Evolutionary Learning of Weighted Linear Composite Dispatching Rules for Scheduling (original) (raw)
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Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations
Evolutionary Computation, 2014
Dispatching rules are frequently used for real-time, on-line scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighborhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm.
Automatic Generation of Dispatching Rules for Large Job Shops by Means of Genetic Algorithms
Proceedings of the 8th International Workshop on Combinations of Intelligent Methods and Applications co-located with 30th International Conference on Artificial Intelligence Tools (ICTAI 2018), 2018
Generating optimized large-scale production plans is an important open problem where even small improvements result in significant savings. Application scenarios in the semiconductor industry comprise thousands of machines and hundred thousands of job operations and are therefore among the most challenging scheduling problems regarding their size. In this paper we present a novel approach for automatically creating composite dispatching rules, i.e. heuristics for job sequencing, for makespan optimization in such large-scale job shops. The approach builds on the combination of event-based simulation and genetic algorithms. We introduce a new set of benchmark instances with proven optima that comprise up to 100000 operations to be scheduled on up to 1000 machines. With respect to this large-scale benchmark, we present the results of an experiment comparing well-known dispatching rules with automatically created composite dispatching rules produced by our system. It is shown that the proposed system is able to come up with highly effective dispatching rules such that makespan reductions of up to 38% can be achieved, and in fact, often near optimal or even optimal schedules can be produced.
A Comprehensive Analysis on Reusability of GP-Evolved Job Shop Dispatching Rules
Genetic Programming (GP) has been extensively used to automatically design dispatching rules for job shop scheduling problems. However, the previous studies only focus on the performance on the training instances. So far, there is no systematic investigation of the reusability of the GP-evolved rules on unseen instances. In practice, it is desirable to train the rules on smaller job shop instances, and apply them to larger instances with more jobs and machines to save training time. In this case, the reusability of the GP-evolved rules under different numbers of jobs and machines is an important issue. In this paper, a comprehensive investigation is conducted to analyse how the variation in the numbers of jobs and machines from the training set to the test set affects the reusability of the GP-evolved rules. It is found that in terms of minimizing makespan, the reusability of the GP-evolved rules highly depends on variation in the numbers of jobs and machines. A better reusability can be achieved by choosing training instances whose numbers of jobs and machines (or at least the ratio between the numbers of jobs and machines) are closer to that of the test instances. Furthermore, the ratio between the numbers of jobs and machines is demonstrated to be an important factor to reflect the complexity of an instance for dispatching rules. This study is the first systematic investigation on the reusability of GP-evolved dispatching rules.
A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.
Evolving Dispatching Rules for solving the Flexible Job-Shop Problem
2005 IEEE Congress on Evolutionary Computation
We solve the Flexible Job-Shop Problem (FJSP) by using dispatching rules discovered through Genetic Programming (GP). While Simple Priority Rules (SPR) have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite Dispatching Rules (CDR) have been shown to be more effective as they are constructed through human experience. In this paper, we employ suitable parameter and operator spaces for evolving CDRs using GP, with an aim towards greater scalability and flexibility. Experimental results show that CDRs generated by our GP framework outperforms the SPRs and CDRs selected from literature in 74% to 85% of FJSP problem instances.
A Dynamic Selection of Dispatching Rules Based on the Kano Model Satisfaction Scheduling Tool
2018
Production scheduling is a function that can contribute strongly to the competitive capacity of companies producing goods and services. Failure to stagger tasks properly causes enormous waste of time and resources, with a clear decrease in productivity and high monetary losses. The efficient use of internal resources in organizations becomes a competitive advantage and can thus dictate their survival and sustainability. In that sense, it becomes crucial to analyze and develop production scheduling models, which can be simplified as the function of affecting tasks to means of production over time. This report is part of a project to develop a dynamic scheduling tool for decision support in a single machine environment. The system created has the ability, after a first solution has been generated, to trigger a new solution as some tasks leave the system and new ones arrive, allowing the user, at each instant of time, to determine new scheduling solutions, in order to minimize a certai...
Towards improved dispatching rules for complex shop floor scenarios
Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO '10, 2010
Developing dispatching rules for manufacturing systems is a tedious process, which is time-and cost-consuming. Since there is no good general rule for different scenarios and objectives automatic rule search mechanism are investigated. In this paper an approach using Genetic Programming (GP) is presented. The priority rules generated by GP are evaluated on dynamic job shop scenarios from literature and compared with manually developed rules yielding very promising results also interesting for Simulation Optimization in general.
IEEE Transactions on Evolutionary Computation, 2013
Designing effective dispatching rules is an important factor for many manufacturing systems. However, this timeconsuming process has been performed manually for a very long time. Recently, some machine learning approaches have been proposed to support this task. In this paper, we investigate the use of genetic programming for automatically discovering new dispatching rules for the single objective job shop scheduling problem (JSP). Different representations of the dispatching rules in the literature and newly proposed in this work are compared and analysed. Experimental results show that the representation which integrates system and machine attributes can improve the quality of the evolved rules. Analysis of the evolved rules also provides useful knowledge about how these rules can effectively solve JSP.
Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming
Scheduling is an important planning activity in manufacturing systems to help optimise the usage of scarce resources and improve the customer satisfaction. In the job shop manufacturing environment, scheduling problems are challenging due to the complexity of production flows and practical requirements such as dynamic changes, uncertainty, multiple objectives, and multiple scheduling decisions. Also, job shop scheduling (JSS) is very common in small manufacturing businesses and JSS is considered one of the most popular research topics in this domain due to its potential to dramatically decrease the costs and increase the throughput. Practitioners and researchers have applied different computational techniques, from different fields such as operations research and computer science, to deal with JSS problems. Although optimisation methods usually show their dominance in the literature, applying optimisation techniques in practical situations is not straightforward because of the prac...
Efficient dispatching rules for dynamic job shop scheduling
International Journal of Advanced Manufacturing Technology, 2004
This study attempts to provide efficient dispatching rules for dynamic job shop scheduling by combining different dispatching rules. A dispatching rule is used to select the next job to be processed from a set of jobs awaiting service. A job shop will be treated as dynamic, when conditions such as continuously arriving new jobs and deviations from current schedule need to be accommodated, and a job shop should be treated as an integrated part of a manufacturing system. The discussion includes a simulation technique which uses ARENA 4.0. software to simulate the dynamic model of a job shop under various rules and performance measures . Results of the simulation show that, for most of the performance measures, combined rules perform well. In this study, the combined rules MWKR_FIFO and TWKR_SPT do well under most conditions.