Dp 2011 krejci filip en

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Evolutionary Algorithms for Project Scheduling[edit]

Diplomové práce 2011

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In this thesis the algorithm Particle Swarm Optimization (PSO) deals with the problem of RCPS | temp. A new algorithm GPSO that combines good features of PSO with genetic operators unbalanced two-point crossover and random mutation is proposed. The performance of this algorithm is compared with other variants of PSO and GA on the criteria of energetic efficiency and earliness-tardiness.

The concept of energetic efficiency is entirely new in the field of project scheduling with temporal constraints. The optimization is achieved by reducing the number of machines’ switching on and off and shortening delays between tasks, in which must be the machines turned on and vice versa creating a sufficiently long delays, so the device to be put into standby mode.

Designed GPSO outperforms other tested variants of PSO and GA. For some test sets GPSO improved the energetic efficiency up to 25 %. The criterion of earliness-tardiness was improved up to 6.7 %, while simultaneously the length of the schedule (Cmax) was reduced up to 3.7 %.

Keywords: RCPS | temp, PSO, GA, GPSO, energetic efficiency, earliness-tardiness

Dp 2011 krejci filip.pdf