Integrated production and distribution scheduling in distributed hybrid flow shops

Qin, Hu; Li, Tao; Teng, Yinglei; Wang, Kai

doi:10.1007/s12293-021-00329-6

Cited by 31 publications

(8 citation statements)

References 55 publications

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“…They established a mixed integer programming model to minimize total weighted earliness and tardiness and developed an enhanced brain storm optimization algorithm to deal with it. Qin et al [ 32 ] addressed an IDPDSP in distributed hybrid flow shop environment and multiple transportation modes to minimize the sum of earliness, tardiness and delivery costs. They introduced an adaptive human-learning-based genetic algorithm to handle it.…”

Section: Literature Reviewsmentioning

confidence: 99%

Integrated scheduling of distributed production and distribution in group manufacturing with uncertain travel time

Jun

Liu

Zhao

et al. 2022

Complex Intell. Syst.

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This paper presents a novel integrated distributed production and distribution scheduling problem in group manufacturing with uncertain travel time (IDPDSP-GM-UTT), in which products are firstly produced in several distributed hybrid flow shops and then delivered to several retailers in batches. The proposed model considers both geographical dispersion of multi-factories and variable travel time between factories and retailers caused by time-varying dynamics of road network, which describes the production environment more authentic. Additionally, a mathematical model is developed to find the optimal quantity of raw material, delivery plan, and punishment of earliness and tardiness with the objective of minimizing total costs. Then, an improved genetic algorithm with two-stage heuristic mutation scheduling strategy and tabu search for local optimization (GA-2HMS&TS) is designed to solve the proposed model. To verify the performances of the proposed method, several experiments by adopting test experimental examples with different scales are performed. The computational results exhibit that the GA-2HMS&TS not only significantly reduces the total cost of production and distribution, but also outperforms all of its rivals. In addition, the robustness of the proposed models is also analyzed with regard to the different road conditions.

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Section: Literature Reviewsmentioning

confidence: 99%

Integrated scheduling of distributed production and distribution in group manufacturing with uncertain travel time

Jun

Liu

Zhao

et al. 2022

Complex Intell. Syst.

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“…In scheduling problems, the additional resources involved mainly consist of machine operators, tools, pallets, dies, industrial robots, and automated guided vehicles. The additional resources may be renewable or non-renewable, discrete or continuous, and be used in the processing or setup [18]. When addressing scheduling problems, there are additional resources constraints, that is, the sum of each additional resource used by all machines cannot exceed a given limit at any moment.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Two-Class Teaching-Learning-Based Optimization for Energy-Efficient Hybrid Flow Shop Scheduling Problems with Additional Resources

Lei,

Zhang,

Liu

2024

Symmetry

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Energy-efficient scheduling problems with additional resources are seldom studied in hybrid flow shops. In this study, an energy-efficient hybrid flow shop scheduling problem (EHFSP) with additional resources is studied in which there is asymmetry in the machine. An adaptive two-class teaching-learning-based optimization (ATLBO) which has multiple teachers is proposed to simultaneously minimize the makespan and the total energy consumption. After two classes are formed, a teacher phase is first executed, which consists of teacher self-learning and teacher training. Then, an adaptive learner phase is presented, in which the quality of two classes is used to adaptively decide the learner phase or the reinforcement search of the temporary solution set. An adaptive formation of classes is also given. Extensive experiments were conducted and the computational results show that the new strategies are effective and that ATLBO was able to provide better results than comparative algorithms reported in the literature in at least 54 of 68 instances.

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“…Niu et al [16] developed an improved NSGA-II algorithm to solve an energy-efficient distributed assembly blocking flow shop problem. Qin et al [17] utilized a realistic DHFS where a novel integrated production and distribution scheduling problem is focused. In realistic industry system, including the glass manufacturing system, the improvement of glass raw materials processing has been studied by many researches [18][19][20], and therefore, the scheduling efficiency has become increasingly important [21].…”

Section: Introductionmentioning

confidence: 99%

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

Geng¹,

Li²

2023

Computer Modeling in Engineering &Amp; Sciences

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To solve the distributed hybrid flow shop scheduling problem (DHFS) in raw glass manufacturing systems, we investigated an improved hyperplane assisted evolutionary algorithm (IhpaEA). Two objectives are simultaneously considered, namely, the maximum completion time and the total energy consumptions. Firstly, each solution is encoded by a three-dimensional vector, i.e., factory assignment, scheduling, and machine assignment. Subsequently, an efficient initialization strategy embeds two heuristics are developed, which can increase the diversity of the population. Then, to improve the global search abilities, a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions. Furthermore, a local search heuristic based on three parts encoding is embedded to enhance the searching performance. To enhance the local search abilities, the cooperation of the search operator is designed to obtain better non-dominated solutions. Finally, the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms. The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.

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Integrated production and distribution scheduling in distributed hybrid flow shops

Cited by 31 publications

References 55 publications

Integrated scheduling of distributed production and distribution in group manufacturing with uncertain travel time

Integrated scheduling of distributed production and distribution in group manufacturing with uncertain travel time

An Adaptive Two-Class Teaching-Learning-Based Optimization for Energy-Efficient Hybrid Flow Shop Scheduling Problems with Additional Resources

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

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