Evolutionary search for difficult problem instances to support the design of job shop dispatching rules

Branke, Juergen; Pickardt, Christoph W.

doi:10.1016/j.ejor.2011.01.044

Cited by 30 publications

(16 citation statements)

References 23 publications

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“…However, a major disadvantage of dispatching rules is a lack of a global perspective [4]. Dispatching rules are generally myopic (short-sighted) and only take into account the local and current conditions [1,7], i.e., rules look at one machine and the queue of jobs awaiting processing at that machine at that moment in time.…”

mentioning

confidence: 99%

Evolving "less-myopic" scheduling rules for dynamic job shop scheduling with genetic programming

Hunt

Johnston

Zhang

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Job Shop Scheduling (JSS) is a complex real-world problem aiming to optimise a measure of delivery speed or customer satisfaction by determining a schedule for processing jobs on machines. A major disadvantage of using a dispatching rule (DR) approach to solving JSS problems is their lack of a global perspective of the current and potential future state of the shop. We investigate a genetic programming based hyper-heuristic (GPHH) approach to develop "less-myopic" DRs for dynamic JSS. Results show that in the dynamic ten machine job shop, incorporating features of the state of the wider shop, and the stage of a job's journey through the shop, improves the mean performance, and decreases the standard deviation of performance of the best evolved rules.

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mentioning

confidence: 99%

Evolving "less-myopic" scheduling rules for dynamic job shop scheduling with genetic programming

Hunt

Johnston

Zhang

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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“…A similar approach is also adopted in Jakobovic and Marasovic (2012). Branke and Pickardt (2011) employ evolutionary algorithms to construct difficult scheduling problem instances in order to expose the weaknesses of existing priority rules. Based on their findings they propose improved heuristics.…”

Section: Related Workmentioning

confidence: 97%

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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“…Branke and Mattfeld applied an EA to a dynamic scheduling problem [4]. Recently extended studies used EAs to establish optimal dispatching rules [3]. The GA is one of the most popular and widely applied EAs for scheduling problems [33,48].…”

Section: Simulation Optimization and Optimal Computing Budget Allocationmentioning

confidence: 99%

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

Lin

Chen

2015

Simulation Modelling Practice and Theory

100

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Evolutionary search for difficult problem instances to support the design of job shop dispatching rules

Cited by 30 publications

References 23 publications

Evolving "less-myopic" scheduling rules for dynamic job shop scheduling with genetic programming

Evolving "less-myopic" scheduling rules for dynamic job shop scheduling with genetic programming

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

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

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