Article
Automation & Control Systems
Vahid Riahi, M. A. Hakim Newton, Abdul Sattar
Summary: The PFSP-SDST problem with sequence-dependent setup times is NP-hard and has practical applications in industries such as cider and print. The proposed CBLS algorithm transforms constraints into an auxiliary objective function to guide the search towards the optimal value of the actual objective function. Experimental results show that the CBLS algorithm outperforms existing state-of-the-art algorithms and obtains new upper bounds for a significant number of problem instances.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
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
Management
Ahmed Missaoui, Ruben Ruiz
Summary: Hybrid Flowshop Scheduling Problems (HFS) are realistic machine sequencing models. We propose a new local search procedure IG, which produces state-of-the-art results according to comprehensive computational experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Ziyue Wang, Liangshan Shen, Xinyu Li, Liang Gao
Summary: This paper addresses the problem of energy-efficient hybrid flowshop rescheduling under machine breakdown and proposes an improved multi-objective firefly algorithm to optimize production efficiency, energy consumption, and production stability.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Interdisciplinary Applications
Fernando Luis Rossi, Marcelo Seido Nagano
Summary: The distributed permutation flowshop scheduling problem (DPFSP) has been widely studied due to the complex production systems with mixed no-idle flowshops. Although the issue of identical factories with mixed no-idle flowshop environments has not been explored in literature, new solutions including MILP formulation, constructive heuristic, and iterated greedy algorithms have been proposed. Extensive experiments showed that the proposed methods outperformed existing approaches.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Automation & Control Systems
Jiang-Ping Huang, Quan-Ke Pan, Zhong-Hua Miao, Liang Gao
Summary: The study focuses on the DPFSP problem with SDST, proposing three constructive heuristics and a DABC algorithm. The heuristics are based on greedy rule and local search, while the DABC algorithm balances local and global exploration with six composite neighborhood operators. A problem-oriented local search method is introduced to improve the best individual in the population. The proposed methods are shown to be effective compared to existing algorithms in solving the problem.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Engineering, Industrial
Yingli Li, Xinyu Li, Liang Gao, Biao Zhang, Quan-Ke Pan, M. Fatih Tasgetiren, Leilei Meng
Summary: This paper presents a mathematical model and a discrete artificial bee colony algorithm for optimizing the distributed hybrid flowshop scheduling problem with sequence dependent setup times. The proposed algorithm utilizes two-level encoding and effective solution update techniques, showing superior performance in experiments.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Chen-Yang Cheng, Pourya Pourhejazy, Kuo-Ching Ying, Yi-Hsiu Liao
Summary: This study successfully addressed the No-wait Flowshop Group Scheduling Problems, achieving a best-found solution rate of over 99.7% through the development of two metaheuristics. The results indicate that RMSA outperforms existing algorithms for solving the NWFGSP_SDST problem.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Chen-Yang Cheng, Pourya Pourhejazy, Kuo-Ching Ying, Shi-Yao Huang
Summary: This study developed an effective metaheuristic to address Blocking Flowshop Scheduling Problems with Sequence-Dependent Setup-Times, showing superior performance and potential applications in solving other complex scheduling problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Nitin Srinath, I. Ozan Yilmazlar, Mary E. Kurz, Kevin Taaffe
Summary: This paper presents two metaheuristics for solving the multi-objective scheduling problem in the dyeing process. Through comparative analysis, it is found that hybrid-optimal approaches provide higher quality solutions but suffer from longer computation times.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Engineering, Chemical
Kun Li, Huixin Tian
Summary: This paper proposes a learning and swarm based multiobjective variable neighborhood search (LS-MOVNS) algorithm to solve the multiobjective PFSP problem. LS-MOVNS achieves a balance between exploration and exploitation in a multiobjective environment through integrating swarm-based search with VNS using machine learning techniques.
Article
Computer Science, Interdisciplinary Applications
Leonardo C. R. Soares, Marco A. M. Carvalho
Summary: The problem of resource-constrained parallel machine scheduling with setup times in microelectronic components manufacturing is addressed using a biased random-key genetic algorithm hybridized with tailored local search procedures. Two new sets of challenging instances are proposed and experiments show the average percentage distances from the lower and upper bounds were 22.44% and -7.62%, respectively.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Mathematics
Chenyao Zhang, Yuyan Han, Yuting Wang, Junqing Li, Kaizhou Gao
Summary: A distributed blocking flowshop scheduling problem with no buffer and setup time constraints is studied. A mixed integer linear programming model is constructed and verified for correctness. An iterated greedy algorithm is presented to optimize the makespan criterion and collaborative interactions are considered to improve the exploration and exploitation of the algorithm.
