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ReuseThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can't change the article in any way or use it commercially. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request.Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid is beneficial for the environment, since they are going to be used when needed. 88The proposed model also tries to obtain energy storage planning scenarios which 89 minimize maximum power flow between the smart-microgrid and the main grid. 90The two latter objectives evaluate the schedule compared to its extreme scenar- 91ios and also to a wide range of possible scenarios. This is done by measuring 92 the current expected cost compared to other possible costs using Sharpe Ratio the Branch and Bound (BB) optimization tree. 147The major contributions of the current work are: forecasts in order to test best-case and worst-case energy storage scenarios; 152• A novel multi-objective power dispatching problem. 153The remainder of this paper is organized as follows. Section 2 describes the 154 microgrid scenario. Section 3 describes, in detail, the proposed energy storage 155 management framework. Section 4 presents the computational experiments, 156and, finally, Section 5 details our final conclusions and future work.

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Given the important role of machine scheduling in manufacturing industry, we discuss power consumption in sequencing jobs in a scheduling problem, assuming variable speed operation in machines. The problem involves defining the allocation of jobs to machines, the order of processing jobs and the speed of processing each job in each machine. This problem can be viewed as a type of green scheduling problem, dealing with sustainable use of energy consumption and environmental effects. We propose a mixed integer linear programming (MILP) model for the unrelated parallel machine-scheduling problem with sequence-dependent setup times, with independent and non-preemptible jobs, minimizing the makespan and the total consumption of electricity. Furthermore, we employ a novel math-heuristic algorithm, named multi-objective smart pool search matheuristic (or simply smart pool), for finding solutions near the Pareto front, in a restricted computational budget. As a case study, a new set of instances is created for the problem. Those instances are solved using the classical-constrained method and the smart pool method. The obtained sets of non-dominated solutions indicate the conflict between both objectives, highlighting the relevance of the suggested approach to industry. From the obtained results, it was verified that the smart pool achieved good convergence towards the true Pareto front, as indicated by the hyper-volume metric, presenting lower average time for finding solutions on the Pareto front. In small to medium size instances, the smart pool search method can achieve very good approximations of the Pareto front with less computational effort than traditional methods.

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