Article
Computer Science, Artificial Intelligence
Nan Zhu, Guiliang Gong, Dian Lu, Dan Huang, Ningtao Peng, Hao Qi
Summary: This research proposes a solution to the distributed flexible job shop scheduling problem considering order cancellation for the first time. The reformative memetic algorithm designed in this work shows outstanding performance in reducing resource waste.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Automation & Control Systems
Mariappan Kadarkarainadar Marichelvam, Mariappan Geetha
Summary: This paper addresses energy-efficient flow shop scheduling problems with uncertainties, proposing an improved memetic algorithm (IMA) that combines a constructive heuristic and likelihood-based operators. The IMA also incorporates a variable neighborhood search (VNS) algorithm for local exploration. Experimental results show that IMA outperforms other algorithms in solution quality.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Automation & Control Systems
Jing-jing Wang, Ling Wang, Xia Xiu
Summary: Facing the challenges of globalization and sustainable industrial development, this paper addresses the energy-aware distributed welding shop scheduling problem (EADWSP) with the aim of minimizing both makespan and total energy consumption. A mathematical model and a cooperative memetic algorithm (CMA) are proposed to tackle the large scale and multiple objective characteristics of the problem. Various specific designs, such as hybrid initialization, cooperative search based on feedback, cooperative selection strategy, problem-specific operators, and local intensification with Q-learning, are introduced to enhance the algorithm's efficiency and effectiveness. Numerical experiments and comparisons with existing algorithms demonstrate the superiority of the proposed CMA, and a real-life case study further verifies its practicality in solving the EADWSP.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Sezin Afsar, Juan Jose Palacios, Jorge Puente, Camino R. Vela, Ines Gonzalez-Rodriguez
Summary: In this paper, the authors study a job shop scheduling problem with the dual objectives of minimizing energy consumption during machine idle time and minimizing the project's makespan. They consider uncertainty in processing times using fuzzy numbers and propose a multi-objective optimization model along with an enhanced memetic algorithm. Experimental results validate the effectiveness of the proposed method.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Raymond Chiong, Qianwang Deng, Xuran Gong, Wenhui Lin, Wenwu Han, Like Zhang
Summary: The study proposes a mathematical model for energy-efficient flexible job shop scheduling, aiming to minimize total energy consumption and the number of machine restarts. By adjusting the start time of operations, the study effectively reduces the number of restarts and total energy consumption without affecting the makespan.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Raymond Chiong, Qianwang Deng, Xuran Gong, Wenhui Lin, Wenwu Han, Like Zhang
Summary: This paper proposes a mathematical model for the energy-efficient flexible job shop scheduling problem (EEFJSP), aiming to minimize the makespan, total energy consumption, and total number of machine restarts. By adjusting the start time of operations, the number of restarts and energy consumption can be effectively reduced. A two-stage memetic algorithm (TMA) is developed, along with an operation-block moving operator, to further decrease energy consumption and machine restarts without affecting the makespan. Computational experiments demonstrate that the proposed TMA obtains better Pareto solutions for the EEFJSP.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Chengfeng Peng, Zhantao Li, Hongyang Zhong, Xiang Li, Anping Lin, Yong Liao
Summary: With the increasing automation rate of workshops and the significance of energy consumption, more and more enterprises are required not only to make scheduling decisions on production equipment but also to consider whether the scheduling of transportation equipment supports workshop production decisions. Since both workshop production scheduling and transportation scheduling are NP-hard problems, an efficient algorithm is necessary to improve workshop productivity. To solve this problem, a manufacturing-transportation multi-objective joint scheduling optimization mathematical model is established based on problem structure, production environment, and optimization objectives. The proposed algorithm incorporates a design idea of memetic algorithm (MA) and non-dominated sorting genetic algorithm-II (NSGA-II) as the basis framework, along with an effective encoding scheme, initialization method, and neighborhood search mechanism. The algorithm's parameter design is completed through variance analysis, and its advantages in solving the problem are verified by comparing and analyzing it with other algorithms in terms of hypervolume and Set Coverage (SC).
