Article
Engineering, Industrial
Shengluo Yang, Zhigang Xu
Summary: This study proposes a novel distributed assembly permutation flowshop scheduling problem with various algorithms and strategies to find the optimal solution, significantly improving the solution. Experimental results show that the proposed batch allocation strategies can greatly enhance the solutions, while heuristics can provide reasonable solutions in a short period of time, with IG_desJ performing the best in terms of solution quality.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Juan C. Yepes-Borrero, Federico Perea, Fulgencia Villa, Eva Vallada
Summary: The paper addresses the Permutation Flowshop Scheduling problem with additional Resources during Setups (PFSR-S) for the first time. Two Mixed Integer Linear Programming formulations and an exact algorithm are proposed to solve the problem. Due to its complexity, a GRASP metaheuristic is also proposed which provides solutions for larger instances. The computational testing shows that the GRASP metaheuristic finds good quality solutions in short computational times.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Shijin Wang, Hanyu Zhang
Summary: This paper investigates a novel hybrid flowshop scheduling problem and proposes a mixed integer linear programming model and a matheuristic algorithm TSFO. Experimental results show that the TSFO method is more effective than directly solving the problem using a commercial solver.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Korhan Karabulut, Hande Oztop, Damla Kizilay, M. Fatih Tasgetiren, Levent Kandiller
Summary: This paper addresses a distributed permutation flowshop scheduling problem with sequence-dependent setup times. To minimize the maximum completion time among the factories, a new mixed-integer linear programming model and a new constraint programming model are proposed. Additionally, an evolution strategy algorithm is employed to obtain high-quality solutions in a short time. The computational results demonstrate that the proposed algorithm outperforms state-of-the-art metaheuristic algorithms for large instances.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Engineering, Industrial
Kuo-Ching Ying, Pourya Pourhejazy, Chen-Yang Cheng, Ren-Siou Syu
Summary: This research extends the distributed assembly permutation flowshop scheduling problem to account for flexible assembly and sequence-independent setup times in a supply chain-like setting. Constructive heuristic and customised metaheuristic algorithms are proposed to solve this emerging scheduling extension, demonstrating higher performance compared to existing algorithms.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Zi-Qi Zhang, Bin Qian, Rong Hu, Jian-Bo Yang
Summary: This paper proposes a Q-learning-based hyper-heuristic evolutionary algorithm (QLHHEA) to solve the distributed assembly blocking flowshop scheduling problem (DABFSP), and the experimental results and statistical analysis demonstrate its effectiveness and efficiency.
APPLIED SOFT COMPUTING
(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, Artificial Intelligence
Hui Yu, Kai-Zhou Gao, Zhen-Fang Ma, Yu-Xia Pan
Summary: This study focuses on a significant distributed assembly permutation flowshop scheduling problem in practical manufacturing systems. Several meta-heuristics, including artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed to solve the problem. Experimental results demonstrate that the proposed Jaya algorithm with Q-learning-based local search performs well and achieves optimal solutions for the majority of benchmark instances.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Shengluo Yang, Junyi Wang, Zhigang Xu
Summary: This study proposes a solution to the distributed permutation flowshop scheduling problem using deep reinforcement learning. By designing suitable reward function and scheduling actions, an intelligent scheduling agent is trained, which significantly improves the solution quality and computation efficiency.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Zi-Qi Zhang, Rong Hu, Bin Qian, Huai-Ping Jin, Ling Wang, Jian-Bo Yang
Summary: In this paper, a matrix-cube-based estimation of distribution algorithm (MCEDA) is proposed to solve the energy-efficient distributed assembly permutation flow-shop scheduling problem (EE_DAPFSP). The proposed algorithm constructs a high-quality and diverse initial population, designs a matrix-cube-based probabilistic model and its update mechanism, develops a suitable sampling strategy, provides a problem-dependent neighborhood search, and embeds speed adjustment strategies based on problem properties, which improve the quality and efficiency of the obtained solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Mojahid Saeed Osman
Summary: This paper introduces an algorithmic method that hybridizes solution procedures with an optimization model to solve the problem of scheduling and allocating changeover tasks. Three priority rules are identified and investigated as objective functions to minimize total changeover time and maximize worker utilization, while satisfying task-sequence-dependency and worker-limit constraints. The proposed hybrid approach provides effective changeover time and worker utilization.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Industrial
Funda Guner, Abduel K. Gorur, Benhuer Satir, Levent Kandiller, John. H. Drake
Summary: Researchers have studied various assembly line problems, and recently, assembly lines with multiple workers at each workstation have become common. This study focuses on assigning tasks to workers already assigned to a specific workstation, rather than balancing the entire line. Two methods, using mixed integer linear programming and constraint programming, are presented to minimize the number of workers required on a multi-manned assembly line with sequence-dependent setup times. Results show that the constraint programming method outperforms the mixed integer linear programming method on several benchmark instances and leads to significant improvements in productivity in a real-world scenario.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Operations Research & Management Science
Baruch Mor, Gur Mosheiov
Summary: The study focuses on a scheduling problem in an m-machine flowshop with linear deterioration of job processing times and job rejection. The objectives are to minimize makespan and total load, while ensuring that the total permitted rejection cost does not exceed a certain limit. By introducing pseudo-polynomial dynamic programming algorithms, it is proven that these problems are NP-hard in the ordinary sense.
