Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems (original) (raw)
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A Genetic Algorithm-Based Approach for Flexible Job Shop Scheduling
2011
Flexible job shop scheduling is a hard combinatorial optimization problem. This paper introduces a simulation-based Genetic Algorithm approach to solve flexible job shop scheduling problem. Four manufacturing scenarios have been considered to access the performance of a job shop with objective to minimize mean tardiness, mean flow time and makespan. Results show that multiple process plans performs better than single process plan for each job type and if only single process plan is made available, then process plan selected on the basis of minimum production time criterion yields better results than other criterion of randomly selected process plan and minimum number of set-ups. Moreover, embedding restart scheme into regular Genetic Algorithm results improvement in the fitness value.
E3S Web of Conferences, 2021
This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) using an Improved Genetic Algorithm integrated with Rules (IGAR). Machine assignment is done by Genetic Algorithm (GA) and operation selection is done using priority rules. Improvements in GA include a new technique of adaptive probabilities and a new forced mutation technique that positively ensures the generation of new chromosome. The scheduling part also proposed an improved scheduling rule in addition to four standard rules. The algorithm is tested against two well-known benchmark data set and results are compared with various algorithms. Comparison shows that IGAR finds known global optima in most of the cases and produces improved results as compared to other algorithms.
Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems
Mathematical Problems in Engineering
Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.
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.
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers & Operations Research, 2008
In this paper, we present a genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP). The algorithm integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results, with respect to other genetic algorithms. Moreover, results are quite comparable to those obtained by the best-known algorithm, based on tabu search. These two results, together with the flexibility of genetic paradigm, prove that genetic algorithms are effective for solving FJSP. ᭧ Scheduling of operations is one of the most critical issues in the planning and managing of manufacturing processes. To find the best schedule can be very easy or very difficult, depending on the shop environment, the process constraints and the performance indicator [1]. One of the most difficult problems in this area is the Job-shop Scheduling Problem (JSP), where a set of jobs must be processed on a set of machines, each job is formed by a sequence of consecutive operations, each operation requires exactly one machine, machines are continuously available and can process one operation at a time without interruption. The decision concerns how to sequence the operations on the machines, such as a given performance indicator is optimized. A typical performance indicator for JSP is the makespan, i.e., the time needed to complete all the jobs. JSP is a well-known NP-hard problem .
Applying Improved Genetic Algorithm for Solving Job Shop Scheduling Problems
Tehnicki Vjesnik-technical Gazette, 2017
The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling combinatorial problems with considerable importance in industry. When solving complex problems, search based on traditional genetic algorithms has a major drawback - high requirement for computational power. The goal of this research was to develop fast and efficient scheduling method based on genetic algorithm for solving the job-shop scheduling problems. In proposed GA initial population is generated randomly, and the relevant crossover and mutation operation is also designed. This paper presents an efficient genetic algorithm for solving job-shop scheduling problems. Performance of the algorithm is demonstrated in the real-world examples.
Flexible job-shop scheduling is a type of scheduling which is extension of Job-shop scheduling problem. In FJSP, operations are processed on different machines, which means operations are break down to sublots, and these sublots are processed by machines independently. In previous research, mathematical model was developed along with implementation of Genetic Algorithm. This paper gives an overview of improved methods to Flexible Job-Shop scheduling Problem with overlapping in operation[1].
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 Bi-Level Genetic Algorithm to Solve the Dynamic Flexible Job Shop Scheduling Problem
Proceedings of the 15th International Conference on Agents and Artificial Intelligence, 2023
The dynamic flexible job shop scheduling problem (DyFJSP) is an extension of the flexible job scheduling problem (FJSP) as the production environment is characterized by a set of disturbances that require a method capable of reacting in real time in order to generate an efficient schedule in case of production failure. In this paper, we propose a bi-level genetic algorithm (BLGA) to solve the DyFJSP in order to minimize the maximum completion time (Makespan). The dynamic scenario taken into account in this work is job insertion. To evaluate the performance of our approach, we carry out experiments on Brandimarte benchmark instances. The results of the experiments show that the BLGA is characterized by its efficiency and performance in comparison with other methods published in the literature.