Intelligent controllers for bi-objective dynamic scheduling on a single machine with sequence-dependent setups

Xanthopoulos, A. S.; Koulouriotis, Dimitrios E.; Tourassis, Vassilios D.; Emiris, Dimitrios M.

doi:10.1016/j.asoc.2013.07.015

Cited by 28 publications

(9 citation statements)

References 41 publications

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“…Xanthopoulos et al (2013) propose two intelligent methods for dynamic scheduling on a single-machine with sequence-dependent setups. The first approach consists of a Reinforcement Learning-based agent which learns to select the best priority rule from a rule base, in respect to the current state of the system.…”

Section: Related Workmentioning

confidence: 99%

Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing

Xanthopoulos

Koulouriotis

2015

J Intell Manuf

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Most sequencing problems deal with deterministic environments where all information is known in advance. However, in real-world problems multiple sources of uncertainty need to be taken into consideration. To model such a situation, in this article, a dynamic sequencing problem with random arrivals, processing times and due-dates is considered. The examined system is a manufacturing line with multiple job classes and sequence-dependent setups. The performance of the system is measured under the metrics of mean WIP, mean cycle time, mean earliness, mean tardiness, mean absolute lateness, and mean percentage of tardy jobs. Twelve job dispatching rules for solving this problem are considered and evaluated via simulation experiments. A statistically rigorous analysis of the solution approaches is carried out with the use of unsupervised and supervised learning methods. The cluster analysis of the experimental results identified classes of priority rules based on their observed performance. The characteristics of each priority rule class are documented and areas in objective space not covered by existing rules are identified. The functional relationship between sequencing priority rules and performance metrics of the production system was approximated by artificial neural networks. Apart from gaining insight into the mechanics of the sequencing approaches the results of this article can be used (1) as a component for prediction systems of dispatching rule output, (2) as a guideline for building new dispatching heuristic with entirely different characteris-B A. S. Xanthopoulos tics than existing ones, (3) to significantly decrease the length of what-if simulation studies.

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Section: Related Workmentioning

confidence: 99%

Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing

Xanthopoulos

Koulouriotis

2015

J Intell Manuf

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“…The elite count is the number of fittest candidate solutions that pass to the next generation. The crossover fraction specifies the portion of the next generation which is created by the crossover operator ( [28,29]). The initial penalty and the penalty factor refer to parameters used in the formulation of the sub-problem that combines the objective function and the non-linear constraint (referred to [27] for details).…”

Section: Configuration Of Meta-heuristic Solvermentioning

confidence: 99%

Single-stage Kanban system with deterioration failures and condition-based preventive maintenance

Xanthopoulos¹,

Koulouriotis²,

Botsaris³

2015

Reliability Engineering & System Safety

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“…Dynamic scheduling problems that involve standard dispatching rules have been studied in Dominic et al (2004) and Huq et al (2009), among others. More elaborate approaches that consider development composite rules and intelligent methods for priority rule selection can be found in the works of Korytkowski et al (2013) and Xanthopoulos et al (2013).…”

Section: Related Workmentioning

confidence: 99%

“…Sequence-dependent setups (Xanthopoulos et al, 2013) take place when switching from one job type to another in each machine. The length of the setup interval…”

Section: System Descriptionmentioning

confidence: 99%

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Efficient priority rules for dynamic sequencing with sequence-dependent setups

Xanthopoulos¹,

Koulouriotis²,

Γαστεράτος³

et al. 2016

10.5267/j.ijiec

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This article addresses the problem of dynamic sequencing on n identical parallel machines with stochastic arrivals, processing times, due dates and sequence-dependent setups. The system operates under a completely reactive scheduling policy and the sequence of jobs is determined with the use of dispatching rules. Seventeen existing dispatching rules are considered including standard and setup-oriented rules. The performance of the system is evaluated by four metrics. An experimental study of the system is conducted where the effect of categorical and continuous system parameters on the objective functions is examined. In light of the results from the simulation experiments, a parameterized priority rule is introduced and tested. The simulation output is analyzed using rigorous statistical methods and the proposed rule is found to produce significantly better results regarding the metrics of mean cycle time and mean tardiness in single machine cases. In respect to three machine cases, the proposed rule matches the performance of the best rule from the set of existing rules which were studied in this research for three metrics.

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Intelligent controllers for bi-objective dynamic scheduling on a single machine with sequence-dependent setups

Cited by 28 publications

References 41 publications

Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing

Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing

Single-stage Kanban system with deterioration failures and condition-based preventive maintenance

Efficient priority rules for dynamic sequencing with sequence-dependent setups

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