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Freshness-driven vehicle routing problem: Modeling and application to the fresh agricultural product pick-storage-transportation 该论文主要针对生鲜农产品从生产基地到收购站，再转运到加工厂过程中导致生鲜农产品新鲜度降低，使得运输成本增高的问题，建立考虑新鲜度驱动的两阶段车辆路径问题优化模型，并分别设计遗传算法和粒子群算法进行求解，并以湖南炎陵黄桃为实例，验证模型和算法的可靠性，并通过敏感性分析给出管理建议，论文发表在《Journal of Industrial and Management Optimization》。论文摘要The fresh agricultural product (FAP) has highly perishable, hard to storage, and huge cost during the picking, storage, and transportation stage. In this paper, we aim to develop a multi-objective optimization model to minimize the total cost of collecting the FAP, where from the acquisition points aside the planting areas to the collection center, and the cost of transferring them to the processing factories (demand points) as well as to minimize the freshness decay during these processes. A epsilon constraint algorithm is used to convert the objective, freshness decay into constraints, and the model is transformed into a single-objective optimization model. According to the characteristics of the model, a hybrid algorithm based on genetic algorithm is developed. In order to verify the effectiveness of the model and algorithm, an example of yellow peach in Yanling, Hunan, China is constructed. And a comparison algorithm, simulated annealing algorithm, is presented. The results show that the effectiveness and efficiency of the hybrid algorithm based on genetic algorithm is better than that of simulated annealing algorithm. The insights of the sensitivity analysis indicate that the model and algorithm presented in this paper can be extended to other FAPs.论文全文{anote icon="fa-download" href="http://lishuanglin.cn/blog/usr/uploads/2023/01/2463324662.pdf" type="secondary" content="论文PDF下载"/} -
A novel min–max robust model for post-disaster relief kit assembly and distribution 该论文是针对灾后应急物资配送过程中的模块化装配问题，建立两阶段多目标鲁棒优化模型，并采用$\epsilon$-约束算法对模型进行求解，最后用云南地震相关数据对模型和算法进行验证，并通过敏感性分析得到相应管理启示。文章发表在《Expert Systems with Applications》。论文摘要In disaster response phase, different types of emergency relief materials are prepared simultaneously. Assorting and packing a proportion of relief items into relief kits will benefit in improving relief distribution agility and efficiency. This study focuses on the relief kit assembly and distribution problem, which includes two stages. The first stage solves the facility location and relief kit assembly problem with the minimum operation cost. The second stage optimizes the relief kit distribution plan with the minimum distribution cost and maximum demand satisfaction, in which an epsilon-constraint method is adopted to transfer the bi-objective model into a single one with the minimum total cost. Then, a min–max robust model is developed to cope with the uncertain demand and travel time. Computational experiments are provided to validate the effectiveness of the min–max robust model compared with deterministic model and two-stage stochastic model. A realistic case study based on earthquakes in Yunnan Province is provided to illustrate the applicability of the proposed min–max robust model. Some managerial insights are obtained by sensitivity analyses as follows. Assembling relief kits in the distribution centers is more effective than that in the demand points. Specifically, the average cost and 95% percentile of the former are 19.45% and 20.52% lower than those of the latter respectively. The vehicle loading capacity has a greater influence on the optimal solution than that of the available working time. Decision makers can balance the total cost and uncertainty budget by adjusting the conservatism level under expected demand satisfaction.论文全文{anote icon="fa-download" href="http://lishuanglin.cn/blog/usr/uploads/2023/01/2835826775.pdf" type="secondary" content="论文PDF下载"/}
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