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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.

Distribution planning is crucial for most companies since goods are rarely produced and consumed at the same place. Distribution costs, in addition, can be an important component of the final cost of the goods. In this paper, we study a VRP variant inspired on a real case of a large distribution company. In particular, we consider a VRP with a heterogeneous fleet of vehicles that are allowed to perform multiple trips. The problem also includes docking constraints in which some vehicles are unable to serve some particular customers. Given the combinatorial nature and the size of the problem, which discard the use of efficient exact methods for its resolution, a novel heuristic algorithm is proposed. The proposed algorithm, called GILS-VND, combines Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) procedures. Our method obtains better solutions than other approaches found in the related literature, and improves the solutions used by the company leading to * Corresponding author * * Principal corresponding author