Article
Engineering, Industrial
Christos Koulamas, George J. Kyparisis
Summary: The study focuses on the no-wait flow shop scheduling problem with a rejection option and presents polynomial-time algorithms to minimize different objective functions efficiently.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Management
Christos Koulamas, George J. Kyparisis
Summary: This study focuses on two-stage no-wait proportionate flow shops and aims to minimize the variation in service time by introducing the Total Absolute Deviation of mid-processing Points (TADZ) metric. The researchers demonstrate that the TADZ objective in this context can be solved in O(nlogn) time, providing a solution to an open research question. Additionally, they explore the solvability of a generic two-stage no-wait proportionate flow shop scheduling problem and present practical applications of TADZ. Furthermore, a new metric called the sum of all partial schedule lengths (SPSL) is introduced, and its related problem is shown to be solvable. Finally, the option of rejecting a job from the schedule is considered, and the resulting problem is solved using dynamic programming.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Health Care Sciences & Services
Yang-Kuei Lin, Chen-Hao Yen
Summary: This research addresses a deterministic three-stage operating room surgery scheduling problem with no-wait constraint. The objective is to minimize the makespan by proposing a genetic algorithm (GA) for solving the scheduling problem. The computational results show that the GA can efficiently find near-optimal solutions with a small average deviation from the lower bound (LB) and reasonable computation time.
Article
Computer Science, Interdisciplinary Applications
Wisute Ongcunaruk, Pornthipa Ongkunaruk, Gerrit K. Janssens
Summary: This study aims to improve transportation planning decisions for a production company in Thailand through a mixed integer programming model and a genetic algorithm, resulting in reduced costs and increased efficiency.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Xueli Yan, Xingsheng Gu
Summary: This paper proposes an algorithm to solve the multi-product multi-stage production scheduling problem by combining improved differential evolution algorithm and memetic algorithm, effectively improving the scheduling performance.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Engineering, Industrial
S. S. Panwalkar, Christos Koulamas
Summary: This paper analyzes the two-machine no-wait proportionate flow shop problem with the goal of minimizing the total absolute deviation of job completion times. It is shown that the optimality of V-shaped sequences is limited and for larger size problems, the best V-shaped sequence can be determined in linear time. However, the number of non-dominated potentially optimal semi-V-shaped sequences increases rapidly for larger size problems.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper investigates a distributed no-wait flexible flow shop scheduling problem with makespan criterion, presenting a mixed-integer linear programming model and machine selection method, as well as developing greedy factory assignment rules and dispatch rules. Multiple constructive heuristics are obtained by combining different rules, and a variable neighborhood descend constructive heuristic version is designed to tackle the problem.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Ningning Zhu, Fuqing Zhao, Ling Wang, Ruiqing Ding, Tianpeng Xu, Jonrinaldi
Summary: This study proposes a discrete knowledge-guided learning fruit fly optimization algorithm to solve the distributed no-wait flow shop scheduling problem. By introducing a probability knowledge model and a local search strategy, the algorithm performs well in minimizing the total weighted earliness and tardiness.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Lixin Cheng, Qiuhua Tang, Liping Zhang, Chunlong Yu
Summary: This article studies the scheduling problem of a flexible manufacturing cell with two production methods. By establishing a mixed integer linear programming model and a Q-learning-based genetic algorithm, it achieves a reasonable ordering and arrangement of standardized products and individualized products. Experimental results show that the proposed Q-GA algorithm outperforms other algorithms in terms of solution effectiveness and robustness.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Chemical
Hao Sun, Aipeng Jiang, Dongming Ge, Xiaoqing Zheng, Farong Gao
Summary: This work focuses on the study of robust no-wait flow shop scheduling problem under the interval-valued fuzzy processing time, proposing a model involving interval-valued fuzzy sets and an improved simulated annealing algorithm for efficient solution. The research demonstrates high efficiency of the algorithm and the applicability of the proposed model and solution strategy under interval-valued fuzzy sets.
