Article
Engineering, Chemical
Ismet Karacan, Ozlem Senvar, Serol Bulkan
Summary: This paper addresses the no-wait flow shop problem with earliness and tardiness objectives, which is proven to be NP-hard. Previous studies on this problem mainly focused on familiar objectives, while the use of both earliness and tardiness objectives has been less explored. A novel methodology for the parallel simulated annealing algorithm is proposed to overcome the runtime drawback of classical simulated annealing and enhance its robustness.
Article
Computer Science, Interdisciplinary Applications
Damla Kizilay
Summary: This study focuses on the problem of disassembly line balancing, which involves sequence-dependent setup time and complex AND/OR precedence relations. The managerial impacts of this study are crucial for both environmental and industrial sustainability. The problem is solved using mixed-integer linear programming and constraint programming models, and compared with a simulated annealing metaheuristic.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Ece Yagmur, Saadettin Erhan Kesen
Summary: The study investigates a joint production scheduling and outbound distribution planning problem, using a mixed integer programming formulation and genetic algorithm to reduce delivery delays and vehicle travel time, proposing a new splitting procedure. Experimental results indicate that genetic algorithm outperforms simulated annealing in terms of solution quality for medium and large instances.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Mathematics
Fabian Riquelme, Elizabeth Montero, Leslie Perez-Caceres, Nicolas Rojas-Morales
Summary: This work focuses on generating conference programs that organize talks into different tracks, with main contributions in literature review, problem formulation and benchmarking, and heuristic approach. A new track-based conference scheduling problem formulation is introduced, with a proposed heuristic method for solving it efficiently.
Article
Computer Science, Interdisciplinary Applications
L. Munoz, J. R. Villalobos, J. W. Fowler
Summary: This paper discusses a dual resource constrained scheduling problem in the manufacturing industry, focusing on the photolithography area in the semiconductor industry. The paper proposes the use of deterministic scheduling theory to develop more efficient scheduling strategies. Integer Programming (IP) models, a heuristic algorithm, and a hybrid model are developed for scheduling parallel machines with shared, constrained, auxiliary resources and sequence-dependent setups. The IP model combined with the heuristic algorithm provides valuable solutions for real-world resource-constrained scheduling problems. It is important to control problem size and time horizon to achieve near-optimal or efficient solutions within acceptable run times. The proposed heuristic algorithm provides an initial feasible solution for the IP model, reducing the search space and eliminating the need for finding an initial feasible solution. A tighter formulation is also proposed by reducing the time horizon.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Management
Ihsan Yanikoglu, Tonguc Yavuz
Summary: This paper investigates a machine scheduling problem on unrelated parallel machines with the objective of minimizing the worst-case total tardiness. The authors propose a robust optimization model and discuss important properties of the mathematical formulation. The paper also addresses the issue of alternative optimal solutions for scheduling problems and presents a branch-and-price algorithm to solve realistic instances effectively. Numerical results demonstrate the effectiveness of the proposed approach in terms of optimality and improvement in objective function value.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Xiao Wu, Peng Guo, Yi Wang, Yakun Wang
Summary: In this paper, a scheduling problem with deteriorating jobs on parallel machines is studied to minimize costs. A mixed integer linear programming (MILP) model is proposed, and logic-based Benders decomposition (LBBD) is used to address the problem.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Tobias Hofmann, David Wenzel
Summary: The implementation of industrial robot systems in the automotive industry has significantly increased productivity, but has also introduced challenges such as technological complexity and safety issues. The goal is to develop algorithms and tools to optimize robot system scheduling.
