Abstract: Based on the characteristics of customer requirements for instant delivery that are not entirely rigid, and of participants’ enthusiasm that is not high in the crowdsourcing logistics distribution mode, this paper introduces the fuzzy time window, and quantifies the customer satisfaction rate into the fuzzy function of time that the crowdsourcing logistic personnel arrives at purchaser location. Under the premise of ensuring a certain customer satisfaction, the research on logistics scheduling based on instant distribution and revenue incentives model is constructed in order to maximize the crowdsourcing logistics personnel’s earnings considering the opportunity, overtime, overload punishment cost.Then this paper uses particle swarm optimization (PSO) algorithm with the dynamic weight to solve the proposed problems, and through the example analysis, the results show that the model is feasible and effective in keeping balance between customer and crowdsourcing logistics personnel.

Key words: crowdsourcing logistics, instant distribution, revenue incentive, customer’s satisfaction, particle swarm optimization

摘要： 基于参与众包物流配送模式的人员闲散、积极性不高,以及客户对即时配送要求并非完全刚性的特征,引入模糊时间窗,将客户满意度量化为众包物流配送人员到达客户位置时刻的模糊隶属度函数。在一定客户满意度下,以最大化众包物流配送人员收益为目标,构建了基于即时配送和收益激励的众包物流运力调度问题模型,考虑到机会、逾时、超载惩罚成本,利用带有动态权重的粒子群算法,通过算例验证分析,结果表明该模型在保证客户满意度和提高众包物流配送人员积极性方面具有可行性和有效性。

关键词: 众包物流, 即时配送, 收益激励, 客户满意度, 粒子群算法