Article
Automation & Control Systems
Pengyu Zhang, Shiji Song, Shengsheng Niu, Rui Zhang
Summary: In this article, the multiroute job shop scheduling problem with continuous-limited output buffers (MRJSP-CLOBs) is studied, and a hybrid algorithm AIA-SA is proposed, which shows lower computing time and faster and more accurate performance in large-scale instances compared to other algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Camilo Andres Rodriguez-Espinosa, Eliana Maria Gonzalez-Neira, Gabriel Mauricio Zambrano-Rey
Summary: This paper addresses a bi-objective problem in flexible job shop scheduling (FJSS) with stochastic processing times. The first objective is to minimize deterministic Earliness+Tardiness, and the second objective is to minimize the Earliness+Tardiness Risk. The proposed approach is a simheuristic that hybridizes the non-dominated sorting genetic algorithm (NSGA-II) with Monte Carlo simulation to obtain the Pareto frontier of both objectives. The computational results demonstrate the effectiveness of the proposed algorithm under different variability environments.
JOURNAL OF SIMULATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Fantahun M. Defersha, Dolapo Obimuyiwa, Alebachew D. Yimer
Summary: In most published articles on flexible job shop scheduling problems (FJSP), the focus is primarily on the limited capacities of machines as the constraining resources. However, with the increasing adoption of numerically controlled machines with self-controlling capabilities, the role of machine operators has changed from performing sequential steps to becoming machine tenders. This paper proposes a mathematical model for a new setup operator constrained FJSP (SOC-FJSP), where setup operations are assumed to be detached. The proposed simulated annealing algorithm is developed to solve the mathematical model, and further extensions are made to account for sequence-dependent setup time and workload balancing among the setup operators.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Industrial
Lixin Cheng, Qiuhua Tang, Liping Zhang
Summary: This paper investigates the mixed-model assembly job-shop scheduling problem with lot streaming and proposes a mathematical model and an adaptive simulated annealing algorithm to solve the problem. Experimental results show that the algorithm performs well.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lixin Cheng, Qiuhua Tang, Shengli Liu, Liping Zhang
Summary: This paper presents a mathematical model and an augmented simulated annealing algorithm for the mixed-model assembly job-shop scheduling problem with batch transfer. By incorporating production sequencing knowledge and batch transfer knowledge, designing problem-specific neighborhood structures, and implementing a restart mechanism, the proposed algorithm outperforms other comparison algorithms in solving the problem.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xu Liang, Jiabao Chen, Xiaolin Gu, Ming Huang
Summary: This article presents an improved adaptive non-dominated sorting genetic algorithm with elite strategy to tackle the complex flexible job-shop scheduling problem. By introducing a constructive heuristic algorithm and improving the elite strategy, the algorithm achieves faster generation of Pareto optimal solution set for the multi-objective scheduling model.
Article
Computer Science, Interdisciplinary Applications
Yinghe Li, Xiaohui Chen, Youjun An, Ziye Zhao, Hongrui Cao, Junwei Jiang
Summary: This study investigates the multi-objective job-shop scheduling problem with multiple resource constraints and proposes an integrated mathematical model considering machine layout rearrangement, transporter allocation with capacity limitation, and worker assignment with skill variance. An improved non-dominated sorting genetic algorithm with a hybrid local search is designed to solve the problem. The numerical simulation and actual production line application demonstrate the effectiveness and practicability of the proposed model.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Management
Dalila B. M. M. Fontes, S. Mahdi Homayouni, Jose F. Goncalves
Summary: This work addresses the problem of job shop scheduling with transport resources, where jobs need to be transported to machines by a limited number of vehicles. A coordinated approach that considers both machine scheduling and vehicle scheduling simultaneously improves the overall performance of the manufacturing system. The proposed hybrid particle swarm optimization and simulated annealing algorithm (PSOSA) outperforms other solution approaches and is robust.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Hardware & Architecture
Faisal Alkhateeb, Bilal H. Abed-alguni, Mohammad Hani Al-rousan
Summary: The hybrid optimization algorithm DCSA tackles the JSSP scheduling problem by discretizing solutions, showing faster convergence and shorter computational time in experiments compared to other popular optimization-based scheduling algorithms.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Bilal Khurshid, Shahid Maqsood, Muhammad Omair, Biswajit Sarkar, Imran Ahmad, Khan Muhammad
Summary: The research investigates the application of hybrid evolution strategy in the Permutation Flow Shop Scheduling Problem, combining global and local search techniques to improve solution quality. Results show that the algorithm significantly enhances the performance of Taillard instances.
Article
Mathematics
Aidin Delgoshaei, Mohd Khairol Anuar Bin Mohd Ariffin, Zulkiflle B. Leman
Summary: This paper proposes a 4-phased fuzzy framework to optimize the performance of manufacturing systems by considering multiple objective functions and scheduling manufacturing systems in a fuzzy environment. The proposed method, FW-NSGA-II, outperforms other solving algorithms in scheduling manufacturing systems, saving up to 5% in the objective function for small-scale, 11.2% for medium-scale, and 3.8% for large-scale manufacturing systems.
Article
Engineering, Multidisciplinary
Kelvin Ching Wei Lim, Li-Pei Wong, Jeng Feng Chin
Summary: The flexible job-shop scheduling problem (FJSP) is common in high-mix industries. This study proposes a simulated-annealing-based hyper-heuristic algorithm (SA-HH) to solve the problem and investigates two variants. The experimental results show that the method performs well on most instances.
ENGINEERING OPTIMIZATION
(2023)
Article
Computer Science, Hardware & Architecture
Fei Luan, Hongxuan Zhao, Shi Qiang Liu, Yixin He, Biao Tang
Summary: To achieve green targets, manufacturing enterprises need to propose an effective energy-saving strategy for production scheduling. In this paper, a multi-objective energy-saving flexible job shop-scheduling problem (MO_EFJSP) is formulated and solved using an enhanced non-dominated sorting genetic algorithm II (ENSGA-II). Extensive computational experiments prove the applicability of ENSGA-II in saving power consumption and its contribution to the field of green production scheduling.
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
(2023)
Article
Operations Research & Management Science
Yiyi Xu, M'hammed Sahnoun, Fouad Ben Abdelaziz, David Baudry
Summary: This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Automation & Control Systems
S. Alireza Davari, Vahab Nekoukar, Cristian Garcia, Jose Rodriguez
Summary: This article introduces an online weighting factor optimization method based on the simulated annealing algorithm, which converges in a few steps using ripple energy as a convergence criterion and does not require cumbersome computations. It is applicable for an induction motor as well as other applications, and has been validated through experimental tests.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Mathematics
Angyan Tu, Jun Ye, Bing Wang
Summary: This paper presents two methods to solve NNOMs, applied to linear and nonlinear programming problems to obtain optimal solutions in practical productions. The NNOP methods can achieve suitable solutions under indeterminate environments and fulfill specific requirements.
JOURNAL OF MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Bing Wang, Kai Feng, Xiaozhi Wang
Summary: This paper addresses an uncertain unrelated parallel machine scheduling problem (UPMSP) with setup times, and proposes a bi-objective robust optimization formulation to minimize the mean and worst-case makespan. Two versions of swarm intelligent algorithms are developed based on fruit fly optimization algorithm (FOA) framework and scenario-guided local search, and experimental results show their advantages. The contribution of this paper lies in the proposed formulation and algorithm approaches for the discussed problem.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
