A General Model for Job Shop Problems Using Imune-Genetic Algorithm and Multiobjective Optimization Techniques (original) (raw)

A Novel Artificial Immune Algorithm for Solving the Job Shop Scheduling Problem

International Journal of Computer Applications, 2012

Scheduling problems are difficult types of production arrangement problems that enumerated among NP-Complete problems. Some of evolutionary algorithms such as Genetic Algorithm, Ant Colony Optimization etc. have been used to solve this problem. In new years, Artificial Immune Algorithm is used to solve optimization problems such as routing and scheduling. One of complex scheduling problems is Job-shop Scheduling problem. In this article we use immune system concepts of human body, to implement a new artificial immune algorithm for solving Job-shop scheduling problem. A new population generation method was proposed based on G&T algorithm. We use two mutation methods, namely Shift Change method and Inverse method in Job-shop scheduling for first time. Moreover, we describe a vaccination method named MCV, to make maximum advance in solutions, and then achieve to more than one optimal solution concurrently and release from local optimum. Finally, we test our method on the very famous benchmark of JSP, namely FT06, then show experimental results and get some conclusions.

Minimize the makespan for job shop scheduling problem using artificial immune system approach

Journal of theoretical and applied information technology, 2015

In the manufacturing industry, scheduling is a process of arranging, controlling and optimizing work and workloads in a production process. This research discussed about job-shop scheduling problem. The main problem in job-shop scheduling is to optimize the usage of machines in order to obtain the shortest time in completing the activities. Several methods have been used to solve job-shop scheduling problems and the method proposed here is artificial intelligence by using the artificial immune system algorithm (AIS). The advantage of this algorithm is fabricated by imitating the natural immune system. The results produced by this method are compared with the best results of the previous research.

Job-shop Scheduling Problem, Artificial Immune Algorithm

2013

An artificial immune algorithm for the flexible job-shop scheduling problem

Future Generation Computer Systems, 2010

This article addresses the flexible job-shop scheduling problem (FJSP) to minimize makespan. The FJSP is strongly NP-hard and consists of two sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on all machines. To solve this problem, an artificial immune algorithm (AIA) based on integrated approach is proposed. This algorithm uses several strategies for generating the initial population and selecting the individuals for reproduction. Different mutation operators are also utilized for reproducing new individuals. To show the effectiveness of the proposed method, numerical experiments by using benchmark problems are conducted. Consequently, the computational results validate the quality of the proposed approach.

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.

Improved immune algorithm for global numerical optimization and job-shop scheduling problems

Applied Mathematics and Computation, 2007

In this paper, by using the unified procedures, an improved immune algorithm named a modified Taguchi-immune algorithm (MTIA), based on both the features of an artificial immune system and the systematic reasoning ability of the Taguchi method, is proposed to solve both the global numerical optimization problems with continuous variables and the combinatorial optimization problems for the job-shop scheduling problems (JSP). The MTIA combines the artificial immune algorithm, which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimal antibody. In the MTIA, the clonal proliferation within hypermutation for several antibody diversifications and the recombination by using the Taguchi method for the local search are integrated to improve the capabilities of exploration and exploitation. The systematic reasoning ability of the Taguchi method is executed in the recombination operations to select the better antibody genes to achieve the potential recombination, and consequently enhance the MTIA. The proposed MTIA is effectively applied to solve 15 benchmark problems of global optimization with 30 or 100 dimensions. The computational experiments show that the proposed MTIA can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing improved genetic algorithms reported recently in the literature. In addition, the MTIA is also applied to solve the famous Fisher-Thompson and Lawrence benchmarks of the JSP. The computational experiments show that the proposed MTIA approach can also obtain both better and more robust results than those evolutionary methods reported recently.

A two-stage genetic algorithm for multi-objective job shop scheduling problems

Journal of Intelligent Manufacturing, 2011

This paper presents a two-stage genetic algorithm (2S-GA) for multi-objective Job Shop scheduling problems. The 2S-GA is proposed with three criteria: Minimize makespan, Minimize total weighted earliness, and Minimize total weighted tardiness. The proposed algorithm is composed of two Stages: Stage 1 applies parallel GA to find the best solution of each individual objective function with migration among populations. In Stage 2 the populations are combined. The evolution process of Stage 2 is based on Steady-State GA using the weighted aggregating objective function. The algorithm developed can be used with one or two objectives without modification. The genetic algorithm is designed and implemented with the GALIB object library. The random keys representation is applied to the problem. The schedules are constructed using a permutation with m-repetitions of job numbers. Performance of the proposed algorithm is tested on published benchmark instances and compared with results from other published approaches for both the single objective and multi-objective cases. The experimental results show that 2S-GA is effective and efficient to solve job shop scheduling problem in term of solution quality.

A Genetic Algorithm with Priority Rules for Solving Job-Shop Scheduling Problems

Studies in Computational Intelligence, 2009

The Job-Shop Scheduling Problem (JSSP) is one of the most difficult NPhard combinatorial optimization problems. In this chapter, we consider JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. First, we develop a genetic algorithm (GA) based approach for solving JSSPs. We then introduce a number of priority rules to improve the performance of GA, such as partial re-ordering, gap reduction, and restricted swapping. The addition of these rules results in a new hybrid GA algorithm that is clearly superior to other wellknown algorithms appearing in the literature. Results show that this new algorithm obtained optimal solutions for 27 out of 40 benchmark problems. It thus makes a significantly new contribution to the research into solving JSSPs.

An Artificial Immune System Approach for Flexible Job Shop Scheduling Problem

2012

Job Shop Scheduling Problem (JSP) is one of the hardest NP-hard class combinatorial optimization problems. Flexible Job Scheduling Problem (FJSP) occurs with the use of parallel machines in job shop environment and it is more complex than JSP because it contains two sub problems: operation sequencing and operation assignment to machines. There are two main approaches to solve FJSP: Hierarchical approach and integrated approach. In hierarchical approach, machine assignment and operation sequencing are independent from each other whereas in integrated approach they occur simultaneously. There are many heuristic methods to solve FJSP in the literature. Artificial Immune System inspired by the vertebrate immune system (AIS) is one of these methods. In this study, an artificial immune system approach based on hierarchical approach is developed to solve FJSP. To demonstrate the effectiveness of the algorithm, numerical experiments by using three benchmark problem sets are conducted. In pr...

A hybrid genetic algorithm for the job shop scheduling problems

Computers & Industrial Engineering, 2003

This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

A General Model for Job Shop Problems Using Imune-Genetic Algorithm and Multiobjective Optimization Techniques (original) (raw)

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