Article
Computer Science, Artificial Intelligence
Derya Deliktas, Ender Ozcan, Ozden Ustun, Orhan Torkul
Summary: The study introduces evolutionary algorithms to solve the bi-objective flexible job shop scheduling problem and compares their performance across various configurations. The transgenerational memetic algorithm using weighted sum method outperforms others and achieves the best-known results for almost all instances of bi-objective flexible job shop cell scheduling.
APPLIED SOFT COMPUTING
(2021)
Article
Multidisciplinary Sciences
Anita Agardi, Karoly Nehez, Oliver Hornyak, Laszlo T. Koczy
Summary: This paper focuses on the flow shop scheduling problem and introduces a discrete bacterial memetic evolutionary algorithm which improves local search using simulated annealing. Experimental results show that this algorithm outperforms other methods in solving the no-idle flow shop scheduling problem.
Article
Computer Science, Artificial Intelligence
Hong-Bo Song, You-Hong Yang, Jian Lin, Jing-Xuan Ye
Summary: In this paper, an effective Hyper Heuristic-based Memetic Algorithm (HHMA) is proposed to solve the Distributed Assembly Permutation Flow-shop Scheduling Problem (DAPFSP) with the objective of minimizing the maximum completion time. A novel searching-stage-based solution representation scheme is presented for both improving the search efficiency and maintaining potential solutions. Comparison experiments on a benchmark set demonstrate the superiority of HHMA over the state-of-the-art algorithms for the DAPFSP.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Suhaila Saidat, Ahmad Kadri Junoh, Wan Zuki Azman Wan Muhamad, Zainab Yahya
Summary: This paper focuses on solving the job shop scheduling problem of factories by proposing new methods and models to improve production efficiency and worker flexibility. Through the use of genetic algorithms and other methods, the study successfully reduced the overall operation time of products and improved worker flexibility in terms of waiting times.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jing-Jing Wang, Ling Wang
Summary: In this article, the distributed hybrid flow-shop scheduling problem is addressed with an optimization framework comprising a mixed integer linear programming model and a bi-population cooperative memetic algorithm (BCMA). Collaborative initialization and intensification search are used to generate diverse solutions and balance exploration and exploitation. Extensive computational tests show the effectiveness of the BCMA in solving the DHFSP and verifying the optimization capabilities of the specific designs.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2021)
Article
Automation & Control Systems
Si-Chen Liu, Zong-Gan Chen, Zhi-Hui Zhan, Sang-Woon Jeon, Sam Kwong, Jun Zhang
Summary: This article addresses the job-shop scheduling problem with multiple objectives, including completion time, total tardiness, advance time, production cost, and machine loss. A multiple populations for multiple objectives genetic algorithm (MPMOGA) is proposed to optimize these objectives simultaneously. The MPMOGA algorithm utilizes an archive sharing technique and an archive update strategy to improve the quality and diversity of the solutions. Experimental results show that MPMOGA outperforms other state-of-the-art algorithms on most test instances.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper proposes a multi-objective memetic algorithm with a two-level encoding scheme to solve the energy-efficient distributed flexible flow shop scheduling problem. Through comprehensive experiments, the effectiveness of the algorithm in optimizing total weighted tardiness and energy consumption is verified.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Forhad Zaman, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: This study considers the uncertainty of activity durations in Resource Constrained Project Scheduling Problems and proposes a simulation-assisted evolutionary framework to solve the optimization problem. A range of problem instances generated based on uncertain durations is evaluated using simulation, with a new strategy proposed to reduce the number of simulation runs.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Operations Research & Management Science
Marjia Haque, Sanjoy Kumar Paul, Ruhul Sarker, Daryl Essam
Summary: The paper proposes a two-phase planning approach for a multi-echelon, multi-period, decentralized supply chain, focusing on the independence and equally powerful behavior of individual entities to achieve maximum profit. A mathematical model for total SC coordination as a first-phase planning problem and separate ones for each entity as second-phase planning problems are developed. The results show that the two-phase planning method is more realistic and effective than a traditional single-phase approach for a decentralized SC network.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Mohamed Meselhi, Ruhul Sarker, Daryl Essam, Saber Elsayed
Summary: In this paper, a novel algorithm is proposed to minimize the number of common variables between sub-problems in large-scale optimization, which improves the performance of the optimization process.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Tahereh Hassanzadeh, Daryl Essam, Ruhul Sarker
Summary: This paper introduces EvoDCNN, a block-based evolutionary model for developing deep convolutional networks for image classification, using a genetic algorithm. By utilizing a fixed-length encoding model, variable-length networks can be generated with high accuracy and less computation. Comprehensive evaluations on multiple datasets show that the proposed model outperforms previous state-of-the-art accuracy for classification.
