Genetic algorithms to minimize the weighted number of late jobs on a single machine (original) (raw)
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Applied Mathematics and Computation, 2007
Today's importance of on time deliveries according to JIT concept makes managers to consider a desired set of extra 'due date related' criteria in the workshop scheduling. These criteria can be stated as tardiness, number of tardy jobs, and earliness. In this paper we focus on the bi-criteria scheduling problem of minimizing the number of tardy jobs and maximum earliness for single machine, in which the idle time is not allowed. The problem is known to be NP-hard. For this problem we developed a genetic algorithm by exploiting its general structure that further improves the initial population, utilizing a heuristic algorithm on the initial population. We present a computational experiment that shows the performance of the developed GA.
A Genetic Algorithm for the Dynamic Single Machine Scheduling Problem
Advances in Networked Enterprises, 2000
This paper starts by studying the performance of two inte"elated genetic algorithms (GA) for the static Single Machine Scheduling Problem (SMSP). One Is a single start GA, the other, called MetaGA, is a multi-start version GA. The performance is evaluated, for total weighted tardiness, on the basis of the quality of scheduling solutions obtained for a limit on computation time. Then, a scheduling system, based on Genetic Algorithms is proposed, for the resolution of the dynamic version of the same problem. The approach used adapts the resolution of the static problem to the dynamic one in which changes may occur continually. This takes into account dynamic occu"ences in a system and adapts the cu"ent population to a new regenerated population, L. M. Camarinha-Matos et al. (eds.), Advances in Networked Enterprises
Single Machine Total Weighted Tardiness Problem with Genetic Algorithms
2007 IEEE/ACS International Conference on Computer Systems and Applications, 2007
Scheduling problems are important NP-hard problems. Genetic algorithms can provide good solutions for such optimization problems. In this paper, we present a genetic algorithm to solve the single machine total weighted tardiness scheduling problem, which is a strong NP-hard problem. The algorithm uses the natural permutation representation of a chromosome, heuristic dispatching rules combined with random method to create the initial population, position-based crossover and order-based mutation operators, and the best members of the population during generations. The computational results of problem examples with 10 and 25 jobs and general problems with 50, 100, 200 and 500 jobs show the good performance and the efficiency of the developed algorithm.
2011
Abstract. This paper considers a single machine scheduling problem in which n jobs are to be processed and a machine setup time is required when the machine switches jobs from one to the other. All jobs have a common due date that has been predetermined using the median of the set of sequenced jobs. The objective is to find an optimal sequence of the set of n jobs to minimize the sum of the job's setups and the cost of tardy or early jobs related to the common due date.
Genetic algorithm for job shop scheduling with earliness and tardiness penalties
2010
The job-shop scheduling problem with operators is a very interesting problem that generalizes the classic jobshop problem in such a way that an operation must be algorithm to solve this problem considering makespan minimization. The genetic algorithm uses permutations with repetition to encode chromosomes and a schedule generation scheme, termed OG&T, as decoding algorithm. This combination guaranties that at least one of the chromosomes represents and optimal schedule and, at the samhat machines and operators are idle while an operation is available to be processed. To improve the quality of the schedules for large instances, we use Lamarckian evolution and modify the OG&T algorithm to further reduce the idle time of the machines and operators, in this case at the risk of leaving all optimal schedules out of the search space. We conducted a large experimental study showing that these improvements allow the genetic algorithm to reach high quality solutions in very short time, and so it is quite competitive with the current state-of-the-art methods.
International Journal of Production Management and Engineering
This article presents two combinatorial genetic algorithms (GA), unequal earliness tardiness-GA (UET-GA) and job-dependent earliness tardiness-GA (JDET-GA) for the single-machine scheduling problem to minimize earliness tardiness (ET) cost. The sequence of jobs produced in basic UET and JDET as a chromosome is added to the random population of GA. The best sequence from each epoch is also injected as a population member in the subsequent epoch. The proposed improvement seeks to achieve convergence in less time to search for an optimal solution. Although the GA has been implemented very successfully on many different types of optimization problems, it has been learnt that the algorithm has a search ability difficulty that makes computations NP-hard for types of optimization problems, such as permutation-based optimization problems. The use of a plain random population initialization results in this flaw. To reinforce the random population initialization, the proposed enhancement is u...
Solving Job Scheduling Problem Using Genetic Algorithm
Advanced Information Networking and Applications, 2021
The efficient scheduling of independent computational jobs in a computing environment is an important problem where there are some deadlines for each job to become complete. Finding optimal schedules for such an environment is (in general) an NP-complete problem, and so heuristic approaches must be used. Genetic algorithms are known to give the best solutions to such problems. The purpose of this paper is to propound a solution to a job scheduling problem using genetic algorithms. The experimental results show that the most important factor on the time complexity of the algorithm is the size of the population and the number of generations.
Applied Mathematical Modelling, 2009
In this paper, a hybrid genetic algorithm is developed to solve the single machine scheduling problem with the objective to minimize the weighted sum of earliness and tardiness costs. First, dominance properties of (the conditions on) the optimal schedule are developed based on the switching of two adjacent jobs i and j. These dominance properties are only necessary conditions and not sufficient conditions for any given schedule to be optimal. Therefore, these dominance properties are further embedded in the genetic ...