Article
Computer Science, Artificial Intelligence
Arash Amirteimoori, Erfan Babaee Tirkolaee, Vladimir Simic, Gerhard-Wilhelm Weber
Summary: A novel Parallel Two-Step Decomposition-Based Heuristic (PTSDBH) and Mixed Integer Linear Programming (MILP) have been developed to address the concurrent scheduling of jobs and Automated Guided Vehicles (AGVs) in a hybrid job shop system. The importance of conflict-free AGV routing has not been emphasized in previous studies, but it is crucial for preventing system breakdowns. PTSDBH, using parallel computing, is effective for solving large-sized problems quickly. Comparison with metaheuristics shows that PTSDBH outperforms them in terms of objective value quality, while TSDBH has higher runtimes due to its use of a single core. Statistical analysis confirms the superiority of PTSDBH and TSDBH over metaheuristics in terms of objective values.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Sezin Afsar, Camino R. Vela, Juan Jose Palacios, Ines Gonzalez-Rodriguez
Summary: The fuzzy job shop scheduling problem with makespan minimisation has been extensively studied, but little work has been done on proposing and solving mathematical models for this problem. This has resulted in a lack of understanding of the problem's hardness and absence of reliable lower and upper bounds. In this study, two mathematical models are proposed and solved, and a thorough analysis on scalability is carried out. The use of different solvers improves known bounds and enables a structural characterization of the instances' hardness.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Atefeh Momenikorbekandi, Maysam F. Abbod
Summary: Metaheuristics are computational procedures developed to find optimal solutions to optimization problems. This paper presents a novel metaheuristic hybrid Parthenogenetic Algorithm (NMHPGA) for optimizing job shop scheduling problems in single-machine and multi-machine job shops and a furnace model. The NMHPGA achieves better objective functions and faster convergence speed compared to other selection operators.
Article
Management
Willian T. Lunardi, Ernesto G. Birgin, Debora P. Ronconi, Holger Voos
Summary: This work investigates the online printing shop scheduling problem, proposing a local search strategy and metaheuristics which have been shown through extensive numerical experiments to be suitable for solving practical instances and competitive in classical instances of the problem.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Arash Amirteimoori, Iraj Mahdavi, Maghsud Solimanpur, Sadia Samar Ali, Erfan Babaee Tirkolaee
Summary: This paper proposes a Mixed-Integer Linear Programming (MILP) model to schedule jobs and transporters in a flexible flow shop system simultaneously. By employing parallel computing methods, the run time can be significantly reduced. The results show that the PPSOGA algorithm outperforms other algorithms in terms of solution quality, efficiency, and reliability.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Automation & Control Systems
Gai-Ge Wang, Da Gao, Witold Pedrycz
Summary: The job-shop scheduling problem is of great practical significance, but is difficult to solve due to many uncontrollable factors. The introduction of fuzzy processing time and completion time allows for a more comprehensive scheduling model, which can be optimized using a hybrid adaptive differential evolution algorithm. Experimental results show that this algorithm outperforms other state-of-the-art algorithms.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Management
Karim Tamssaouet, Stephane Dauzere-Peres, Sebastian Knopp, Abdoul Bitar, Claude Yugma
Summary: This paper addresses a multiobjective complex job-shop scheduling problem in semiconductor manufacturing by extending a batch-oblivious approach, introducing a criterion for production target satisfaction and a preference model. The proposed approach provides good solutions and significant improvements compared to actual factory schedules.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
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
Automation & Control Systems
Raul Mencia, Carlos Mencia, Ramiro Varela
Summary: We address the task of repairing infeasibility in job shop scheduling problems with a hard constraint on the maximum makespan. By adopting a job-based view of repairs and proposing enhancements to a genetic algorithm, we aim to improve efficiency and effectiveness in solving the problem. The proposed methods show significant improvements in experimental results.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Cunli Song
Summary: This study investigates an energy-oriented scheduling problem derived from a hybrid flow shop with unrelated parallel machines. A hybrid multi-objective teaching-learning based optimization algorithm is proposed, which effectively reduces standby and turning on/off energy consumption, improves algorithm convergence speed, and enhances exploration and exploitation capabilities. Experimental results across 15 cases verify the effectiveness and superiority of the proposed algorithm.
