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
Computer Science, Artificial Intelligence
Shih-Wei Lin, Chen-Yang Cheng, Pourya Pourhejazy, Kuo-Ching Ying
Summary: Scheduling problems are crucial in modern manufacturing, and an improved meta-heuristic algorithm, MTSA, has been proposed for Permutation Flowshop Scheduling Problem with Mixed-Blocking Constraints, outperforming existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(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
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
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, Artificial Intelligence
Chin-Chia Wu, Win-Chin Lin, Xin-Gong Zhang, Dan-Yu Bai, Yung-Wei Tsai, Tao Ren, Shuenn-Ren Cheng
Summary: This study explores the importance of setup times in scheduling decisions in real-world industrial environments, as well as the issue of job processing times in the face of various uncertainties. By introducing a single-machine scheduling problem and various algorithms, and optimizing solutions to find the best approach.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
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
Operations Research & Management Science
Muberra Allahverdi
Summary: This paper addresses the two-machine flowshop scheduling problem with total tardiness as the performance measure. The problem is addressed for the first time in this paper and nineteen algorithms are proposed. The algorithms are extensively evaluated and compared to a random solution, showing significant improvement in performance. The best algorithm reduces the error by 99.99% compared to the random solution and is recommended based on hypothesis testing.
RAIRO-OPERATIONS RESEARCH
(2023)
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, Artificial Intelligence
Wei Fang, Haolin Zhu, Yi Mei
Summary: This study proposes a hybrid meta-heuristic method based on LA-ALNS and tabu search to solve an unrelated parallel machine scheduling problem aiming to minimize the makespan. The proposed method achieves a balance between search efficiency and solution quality by guiding exploration, alleviating the short-term cycle, and designing local search operators. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics, Applied
Wanlei Wang
Summary: This paper investigates the single-machine due-date assignment problem with past-sequence-dependent setup times, proposing optimal solutions under different due-date assignment scenarios. The problem is proven to be solvable in polynomial time by minimizing a linear weighted sum. Furthermore, three extensions are provided by considering various dependencies in processing times.
JOURNAL OF APPLIED MATHEMATICS AND COMPUTING
(2022)
Article
Engineering, Industrial
Pourya Pourhejazy, Chen-Yang Cheng, Kuo-Ching Ying, Su-Yuan Lin
Summary: This study contributes to the literature of distributed scheduling by developing an original Mixed-Integer Linear Programming (MILP) formulation and extending the Iterated Greedy algorithm to solve the Distributed Two-Stage Assembly Flowshop Scheduling Problem with Sequence-Dependent Setup Times. Extensive numerical tests show that the Improved Iterated Greedy (IIG) algorithm yields the best solution in large-scale instances, with statistical analysis confirming its superiority over other algorithms.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
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
Engineering, Multidisciplinary
Keyvan Kamandanipour, Siamak Haji Yakhchali, Reza Tavakkoli-Moghaddam
Summary: This article proposes a data-driven ticket dynamic pricing methodology for passenger railway service providers. The methodology aims to maximize revenue under constrained train capacity by using machine learning and optimization tools. The results of the study indicate that the proposed methodology has the potential to improve service provider's revenue.
ENGINEERING OPTIMIZATION
(2023)
Review
Engineering, Industrial
Sohrab Faramarzi-Oghani, Parisa Dolati Neghabadi, El-Ghazali Talbi, Reza Tavakkoli-Moghaddam
Summary: This article provides a literature review on the application of meta-heuristic algorithms in sustainable supply chain management (SSCM), based on the analysis of 160 selected papers. The findings show a significant growth in research in this field in recent years, with hybrid meta-heuristics overtaking pure meta-heuristics. The genetic algorithm (GA) and the non-dominated sorting GA (NSGA-II) are the most commonly used single- and multi-objective algorithms. The study also highlights the importance of addressing sustainability aspects in product distribution and vehicle routing, as well as the growing attention to the economic-environmental category of sustainability.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Hamidreza Arbabi, Ali Bozorgi-Amiri, Reza Tavakkoli-Moghaddam
Summary: This paper presents a novel cloud manufacturing system that integrates distributed manufacturing enterprises to collaborate in a dynamic environment. The paper develops a configuration design and capacity planning problem for the system, considering the dynamic nature of service providers and demand. A multi-objective mathematical model is proposed and extensions of a grey wolf optimiser algorithm are devised to solve large-scale instances. Computational experiments and sensitivity analysis provide insights for managers.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Operations Research & Management Science
Kamyab Karimi, Ali Ghodratnama, Reza Tavakkoli-Moghaddam
Summary: Breast cancer has become a leading cause of mortality among women in recent decades. Machine learning can be used to improve treatment outcomes and reduce costs and time. This research proposes two novel feature selection methods based on imperialist competitive algorithm and bat algorithm, aiming to enhance diagnostic models' efficiency.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Engineering, Biomedical
Fatemeh Hamedani-KarAzmoudehFar, Reza Tavakkoli-Moghaddam, Amir Reza Tajally, Seyed Sina Aria
Summary: Deep learning-based approaches have achieved significant success in medical fields, but there are challenges in quantifying the uncertainty of their outputs. This study proposes three uncertainty quantification models for the classification of breast tumor tissue types. The analysis demonstrates that these models show high uncertainty in misclassifications, which is crucial for assessing medical diagnosis risks. The Bayesian Ensemble model shows more reliable uncertainty quantification.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Operations Research & Management Science
