Article
Management
Karim Tamssaouet, Stephane Dauzere-Peres
Summary: This article presents a framework that unifies and generalizes well-known literature results on local search for job-shop and flexible job-shop scheduling problems. The proposed framework focuses on quickly ruling out infeasible moves and evaluating the quality of feasible neighbors, which are crucial for the success of local search approaches. It can be applied to any scheduling problem with an appropriate defined neighborhood structure. The proposed framework introduces novel procedures for evaluating feasibility and estimating the value of objective functions for neighbor solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper investigates the distributed flow shop scheduling problem in heterogeneous multi-factories and proposes a mixed-integer linear programming model and a multi-local search algorithm to solve it. The effectiveness and efficiency of the proposed methods are demonstrated through experiments.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Interdisciplinary Applications
Jin Xie, Xinyu Li, Liang Gao, Lin Gui
Summary: This paper proposes a hybrid algorithm combining genetic algorithm and tabu search to solve job shop scheduling problems. The evaluation of famous benchmark instances shows that the algorithm outperforms other methods in terms of computational efficiency and solution quality.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Cuiyu Wang, Li Zhao, Xinyu Li, Yang Li
Summary: This paper proposes a model and algorithm for the welding shop inverse scheduling problem (WSISP) and validates the effectiveness of the proposed method through experiments.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Industrial
Jin Xie, Xinyu Li, Liang Gao, Lin Gui
Summary: This paper proposes a hybrid genetic tabu search algorithm for the distributed flexible job-shop scheduling problem, which outperforms other comparison algorithms in terms of solution quality and computation efficiency.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Xiaotong Hu, Ling Wang, Zekai Li
Summary: The paper proposes a MDDE algorithm to solve the distributed permutation flow shop scheduling problem, with improved efficiency through optimization of NEH method, Taillard acceleration method, discrete mutation strategy, and neighborhood structures. The experimental results demonstrate the effectiveness of the algorithm in solving the DPFSP.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiuli Wu, Xiaoyan Yan, Ling Wang
Summary: This study focuses on optimizing job release and scheduling in a reentrant hybrid flow shop, proposing a mathematical model for minimizing makespan and total energy consumption. An improved multi-objective evolutionary algorithm is introduced to effectively solve this strongly NP-hard problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Nayeli Jazmin Escamilla Serna, Juan Carlos Seck-Tuoh-Mora, Joselito Medina-Marin, Norberto Hernandez-Romero, Irving Barragan-Vite, Jose Ramon Corona Armenta
Summary: The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that has been extensively studied to model and optimize more complex situations reflecting the current needs of the industry. This work introduces a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), which utilizes the concepts of a cellular automaton to generate and share information among a set of leading solutions called smart-cells. Experimental results demonstrate the satisfactory performance of the GLNSA algorithm when compared with recent algorithms, using four benchmark sets and 101 test problems.
PEERJ COMPUTER SCIENCE
(2021)
Article
Automation & Control Systems
Chunjiang Zhang, Yin Zhou, Kunkun Peng, Xinyu Li, Kunlei Lian, Suyan Zhang
Summary: This paper proposes a dynamic scheduling framework based on an improved gene expression programming algorithm to address the dynamic flexible job shop scheduling problem considering setup time and random job arrival. Experimental results demonstrate that the improved gene expression programming outperforms standard gene expression programming, genetic programming, and scheduling rules.
MEASUREMENT & CONTROL
(2021)
Article
Operations Research & Management Science
Moussa Abderrahim, Abdelghani Bekrar, Damien Trentesaux, Nassima Aissani, Karim Bouamrane
Summary: This paper addresses the problem of job assignment in a job-shop manufacturing system and proposes an improved algorithm to minimize the maximum completion time of a job set. Experimental tests demonstrate the effectiveness of the proposed approach.
OPTIMIZATION LETTERS
(2022)
Review
Computer Science, Information Systems
Mei Li, Gai-Ge Wang
Summary: The manufacturing industry plays a crucial role in a country's productivity level and economic development, but it also brings about environmental pollution and resource scarcity. Therefore, researching Green Shop Scheduling Problems (GSSPs) has become an important topic, aiming to reduce resource consumption and environmental pollution while achieving economic benefits through behavior control. In the context of Industry 4.0, GSSPs require re-examination and study.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Alexander Kinast, Roland Braune, Karl F. Doerner, Stefanie Rinderle-Ma, Christian Weckenborg
Summary: A hybrid genetic algorithm was proposed to solve the task shop scheduling problem with cobots allocation, demonstrating that deploying additional robots can significantly improve production efficiency.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2022)
Article
Computer Science, Artificial Intelligence
Radoslaw Rudek
Summary: This study introduces a fast insert neighborhood search (FINS) for scheduling problems with general learning curves, which significantly outperforms traditional methods.
