A Framework to Minimize Total Energy Consumption and Total Tardiness on a Single Machine

A great amount of energy is wasted in industry by machines that remain idle due to underutilization. A way to avoid wasting energy and thus reducing the carbon print of an industrial plant is to consider minimization of energy consumption objective while making scheduling decisions. In order to minimize energy consumption, the decision maker has to decide the timing and length of turn off /turn on operation (a setup) and also provide a sequence of jobs that minimizes the scheduling objective, assuming that all jobs are not available at the same time. In this paper, a framework to solve a multiobjective optimization problem that minimizes total energy consumption and total tardiness is proposed. Since total tardiness problem with release dates is an NP-hard problem, a new greedy randomized multiobjective adaptive search metaheuristic (GRASPTETT) is utilized to obtain an approximate pareto front (i.e., an approximate set of non-dominated solutions). Analytic Hierarchical Process is utilized to determine the ”best” alternative among the solutions on the pareto front. The proposed framework is illustrated on a case study. It is shown that a wide variety of dispersed solutions can be obtained via the proposed framework, and as total tardiness decreases, total energy consumption increases.