shi, 2015). The flexible flow shop scheduling problem (FFSP) is a class of the well-known problems in the production scheduling area (Zandieh et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Multiple rules with game theoretic analysis for flexible flow shop scheduling problem with component altering times

Han

Zhu

et al. 2016

IJMIC

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This paper studies the flexible flow shop scheduling problem with component altering times (FFSP-CAT), which is a specific form of the flexible flow shop scheduling problem with sequence dependent setup times in the practical scenario. Dealing with FFSP-CAT includes the jobs' machine assignment determination and the globe optimisation. We develop six rules for jobs' machine assignment, and since these rules will easily conflict with one another if they are used with the same priorities, we construct a repeated cooperative model and provide a game theoretical analysis, then derive a Nash equilibrium machine assignment scheme (NEMAS) to effectively manage these rules for jobs' machines assignment at each stage. Furthermore, to achieve the global optimum, we design an advanced compact genetic algorithm (ACGA). By combining NEMAS and ACGA, an approach named advanced compact genetic algorithm with Nash equilibrium machine assignment scheme (ACGA-NEMAS) is used for minimising the makespan of FFSP-CAT. Through extensive comparison experiments with different scales of instances, we show that the algorithm with NEMAS acquires 56.85% improvement over the algorithm without NEMAS, and ACGA-NEMAS performs 80.28% better than genetic algorithm.

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“…In DE, each individual of the population will undergo evolutionary operation, the good ones will be kept, and then the fast evolution will be realised. In recent years, DE algorithm and its improved algorithms have been widely applied in the scheduling problems (Andreas, 2008;Godfrey and Donald, 2006;Vasna and Raju, 2007;Chung and Lau, 2009;Han et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Time-window-based combined objective DE algorithm for hybrid flow-shop

Han

Shi

Yao

2014

IJMIC

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Abstract:In this paper, a time-window-based hybrid flow-shop scheduling model is presented for manufacturing enterprise for solving production scheduling optimisation problem in the rolling production process. Time window deviation sum and the earliness/tardiness (E/T) penalty sum's optimisation goal are modelled, and differential evolution (DE) global optimisation algorithm is used to conduct the global optimisation. The assigned tasks to determine a job's machining path in the time window frame can be finished in each stage. The hybrid flow-shop scheduling optimisation method based on time window can improve the equipment utilisation and reduce scheduling conflict situations. Our time-window-based hybrid flow-shop scheduling optimisation method is evaluated by multiple sets of programs' numerical analysis, and the results show that the proposed approach can meet the requirement of business rolling production scheduling very well.

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Multiple rules decision-based DE solution for the earliness-tardiness case of hybrid flow-shop scheduling problem

Cited by 10 publications

References 6 publications

A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

Multiple rules with game theoretic analysis for flexible flow shop scheduling problem with component altering times

Time-window-based combined objective DE algorithm for hybrid flow-shop

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