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Ying-Ying Huang, Quan-Ke Pan, Jiang-Ping Huang, P. N. Suganthan, Liang Gao
Summary: This paper proposes an improved iterative greedy algorithm based on groupthink for solving the distributed assembly permutation flowshop scheduling problem with total flowtime criterion, and experimental results show that the proposed algorithm significantly outperforms other algorithms in comparison.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Hong-Bo Song, Jian Lin
Summary: The paper introduces a GP-HH algorithm to address the DAPFSP-SDST problem by using genetic programming to generate heuristic sequences and incorporating simulated annealing for local search, achieving effective solutions and improving upon existing benchmarks.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Editorial Material
Computer Science, Information Systems
Chi-Hua Chen, Kuo-Ming Chao, Feng-Jang Hwang, Chunjia Han, Lianrong Pu
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
(2021)
Article
Operations Research & Management Science
F. J. Hwang, Yao-Huei Huang
Summary: This study proposes an effective alternative logarithmic scheme for tackling nonconvex optimization problems with univariate nonlinear terms, which does not incur any inequality constraints. Although the proposed scheme requires more continuous variables, it offers the advantage of simultaneously including a system of equality constraints and no inequality constraints.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
(2021)
Article
Management
Xin Chen, Qian Miao, Bertrand M. T. Lin, Malgorzata Sterna, Jacek Blazewicz
Summary: This paper addresses the scheduling problem in a two-machine flow shop to maximize the total early work. A dynamic programming approach and an approximation scheme are proposed, and the performance of a classical algorithm is analyzed.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Information Systems
Xiongfei Shan, Mingyang Pan, Depeng Zhao, Deqiang Wang, Feng-Jang Hwang, Chi-Hua Chen
Summary: The proposed algorithm utilizes electronic image stabilization technology for maritime target detection, involving models such as PLM, PCM, and ICM for feature points classification, video stability, and target detection. It outperformed benchmark models in common metrics like MSE, PSNR, SSIM, and mAP, demonstrating its superiority in image stabilization and target ship detection.
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
(2021)
Article
Physics, Multidisciplinary
Xin Fu, Chengyao Xu, Yuteng Liu, Chi-Hua Chen, F. J. Hwang, Jianwei Wang
Summary: This study utilizes trajectory data of taxis in Ningbo city, China to analyze the spatial dependence of urban traffic activities and the spatial migration characteristics of congestion, aiming to provide valuable insights for formulating targeted congestion management strategies. Through calculating average driving speeds and analyzing congestion spatial features, the research identifies high-clustering areas located at urban fringes and low-clustering areas at the geometric center of major urban areas, contributing to a better understanding of congestion space migration at the urban scale. Changes in congestion trends on working days are compared with non-working days, revealing larger offsets and azimuths of low-value areas in downtown areas during working days. Accurately capturing the spatial-temporal migration characteristics of congestion space will assist in developing more effective congestion management strategies.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Hao Fang, Chi-Hua Chen, Dewang Chen, Feng-Jang Hwang
Summary: This study proposes a novel neuron-network-based mixture probability model to predict passenger walking time in metro stations. The experiments conducted in Fuzhou demonstrate that this model outperforms all the other compared models. It is applicable for analyzing passenger flow.
