Abstract: Aiming at the impact of dynamic demand on the carbon emissions of location and vehicle routing,a two-stage location-routing problem including pre-optimization stage and real-time optimization was studied.The corresponding carbon emissions minimization Open Location-Routing Problem (OLRP) with dynamic demands was proposed,and a four-stage hybrid quantum differential evolution algorithm was designed to solve the problem.Dynamics quantum revolving door and the greedy quantum selection method were used to update and improve the solution.The established model and algorithm were applied to solve the dynamic demand instances which were randomly generated,and the effects of dynamic demands on carbon emissions and distance were analyzed.The experiment results showed that the demand was positively correlated with carbon emissions,and the real-time response strategy could reduce target cost 15.5%.The proposed quantum differential evolution algorithm was compared with other intelligent algorithms through standard test instances,and the result verified that the algorithm had better search ability and could meet the requirements of real-time dynamic demands.

Key words: location-routing problem, dynamic demand, low carbon, quantum differential evolution algorithm, logistics distribution

摘要： 针对需求动态变化对选址及配送路径碳排放的影响,提出了预优化和实时优化的两阶段选址—路径问题,建立了相应的低碳动态需求开放式选址—路径模型,设计了四阶段混合量子差分进化算法进行求解。通过构造最优差分变异策略,并将其与动态量子旋转门及贪婪量子选择方式相结合进行解的更新及改善。仿真实验结果表明碳排放量与需求正相关,采用实时响应策略使目标成本降低15.05%;与其他3种算法相比,所提算法具有较好的搜索能力,能满足实时调度的要求。

关键词: 选址&mdash, 路径问题, 低碳, 动态需求, 量子差分进化算法, 物流配送