Keyvan Kamandanipour, Siamak Haji Yakhchali, Reza Tavakkoli-Moghaddam
Summary: This study proposes an intelligent decision support system by integrating dynamic pricing, fleet management, and capacity allocation for passenger rail service providers, aiming to improve their profitability. Travel demand and price-sale relations are quantified based on historical sales data. A mixed-integer non-linear programming model is presented to maximize the company's profit considering various cost types in a multi-train multi-class multi-fare passenger rail transportation network. A fix-and-relax heuristic algorithm is applied for large-scale problems. Real numerical cases show that the proposed model has a high potential to improve total profit compared to current sales policies.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Ali Ghodratnama, Mehdi Amiri-Aref, Reza Tavakkoli-Moghaddam
Summary: This paper presents a bi-objective mathematical model for a hybrid flow shop scheduling problem with robots and fuzzy maintenance time. The model aims to minimize the mean completion time of jobs and the total cost of processes based on maintenance and transportation costs involving robots. Two multi-objective decision-making approaches, LP-metric and goal attainment, are employed to solve the deterministic-equivalent problem formulated using a fuzzy chance-constrained programming model. Sensitivity analysis of objective function weights is conducted, and the efficiency of the proposed solution approaches is evaluated using the TOPSIS method. Future research areas are suggested.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Elmira Gheisariha, Farhad Etebari, Behnam Vahdani, Reza Tavakkoli-Moghaddam
Summary: This study introduces an unprecedented integrated supply-production-distribution problem in the dairy industry, addressing various planning aspects at each level. A mathematical model is developed to minimize the total cost of the supply chain, considering a wide range of decisions and realistic concerns. Robust optimization technique is applied to handle supply and demand uncertainties. A real case study validates the proposed model and approach.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Hadi Abdollahzadeh-Sangroudi, Elham Moazzam-Jazi, Reza Tavakkoli-Moghaddam, Mehdi Ranjbar-Bourani
Summary: This paper proposes a dynamic opportunistic approach to model maintenance planning for a job-shop production system. It addresses the issue of economic dependency in such a system, where maintenance activities can either decrease or increase system costs. Unlike many existing maintenance models that only consider long-term planning, this approach takes into account short-term and real circumstances, such as system characteristics, workload, and available maintenance resources. A mathematical model is developed to simultaneously make decisions on maintenance selection, grouping, lot sizing, and production scheduling, with the objective of minimizing costs. The proposed model is solved using a self-adaptive Cuckoo Optimization Algorithm, and numerical experiments are conducted to validate its effectiveness.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Cybernetics
Mahdi Bastan, Reza Tavakkoli-Moghaddam, Ali Bozorgi-Amiri
Summary: Commercial banks face various risks, and crises and disasters can worsen these risks and lead to severe damage and bankruptcy. This study aims to assess the effectiveness of business continuity management policies in achieving resilient banking through a simulation model.
Article
Energy & Fuels
Maryam Arabahmadi, Amirabbas Shojaie, Reza Tavakkoli-Moghaddam, Shahrzad Majdabadi Farahani, Hassan Javanshir
Summary: This study aims to predict the number of accidents in the National Iranian Oil Products Distribution Company (NIOPDC) in 2022 using artificial neural network models. The results show that the NARX network has higher prediction accuracy compared to the MLP network.
PETROLEUM SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Industrial
Keivan Tafakkori, Fariborz Jolai, Reza Tavakkoli-Moghaddam
Summary: This paper presents decentralized capacity planning models for different types of supply chain entities, aiming to enhance their resilience. Novel resilience metrics are developed to measure the proximity of capacities to disruptions, and optimization models are used to select business continuity plans that maximize resilience and cost-efficiency. Uncertainties associated with recovery time and disruptions are addressed using a robust-stochastic optimization method, and disruption scenarios are simulated using a discrete-time Markov chain. Computational tests confirm the robustness, validity, and generality of the proposed models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Keyvan Kamandanipour, Reza Tavakkoli-Moghaddam, Siamak Haji Yakhchali
Summary: This paper focuses on the discrete time/resource trade-off problem (DTRTP) in the context of resource-constrained project scheduling. A mathematical model is presented for the DTRTP with renewable resource types, and a hybrid heuristic/meta-heuristic algorithm is proposed to solve the deterministic model. The proposed approach is evaluated using numerical examples and compared with existing optimization tools, showing promising results in terms of performance and handling uncertainty.
Article
Economics
Abolghasem Yousefi-Babadi, Ali Bozorgi-Amiri, Reza Tavakkoli-Moghaddam, Kannan Govindan
Summary: With the passage of time, the grain supply chain may become sub-optimal and disrupted, necessitating a redesign to avoid irreversible costs. This paper presents a redesign of the wheatflour-bread supply chain network considering sustainable development under uncertainty. The model addresses economic, environmental, social, and technical uncertainties using a robust optimization method, resulting in enhanced objective functions.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Environmental Sciences
Arezoo Dahesh, Reza Tavakkoli-Moghaddam, AmirReza Tajally, Aseman Erfani-Jazi, Milad Babazadeh-Behestani
Summary: A robust model using machine learning algorithms was built to classify subscribers in Tehran Province Water and Wastewater (TPWW) and identify high-consumption subscribers. The random forest algorithm was considered the best model with high accuracy in train and test.
WATER AND ENVIRONMENT JOURNAL
(2023)