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
(2022)
Article
Computer Science, Information Systems
Hao Fang, Yiwei Liu, Chi-Hua Chen, Feng-Jang Hwang
Summary: This paper proposes a travel time prediction method based on spatial-feature-based hierarchical clustering and deep multi-input gated recurrent unit. Experimental results demonstrate that this method can achieve more accurate travel time prediction, outperforming various combinations of baseline clustering algorithms and prediction models.
ACM TRANSACTIONS ON SENSOR NETWORKS
(2023)
Article
Operations Research & Management Science
Alexandre Dolgui, Mikhail Y. Kovalyov, Bertrand M. T. Lin
Summary: This paper investigates the problem of maximizing total early work in a two-machine flow-shop. Dynamic programming algorithms are proposed for both weighted and unweighted cases, and a solution is also provided for the distributed setting. Computational experiments are conducted to evaluate the proposed models.
NAVAL RESEARCH LOGISTICS
(2022)
Editorial Material
Engineering, Electrical & Electronic
Abel C. H. Chen, Wen-Kang Jia, Feng-Jang Hwang, Genggeng Liu, Fangying Song, Lianrong Pu
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
(2022)
Article
Telecommunications
Canyang Guo, Chi-Hua Chen, Ching-Chun Chang, Feng-Jang Hwang, Chin-Chen Chang
Summary: Intelligent equipment in the IoT requires privacy protection due to its massive and frequent data communication. This study proposes a De-correlation neural network (DeCNN) that synchronously estimates and protects privacy using a comprehensive loss function. A two-stage learning algorithm is utilized for solution optimization and computation enhancement. Experimental results in deep fingerprint positioning show that the proposed method reduces the maximal correlation coefficient between transmission and target data from 0.95 to 0.34 (and 0.13) at positioning errors of 1.31 m (and 2.88 m).
IEEE COMMUNICATIONS LETTERS
(2023)
Article
Management
Fang Yang, F. J. Hwang, Yao-Huei Huang
Summary: The study considers the Shipper Lane Selection Problem (SLSP), which determines which lanes are served by a shipper's vehicle fleet or outsourced. Unlike previous studies, this study considers a generalized version with time windows for each lane, known as the SLSP with Time Windows (SLSPTW). A mixed integer linear programming model is used to minimize transportation and service/setup costs in order for the shipper to auction off the lanes. The proposed two-stage solution approach efficiently generates possible solutions and verifies the best solution using a decomposed model of the SLSPTW.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2023)
Article
Optics
Han-Lin Li, Shu-Cherng Fang, Bertrand M. T. Lin, Way Kuo
Summary: RGB and CYMK are two major coloring schemes for light colors and pigment colors. However, they have limitations in manipulating colors and lack interchangeability or unification. This study proposes a universal color system (C-235) based on prime number theory and Goldbach's conjecture, which offers a unified representation for efficient encoding and effective manipulation of color. The C-235 system has potential applications in designing high-rate LCD systems and colorizing objects with multiple attributes and DNA codons.
LIGHT-SCIENCE & APPLICATIONS
(2023)
Article
Engineering, Civil
Canyang Guo, Chi-Hua Chen, Feng-Jang Hwang, Ching-Chun Chang, Chin-Chen Chang
Summary: This paper proposes a fast spatiotemporal learning (FSTL) framework with a fast spatiotemporal GCN module, which efficiently and accurately defines and learns the spatiotemporal characteristics and relationships of traffic networks, solving the problem of spatiotemporal dependency learning of traffic flow data.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Bertrand M. T. Lin, Shin-Mei Lin, Shyong Jian Shyu
Summary: In this paper, the potential applications of Goldbach's conjecture are discussed by investigating it as an optimization problem. The research focuses on selecting a minimum number of primes to cover a given set of target even integers, which is modelled based on the design of base components. The problem is formally defined, integer programming formulations are proposed, solution procedures are developed, and a computational study is conducted to test the solutions.
Proceedings Paper
Computer Science, Theory & Methods
Yi-Zhuo Zhang, Yiwei Liu, Chan-Liang Chung, Chi-Hua Chen, Feng-Jang Hwang
Summary: This study introduces a multiple linear regression architecture based on stream homomorphic encryption computing to analyze ciphertext for massive secure data computing. Through a case study of traffic information prediction, the proposed architecture was shown to effectively and promptly obtain the predicted traffic information.
2020 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C 2020)
(2021)