OPTIMIZATION AND ENGINEERING
(2021)
Article
Engineering, Industrial
Massimo Pinto Antonioli, Carlos Diego Rodrigues, Bruno de Athayde Prata
Summary: This paper presents a customer order scheduling environment with explicit setup times dependent on production sequence, aiming to minimize total tardiness. A variety of algorithms are proposed and compared on randomly generated test instances, with SPAM performing best for small instances and the hybrid matheuristic SPAM-JPO and MILP model being most efficient for large instances.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2022)
Article
Management
Masoumeh Ghorbanzadeh, Mohammad Ranjbar
Summary: This paper investigates an energy-aware flow shop scheduling problem with sequence-dependent setup times, group scheduling, and renewable energy constraints. The objective is to minimize the total energy cost based on time-of-use electricity tariffs. Two mixed-integer linear programming models are developed, along with a decomposition-based heuristic algorithm for efficiently solving medium-size instances. Computational experiments show that the heuristic algorithm outperforms the developed models, with the time-interval index model exhibiting superior performance compared to the time-unit index model. Sensitivity analyses and economic performance evaluation are also provided.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Quang-Vinh Dang, Thijs van Diessen, Tugce Martagan, Ivo Adan
Summary: This paper addresses the scheduling problem of a set of tasks on identical parallel machines in a work center, considering the complex characteristics, objectives, and decision-making process, and proposes a mathematical model and a new matheuristic to solve the problem, demonstrating its superiority through empirical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Leilei Meng, Kaizhou Gao, Yaping Ren, Biao Zhang, Hongyan Sang, Zhang Chaoyong
Summary: This study addresses the problem of distributed hybrid flow shop scheduling with sequence-dependent setup times. Three novel mathematical models are proposed and compared with existing models, demonstrating their effectiveness. Experimental results show that the model based on sequence-based modeling performs the best, while the existing model performs the worst. Additionally, the constraint programming model is more efficient and effective than the mathematical models.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Operations Research & Management Science
Kaining Shao, Wenjuan Fan, Zishu Yang, Shanlin Yang, Panos M. Pardalos
Summary: This paper studies a PET/CT examination scheduling problem considering multi-stage processes. An integer programming model and a set partitioning model are formulated to minimize the total dose of purchased imaging agents. A variable neighborhood search heuristic with a scheduling rule based on optimal properties is proposed. Experimental results show that the proposed algorithm can obtain near-optimal solutions in a short time and outperforms the commonly used First Come First Service rule.
OPTIMIZATION LETTERS
(2023)
Article
Operations Research & Management Science
Kaining Shao, Wenjuan Fan, Zishu Yang, Shanlin Yang, Panos M. Pardalos
Summary: This paper discusses the treatment sequencing problem of cancer patients, introducing the dual factors of treatment value and waiting time, and solving the original problem through a column generation approach.
OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Quang-Vinh Dang, Koen Herps, Tugce Martagan, Ivo Adan, Jasper Heinrich
Summary: This paper addresses the problem of scheduling jobs on identical parallel machines with tool switches in a high-mix, low-volume manufacturing environment. The objective is to maximize the profit generated by the manufacturing system by assigning operations to machines, sequencing these operations, and determining a tool-switching plan. A mix-integer linear programming model is formulated, and a genetic algorithm is proposed to solve industry-size problem instances. Computational experiments show that the proposed GA achieves approximately 26% profit improvement compared to the current scheduling method.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Information Systems
Sharan Srinivas, Haya Salah
Summary: This study demonstrates the successful prediction of consultation length and patient no-shows using machine learning algorithms. By integrating models, the prediction accuracy was improved, reducing resource wastage and significantly decreasing patient waiting time and doctor idle time. It provides an effective approach to enhancing resource utilization.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2021)
Article
Engineering, Industrial
Mohamed R. Salama, Ronald G. McGarvey
Summary: This paper investigates the design and management of supply chains during a global pandemic, proposing a stochastic mixed integer linear programming model to evaluate the SC designs under different pandemic scenarios by maximizing the conditional value at risk (CVaR) of the SC profit. The study also explores the impact of SC network expansion on meeting demand and the effects of diversifying network node locations on SC performance. The paper provides managerial insights for SC planners to guide viable designs and management plans.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Management
Sharan Srinivas, Kavin Anand, Anand Chockalingam
Summary: This study explores the inverse association between adolescent psychological well-being and adulthood CVD risk, suggesting that improving psychological well-being in adolescence through quality management principles could lead to a reduction in CVD risk later in life. The findings highlight the importance of long-term positive well-being in preventing cardiovascular disease, offering implications for improving CVD care from a quality management perspective.
BENCHMARKING-AN INTERNATIONAL JOURNAL
(2022)
Review
Management
Suchithra Rajendran, Sharan Srinivas, Emily Pagel
Summary: This study is the first to propose recommendations for improving the service quality of insurance companies using the feedback from online customers and employees. The findings indicate that major issues include improper client assistance, inefficient claims processing, payment/fee-related problems, and unresponsive ancillary services. The study also identifies long and complex training, lack of social events, and incentive programs as the main reasons for workforce frustrations. The satisfaction level of employees is positively influenced by coworkers, workplace facilities, clean work environment, and other amenities.
