Article
Computer Science, Artificial Intelligence
Qing-qing Zeng, Jun-qing Li, Rong-hao Li, Ti-hao Huang, Yu-yan Han, Hong-yan Sang
Summary: This paper addresses a multi-objective energy-efficient scheduling problem of the distributed permutation flowshop with sequence-dependent setup time and no-wait constraints. It proposes a new mixed-integer linear programming model and an improved non-dominated sorting genetic algorithm, along with problem-specific heuristics and search operators, to enhance the algorithm performance.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
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
Engineering, Industrial
Tuane Tonani Yamada, Marcelo Seido Nagano, Hugo Hissashi Miyata
Summary: The study proposes constructive methods to minimize total tardiness in production scheduling, with the HENLL algorithm using insertion logic showing the best performance. Additionally, a metaheuristic based on the iterated greedy search method is presented to significantly improve results obtained by the heuristics alone.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2021)
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
Xinnian Wang, Keyi Xing, Yanxiang Feng, Yunchao Wu
Summary: This study addresses the scheduling problem of deadlock-prone flexible manufacturing systems subject to no-wait constraints for the first time, and develops a new scheduling algorithm based on the place-timed Petri net (PN) model and heuristic search. The problem is solved through timetabling and sequencing, using a controlled PN model for timetabling and a hybrid heuristic search approach for sequencing.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(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
Automation & Control Systems
Mustafa Avci
Summary: The distributed no-wait flowshop scheduling problem (DNWFSP) is a variant of the classical flowshop scheduling problem. An iterated local search (ILS) algorithm is proposed to solve the DNWFSP, which incorporates specialized local search and adaptively adjusted perturbation strength. The ILS is able to produce high-quality solutions in short computing times.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Mathematics
Muberra Allahverdi
Summary: This article investigates the problem of minimizing total completion time in an uncertain environment. By establishing a more effective mathematical dominance relation, it is found that the average improvement compared to the existing literature is 1407.80%. Furthermore, statistical hypothesis testing and confidence intervals confirm the accuracy of the established dominance relation.
HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Karam Allali, Said Aqil, Jabrane Belabid
Summary: This paper investigates a multi-objective optimization distributed no-wait permutation flow shop scheduling problem under the constraint of sequence dependent setup time. The study proposes mixed integer linear programming and several efficient metaheuristics to solve this industrial problem. The combination of the genetic algorithm and Nawaz-Enscore-Ham algorithm yields the best results.
SIMULATION MODELLING PRACTICE AND THEORY
(2022)
Article
Management
Fernando Luis Rossi, Marcelo Seido Nagano
Summary: This paper investigates the mixed no-idle flowshop scheduling problem with sequence-dependent setup times and makespan minimisation criterion. A mathematical formulation and a constructive heuristic are proposed for this new problem, and extensive experiments show that the new heuristic outperforms methods from the literature.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Computer Science, Information Systems
Jianming Dong, Hong Pan, Cunkui Ye, Weitian Tong, Jueliang Hu
Summary: Efficient resource arrangement is crucial for creating intelligent management systems in hospitals. This study introduces the no-wait two-stage flowshop scheduling problem with multi-task flexibility on the first-stage machine, aiming to minimize the maximum completion time of all jobs. Several novel structural properties are discovered, leading to a linear-time combinatorial algorithm with an approximation ratio of 13/8.
INFORMATION SCIENCES
(2021)
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
Engineering, Chemical
Majharulislam Babor, Julia Senge, Cristina M. Rosell, Dolores Rodrigo, Bernd Hitzmann
Summary: This study introduces a hybrid no-wait flowshop scheduling model and demonstrates through simulations and the implementation of various optimization algorithms that this model can effectively reduce machine idle time and shorten the manufacturing cycle, providing a powerful tool for improving production efficiency in small and medium-sized bakery industries.
