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Determining an optimal solution is almost impossible [2] but trying to improve an existing solution is a way to lead to a better scheduling. By crossover and mutation of agents, according to their fitness function, we improve an existing solution. So, determining a good and valid solution in a Job-Shop scheduling problem mustn't be the optimization...
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In this paper we present a heuristic algorithm for opti-mization of mathematical functions. Our algorithm has been tested on several function optimization problems. The performance of the heuristic algorithm is compared with the performance of genetic algorithms. Experimental results using benchmark optimization problems confirm that our algorithm...
The single machine total weighted tardiness problem with sequence dependent setup times is a challenging and heavily studied problem. This problem is NP-hard, so several heuristics have been proposed in the literature so far. One of them is the genetic algorithm. The genetic algorithm is both powerful solution technique and applicable to wide range...
Citations
... A genetic algorithm (GA) has been developed in Ref. [16] for minimizing the average residence time to produce a set of batches in function of batch order in a multipurpose-multiproduct batch plant. Multi objective genetic algorithm to find a balance point in respect of a solution of the Pareto front is presented in Ref. [17]. A decomposition heuristics algorithm based on multibottleneck processors for large-scale job shop scheduling problems is proposed in Ref. [18]. ...
Scheduling is an important aspect of automation in manu- facturing systems. It consists in allocating a finite set of resources
or machines over time to perform a collection of tasks or jobs while satisfy- ing a set of constraints. One of the most known
and hardest scheduling problems is the Job Shop, to which a distributed approach is proposed in this paper based on agent
cooperation. There are essentially two types of agents: Job agents and Resource agents. Different agent behaviours based on
heuristics are proposed and experimentally compared on ran- domly generated examples.
Early and seminal work which applied evolutionary computing methods to scheduling problems from 1985 onwards laid a strong and exciting foundation for the work which has been reported over the past decade or so. A survey of the current state-of-the-art was produced in 1999 for the European Network of Excellence on Evolutionary Computing EVONET—this paper provides a more up-to-date overview of the area, reporting on current trends, achievements, and suggesting the way forward.
