SIMHEURISTIC APPROACH TO A SINGLE-MACHINE SCHEDULING PROBLEM WITH ENVIRONMENTAL CONSIDERATIONS AND STOCHASTIC TIMES

This paper addresses a stochastic version of the single-machine scheduling problem with environmental considerations, where jobs processing times behave as a random variable, and the energy cost depends on which jobs will be processed in which periods, since each job has its own energy consumption and each period has its own energy tariff due to the Time-of-use tariff policy. This work aims to minimize the total energy consumption under different scenarios of uncertainty for processing times. To solve the problem, a simheuristic approach is presented, which combines strategies from the Simulated Annealing and Greedy Randomized Adaptive Search Procedure metaheuristics for solution optimization, along with Monte Carlo Simulation to generate multiple scenarios of a solution. Thus, it is possible to observe the impact of process variations on total energy consumption.