Article
Computer Science, Interdisciplinary Applications
Mustafa Avci, Mualla Gonca Avci, Alper Hamzadayi
Summary: This article proposes a branch-and-cut algorithm for solving the DNWFSP problem. By combining with a heuristic algorithm and employing symmetry breaking constraints to strengthen the model, this algorithm can improve the solution effectiveness of the DNWFSP problem to a certain extent.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Management
Fernando Siqueira de Almeida, Marcelo Seido Nagano
Summary: This paper proposes four algorithms for the no-wait flow shop scheduling problem, addressing the problem with sequence-dependent setup times. The algorithms improve the incumbent solution through a process of destruction and repair, exploring search intensification-diversification at different levels. Computational experiments demonstrate that the best proposed algorithm significantly outperforms existing methods.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Operations Research & Management Science
Sina Navidi, Muhammad Motamedi, Amir Aghsami, Fariborz Jolai
Summary: This paper proposes a new profit function based on queueing theory for investing operations in open-pit mines. The function aims to maximize profit while considering budget constraints, equipment's price, and other costs. By making meticulous decisions to find the optimal equipment combination, the transportation costs can be reduced, leading to improved mining efficiency and increased profit.
INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT
(2023)
Article
Engineering, Industrial
Pedram Memari, Seyedeh Samira Mohammadi, Fariborz Jolai, Seyed Farid Ghaderi
Summary: This paper addresses the decision problem of selecting a suitable location for a landfill, aiming to achieve urban waste management, cost reduction, efficiency improvement, and community social issues resolution. A combinatorial multi-criteria decision model is developed using fuzzy-DEMATEL, fuzzy-ANP, and Z-number-DEA methods, considering uncertainty and data reliability.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(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
Yahya Dorfeshan, Fariborz Jolai, Seyed Meysam Mousavi
Summary: Simultaneous decision-making in the supply chain and project management, known as the project-driven supply chain, is an important topic. This paper presents a new method to quantify supply risks in project activities, with a focus on critical path determination and resilience assessment. The proposed MABACODAS model considers parametric fuzzy values and interval type-2 fuzzy sets to handle uncertainties. A real case study in the construction industry is provided to demonstrate the applicability of the introduced model.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Industrial
Mohammad Rahiminia, Sareh Shahrabifarahani, Mohammad Alipour-Vaezi, Amir Aghsami, Fariborz Jolai
Summary: It is necessary to control patient congestion and manage medical waste in medical centers during pandemics. However, there is a lack of models that simultaneously address both issues using queueing systems. This paper develops a mathematical model to manage patient congestion and medical waste during the pandemic, using machine learning algorithms to categorize patients and a Markovian healthcare waste queueing-inventory system to model the number of outpatients, inpatients, and medical waste. The model is validated and applied to a case study on Covid-19, resulting in minimized waiting times for patients and reduced waste accumulation.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
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, 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
Operations Research & Management Science
Babak Aslani, Meysam Rabiee, Mona Jabbari, Dursun Delen
Summary: Optimal determination of healthcare facility locations is crucial for public health, well-being, and social welfare. This paper developed a multi-objective mixed-integer linear programming model to address the complexities of this problem, which includes multiple sustainability criteria. The model considered total travel distance, equity, local covering, effectiveness, and overlap functions as objective functions. The proposed framework was successfully applied to a real-world case of locating preventive healthcare centers in Iran and demonstrated its robustness and applicability through sensitivity analysis.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Parisa Fallah, Meysam Rabiee, Abolghasem Yousefi-Babadi, Emad Roghanian, Mostafa Hajiaghaei-Keshteli
Summary: This research proposes a stochastic multi-objective mixed-integer linear programming model for an agile, flexible disaster supply chain network, and utilizes a novel group decision-making framework to select qualified suppliers. A robust probabilistic programming approach and metaheuristic algorithms are used to handle uncertainty and large-scale problems. The results of the study demonstrate the applicability of the proposed framework and methodologies.
COMPUTERS & INDUSTRIAL ENGINEERING
(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)
Article
Engineering, Aerospace
Naimeh Borjalilu, Fariborz Jolai, Mahdieh Tavakoli
Summary: This study developed integrated multi-class classification machine learning models and Markov chain to evaluate cockpit crew performance during flights. The main features related to flights were identified and random forest classifier showed the highest performance in the evaluation metrics.
Article
Computer Science, Artificial Intelligence
Abolfazl Maleki, Erfan Nejati, Amir Aghsami, Fariborz Jolai
Summary: Bike-Sharing Systems have gained popularity worldwide due to their positive impact on traffic, pollution, and public health. However, traditional simulation-based methods are inefficient for real-time decision-making in large-scale and complex systems. This paper introduces a Supervised Learning-Based Simulation method as an alternative to address the rebalancing problem in bike-sharing systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
