Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming (original) (raw)
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Learning iterative dispatching rules for job shop scheduling with genetic programming
The International Journal of Advanced Manufacturing Technology, 2013
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)
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 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 genetic algorithm-based approach for optimization of scheduling in job shop environment
Journal of Advanced Manufacturing Systems, 2011
The present work aims to develop a genetic algorithm-based approach to solve the scheduling optimization problem in the Job Shop manufacturing environment. A new encoding scheme for chromosome representation has been developed for this purpose that denotes a priority sequence of operations, from which a schedule can be generated if the precedence constraints are known. The successful implementation of the proposed encoding scheme has been presented and its performance has been compared with the existing operation-based scheme found in literatures across different test cases by varying the number of jobs and machines in the shop floor.
Genetic Algorithm for Job Shop Scheduling Problem: A Case Study
The job-shop scheduling (JSS) is a schedule planning for low volume systems with many variations in requirements. In job-shop scheduling problem (JSSP), there are k operations and n jobs to be processed on m machines with a certain objective function to be minimized. Due to complexity of transferring work in process product, this research add transfer time variable from one machine to another for each different operation. Performance measures are mean flow time and make span. In this paper we used genetic algorithm (GA) with some modifications to deal with problem of job shop scheduling. The result than is compared with dispatching rules such as longest processing time, shortest processing time and first come first serve. The numerical example showed that GA result can outperform the other three methods.
A Decision Support System for Solving Job-Shop Scheduling Problems Using Genetic Algorithms
2008
The primary objective of this research is to solve the job-shop scheduling problems by minimizing the makespan. In this paper, we first developed a genetic algorithm (GA) for solving JSSPs, and then improved the algorithm by integrating it with three priority rules. The performance of the developed algorithm was tested by solving 40 benchmark problems and comparing their results with that of a number of well-known algorithms. For convenience of implementation, we developed a decision support system (DSS). In the DSS, we built a graphical user interface (GUI) for user friendly data inputs, model choices, and output generation. An overview of the DSS and the analysis of experimental results are provided.
Solving job-shop scheduling problems by genetic algorithm
Proceedings of IEEE International Conference on Systems, Man and Cybernetics
Job-shop Scheduling Problem (JSP) is one of extremely hard problems because it requires very large combinatorial search space and the precedence constraint between machines. The traditional algorithm used t o solve the problem is the branch-and-bound method, which takes considerable computing time when the size of problem is large. W e propose a new method for solving JSP using Genetic Algorithm (G A) and demonstrate its efficiency by the standard benchmark of job-shop scheduling problems. Some important points of G A are how t o represent the schedules as an individuals and t o design the genetic operators for the representation in order t o produce better results.
PERFORMANCE OF GENETIC ALGORITHMS FOR SOLVING FLEXIBLE JOB-SHOP SCHEDULING PROBLEM
Serials Publications, 2011
A Job-Shop Scheduling is a process-organized manufacturing facility. Its main characteristics are that a great diversity of jobs is performed. A Job-Shop produces goods (parts) and these parts have one or more alternatives process plans. Each process plan consists of a sequence of a operations and these operations require resources and have certain (predefined) duration on machines. The Job-Shop Scheduling is a problem of planning and organization of a set of tasks to be performed on a set of resources with variable performance. In this paper, two approaches Jobs Sequencing List Oriented Genetic Algorithm and Operations machines Coding Oriented Genetic Algorithm have been implemented and compared for solution of the Job-Shop scheduling problem. Each approach has its own coding, evaluation function, crossovers and mutations applicable in Job-Shop scheduling problem to minimize the makespan, the workload of the most loaded machine and the total workload of the machines. Jobs Sequencing List Oriented Genetic Algorithm has been found to be the best out of two approaches to minimize the objectives.
POLICY REFINEMENT WITH GENETIC ALGORITHMS FOR JOB SHOP SCHEDULING
Proceedings of 8th International Symposium on Intelligent and Manufacturing Systems
Evolutionary approach has been a well known and widely used method in solving scheduling problems besides other soft computing techniques. A policy refinement approach is used in this research that evolves the set of dispatching rules with use of Genetic Algorithms (GAs) in order to solve semi-dynamic job shop scheduling problems. In other words, the set of dispatching rules are considered as policies for allocation of jobs to a number of resources (machines) and these policies are refined through evolution with use of GA for optimisation. The objective of this research is to refine scheduling policies to gain better results for solving dynamic job-shop scheduling problems. The criterion considered to be optimised in this research is Cmax as it had been for many researches in last few decades.
Genetic Algorithms for Job-Shop Scheduling Problems
time 0 2 4 6 8 1 0 1 2 ,, ,, ,, J 3 Figure 1: A Gantt-Chart representation of a solution for a 3 × 3 problem a set of disjunctive arcs representing pairs of operations that must be performed on the same machines. The processing time for each operation is the weighted value attached to the corresponding nodes. shows this in a graph representation for the problem given in .