BENCHMARKING-AN INTERNATIONAL JOURNAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohamed R. Salama, Ronald G. McGarvey
Summary: This study proposes a method to reduce passengers' in-vehicle time by implementing skip-stop operation, aiming to protect passengers from infection during pandemics. By applying a mixed integer linear programming model and a multi-start genetic algorithm, the study successfully finds solutions to minimize in-vehicle time while ensuring safety measures.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Economics
Mohamed R. Salama, Sharan Srinivas
Summary: This paper addresses the coordination problem of a truck and multiple heterogeneous unmanned aerial vehicles (UAVs or drones) for last-mile package deliveries. It introduces a new variant of truck-drone tandem that allows the truck to stop at non-customer locations for drone launch and recovery operations. The paper formulates a mixed integer linear programming model to optimize three key decisions and proposes an optimization-enabled two-phase search algorithm. Numerical analysis shows significant improvement in delivery efficiency by using flexible sites for drone operations.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Review
Operations Research & Management Science
Sharan Srinivas, Surya Ramachandiran
Summary: In the fiercely competitive airline industry, carriers aim to increase customer satisfaction by understanding expectations and personalizing service offerings. With the increasing use of social media, airlines can leverage online customer reviews to gain insights about their services and competitors. This study proposes a framework to automatically extract airline-specific intelligence from online customer reviews, using topic modeling, sentiment analysis, and collocation analysis. The proposed framework achieves higher classification accuracy compared to benchmark models and provides valuable insights about different aspects of airline service quality and reasons for passenger satisfaction/dissatisfaction.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Engineering, Industrial
Sharan Srinivas, Shitao Yu
Summary: This research proposes a collaborative human-robot order-picking system using autonomous mobile robots (AMRs) and human pickers to improve picking efficiency. By discussing order batching, batch assignment and sequencing, and picker-robot routing, an optimization model is developed to minimize the total tardiness of orders. A simulated annealing algorithm is proposed to handle large instances, and the experimental results demonstrate the superior performance of the proposed solution approach compared to existing methods.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2022)
Article
Business
Muzhen Li, Li Zhao, Sharan Srinivas
Summary: This study aims to understand the perspectives of online adaptive clothing consumers by analyzing customer reviews scraped from online platforms. The findings reveal that clothing function, customer service, and clothing aesthetics are the most discussed themes. Collocation analysis identifies the underlying causes of customer satisfaction and dissatisfaction. This research provides insights into the clothing needs of people with disabilities and offers practical guidelines for adaptive clothing retailers.
INTERNATIONAL JOURNAL OF CONSUMER STUDIES
(2023)
Article
Operations Research & Management Science
Teena Thomas, Sharan Srinivas, Chandrasekharan Rajendran
Summary: This research introduces the single truck multi-drone routing and scheduling problem for last-mile parcel delivery. A mixed integer linear programming (MILP) model is developed to minimize the delivery completion time, and a variant is introduced to minimize the total delivery cost. The proposed relax-and-fix with re-couple-refine-and-optimize (RF-RRO) heuristic approach and deep learning-based clustering procedure show promising results in solving the problem and improving delivery efficiency.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Engineering, Industrial
S. A. Ezhil, Chandrasekharan Rajendran, Sharan Srinivas
Summary: This paper proposes a general mathematical model for the Multi-Period, Multi-Stage Fixed-Charge Transportation Problem (MPMS-FCTP) in a supply chain, aiming to minimize the fixed and variable transportation, storage and backlog costs. Two improved Lagrangian-Relaxation (LR)-based approaches are developed to efficiently solve the MPMS-FCTP, and heuristics based on these approaches yield better solutions than existing ones in the literature. The experimental results demonstrate the effectiveness of the proposed LR approaches and the convergence to optimality is also discussed.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2023)
Article
Transportation
Suchithra Rajendran, Sharan Srinivas, Trenton Grimshaw
Summary: This research focuses on using machine learning algorithms to predict the demand for air taxi urban air mobility (UAM) services in different geographic regions of New York City at different times of the day. The experimental results suggest that gradient boosting consistently provides higher prediction performance, and specific locations, time periods, and weekdays have emerged as critical predictors.
JOURNAL OF AIR TRANSPORT MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)