Article
Automation & Control Systems
Fuqing Zhao, Tao Jiang, Ling Wang
Summary: Green manufacturing has gained increasing attention in the context of carbon peaking and carbon neutrality. Distributed production is prevalent in various manufacturing industries due to globalization. This article addresses the energy-efficient distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (DNWFSP-SDST) for minimizing makespan and total energy consumption (TEC). A mixed-integer linear programming model is formulated, and a cooperative meta-heuristic algorithm based on Q-learning (CMAQ) is proposed. The experimental results demonstrate that CMAQ outperforms state-of-the-art comparison algorithms in solving energy-efficient DNWFSP-SDST.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Shu Luo, Linxuan Zhang, Yushun Fan
Summary: In this study, a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named HMAPPO is proposed to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem. The method consists of objective agent, job agent, and machine agent, with various job selection rules and machine assignment rules designed to achieve temporary objectives at each rescheduling point. Extensive numerical experiments have confirmed the effectiveness and superiority of HMAPPO compared to other known dynamic scheduling methods.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
M. Salehi Sarbijan, J. Behnamian
Summary: This paper introduces the real-time collaborative feeder vehicle routing problem (RTCFVRP) with flexible time windows and proposes algorithms including mixed-integer linear programming, multi-objective particle swarm optimization (MOPSO), and MOPSO-variable neighborhood search (MOPSO-VNS) to solve the problem. The results show that the proposed algorithm outperforms other algorithms in both static and dynamic modes.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Engineering, Industrial
Jinsheng Gao, Xiaomin Zhu, Kaiyuan Bai, Runtong Zhang
Summary: This paper addresses the no-wait job shop scheduling problem with due date and subcontracting cost constraints, introducing two mathmatical models to find solutions for the problem.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Management
Czeslaw Smutnicki, Jaroslaw Pempera, Grzegorz Bocewicz, Zbigniew Banaszak
Summary: This paper investigates the problem of cyclic scheduling in a manufacturing system, considering the flow of jobs with identical technological routes, no-wait constraints, and missing operations. The problem is decomposed into two sub-problems, and alternative methods are provided for finding the minimal cycle time and optimal processing order of jobs. A metaheuristic approach is used to solve the latter sub-problem. Experimental examination demonstrates the efficiency and quality of the proposed algorithm.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Behdad Ehsani, Hamed Karimi, Alireza Bakhshi, Amir Aghsami, Masoud Rabbani
Summary: Along with global catastrophes, the COVID-19 outbreak has worsened the severity of disasters. Current actions by aid organizations and philanthropists are not effectively dealing with epidemic outbreaks in disaster situations. This paper proposes a novel humanitarian model using IoT-based technology to prevent COVID-19 outbreaks in response to disasters. The model utilizes IoT systems for remote monitoring, suspect detection, surveillance, disinfection, and transportation of relief items. It is divided into two stages: identifying infected cases and promptly transferring patients to temporary hospitals, and locating distribution centers to evenly distribute relief items to hospitals and evacuation centers. The model is applied to a real case study and sensitivity analysis on crucial parameters is conducted, showing that the use of IoT systems reduces infections and shortages of relief items. Valuable insights are gained for healthcare managers.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Automation & Control Systems
Hanieh Heydari, Ata Allah Taleizadeh, Fariborz Jolai
Summary: One of the crises of human beings today is the lack of natural resources and the increase in industrial waste due to overproduction. The warming of the earth has also become a major topic. To prevent this trend, a sustainable supply chain should be used and producers should participate in green projects. Attention should be given to the environment without sacrificing profits. Pricing of products should consider factors such as inflation and production costs. An initial budget can be repaid with green bonds. The study introduces a sustainable supply chain model that calculates optimal prices and finances the chain with green bonds.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Mahdi Hamid, Mohammad Mahdi Nasiri, Masoud Rabbani
Summary: Meal delivery services is a competitive market, and customer experience is crucial. Enhancing delivery operations by adding drones and crowdsourcing can improve cost, meal freshness, and due-date satisfaction. A mathematical model and an efficient self-adaptive hyper-heuristic method based on genetic algorithm and modified particle swarm optimization are developed.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Benyamin Moghaddasi, Amir Salar Ghafari Majid, Zahra Mohammadnazari, Amir Aghsami, Masoud Rabbani
Summary: This paper develops a mixed integer nonlinear programming routing-location model to improve the design of the cold chain logistics network and solve the optimization problem of the marine product distribution logistics system. The proposed model considers the characteristics of the cold chain logistics industry and aims to maximize customer satisfaction while minimizing costs and greenhouse gas emissions. It offers decision support for supply chain managers to select the optimal number of distribution centers and routes while also considering the environmental impact.
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2023)
Article
Computer Science, Cybernetics
Hossein Shakibaei, Mohammad Reza Farhadi-Ramin, Mohammad Alipour-Vaezi, Amir Aghsami, Masoud Rabbani
Summary: A new model is developed to optimize the humanitarian supply chain and minimize human, financial, and moral losses. By applying multiple attribute decision-making methods and machine learning algorithms, the study successfully reduces losses and improves efficiency.
Article
Computer Science, Artificial Intelligence
Mahdieh Tavakoli, Amirreza Tajally, Mohssen Ghanavati-Nejad, Fariborz Jolai
Summary: The study investigates customer-based sustainable-resilient supplier selection problem and proposes a Markovian-based fuzzy decision-making method. Customer preferences are evaluated using quality function deployment and Markov transition matrix, and indicator weights are calculated using the transition matrix and fuzzy best-worst method. The performance of suppliers is measured based on the decision matrix and sub-criteria weights. The study also considers an online marketplace as a case study and finds that customer preferences change during a pandemic situation.
