This paper describes an evolutionary search scheduling algorithm (ESSA) developed to solve the most difficult job shop scheduling problems (JSSP) that are known to be NP-hard combinatorial optimization problems. The ESSA proposed is a hybrid approach that focuses on optimization of locally optimized solutions. The differences versus other ESSA strategies are the new proposed encoding, decoding and forcing scheme, the local search optimizer that uses a new repair based neighborhood structure and a new bootstrapping strategy. Experimental results on common benchmarks indicate the power of the hybrid ESSA. The results clearly show that optimal schedules can be found. Moreover, the algorithm outperformed several ESSAs on average results with moderate computation time needed.
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