Article
Computer Science, Software Engineering
Ali Ahrari, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of predefined problems for non-experienced users, as well as highly customized problems for experienced users. It can integrate with any optimization method and calculate optimization performance based on robust mean peak ratio. PyDDRBG is expected to advance the fields of static and dynamic multimodal optimization by providing a common platform for numerical analysis, evaluation, and comparison.
Article
Automation & Control Systems
Kyle Robert Harrison, Saber M. Elsayed, Terence Weir, Ivan L. Garanovich, Sharon G. Boswell, Ruhul A. Sarker
Summary: This article proposes a novel model for project portfolio selection and scheduling problem (PPSSP) that can handle multiple groups of projects in real-world scenarios. It also presents three hybrid meta-heuristic algorithms to provide high-quality solutions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Ali Ahrari, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: This study introduces a second variant of the successful RS-CMSA-ES method, called RS-CMSA-ESII, which improves upon certain components and enhances the performance of the method in multimodal optimization.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Forhad Zaman, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: This article introduces a novel auto-configured multioperator evolutionary approach for handling disruptions in project scheduling, which outperforms state-of-the-art algorithms in terms of solution quality.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Lisa Liu, Daryl Essam, Timothy Lynar
Summary: This article examines the complexity of IoT traffic and proposes two new metrics. Through comparative experiments, the new methods are proven to outperform existing approaches, particularly in heterogeneous conditions.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Setyo Tri Windras Mara, Ruhul Sarker, Daryl Essam, Saber Elsayed
Summary: This paper examines the cooperation between electric vehicles and drones in last-mile logistics operations, known as the electric vehicle-drone routing problem (EVDRP). A mixed-integer programming model is formulated to minimize the total completion time by equipping electric vehicles with drones to deliver parcels. Recharging stations are considered to overcome the battery limitation of electric vehicles. A memetic algorithm-based approach with problem-specific operators is developed to solve the model. The algorithm's effectiveness is demonstrated through comprehensive numerical experiments, leading to valuable managerial insights.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Aaliya Sarfaraz, Ripon K. Chakrabortty, Daryl L. Essam
Summary: The profitability of a supply chain depends on the stability of its stakeholders and their consistent information sharing. However, a lack of coordination can cause inefficiencies, such as the bullwhip effect and product unavailability. This study proposes a simulation model using blockchain technology to improve information sharing in a supply chain. An improved proof-of-authority consensus algorithm is also introduced to enhance trust in a decentralized supply chain model. Multiple experiments demonstrate the effectiveness of this approach in reducing inefficiencies and increasing overall supply chain efficiency.
BLOCKCHAIN-RESEARCH AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Md Juel Rana, Forhad Zaman, Tapabrata Ray, Ruhul Sarker
Summary: Community microgrids provide resiliency in smart grid operation and have seen an increased penetration of eco-friendly electric vehicles (EVs) in recent years. However, the uncontrolled charging of EVs can overwhelm electric networks. In this work, an efficient demand response (DR) scheme based on dynamic pricing is proposed to enhance the capacity of microgrids in hosting a large number of EVs. The scheme utilizes a hierarchical optimization framework and employs evolutionary algorithms and mixed-integer linear programming models to solve the problems. The proposed DR scheme is tested on a microgrid system and proves to be effective compared to benchmark pricing policies.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
