Article
Management
Davide Anghinolfi, Massimo Paolucci, Roberto Ronco
Summary: This paper addresses the multi-objective combinatorial optimization problem of scheduling jobs on multiple parallel machines while minimizing both the makespan and total energy consumption. A heuristic method is developed to tackle this problem, with experimental results demonstrating its effectiveness compared to three competitors.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Management
Xiaojuan Jiang, Kangbok Lee, Michael L. Pinedo
Summary: This paper considers bicriteria scheduling problems with identical machines, involving conflicting objectives of makespan and total completion time. The authors propose a fast approximation algorithm and analyze the problem's inapproximability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Arthur Kramer, Manuel Iori, Philippe Lacomme
Summary: This paper addresses the parallel machine scheduling problem with family dependent setup times and total weighted completion time minimization by introducing five novel mixed integer linear programs. Numerical experiments show that one of the arc-flow models and the set covering model are quite efficient.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
N. Sivarami Reddy, D. Ramamurthy, M. Padma Lalitha, K. Prahlada Rao
Summary: This paper focuses on the simultaneous scheduling of machines, AGVs, and tools in a multi-machine FMS. It introduces a flower pollination algorithm (FPA) to solve this complex scheduling problem, and the results show that the proposed FPA outperforms other methods in terms of solution quality, efficiency, convergence rate, and robustness.
Article
Operations Research & Management Science
Mostafa Khatami, Daniel Oron, Amir Salehipour
Summary: This paper introduces the problem of scheduling a set of coupled-task jobs on parallel identical machines with the objective of minimizing makespan in the context of patient appointment scheduling. The majority of these problems are proven to be (strongly) NP-hard, but optimal scheduling policies are provided for two settings consisting of identical jobs. An important result is that the existence of a (2-ε)-approximation algorithm for the problem implies P=NP, improving a recently proposed bound for the open-shop counterpart.
OPTIMIZATION LETTERS
(2023)
Article
Management
Han Zhang, Kai Li, Zhao -hong Jia, Chengbin Chu
Summary: This paper addresses the scheduling problem of a group of jobs on non-identical parallel batch processing machines with arbitrary release times, non-identical sizes, and different processing times, aiming to minimize the total completion time. A mixed-integer programming model is constructed to solve the problem, and a modified elite ant system algorithm with local search (MEASL) is proposed due to the problem's strong NP-hardness. Extensive simulation experiments compare the performance of MEASL with other meta-heuristic algorithms and the commercial optimization solver (Gurobi), and validate the effectiveness of the proposed algorithm.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Antoine Perraudat, Stephane Dauzere-Peres, Philippe Vialletelle
Summary: This paper investigates the problem of optimizing the qualification configuration in semiconductor manufacturing to improve utilization rate. New solution approaches based on empirical observations and dual variable analysis are proposed and validated in practice.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Engineering, Industrial
Yuri N. Sotskov
Summary: This article investigates the scheduling problem of a set of jobs on identical machines, aiming to minimize the makespan. The stability analysis of the optimal semi-active schedule is conducted, revealing necessary and sufficient conditions for instability and the potential for infinite stability radius. The article also establishes lower and upper bounds on the stability radius and proposes a formula and algorithm for its calculation.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Jun Kim, Hyun-Jung Kim
Summary: This paper develops an exact algorithm to solve the identical parallel additive machine scheduling problem by considering multiple processing alternatives to minimize the makespan. Experimental results show that the algorithm's computational time outperforms a commercial solver (CPLEX), and useful insights for designing processing alternatives of products are derived from how the parts are comprised when the processing alternatives are optimally selected.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
J. Adan
Summary: This paper proposes a new hybrid genetic algorithm for the unrelated parallel machine scheduling problem and demonstrates its superiority through a comparative study on large instances. It also highlights the importance of calibration.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Computer Science, Interdisciplinary Applications
Raphael Kramer, Arthur Kramer
Summary: The discrete parallel machine makespan scheduling location (ScheLoc) problem is an integrated combinatorial optimization problem that involves choosing machine locations and job scheduling to minimize makespan. A new arc-flow formulation, column generation, and three heuristic procedures are proposed to solve the problem effectively, achieving proven optimal solutions for benchmark instances and small percentage gaps for challenging instances.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Pedram Beldar, Milad Moghtader, Adriana Giret, Amir Hossein Ansaripoor
Summary: The combination of job scheduling and maintenance activity is investigated in this paper. A new mixed integer linear programming model is proposed, and two meta-heuristic approaches based on Simulated Annealing and Variable Neighborhood Search are developed. The results indicate that the proposed methods have a competitive behavior and outperform other algorithms in most cases.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yan Wang, Zhao-hong Jia, Kai Li
Summary: This paper proposes a co-evolutionary algorithm based on three populations, with an adaptive search strategy and improved pheromone update method, to solve the job scheduling problem on parallel batch processing machines. Experimental results show that the proposed algorithm outperforms existing multi-objective algorithms in terms of makespan and total energy consumption.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Management
Haidong Li, Zhen Li, Yaping Zhao, Xiaoyun Xu
Summary: This paper addresses the scheduling problem for customer orders on unrelated parallel machines, developing optimality properties, an easily computable lower bound, and providing optimal schedules for two special cases. Three heuristic algorithms are proposed and their worst case performances are bounded. Numerical experiments demonstrate the effectiveness of the lower bound and proposed algorithms in managing differentiated customers in a complex production environment.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Management
Qi Wei, Yong Wu, T. C. E. Cheng, Fengxin Sun, Yiwei Jiang
Summary: This study focuses on online hierarchical scheduling in shared manufacturing and proposes a greedy algorithm to minimize the total completion time. The lower bound and competitive ratio of the problem are derived and an improved online algorithm is presented. Numerical experiments show good performance of the greedy online algorithm.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Engineering, Industrial
Xiaoliang Yan, Reed Williams, Elena Arvanitis, Shreyes Melkote
Summary: This paper extends prior work by developing a semantic segmentation approach for machinable volume decomposition using pre-trained generative process capability models, providing manufacturability feedback and labels of candidate machining operations for query 3D parts.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)
Article
Engineering, Industrial
Jing Huang, Zhifen Zhang, Rui Qin, Yanlong Yu, Guangrui Wen, Wei Cheng, Xuefeng Chen
Summary: In this study, a deep learning framework that combines interpretability and feature fusion is proposed for real-time monitoring of pipeline leaks. The proposed method extracts abstract feature details of leak acoustic emission signals through multi-level dynamic receptive fields and optimizes the learning process of the network using a feature fusion module. Experimental results show that the proposed method can effectively extract distinguishing features of leak acoustic emission signals, achieving higher recognition accuracy compared to typical deep learning methods. Additionally, feature map visualization demonstrates the physical interpretability of the proposed method in abstract feature extraction.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)
