Article
Computer Science, Artificial Intelligence
Bentao Su, Naiming Xie, Yingjie Yang
Summary: This paper focuses on an unrelated parallel workgroup scheduling problem, with a particular emphasis on personnel with similar work skills and eligibility and human resource constraints. By constructing an integer programming model and using meta-heuristic methods, an effective hybrid genetic algorithm is proposed to address the issue.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Computer Science, Interdisciplinary Applications
Feifeng Zheng, Kaiyuan Jin, Yinfeng Xu, Ming Liu
Summary: This work investigates a new production order scheduling problem on unrelated parallel machines under the background of shared manufacturing economics. By considering customer orders with release times and the eligibility of machines, a mixed integer linear programming model is established to minimize the weighted sum of total completion time and total processing cost. Two heuristic algorithms are developed for solving large-scale instances.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Mohammed A. A. Al-qaness, Ahmed A. Ewees, Mohamed Abd Elaziz
Summary: In this study, a new method is proposed to address the unrelated parallel machine scheduling problem with sequence-dependent and machine-dependent setup times, using the features of the whale optimization algorithm and the firefly algorithm. The FA operators are employed to improve the exploitation ability of the WOA, and the quality of the proposed WOAFA method is tested against other well-known meta-heuristic algorithms.
Article
Engineering, Multidisciplinary
Ahmed A. Ewees, Mohammed A. A. Al-qaness, Mohamed Abd Elaziz
Summary: This paper proposes a modified salp swarm algorithm (SSAFA) to solve the unrelated parallel machine scheduling problem with sequence-dependent setup times. By using the operators of the firefly algorithm as a local search, the quality of the solution is improved. Evaluation outcomes confirm the competitive performance of SSAFA in various problem instances using different performance measures.
APPLIED MATHEMATICAL MODELLING
(2021)
Article
Computer Science, Information Systems
Salwani Abdullah, Ayad Turky, Mohd Zakree Ahmad Nazri, Nasser R. Sabar
Summary: This article introduces a two-stage evolutionary variable neighbourhood search method for effectively solving the challenging industrial problem of unrelated parallel machine scheduling with sequence-dependent setup times.
Article
Mathematics
Dung-Ying Lin, Tzu-Yun Huang
Summary: In this study, we propose a population-based simulated annealing algorithm embedded with a variable neighborhood descent technique to solve the unrelated parallel machine scheduling problem with sequence-dependent setup times. Empirical results show that this solution strategy outperforms a commonly used commercial optimization package and provides better schedules in a more efficient manner.
Article
Engineering, Industrial
Hamid Safarzadeha, Seyed Taghi Akhavan Niakia
Summary: This paper investigates a general unrelated parallel machine scheduling problem with machine processing cost and proposes a mathematical programming approach to solve it. A multiobjective solution procedure is proposed to generate Pareto optimal solutions, and the performance of the approach is evaluated through comprehensive numerical experiments. The results demonstrate that the mathematical programming solution approach is effective in solving the problem.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2022)
Article
Automation & Control Systems
Zhi Pei, Mingzhong Wan, Zhong-Zhong Jiang, Ziteng Wang, Xu Dai
Summary: In this study, an optimized scheduling strategy is proposed to reduce electricity cost and increase productivity in the manufacturing sector through the use of a nonlinear mathematical programming model and tailored cutting planes. It is important for manufacturing companies to adapt time-of-use electricity pricing policies to achieve the most desirable outcome, according to their specific circumstances.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
A. Berthier, A. Yalaoui, H. Chehade, F. Yalaoui, L. Amodeo, C. Bouillot
Summary: This paper tackles an unrelated parallel machines scheduling problem with various characteristics and constraints. It proposes a mathematical programming approach and an improved genetic algorithm to provide exact and efficient solutions.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Review
Green & Sustainable Science & Technology
Sana Iqbal, Mohammad Sarfraz, Mohammad Ayyub, Mohd Tariq, Ripon K. Chakrabortty, Michael J. Ryan, Basem Alamri
Summary: This paper reviews research on Demand Side Management strategies, identifying challenging perspectives for future study. Researchers use soft computing and optimization techniques to address energy management challenges, with DSM implementation playing an important role in smart energy management.
Article
Automation & Control Systems
Weihao Wang, Chutong Gao, Leyuan Shi
Summary: This paper addresses the robust makespan optimization problem on unrelated parallel machine scheduling with sequence-dependent setup times. A robust optimization model with the min-max regret criterion is proposed to solve this problem. The worst-case scenario with the maximum regret is proven to belong to a finite set of extreme scenarios, and an enhanced regret evaluation method is designed to accelerate the process. Finally, a heuristic algorithm based on property analysis is proposed to solve the practical problem.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Manufacturing
Haichao Chen, Peng Guo, Jesus Jimenez, Zhijie Sasha Dong, Wenming Cheng
Summary: This paper addresses the emerging problem of an unrelated parallel machine photolithography scheduling problem with dual resource constraints in the semiconductor industry. It proposes two mixed-integer programming (MIP) models and an improved naked mole-rat algorithm hybridized with genetic algorithm (GA) and variable neighborhood search algorithm (VNS). Experimental results demonstrate that Model 2 outperforms Model 1 and the improved naked mole-rat algorithm significantly improves upon GA and VNS.
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
(2023)
Article
Chemistry, Analytical
Mikhak Samadi, Javad Fattahi, Henry Schriemer, Melike Erol-Kantarci
Summary: This paper presents a multi-layer architecture that utilizes a multi-objective optimization model to consider consumer comfort and experience, enhances consumer comfort through a Clustered Sequential Management (CSM) approach, and finds the optimal usage time for thermal loads using thermodynamic solutions and scheduling models to reduce costs and increase consumer satisfaction.
Article
Computer Science, Artificial Intelligence
Deming Lei, Shaosi He
Summary: In this paper, an adaptive artificial bee colony algorithm is proposed to solve the unrelated parallel machine scheduling problem with preventive maintenance and minimize the makespan. The computational results demonstrate that the new strategies are effective and provide better results than the algorithms from the literature.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Fahad R. Albogamy
Summary: This paper develops an energy consumption scheduler (ECS) to solve the power usage scheduling problem for optimal energy management (EM). The ECS, based on the GWDO algorithm, determines the optimal operation schedule of household appliances and batteries charge/discharge. Simulation results validate the applicability of the proposed model in EM problems.
APPLIED SCIENCES-BASEL
(2023)