Comparing to other algorithms, the FINS method is 120 times faster and improves criterion values by up to 25% for instances with 100-800 jobs and 5-80 machines.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Industrial
Jiaxin Fan, Weiming Shen, Liang Gao, Chunjiang Zhang, Ze Zhang
Summary: The paper proposes a hybrid Jaya algorithm integrated with Tabu search to solve the flexible job shop scheduling problem, addressing the issue of multiple critical paths. By utilizing two Jaya operators and three approaches during the local search phase, the algorithm demonstrates superiority in both optimality and stability on various benchmark sets for FJSP.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Mathematics
Yuanfei Wei, Zalinda Othman, Kauthar Mohd Daud, Shihong Yin, Qifang Luo, Yongquan Zhou
Summary: In this paper, a hybrid algorithm called EOSMA is proposed to solve the Job Shop Scheduling Problem (JSSP). The algorithm combines the update strategy of Equilibrium Optimizer (EO) with Slime Mould Algorithm (SMA) to achieve a better balance between exploration and exploitation. The addition of Centroid Opposition-based Computation (COBC) improves exploration and exploitation, increases population diversity, enhances convergence speed and accuracy, and prevents falling into local optima. The algorithm also introduces a Sort-Order-Index (SOI)-based coding method and a neighbor search strategy to improve the efficiency of solving JSSP. Experimental results and statistical analysis demonstrate that EOSMA outperforms other competing algorithms.
Article
Computer Science, Artificial Intelligence
Keyvan Kamandanipour, Mohammad Mahdi Nasiri, Dincer Konur, Siamak Haji Yakhchali
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Engineering, Multidisciplinary
Hamidreza Arbabi, Mohammad Mahdi Nasiri, Ali Bozorgi-Amiri
Summary: A hub-and-spoke architecture for a parcel delivery system is proposed in this article, considering various real-world assumptions. A novel algorithm is developed to solve the problem, with a comprehensive computational analysis to validate its performance.
ENGINEERING OPTIMIZATION
(2021)
Article
Psychology, Multidisciplinary
Mohammadali Amini-Tehrani, Mohammad Nasiri, Tina Jalali, Raheleh Sadeghi, Mehri Mehrmanesh, Hadi Zamanian
Summary: This study developed and validated a questionnaire on relational adverse childhood experiences in the context of home and school. The questionnaire showed good reliability and validity, making it a useful tool for studying relational adverse childhood experiences.
CURRENT PSYCHOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Mohammad Mahdi Nasiri, Naeime Ahmadi, Dincer Konur, Ali Rahbari
Summary: The study focuses on cross-docking systems and investigates the problem of rescheduling at a cross-dock facility. A predictive-reactive rescheduling system is proposed to handle uncertainties in truck arrival times. The system consists of a rescheduling optimization model and a short interval repair policy. Computational experiments are performed to analyze and compare the performance of different repair policies.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Green & Sustainable Science & Technology
Alireza Shahedi, Mohammad Mahdi Nasiri, Mohamad Sadegh Sangari, Frank Werner, Fariborz Jolai
Summary: This study aims to develop a sustainable closed-loop supply chain network model for the automotive industry and formulates three objective functions based on sustainability criteria. A case study of Iran's automotive industry is used for validation, and a scenario-based approach using stochastic programming is applied to handle uncertainties. The results show that the stochastic programming approach is successful in mitigating the effects of uncertainties and the preferred Pareto optimal solution achieves a significant decrease in environmental impact with minimal increase in economic value.
PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY
(2022)
Article
Computer Science, Artificial Intelligence
Mojtaba Ranjbar, Mohammad Mahdi Nasiri, S. Ali Torabi
Summary: This study utilizes a fuzzy hybrid multi-criteria method and a fuzzy bi-objective mathematical programming model to address the project portfolio selection and scheduling problem, optimizing the project portfolio through weighted qualitative criteria and a bi-objective fuzzy mathematical model.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Thermodynamics
Mohammad Mahdi Nasiri, Ali Dolatabadi, Christian Moreau
Summary: The flattening process of a droplet impacting a solid surface is crucial in various industrial applications. In this study, a numerical model is developed to investigate the formation of fragmented splats during droplet flattening and solidification in plasma spraying conditions. The numerical results show that gas desorption from the surface produces a barrier layer, affecting the heat transfer and spreading of the droplet.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2022)
Article
Construction & Building Technology
Maedeh Motalebi, Ali Rashidi, Mohammad Mahdi Nasiri
Summary: This study presents a framework that integrates mathematical optimization, BIM, and LCA to enhance the energy efficiency of existing buildings. The results show that improving building envelopes and evaporative coolers can significantly reduce energy consumption.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Operations Research & Management Science
Fatemeh Faghih-Mohammadi, Mohammad Mahdi Nasiri, Dincer Konur
Summary: In an emergency logistics operation, quick delivery and fair distribution of relief items are crucial. Direct shipments from suppliers to affected areas may be challenging due to truck scarcity and underutilization. Warehouses or cross-docks can help overcome these challenges. This research incorporates opportunistic cross-docking into emergency logistics operations to improve the efficiency of relief item delivery.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Environmental Sciences
Kannan Govindan, Saeede Nosrati-Abarghooee, Mohammad Mahdi Nasiri, Fariborz Jolai
Summary: Proper management of medical waste is crucial for environmental protection and public health. This paper introduces a novel circular economy transition model for medical waste management, aiming to minimize cost and population risk by optimizing the uncertainty in waste generation and treatment.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
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, Artificial Intelligence
Saeede Nosrati-Abarghooee, Mohammad Sheikhalishahi, Mohammad Mahdi Nasiri, Seyed Mohammad Gholami-Zanjani
Summary: Population growth and disruptions caused by COVID-19 have increased the demand for medical services, resulting in more medical waste generation. This paper proposes a mathematical model for designing a reverse logistics network to manage healthcare waste under uncertainty and epidemic disruptions. The model aims to minimize costs and population risk simultaneously. The effectiveness of the proposed model is confirmed through sensitivity analysis. Rating: 7/10.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohammad Amin Amani, Mohammad Mahdi Nasiri
Summary: In this article, a new cross-docking approach with paired-doors and preemption is proposed based on the SDG12 paradigm. It is suitable for distributing perishable products due to their time-sensitive nature. The proposed approach is compared with the conventional approach and shown to be faster in transferring products. Additionally, a predictive model is built using a machine learning algorithm to predict the makespan with an average accuracy of 92.8%.
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2023)
Proceedings Paper
Automation & Control Systems
Ahmad Ghasemkhani, Reza Tavakkoli-Moghaddam, Mahdi Hamid, Mohammad Mahdi Nasiri
Summary: This study utilizes a bi-objective linear model to minimize makespan and cost in the stage shop problem by allocating jobs to humans and robots. An interactive method is used to convert the model into a single objective one. Sensitivity results demonstrate that human-robot collaboration significantly reduces makespan.
Article
Engineering, Multidisciplinary
M. M. Nasiri, M. Hamid
Article
Computer Science, Interdisciplinary Applications
Xiaolin Wang, Liyi Zhan, Yong Zhang, Teng Fei, Ming-Lang Tseng
Summary: This study proposes an environmental cold chain logistics distribution center location model to reduce transportation costs and carbon emissions. It also introduces a hybrid arithmetic whale optimization algorithm to overcome the limitations of the conventional algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hong-yu Liu, Shou-feng Ji, Yuan-yuan Ji
Summary: This study proposes an architecture that utilizes Ethereum to investigate the production-inventory-delivery problem in Physical Internet (PI), and develops an iterative heuristic algorithm that outperforms other algorithms. However, due to gas prices and consumption, blockchain technology may not always be the optimal solution.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Paraskevi Th. Zacharia, Elias K. Xidias, Andreas C. Nearchou
Summary: This article discusses the assembly line balancing problem in production lines with collaborative robots. Collaborative robots have the potential to improve automation, productivity, accuracy, and flexibility in manufacturing. The article explores the use of a problem-specific metaheuristic to solve this complex problem under uncertainty.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