Article
Green & Sustainable Science & Technology
Mahsa Taherifar, Negin Hasani, Mahsa Zokaee, Amir Aghsami, Fariborz Jolai
Summary: In the midst of the COVID-19 pandemic, utilizing online markets to sell products has become increasingly crucial for businesses to remain competitive and survive. Traditional business practices have been disrupted, leading consumers to rely on online channels for their needs. Therefore, businesses that have quickly adapted to online markets have been able to maintain their customer base and generate revenue. Considering the potential of online markets is vital in the current pandemic situation.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Computer Science, Interdisciplinary Applications
Fatemeh Hirbod, Masoud Eshghali, Mohammad Sheikhasadi, Fariborz Jolai, Amir Aghsami
Summary: In order to control and maintain public health, effective implementation of response strategies, including vaccine distribution, is necessary. This paper proposes a novel model that combines location-allocation problems with queueing systems methodologies to optimize the efficiency of vaccine distribution. The model considers various factors such as uncertain demand and varying service rates, aiming to minimize total costs and improve decision-making frameworks in health systems.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Majid Heidari, Masoud Rabbani
Summary: In today's turbulent and risky world, the importance of risk management has been emphasized in all areas, including supply chain network design. The goal is to minimize potential risks and effectively utilize resources to meet future needs. A leagile supply chain is designed to optimize resource consumption and respond quickly to customer demands. The study also focuses on designing a resilient supply chain that minimizes the environmental and social impact. A mathematical model with three objectives, economic, environmental, and social, is developed and validated using the NSGA-III algorithm. The case study of a pharmaceutical supply chain in Iran is conducted to analyze the applicability and solution results, as well as analyzing critical parameters.
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
(2023)
Article
Engineering, Industrial
Mohammadreza Eslamipirharati, Fariborz Jolai, Amir Aghsami
Summary: This paper investigates the bi-objective optimization of a sustainable reverse supply chain network considering two key sources of uncertainty. The study also examines the effects of carbon tax policies and government subsidies on remanufactured products, as well as three important sustainability aspects - economic, social, and environmental. Numerical analysis demonstrates the sensitivity of objective functions to uncertain parameters and minimum acceptable quality levels, and shows that government subsidies can offset the negative effects of carbon tax policies.
JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING
(2023)
Article
Business
Mohammad Rahiminia, Sareh Shahrabifarahani, Zahra Mojaradi, Amir Aghsami, Fariborz Jolai
Summary: During epidemics, controlling patient congestion is important for reducing disease spreading. This study developed a mathematical model to manage patient congestion and waste in a medical center, considering environmental issues. The model includes a queueing system for patients and vehicles, and an inventory model to prevent waste accumulation. The model was solved using the Grasshopper algorithm and demonstrated applicability in a case study. Sensitivity analysis and valuable managerial insights were provided.
JOURNAL OF MANAGEMENT ANALYTICS
(2023)
Article
Management
Alimohammad Lotfi, Mandana Shakouri, Seyed Reza Abazari, Amir Aghsami, Masoud Rabbani
Summary: This paper discusses the management and design of a sustainable pharmaceutical supply chain network considering recycling. The authors use the analytical hierarchy process to select green manufacturers and propose a multi-objective mathematical model to design the network. A case study in Iran demonstrates the improvement in environmental and social issues while minimizing costs.
JOURNAL OF ADVANCES IN MANAGEMENT RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Fatemeh Azizi, Mahdi Hamid, Behnaz Salimi, Masoud Rabbani
Summary: This study introduces an integrated intelligent algorithm to evaluate OR performance based on ergonomics indicators and examines their impact on job satisfaction. The results show that OR performance is appropriate in terms of knowledge, situation assessment, situation analysis, work and equipment specifications, and mental power. However, it is inadequate in terms of information and communication system, teamwork and effective communication, physical condition, and OR condition.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Construction & Building Technology
Yasamin Sadat Babaei, Shiva Malekkhouyan, Amir Aghsami, Masoud Rabbani
Summary: Vaccines are crucial for monitoring and preventing contagious diseases, especially with the global prevalence of the coronavirus. This study develops a sustainable multi-objective mixed-integer non-linear programming model to address the challenges of vaccine inventory ordering and waste management. A real case study in Kashan province, Iran, along with two test problems, is considered to explore the applicability of the proposed model. Sensitivity analysis suggests that extending the review period could lead to increased order quantity and higher costs for the supply chain.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Management
Ehsan Aghakarimi, Hamed Karimi, Amir Aghsami, Fariborz Jolai
Summary: This paper proposes a comprehensive framework to evaluate the performance of different branches of a retailer, using the best-worst method (BWM) and data envelopment analysis (DEA). The branches are ranked and weaknesses and strengths are identified through sensitivity analysis and statistical tests. Strategies are then proposed using SWOT analysis to improve the performance of weak branches.
INTERNATIONAL JOURNAL OF PRODUCTIVITY AND PERFORMANCE MANAGEMENT
(2023)
