Scheduling using artificial immune system metaphors: A review (original) (raw)
Related papers
An immune system approach to scheduling in changing environments
GECCO 99, Proceedings of the Genetic and …, 1999
This paper describes the application of an arti cial immune system, AIS, model to a scheduling application, in which sudden changes in the scheduling environment require the rapid production of new schedules. The model operates in two phases: In the rst phase of the system, the immune system analogy, in conjunction with a genetic algorithm, GA, is used to detect common patterns amongst scheduling sequences frequently used by a factory. In phase II, some of the combinatoric features of the natural immune system are modelled in order to use the detected patterns to produce new schedules, either from scratch or starting from a partially completed schedule. The results are compared to those calculated using an exhaustive search procedure to generate patterns. The AIS GA analogy appears to be extremely promising, in that schedules corresponding to situations previously encountered can easily be reconstructed, and also in that the patterns are shown to incorporate su cient information to potentially construct schedules for previously unencountered situations.
Artificial Immune System Applied to Job Shop Scheduling
Journal of Industrial and Intelligent Information, 2021
Job Shop Scheduling is a problem to schedule n number of jobs in m number of machines with a different order of processing. Each machine processes exactly one job at a time. Each job will be processed in every machine once. When a machine is processing one particular job then the other machine can’t process the same job. Different schedule’s order might produce different total processing time. The result of this scheduling problem will be total processing time and schedule’s order. This paper uses clonal selection as the algorithm to solve this problem. The clonal selection algorithm comes from the concept of an artificial immune system. It's developed by copying a human’s immune system behavior. A human’s immune system can differentiate foreign objects and eliminate the objects by creating an antibody. An antibody will go to a cloning process and will mutate to further enhance itself. Clonal selection algorithm applies this cloning and mutation principle to find the most optima...
Artificial immune system for static and dynamic production scheduling problems
2017
Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in production scheduling is to allocate the machines to perform the tasks. Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP) are two of the areas in production scheduling problems for these machines. One of the main objectives in solving JSSP and FJSSP is to obtain the best solution with minimum total completion processing time. Thus, this thesis developed algorithms for single and hybrid methods to solve JSSP and FJSSP in static and dynamic environments. In a static environment, no change is needed for the produced solution but changes to the solution are needed. On the other hand, in a dynamic environment, there are many real time events such as random arrival o...
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...
Investigating artificial immune systems for job shop rescheduling in changing environments
2008
Artificial immune system can be used to generate schedules in changing environments and it has been proven to be more robust than schedules developed using a genetic algorithm. Good schedules can be produced especially when the number of the antigens is increased. However, an increase in the range of the antigens had somehow affected the fitness of the immune system. In this research, we are trying to improve the result of the system by rescheduling the same problem using the same method while at the same time maintaining the robustness of the schedules.
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
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 be...
Journal of Manufacturing Systems, 2013
We address the two-stage multi-machine assembly scheduling problem. The first stage consists of m independently working machines where each machine produces its own component. The second stage consists of two independent and identical assembly machines. The objective is to come up with a schedule that minimizes total or mean completion time for all jobs. The problem has been addressed in the scheduling literature and several heuristics have been proposed. In this paper, we propose a new heuristic called artificial immune system (AIS). We conduct experimental analysis for comparing the newly proposed heuristic AIS with the best known heuristic in the literature. Experimental results show that our proposed heuristic AIS performs better than the best known existing heuristic. More specifically, our new heuristic AIS reduces the error of the best known heuristic by 60% while the computational times of both AIS and the best known heuristic are almost the same.
A New Approach to Solve Flowshop Scheduling Problems By Artificial Immune Systems
Dogus Universitesi Dergisi, 2011
The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the criteria of makespan minimization for the flow shop scheduling problem. Artificial Immune Systems (AIS) are new intelligent problem solving techniques that are being used in scheduling problems. AIS can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanisms of the immune response is used. The operation parameters of meta-heuristics have an important role on the quality of the solution. Thus, a generic systematic procedure which bases on a multistep experimental design approach for determining the efficient system parameters for AIS is presented. Experimental results show that, the artificial immune system algorithm is more efficient than both the classical heuristic flow shop scheduling algorithms and simulated annealing.