Abstract
In this work, we are interested in a job shop scheduling problem (JSSP) with resources availability constraints. The aim consists in scheduling a set of N jobs on M machines. To be processed in the system, each job needs an number of consumable resources that are available in a limited quantity. Solving such a problem means finding better jobs sequencing in order to minimise the maximum execution time. We suggest two different methods to solve the above-mentioned problem. We firstly propose a set of four heuristics based on priority rules. Then, we make call to genetic algorithm. Using a real job shop manufacturing system data, a large-scale experiment was performed in order to analyse the performance of the proposed methods. The studied system is called iCIM 3000. The simulation results reveal that the new proposed heuristics are better than genetic algorithms and achieve to good solutions in shorter time.
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