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These autonomous electric robots operate on sidewalks and deliver time-sensitive goods, such as express parcels, medicine and meals. However, their limited cargo capacity and battery life require a return to a depot after each delivery. This challenge can be modeled as an electric vehicle-routing problem with soft time windows and single-unit capacity constraints. The objective is to serve all customers while minimizing the quadratic sum of delivery delays and ensuring each vehicle operates within its battery limitations. To address this problem, we propose a mixed-integer quadratic programming model and introduce an enhanced formulation using a layered graph structure. For this layered graph, we present two solution approaches based on relaxations that reduce the number of nodes and arcs compared to the expanded formulation. 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    <id>30455</id>
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    <title language="eng">Editorial: Environmental waste and renewable energy optimization for the sustainable development goals achievement</title>
    <parentTitle language="eng">Frontiers in Environmental Science</parentTitle>
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      <firstName>Biswajit</firstName>
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      <firstName>Syed Mithun</firstName>
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    <title language="eng">OPTE special issue on technical operations research (TOR)</title>
    <parentTitle language="eng">Optimization and Engineering</parentTitle>
    <identifier type="issn">1389-4420</identifier>
    <identifier type="issn">1573-2924</identifier>
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    <author>
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    <author>
      <firstName>Ulf</firstName>
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      <firstName>Peter F.</firstName>
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    <title language="eng">MEDEVAC mission planning and decision making with mixed-integer programming and wargaming</title>
    <abstract language="eng">In the highly dynamic environment of military operations, efficient medical evacuation (MEDEVAC) of casualties is a critical strategic challenge. This paper presents a method for planning MEDEVAC missions using mathematical optimization and gamification. By applying Mixed-Integer Programming (MIP), a mathematical optimization technique, to solve a MEDEVAC dispatching and routing problem, a structured approach to decision making in emergency situations is presented. Our MIP model enables the detailed consideration of operational conditions and the mission support with combat helicopters as escorts, and thus improves the strategic planning and efficiency of MEDEVAC operations. When it comes to balance mission safety versus execution speed, we will enter the realm of multicriteria optimization models. As a further aspect, the role of gamification is emphasized by developing a board game that simulates the planning task and thus promotes understanding of the planning software. This approach allows planners to compare the quality of their manual planning with the computer-generated solution, improve their own planning skills and realize the benefits of software support for automated, AI- (Artificial Intelligence-) -assisted planning.</abstract>
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