Estimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problem

Xue, Yue Guang; Rui, Zhijian; Yu, Xianyu; Sang, Xiuzhi; Liu, Wenjie

doi:10.1007/s12293-019-00295-0

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…In literature, parallel machine scheduling problems involving energy consumption concern objectives such as the total penal cost of tardy jobs and the extra energy consumption of machines [32], the total electricity cost with bounded makespan [10], the total energy consumption and the makespan [3,51,53,58], the total tardiness and total energy consumption [37], the makespan and the total carbon emission [56], the total energy consumption and total completion time [57].…”

Section: Energy-efficient Parallel Machine Scheduling Problemsmentioning

confidence: 99%

“…Methods used to solve an energy-related parallel machine scheduling problem include dispatching rules [32], two-stage heuristic algorithm [10,57], a fixed and relax algorithm [43], an imperialist competitive algorithm [37], a memetic differential evolution algorithm [53], the distribution evolution memetic algorithm [56], an evolutionary algorithm [58], and a bi-objective local search algorithm [3].…”

Section: Energy-efficient Parallel Machine Scheduling Problemsmentioning

confidence: 99%

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Decomposition approaches for parallel machine scheduling of step-deteriorating jobs to minimize total tardiness and energy consumption

Guo

Wang

et al. 2021

Complex Intell. Syst.

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In this paper, an identical parallel machine scheduling problem with step-deteriorating jobs is considered to minimize the weighted sum of tardiness cost and extra energy consumption cost. In particular, the actual processing time of a job is assumed to be a step function of its starting time and its deteriorating threshold. When the starting time of a job is later than its deteriorating threshold, the job faces two choices: (1) maintaining its status in holding equipment and being processed with a base processing time and (2) consuming an extra penalty time to finish its processing. The two work patterns need different amounts of energy consumption. To implement energy-efficient scheduling, the selection of the pre-processing patterns must be carefully considered. In this paper, a mixed integer linear programming (MILP) model is proposed to minimize the total tardiness cost and the extra energy cost. Decomposition approaches based on logic-based Benders decomposition (LBBD) are developed by reformulating the studied problem into a master problem and some independent sub-problems. The master problem is relaxed by only making assignment decisions. The sub-problems are to find optimal schedules in the job-to-machine assignments given by the master problem. Moreover, MILP and heuristic based on Tabu search are used to solve the sub-problems. To evaluate the performance of our methods, three groups of test instances were generated inspired by both real-world applications and benchmarks from the literature. The computational results demonstrate that the proposed decomposition approaches can compute competitive schedules for medium- and large-size problems in terms of solution quality. In particular, the LBBD with Tabu search performs the best among the suggested four methods.

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Section: Energy-efficient Parallel Machine Scheduling Problemsmentioning

confidence: 99%

Section: Energy-efficient Parallel Machine Scheduling Problemsmentioning

confidence: 99%

Decomposition approaches for parallel machine scheduling of step-deteriorating jobs to minimize total tardiness and energy consumption

Guo

Wang

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…The key to solve this extremely difficult scheduling problem is to balance the workload in the flexible workshop [35]. In the literature, workshop scheduling is divided into various types, including parallel machine scheduling [36,37], flow shop scheduling [38,39], job shop scheduling [40,41], and single machine scheduling [42,43]. It is concluded that workshop scheduling is mostly connected with a learning algorithm due to its complexity [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based scheduling: a bibliometric perspective

Cao

et al. 2021

IET Collab Intel Manufact

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In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls into such a category, which is often non‐linear in nature, time varying, multi‐objective, and subject to interruptions. Ergo, the machine learning‐based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives, such as mathematical programming, combinatorial optimization, and heuristic rules. However, due to the inherent complexity of the problem, many issues remain to be addressed. In particular, with the availability of production data, the progress of computing power, and the breakthrough in intelligent algorithms, a novel branch of data driven algorithms present great potential, for example, the deep learning and reinforcement learning‐based algorithms. To reveal the value of machine learning‐based scheduling methods, bibliometric analysis was conducted to analyse the relevant articles and documents from the year 1980 to 2019. Finally, the future research trend in the domain of machine learning‐based scheduling is considered and tips are provided for researchers as well as practitioners to find leading scientists for collaborations.

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“…However, it needs much effort to construct a Bayesian network, especially when the structure of the Bayesian network is needed to learn from data. Therefore, in the EDA domain, traditional statistical models are still widely used [17], [18]. Recently, scholars have tried to apply EDA to GEP to realize improved performance.…”

Section: Introductionmentioning

confidence: 99%

Information Fusion in Offspring Generation: A Case Study in Gene Expression Programming

Liu

Zhang

et al. 2020

IEEE Access

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Gene expression programming (GEP), which is a variant of genetic programming (GP) with a fixed-length linear model, has been applied in many domains. Typically, GEP uses genetic operators to generate offspring. In recent years, the estimation of distribution algorithm (EDA) has also been proven to be efficient for offspring generation. Genetic operators such as crossover and mutation generate offspring from an implicit model by using the individual information. By contrast, EDA operators generate offspring from an explicit model by using the population distribution information. Since both the individual and population distribution information are useful in offspring generation, it is natural to hybrid EDA and genetic operators to improve the search efficiency. To this end, we propose a hybrid offspring generation strategy for GEP by using a univariate categorical distribution based EDA operator and its original genetic operators. To evaluate the performance of the new hybrid algorithm, we apply the algorithm to ten regression tasks using various parameters and strategies. The experimental results demonstrate that the new algorithm is a promising approach for solving regression problems efficiently. The GEP with hybrid operators outperforms the original GEP that uses genetic operators on eight out of ten benchmark datasets. INDEX TERMS Genetic programming, gene expression programming, estimation of distribution algorithm, offspring generation.

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Estimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problem

Cited by 13 publications

References 25 publications

Decomposition approaches for parallel machine scheduling of step-deteriorating jobs to minimize total tardiness and energy consumption

Decomposition approaches for parallel machine scheduling of step-deteriorating jobs to minimize total tardiness and energy consumption

Machine learning‐based scheduling: a bibliometric perspective

Information Fusion in Offspring Generation: A Case Study in Gene Expression Programming

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