Article
Engineering, Industrial
Hamid Safarzadeha, Seyed Taghi Akhavan Niakia
Summary: This paper investigates a general unrelated parallel machine scheduling problem with machine processing cost and proposes a mathematical programming approach to solve it. A multiobjective solution procedure is proposed to generate Pareto optimal solutions, and the performance of the approach is evaluated through comprehensive numerical experiments. The results demonstrate that the mathematical programming solution approach is effective in solving the problem.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2022)
Article
Multidisciplinary Sciences
Gulcin Bektur
Summary: This study focuses on an energy-efficient unrelated parallel machine scheduling problem, incorporating speed scaling as an energy-efficient strategy. It proposes a multiobjective MILP model and a NSGA-II-based memetic algorithm for the problem.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Industrial
Xin Feng, Hongjun Peng
Summary: This study investigates the robust identical parallel machine scheduling problem with a two-stage TOU tariff and NAM option, aiming to improve energy efficiency in the manufacturing industry by regulating the electricity imbalance between supply and demand. The problem is formulated into a min-max regret model to maximize robustness, and both an iterative relaxation-based exact algorithm and a memetic differential evolution-based heuristic are developed to solve the problem. Computational experiments are conducted on randomly generated instances with up to 20 jobs and large-sized instances with up to 150 jobs to evaluate the performance of the developed methods and identify managerial insights for achieving energy-efficient schedules.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Qihao Liu, Xinyu Li, Liang Gao, Guangchen Wang
Summary: This paper studies the multiobjective distributed integrated process planning and scheduling problem, establishing a mixed-integer linear programming model and proposing a new encoding method based on the process network graph and a multiobjective memetic algorithm to solve the problem. The algorithm introduces a simulated annealing mechanism to avoid falling into a local optimum.
Article
Computer Science, Interdisciplinary Applications
Victor Abu-Marrul, Rafael Martinelli, Silvio Hamacher, Irina Gribkovskaia
Summary: This paper addresses a variant of a batch scheduling problem with identical parallel machines and non-anticipatory family setup times. New methods have been developed to overcome current solution approaches and provide improved results for ship scheduling problems, achieving a reduction of more than 10% in the objective function.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Management
Izack Cohen, Krzysztof Postek, Shimrit Shtern
Summary: Real-life parallel machine scheduling problems have limited information about task duration at scheduling time and allow rescheduling of tasks when a machine becomes idle. This paper proposes an adaptive robust optimization scheduling approach that considers the possibility of adjusting scheduling decisions based on new information. The approach leads to better immediate decisions and improved makespan guarantees. A mixed integer linear programming model and a two-stage approximation heuristic are developed to minimize the worst-case makespan. Numerical study results show that adaptive scheduling achieves solutions with better and more stable makespan realizations compared to static approaches.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Davi Mecler, Victor Abu-Marrul, Rafael Martinelli, Arild Hoff
Summary: This paper addresses a complex parallel machine scheduling problem with jobs divided into operations and operations grouped in families. The proposed algorithms outperform existing methods in known benchmark instances and provide new upper bounds for some instances. Furthermore, variants using a greedy repair operator find more than 70% of the best solutions in more challenging instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Yujie Xiao, Yan Zheng, Yuantang Yu, Lianmin Zhang, Xiaowei Lin, Bin Li
Summary: The concept of green manufacturing creating social value has gained global attention for its potential to reduce carbon emissions and protect the environment. The study focuses on the parallel machine scheduling problem (PMSP) in manufacturing, highlighting the need to consider social value alongside traditional efficiency measures. A model incorporating energy costs and social value is proposed, with a branch and bound algorithm developed to efficiently solve the problem and tested in computational experiments with data from an auto parts manufacturer.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Automation & Control Systems
Gilberto Rivera, Raul Porras, J. Patricia Sanchez-Solis, Rogelio Florencia, Vicente Garcia
Summary: This paper introduces a novel metaheuristic called Outranking-based Particle Swarm Optimization (O-PSO) for addressing the multi-objective Unrelated Parallel Machine Scheduling Problem. O-PSO is an optimization algorithm that combines particle swarm optimization with the preferences of the Decision Maker (DM) expressed in a fuzzy relational system based on ELECTRE III. Unlike other multi-objective metaheuristics, O-PSO focuses on finding the Region of Interest (RoI) instead of approximating a sample of the complete Pareto frontier. The efficiency of O-PSO is validated through experiments on synthetic instances and a real-world case study, showing its capability of generating high-quality solutions and supporting multicriteria decision analysis.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Theory & Methods
Aldenio Burgos, Eduardo Alchieri, Fernando Dotti, Fernando Pedone
Summary: State machine replication (SMR) is a fault-tolerant approach to provide high availability and strong consistency in services. To improve performance, two classes of protocols have been proposed, with each having its own advantages and disadvantages depending on the workload characteristics. To reconcile the advantages, a hybrid scheduling technique has been introduced, which demonstrated improved system performance by up to 3x compared to individual protocols in a workload with conflicting commands.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Mathematics
Raul Mencia, Carlos Mencia
Summary: This paper introduces new memetic algorithms to solve the problem of scheduling a set of jobs on a machine with time-varying capacity, with new local search procedures that enable the algorithms to achieve higher-quality solutions.