Article
Computer Science, Artificial Intelligence
Mehmet Akif Sahman, Sedat Korkmaz
Summary: The Job-Shop Scheduling Problem is a complex optimization problem that can be solved using heuristic algorithms. This study proposes a new version of the Artificial Algae Algorithm and integrates three different encoding schemes with this algorithm to solve high-dimensional Job-Shop Scheduling Problems. Through comparison and analysis, it is found that integrating the Smallest Position Value encoding scheme into the Artificial Algae Algorithm produces the best makespan value results.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Jesus Para, Javier Del Ser, Antonio J. Nebro
Summary: This paper provides an in-depth review and analysis of multi-objective job shop scheduling optimization, with a focus on minimizing energy consumption. Through performance comparisons across various algorithms and synthetic test instances, the paper offers insights for good practices and further improvements in this vibrant research area.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Yarong Chen, Ya-Chih Tsai, Fuh-Der Chou
Summary: This paper focuses on the hybrid flow shop scheduling problem and proposes a new mixed integer programming model and two new lower bounds based on the bin-packing concept. The proposed model is compared with existing models using two sets of small and small-to-medium problems, and the effectiveness of the proposed lower bound is also demonstrated.
Article
Computer Science, Information Systems
Andrea Corsini, Simone Calderara, Mauro Dell'Amico
Summary: In recent years, the optimization community has shown increasing interest in leveraging Machine Learning (ML) to enhance algorithm design. Research focusing on using ML to predict the quality of machine permutations has led to improvements in the performance of algorithms like Tabu Search, showcasing the value of such predictive modeling techniques.
Article
Automation & Control Systems
Pengyu Zhang, Shiji Song, Shengsheng Niu, Rui Zhang
Summary: In this article, the multiroute job shop scheduling problem with continuous-limited output buffers (MRJSP-CLOBs) is studied, and a hybrid algorithm AIA-SA is proposed, which shows lower computing time and faster and more accurate performance in large-scale instances compared to other algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Operations Research & Management Science
Eduardo Segredo, Ben Paechter, Carlos Segura, Carlos I. Gonzalez-Vila
OPTIMIZATION LETTERS
(2018)
Article
Green & Sustainable Science & Technology
Dennis Wilson, Silvio Rodrigues, Carlos Segura, Ilya Loshchilov, Frank Hutter, Guillermo Lopez Buenfil, Ahmed Kheiri, Ed Keedwell, Mario Ocampo-Pineda, Ender Ozcan, Sergio Ivvan Valdez Pena, Brian Goldman, Salvador Botello Rionda, Arturo Hernandez-Aguirre, Kalyan Veeramachaneni, Sylvain Cussat-Blanc
Article
Operations Research & Management Science
Joel Chacon Castillo, Carlos Segura
OPTIMIZATION LETTERS
(2020)
Article
Physics, Multidisciplinary
Isaac Lopez-Lopez, Guillermo Sosa-Gomez, Carlos Segura, Diego Oliva, Omar Rojas
Article
Mathematics
Jose Alejandro Cornejo-Acosta, Jesus Garcia-Diaz, Julio Cesar Perez-Sansalvador, Carlos Segura
Summary: The paper introduces novel compact integer programs for the depot-free multiple traveling salesperson problem (DFmTSP). These programs are adapted to different variants of the DFmTSP and have been empirically tested to show their theoretical and practical advantages over the state of the art.
Article
Mathematics
Alejandro Marrero, Eduardo Segredo, Coromoto Leon, Carlos Segura
Proceedings Paper
Engineering, Electrical & Electronic
Carlos Segura, Gara Miranda, Eduardo Segredo, Joel Chacon
2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2019)
Article
Computer Science, Information Systems
Eduardo Segredo, Gabriel Luque, Carlos Segura, Enrique Alba
Proceedings Paper
Computer Science, Artificial Intelligence
Joel Chacon, Carlos Segura
2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Nayeli Angel, Carlos Segura, Oscar Dalmau Cedeno
2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Ricardo Nieto-Fuentes, Carlos Segura, S. Ivvan Valdez
2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2018)
Article
Computer Science, Information Systems
Emmanuel Romero Ruiz, Carlos Segura
COMPUTACION Y SISTEMAS
(2018)
Article
Computer Science, Information Systems
Sergio Alvarado, Carlos Segura, Oliver Schutze, Saul Zapotecas
COMPUTACION Y SISTEMAS
(2018)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Carlos Segura, Eduardo Segredo, Gara Miranda
2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2017)