Article
Computer Science, Interdisciplinary Applications
Fehmi Burcin Ozsoydan, Mujgan Sagir
Summary: The paper presents a learning iterated greedy search metaheuristic algorithm to minimize the maximum completion time in a hybrid flexible flowshop problem. Through four main phases, the algorithm adaptively learns and promotes efficient low-level heuristics, leading to significant improvements demonstrated by statistical tests compared to eight other algorithms in related literature.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Engineering, Industrial
Yenny Alexandra Paredes-Astudillo, Valerie Botta-Genoulaz, Jairo R. R. Montoya-Torres
Summary: Inspired by real-life applications, this paper focuses on incorporating learning effects into scheduling problems, specifically in hand-intensive manufacturing. Four well-known approaches for modeling learning effects are considered and mathematical models are provided for each case. A solver is used to find optimal solutions for small problem instances, while a Simulated Annealing algorithm is proposed for large problem instances. Computational experiments show that the Simulated Annealing algorithm performs better for fast learning effects compared to slow learning effects. Insights are provided for industrial decision makers regarding the impact of the learning effect model on the makespan minimisation flowshop scheduling problem.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Navid Salmanzadeh-Meydani, S. M. T. Fatemi Ghomi, Seyedhamidreza Shahabi Haghighi, Kannan Govindan
Summary: This paper presents a method for evaluating the sustainability performance of an organization using PCA, NT, and statistical analysis. The results show that the factors related to the outcomes are of great importance for organizational performance, and there has been a decline in sustainability performance in recent years.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Management
Mohsen Abdoli, Mostafa Zandieh, Sajjad Shokouhyar
Summary: This study determines the optimal queuing system capacity by analyzing the properties of the queuing system and appointment window, aiming to minimize the total costs. The findings can guide the management decisions of both public and private healthcare centers.
JOURNAL OF MODELLING IN MANAGEMENT
(2023)
Review
Green & Sustainable Science & Technology
K. Mostaghimi, J. Behnamian
Summary: Waste management is crucial for environmental preservation and public health. Green production, focusing on prevention and waste reduction, is identified as the best approach for waste management. Collaboration between citizens, businesses, industries, and government is essential for efficient waste management.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Review
Computer Science, Interdisciplinary Applications
M. Niyazi, J. Behnamian
Summary: This paper reviews the application of CC, BD, and UAVs in disaster management, exploring their potential and challenges in collecting and analyzing disaster data, as well as optimizing relief operations. It provides future research directions based on the analysis of about 170 related papers.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Review
Computer Science, Interdisciplinary Applications
M. Salehi Sarbijan, J. Behnamian
Summary: Vehicle routing problems (VRPs) have been a popular research topic in combinatorial optimization for the past six decades, and this paper reviews and analyzes recent research in this area, focusing on emerging topics like FVRP, RTVRP, and CVRP. The study suggests using multi-agent solving approaches and novel metaheuristic algorithms in FVRP and exploring real-time methodologies in various routing problems.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
J. Behnamian, M. Ghadimi, M. Farajiamiri
Summary: The importance of green vehicle routing lies in the unsustainable nature of current distribution systems and the lack of consideration for environmental impacts. This study presents a mathematical formulation for a green heterogeneous vehicle routing problem and develops a firefly algorithm to solve it. The use of data mining significantly improves the algorithm's performance.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Environmental Sciences
Mehrdad Niyazi, Javad Behnamian
Summary: Collecting and sharing information about affected areas is crucial for making optimal decisions in relief processes. Cloud computing, as a versatile platform for processing and storing big data, has been recognized for its effectiveness in disaster situations. A three-stage dynamic procedure is proposed for handling evacuation operations and logistics issues, using image processing, tweet mining, and a system of equations to improve decision-making and efficiency.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Engineering, Chemical
Navid Salmanzadeh-Meydani, S. M. T. Fatemi Ghomi, Seyedhamidreza Shahabi Haghighi, Kannan Govindan
Summary: This paper presents a quantitative approach to evaluate the resilience of organizations in sudden-onset disasters, taking into account preparedness actions. The concept of the resilience triangle is expanded and the gradual improvement of functionality level is examined as a type of preparedness action. Measures of robustness and rapidity are used to indicate the loss of functionality and recovery time, while resourcefulness and redundancy measures are used to improve disaster resilience. Mathematical models are developed to assess the impact of these measures on resilience. The approach is applied to an oil and gas company and found to be effective in disaster response, planning, and mitigation.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2023)
