The objective of this study is to address a scheduling issue arising in production workshops, specifically dealing with unrelated parallel machine scheduling integrating flexible and periodic maintenance interventions. Machines, while operational, consume varying amounts of energy based on their state: idle states consume less energy than active ones. Additionally, energy consumption is influenced by both the production job and the specific machine.Our goal is to minimize two functions: the first pertains to reducing production and maintenance earliness/tardiness, while the second focuses on minimizing energy consumption. To address this problem, a Mixed Integer Linear Program (MILP) is proposed. The ϵ—constrained method is employed for computing the Pareto front, and further adaptation involves applying the Multi-Objective Simulated Annealing algorithm (MOSA) to handle instances of substantial size.The proposed MILP model demonstrates the ability to accurately determine the Pareto front for up to 30 production jobs and 2 machines on literature benchmarks within a timeframe of less than 1 hour. Moreover, the computed metrics proves the effectiveness of the proposed MOSA.

This paper addresses the problem of scheduling different non-preemptive jobs on a single machine under time of use electricity tariffs consideration. The considered machine has three main states (OFF, ON, Idle) and two transition states (Turn-on and Turn-off), Each of these machine's states as well as the processing jobs, consume a specific amount of energy. Moreover, a speed-scalable case of the problem is considered, in which jobs can be processed at an arbitrary speed with a trade-off between speed and energy consumption. First, an integer linear programming model with the objective of minimizing total energy consumption costs formulates this scheduling problem. Then, since this problem is strongly NP-hard, different approximate optimization methods are investigated to provide near-optimal solutions. Finally, an extensive computational study is carried out to establish the efficiency of the proposed algorithms. The obtained results show, how a well-tuned genetic algorithm combined with an adequate local search procedure constitutes an efficient method for solving this energy-aware scheduling problem.

This paper proposes a planning approach that integrates two levels of decision-making (strategic/tactical), using a mixed integer non-linear programming (MINLP) model. This model is designed to minimize costs in a supply chain process. It takes into account the management of several actors, the integration of several sources of energy, fuel consumption and the control of travel speeds during transport operations. This model could be used as a decision support tool to assess and control the effects of carbon emissions in a global supply chain. The model is an extension of the model proposed in [1], with a new contribution to the integration of energy aspects and vehicle speed control. The approach is tested on a real case from the literature taken from the textile and clothing industry. The results show that, considering the control of vehicle speed, the type of fuel, the energy used, and the carbon tax would significantly influence the economic and environmental activities of an industry.