Decoder-based evolutionary algorithm for bi-objective just-in-time single-machine job-shop

The bi-objective just-in-time single-machine job-shop scheduling problem (JIT-JSP) aims at simultaneously minimizing earliness and tardiness. In this paper, a multi-objective decoder-based evolutionary algorithm is proposed. The decoding strategy divides the search into two steps. In the first step, the search of the permutation order of the jobs is realized thanks to a multi-objective evolutionary algorithm. For a fixed permutation, the decoder algorithm optimizes the multi-objective timing sub-problem in the second step. Thus each permutation order induces a Pareto set of solutions. Two different decoding strategies to fix the idle times are proposed, one approximate and one exact. A comparison study with a classical multi-objective evolutionary algorithm underlines the performance of the proposed decoding strategy and the interest of the approximate decoder.