Article
Engineering, Civil
F. Radan, S. M. T. Fatemi Ghomi, S. M. J. Mirzapour Al-e-hashem, Moeen Sammak Jalali
Summary: This paper addresses the maritime inventory routing problem (MIRP) and develops a mixed integer nonlinear programming model considering various constraints. Through studying ports in Iran and nearby areas, it is found that wind force and wave angle do not affect the routing, but only change the ship speed and costs. Tide, on the other hand, influences the route and increases costs.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Economics
Mahnaz Sohrabi, Mostafa Zandieh, Mohammad Shokouhifar
Summary: This study examines the challenges of healthcare systems in achieving sustainable inventory management of blood products. The study aims to promote social equity in healthcare provision, optimize cost management, and minimize environmental pollution. A demand-driven multi-objective inventory model is proposed, utilizing hybrid policies in an uncertain environment. The model considers different types of demands, applies a robust fuzzy stochastic programming approach, and implements a combined metaheuristic technique for solution finding. The results demonstrate the superior performance of the proposed model in minimizing costs, reducing shortages and wastage, and addressing health equity and emergencies.
SOCIO-ECONOMIC PLANNING SCIENCES
(2023)
Article
Green & Sustainable Science & Technology
Erfan Shafiee Roudbari, S. M. T. Fatemi Ghomi, Ursula Eicker
Summary: The global population growth leads to increased demand for raw materials, while governments are implementing circular economy strategies in cities and industries. This paper presents a comprehensive model of a multi-echelon closed-loop supply chain network that operates under uncertainty. The model optimizes three contradicting objectives: maximizing profit, minimizing emissions, and maximizing job creation. The augmented epsilon constraint method is applied to improve the model. Applied in the clothing industry in Montreal, Canada, the results show the attractiveness of such a network for companies seeking profit, sustainability, and entrepreneurship.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Computer Science, Information Systems
Amin Rahimi, Seyed Mojtaba Hejazi, Mostafa Zandieh, Mirpouya Mirmozaffari
Summary: This paper proposes a surgical case scheduling problem that assigns n surgeries to m identical operating rooms or machines. Since optimization problems in operating rooms are NP-hard, mathematical and metaheuristic methods are used. The ordering of surgical operations in each room is a crucial part of sequencing and scheduling problems. The study introduces a no-wait open-shop surgical case scheduling problem with multi-transportation times and develops a mixed-integer linear program (MILP) to solve small-sized instances. Moreover, a hybrid simulated annealing (SA) algorithm is suggested for solving large-sized problems in an acceptable computational time.
APPLIED SYSTEM INNOVATION
(2023)
Article
Computer Science, Interdisciplinary Applications
J. Behnamian, Z. Gharabaghli
Summary: Hospitals are important service organizations in the health system, and it is crucial to provide high-quality services due to the high risk involved. This study presents a patient admission scheduling model considering resource constraints and uncertainty in service quality. Mathematical modeling and optimization algorithms are used to enhance healthcare services and patient satisfaction.
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2023)
Article
Computer Science, Interdisciplinary Applications
M. Hajibabaei, J. Behnamian
Summary: A mathematical model is developed for a flexible job-shop scheduling problem with assembly operation. Scheduling is performed based on job release times and machine breakdowns in the first stage, followed by assembly operations in a flow-shop environment, and then the assembled products are sent to customers. Three objective functions, including various costs, CO2 emission, and noise pollution, need to be minimized simultaneously. The proposed model is linearized and its complexity is reduced using the epsilon-constraint methods and Lagrangian relaxation algorithm, and the experimental results show that the Lagrangian relaxation algorithm is highly efficient.
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2023)
Article
Automation & Control Systems
N. Bagheri Rad, J. Behnamian
Summary: The changing market environment necessitates the use of job shop systems based on real-time data. Intelligent factories, created through the integration of physical-virtual systems, offer higher quality and faster production speed compared to traditional methods. Radio Frequency Identification System is employed for virtual connections between factories, allowing quick and careful decision-making regarding events such as new job arrivals and machine breakdowns. This research addresses the real-time scheduling problem in multi-agent production networks distributed in smart factories, highlighting its importance in today's industry.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)