A Comparison of Decoding Strategies for the 0/1 Multi-objective Unit Commitment Problem

In the single objective Unit Commitment Problem (UCP) the problem is usually separated in two sub-problems : the commitment problem which aims to fix the on/off scheduling of each unit and the dispatching problem which goal is to schedule the production of each turned on unit. The dispatching problem is a continuous convex problem that can easily be solved exactly. For the first sub-problem genetic algorithms (GA) are often applied and usually handle binary vectors representing the solutions of the commitment problem.Then the solutions are decoded in solving the dispatching problem with an exact method to obtain the precise production of each unit. In this paper a multiobjective version of the UCP taking the emission of gas into account is presented. In this multi objective UCP the dispatching problem remains easy to solve whereas considering it separatly remains interesting. A multi-objective GA handling binary vectors is applied. However for a binary representation there is a set of solutions of the dispatching problem that are pareto equivalent. Three decoding strategies are proposed and compared. The main contribution of this paper is the third decoding strategy which attaches an approximation of the Pareto front from the associated dispatching problem to each genotypic solution. It is shown that this decoding strategy leads to better results in comparison to the other ones.