Article
Computer Science, Information Systems
Han Song, Guangmin Jia, Wuliang Peng
Summary: This paper proposes a novel bi-objective reactive project scheduling problem under the condition of resource uncertainty, and presents a regret-based biased random sampling heuristic algorithm to solve it. Additionally, the advantages and disadvantages of priority rules are compared and the combinations of parameter values and schedule generating schemes (SGS) are investigated.
Article
Management
Rachid Hassani, Guy Desaulniers, Issmail Elhallaoui
Summary: A fast re-scheduling heuristic is developed in this study to correct minor disruptions in a retail industry context, achieving a good compromise between cost and number of shift changes.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Industrial
Fei Zuo, Enrico Zio, Yue Xu
Summary: Risk response is an essential part of project risk management, which helps reduce the negative impacts of risks. The paper proposes a flow-based continuous-time bi-objective optimization model for risk-related resource planning in response to the scarcity of resources. The model is successfully applied to a case study, obtaining the global optimal solution through parameter tuning. Additionally, a rule-based metaheuristic algorithm is developed to handle large-scale projects by incorporating improved population initialization and genetic operators. The computational results of the case study and numerical experiments validate the effectiveness of the algorithm and highlight the significance of the precautionary principle and redundant resources in project risk management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Mirsaeid Hosseini Shirvani, Reza Noorian Talouki
Summary: This paper proposes a hybrid algorithm for scheduling scientific workflows on hybrid cloud architecture, addressing the optimization of both makespan and monetary cost. By utilizing simulated annealing and task duplication algorithms, the proposed approach significantly improves efficiency and performance compared to existing methods.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Shuai Liu, Lin Liu, Dongmei Pei, Jue Wang
Summary: With the development of big traffic data, bus schedules should be changed to meet passengers' travel needs. A Dual-Cost Bus Scheduling Optimization Model is established to minimize bus operation and passenger travel costs. The Adaptive Double Probability Genetic Algorithm is used to solve the model. The results show that the built model can better meet the passenger travel demand and the optimized algorithm has faster convergence and better results.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Niteesh Yadav, Ajinkya Tanksale
Summary: We propose a mixed-integer programming model for a multi-objective home healthcare delivery problem, which can handle most of the commonly imposed restrictions in this field. Our model includes selection, assignment, scheduling, and routing decisions, focusing on improving the quality of the schedule for selected patients. We calculate the inconvenience caused by scattered visits and their overlap with patient-specific inconvenient time windows to minimize the total inconvenience cost for patients while considering other stakeholders' competitive goals.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Tamer F. Abdelmaguid
Summary: This paper introduces two heuristic methods based on NSGA-II and MOGWO for addressing a bi-objective dynamic multiprocessor open shop scheduling problem, focusing on the generation of Pareto optimal solutions, with findings indicating that NSGA-II generally outperforms MOGWO in various settings.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Theory & Methods
Huifang Li, Danjing Wang, Mengchu Zhou, Yushun Fan, Yuanqing Xia
Summary: This paper proposes a Multi-swarm Co-evolution-based Hybrid Intelligent Optimization (MCHO) algorithm for multiple-workflow scheduling. The algorithm uses a multi-swarm co-evolutionary mechanism, incorporates local and global guiding information, and applies genetic algorithm and simulated annealing strategies to improve the scheduling performance.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Management
Juan C. Yepes-Borrero, Federico Perea, Ruben Ruiz, Fulgencia Villa
Summary: A bi-objective parallel machine scheduling problem is addressed in this study, considering setup times, limited resources, and using an algorithm based on iterated greedy approaches to search for the optimal solution. Experimental results show that the proposed method outperforms other tested procedures.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Operations Research & Management Science
Wuliang Peng, Jiali Lin, Jingwen Zhang, Liangwei Chen
Summary: This paper investigates the project planning and resource allocation issues in enterprise project management systems, considering the precedence relationships and resource sharing between projects, proposing a bi-objective hierarchical resource-constrained project scheduling problem. Through a two-phase algorithm running at the operational level and the tactical level, as well as conducting bi-objective program planning based on Pareto fronts, an effective integrated scheduling method is provided.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Shahed Mahmud, Ripon K. Chakrabortty, Alireza Abbasi, Michael J. Ryan
Summary: The study introduces an integrated supply chain scheduling model to address highly customized and on-time delivery requirements, enhancing the performance of multi-objective particle swarm optimization. Two new meta-heuristic algorithms are developed with specific search mechanisms and mutation operators for the flexible job shop problem.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Lixin Cheng, Qiuhua Tang, Liping Zhang, Zikai Zhang
Summary: This study addresses the scheduling problem in mixed shop production systems and proposes an optimization method using speed-scaling policy and no-idle time strategy. By formulating a mathematical model and developing a Q-learning algorithm, simultaneous optimization of production efficiency and energy consumption is achieved.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Environmental Studies
Huilin Feng, Rong Hu, Deyun Wang, Junfeng Zhang, Chuntao Wu
Summary: This paper explores the contribution of slot allocation to noise abatement at schedule coordinated airports. A bi-objective model is built to incorporate scheduling efficiency and noise reduction. A solution is found that maximizes airport noise reduction with the greatest allocation efficiency. The price of noise reduction is proposed to balance efficiency and environmental benefits. The research shows that slot scheduling can effectively reduce noise at Shanghai Hongqiao International Airport. The temporal distribution of slots in the maximum noise reduction schedule fluctuates significantly. The cost of noise reduction is high and increases sharply as the noise reduction value rises. Additionally, airport capacity expansion plays a vital role in reducing the cost of noise abatement.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2023)
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
Automation & Control Systems
Sheng-Long Jiang, Qie Liu, I. David L. Bogle, Zhong Zheng
Summary: Scheduling is crucial in steelmaking manufacturing systems. This study introduces a resilient scheduling model that allows for flexible decisions and quick recovery from random disturbances in steelmaking plants. A dynamic multi-objective optimization problem (DMOP) is formulated and a resilient scheduling optimization framework is proposed to solve it. Experimental evidence confirms the effectiveness of the proposed model and framework in solving dynamic scheduling problems in steelmaking plants.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Ming Liu, Dapeng Yang, Fengxia Hao
MATHEMATICAL PROBLEMS IN ENGINEERING
(2017)
Article
Mathematics, Applied
Ming Liu, Dapeng Yang, Qiang Su, Lujun Xu
COMPUTATIONAL & APPLIED MATHEMATICS
(2018)
Article
Green & Sustainable Science & Technology
Dapeng Yang, Daqing Wu, Luyan Shi
Article
Computer Science, Interdisciplinary Applications
Ming Liu, Feng Chu, Junkai He, Dapeng Yang, Chengbin Chu
COMPUTERS & OPERATIONS RESEARCH
(2019)
Article
Environmental Sciences
Bian Liang, Dapeng Yang, Xinghong Qin, Teresa Tinta
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2019)
