Fuzzy demand electric vehicle routing problem with soft time windows
研究了电动汽车在模糊需求和软时间窗下的路径规划问题，构建混合整数线性规划模型并用LINGO求解，通过参数分析和案例验证，为车队管理决策提供框架。
Abstract This research introduces a novel contribution to the Electric Vehicle Routing Problem (EVRP) field by addressing fuzzy demands, soft time windows, and recharging at demand points. The problem is formulated as a mixed-integer linear programming model that incorporates uncertainties in demand levels and allows flexibility in time windows with a penalty for its violation. LINGO software is utilized to solve the proposed model. To assess the effect of fuzzy demand, a parametric analysis is conducted by varying the fuzzy demand parameters using lexicographic fuzzy method implemented by LINGO. The model’s validity and effectiveness are verified using Solomon’s benchmark dataset, and further applied to a case study from the Egyptian local market. The solutions obtained are evaluated based on total costs incurred and total CO 2 emissions. By analyzing the resulting solutions, managerial implications are deduced, providing a framework for decision-makers in electric vehicle fleet management. Recommendations are made to decision makers on fuzzy demand modeling techniques, charging infrastructure, pricing and incentive strategies, and management systems. This study contributes to the advancement of EVRP research, offering practical solutions for real-world transportation planning and logistics management.