Article
Computer Science, Artificial Intelligence
Francisco J. Gil-Gala, Maria R. Sierra, Carlos Mencia, Ramiro Varela
Summary: The proposed Memetic Algorithm combines Genetic Program and Local Search algorithm to evolve priority rules for scheduling a set of jobs on a machine with time-varying capacity, with specifically designed neighborhood structures for the problem. Experimental results demonstrate that proper selection and combination of neighborhood structures allow the Memetic Algorithm to outperform previous approaches to the same problem.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Hua Wang, Rui Li, Wenyin Gong
Summary: This work aims to solve the distributed heterogeneous unrelated parallel machine scheduling problem with minimizing total tardiness and makespan. A knowledge and Pareto-based memetic algorithm (KPMA) is proposed, which includes heuristic rules, heuristic neighborhood structures, and an elite strategy. Numerical experiments show that KPMA has better performance and a strong ability to solve DHUPMSP compared to other state-of-art methods.
EGYPTIAN INFORMATICS JOURNAL
(2023)
Article
Operations Research & Management Science
Victor Abu-Marrul, Rafael Martinelli, Silvio Hamacher, Irina Gribkovskaia
Summary: This paper addresses a parallel machine scheduling problem with non-anticipatory family setup times and batching. It proposes an Iterated Greedy simheuristic with built-in Monte Carlo Simulation to handle the stochastic parameters. Experimental results show that the proposed simheuristic outperforms other algorithms in terms of both objective values and computational times.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Mathematics
Dung-Ying Lin, Tzu-Yun Huang
Summary: In this study, we propose a population-based simulated annealing algorithm embedded with a variable neighborhood descent technique to solve the unrelated parallel machine scheduling problem with sequence-dependent setup times. Empirical results show that this solution strategy outperforms a commonly used commercial optimization package and provides better schedules in a more efficient manner.
Article
Ecology
Francisco P. Vergara, Cristian D. Palma, Hector Sepulveda
Article
Forestry
Cristian D. Palma, Francisco P. Vergara
Article
Forestry
Cristian D. Palma, John D. Nelson
CANADIAN JOURNAL OF FOREST RESEARCH
(2009)
Article
Forestry
Mauricio A. Acuna, Cristian D. Palma, Wenbin Cui, David L. Martell, Andres Weintraub
CANADIAN JOURNAL OF FOREST RESEARCH
(2010)
Article
Forestry
Cristian D. Palma, John D. Nelson
EUROPEAN JOURNAL OF FOREST RESEARCH
(2010)
Article
Forestry
Cristian D. Palma, John D. Nelson
Article
Forestry
Francisco P. Vergara, Cristian D. Palma, John Nelson
Article
Environmental Sciences
Mathias Kuschel-Otarola, Diego Rivera, Eduardo Holzapfel, Cristian D. Palma, Alex Godoy-Faundez
Article
Forestry
Sattar Ezzati, Cristian D. Palma, Pete Bettinger, Ljusk Ola Eriksson, Anjali Awasthi
Summary: This study presents a generic and cost-effective approach for spatially explicit decision support regarding the allocation of road repair treatments. By formulating an integer programming model that combines expert opinions with operational costs, repair schedules for each road segment are determined. Sensitivity analysis reveals that 76% of roads in the Hyrcanian forests need prioritized treatment. Incorporating environmental dimensions into operational costs enables the generation of an optimal tradeoff curve for road network segments.
CANADIAN JOURNAL OF FOREST RESEARCH
(2021)
Article
Mathematics
Cristian D. Palma, Patrick Bornhardt
Article
Engineering, Industrial
Cristian D. Palma
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE
(2018)
Article
Forestry
Cristian D. Palma, Wenbin Cui, David L. Martell, Dario Robak, Andres Weintraub
INTERNATIONAL JOURNAL OF WILDLAND FIRE
(2007)