Hybridizing genetic algorithm and single-based metaheuristics to solve unrelated parallel machine scheduling problem with scarce resources

<span lang="EN-US">This paper focuses on solving unrelated parallel machine scheduling with resource constraints (UPMR). There are </span><em><span lang="EN-US">j</span></em><span lang="EN-US"> jobs, and each job needs to be processed one of the machines aim at minimizing lang="EN-US">makespan</span></em><span lang="EN-US">. Besides dependence machine, processing time any depends usage a rare renewable resource. A certain number those resources R</span><em><span lang="EN-US">max</span></em><span can disseminated jobs for purpose them time, j units (</span><em><span lang="EN-US">rjm</span></em><span lang="EN-US">) when in m. When more assigned job, minimizes. However, available is limited, this makes problem difficult solve good quality solution. Genetic algorithm shows promising results UPMR. genetic suffers from premature convergence, which could hinder resulting quality. Therefore, work hybridizes single-based metaheuristics (SBH) handle convergence escape local optima improve solution further. The replaces mutation algorithm. evaluation performance was conducted through extensive experiments. </span>