Unrelated parallel machines scheduling with dependent setup times in textile industry
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Unrelated parallel machines scheduling with dependent setup times in textile industry
Authors
Keywords
-
Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume -, Issue -, Pages 108736
Publisher
Elsevier BV
Online
2022-10-08
DOI
10.1016/j.cie.2022.108736
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Unrelated parallel machine scheduling with new criteria: Complexity and models
- (2021) Abdoul Bitar et al. COMPUTERS & OPERATIONS RESEARCH
- Exact and heuristic methods to solve the parallel machine scheduling problem with multi-processor tasks
- (2018) Lingxiao Wu et al. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
- A genetic algorithm for solving the steel continuous casting problem with inter-sequence dependent setups and dedicated machines
- (2018) Abdelkader Sbihi RAIRO-OPERATIONS RESEARCH
- Reformulations and an exact algorithm for unrelated parallel machine scheduling problems with setup times
- (2018) Luis Fanjul-Peyro et al. COMPUTERS & OPERATIONS RESEARCH
- A mathematical model and heuristic algorithms for an unrelated parallel machine scheduling problem with sequence-dependent setup times, machine eligibility restrictions and a common server
- (2018) Gulcin Bektur et al. COMPUTERS & OPERATIONS RESEARCH
- Solution method for a large-scale loom scheduling problem with machine eligibility and splitting property
- (2017) D. Yilmaz Eroglu et al. JOURNAL OF THE TEXTILE INSTITUTE
- Resource-constrained unrelated parallel machine scheduling problem with sequence dependent setup times, precedence constraints and machine eligibility restrictions
- (2016) Mojtaba Afzalirad et al. COMPUTERS & INDUSTRIAL ENGINEERING
- Hybrid genetic algorithms with dispatching rules for unrelated parallel machine scheduling with setup time and production availability
- (2015) Cheol Min Joo et al. COMPUTERS & INDUSTRIAL ENGINEERING
- Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem
- (2014) Gökçe Candan et al. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times
- (2014) Duygu Yilmaz Eroglu et al. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Workload balancing in identical parallel machine scheduling using a mathematical programming method
- (2013) Yassine Ouazene et al. International Journal of Computational Intelligence Systems
- A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times
- (2011) Eva Vallada et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
- Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints
- (2009) R. Tavakkoli-Moghaddam et al. COMPUTERS & OPERATIONS RESEARCH
- A two-stage Ant Colony Optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times
- (2009) Jean-Paul Arnaout et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Non-identical parallel-machine scheduling research with minimizing total weighted completion times: Models, relaxations and algorithms
- (2008) Kai Li et al. APPLIED MATHEMATICAL MODELLING
- Exact algorithms for a scheduling problem with unrelated parallel machines and sequence and machine-dependent setup times
- (2006) Pedro Leite Rocha et al. COMPUTERS & OPERATIONS RESEARCH
- A survey of scheduling problems with setup times or costs
- (2006) Ali Allahverdi et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started