text: Determining the optimal location of control cabinet components requires the exploration of a large configuration space. For real-world control cabinets it is impractical to evaluate all possible cabinet configurations. Therefore, we need to apply methods for intelligent exploration of cabinet configuration space that enable to find a near-optimal configuration without evaluation of all possible configurations. In this paper, we describe an approach for multi-objective optimization of control cabinet layout that is based on Pareto Simulated Annealing. Optimization aims at minimizing the total wire length used for interconnection of components and the heat convection within the cabinet. We simulate heat convection to study the warm air flow within the control cabinet and determine the optimal position of components that generate heat during the operation. We evaluate and demonstrate the effectiveness of our approach empirically for various control cabinet sizes and usage scenarios.
  @cite_16: Display Omitted This research proposes the hybrid vehicle routing problem (HVRP), which is an extension of the green vehicle routing problem.A simulated annealing (SA) heuristic is proposed to solve HVRP.Computational results show that the proposed SA effectively solves HVRP.Sensitivity analysis has been conducted to understand the effect of hybrid vehicles and charging stations on the travel cost. This study proposes the Hybrid Vehicle Routing Problem (HVRP), which is an extension of the Green Vehicle Routing Problem (G-VRP). We focus on vehicles that use a hybrid power source, known as the Plug-in Hybrid Electric Vehicle (PHEV) and generate a mathematical model to minimize the total cost of travel by driving PHEV. Moreover, the model considers the utilization of electric and fuel power depending on the availability of either electric charging or fuel stations.We develop simulated annealing with a restart strategy (SA_RS) to solve this problem, and it consists of two versions. The first version determines the acceptance probability of a worse solution using the Boltzmann function, denoted as SA_RSBF. The second version employs the Cauchy function to determine the acceptance probability of a worse solution, denoted as SA_RSCF. The proposed SA algorithm is first verified with benchmark data of the capacitated vehicle routing problem (CVRP), with the result showing that it performs well and confirms its efficiency in solving CVRP. Further analysis show that SA_RSCF is preferable compared to SA_RSBF and that SA with a restart strategy performs better than without a restart strategy. We next utilize the SA_RSCF method to solve HVRP. The numerical experiment presents that vehicle type and the number of electric charging stations have an impact on the total travel cost.
summary: @cite_16 propose to use Simulated Annealing for solving the hybrid vehicle routing problem. The aim is to minimize the total travel cost for hybrid vehicles that use fuel and electricity while considering the time limit, electric capacity, fuel capacity, locations of fuel stations and electricity charging stations.