Logistics Service Composition Based on Multi-Objective Optimization
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摘要:为了发展铁路货运组织改革提出的门到门的全程物流服务，达到更及时有效地响应货运客户日益多样化、个性化的物流需求的目的. 针对物流服务的特点，构建物流Web服务组合模型，采用Web服务组合技术将物流服务提供商提供的物流服务进行实时、动态的组合，并将组合问题转化为求解多目标优化问题，提出了改进的NSGA-Ⅱ （non-dominated sorting in genetic algorithm-Ⅱ）算法. 首先考虑了物流业务流程中物流服务的逻辑关系以及由于物流服务提供商联盟而产生的物流服务联合关系，设计了隐含逻辑关系和联合关系的染色体编码方式和遗传因子；其次，将实际铁路物流服务中客户的QoS （quality of service）属性需求作为组合服务的目标函数的惩罚函数，有效避免组合服务重计划问题，促使染色体向满足约束的方向进化；最后对算法进行实验仿真，结果表明算法解决物流服务组合问题的有效性与可行性，某次实际铁路物流服务选择的具体服务与组合服务的QoS属性都符合客户要求，组合服务从三个目标角度来看都是非劣的，而在物流任务与候选服务增加的情况下，算法运行时间满足实际需要. 并且较于NSGA-Ⅱ算法，考虑了物流服务提供商的联盟需求，得到满足客户物流服务需求的Pareto解在Pareto解集中所占比例达到100%，即得到了质量更优的物流服务组合方案.Abstract:Given the reform in railway freight transportation, the authors developed an end-to-end logistics service to respond more promptly and effectively to customers’ diverse, individual needs. They built a Web-based service model, incorporating all the characteristics of traditional logistics services. This Web-based model dynamically composed the services offered by logistics providers in real time and transformed them into multi-objective optimization problems. Then the authors applied an improved NSGA-Ⅱ（non-dominated sorting in genetic algorithm-Ⅱ） algorithm to the optimization problem. Firstly, in the chromosome encoding and genetic factor of the algorithm, they considered the logical relationship of the logistic services and the associations between the logistics service provider alliances. Secondly, they added customers’ demand for the QoS （quality of service） attribute as a penalty function to the fitness function, effectively avoiding the problem of service re-planning and encouraging the evolution of the chromosomes in the direction of the constraint. The experimental simulation of the algorithm shows that it is a feasible and effective solution to the logistics service composition problem. In an actual railway logistics service, the QoS attribute of specific and composite services meets customer requirements and the composite service is non-inferior for all objectives. The running time of the algorithm meets customer needs even when the number of logistics tasks and candidate services increases. The proposed algorithm offers a better logistics service composition plan compared to the NSGA-Ⅱ algorithm, because the Pareto solution that meets customers’ needs accounts for 100% of the Pareto solution set and also considers the demands of the allied logistics service providers.
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图 5不同物流任务数量与候选服务规模下的运行时间
Figure 5.Run time of various logistics task number and candidate service size
图 6满足需求的Pareto解在Pareto解集中所占比例
Figure 6.Proportion of Pareto solution meeting demand to Pareto solution set
表 1部分Pareto解
Table 1.Part of Pareto set
组合服务编号 物流任务1 物流任务2、3 物流任务4 物流任务5 组合服务质量属性 服务
编号质量
属性服务
编号质量
属性服务
编号质量
属性服务
编号质量
属性1 1 1.10 26 1.50 4 2.70 50 4.80 10.10 18.00 16.00 20.00 66.00 120.00 0.90 0.88 0.84 0.96 0.64 2 8 1.50 0 2.00 4 2.70 48 4.00 10.20 12.00 11.50 20.00 66.00 109.50 0.84 0.85 0.84 0.95 0.60 3 1 1.10 0 2.00 4 2.70 49 3.90 9.70 18.00 11.50 20.00 66.00 115.50 0.90 0.85 0.84 0.95 0.61 4 2 1.40 11 1.20 4 2.70 49 3.90 9.20 18.00 15.00 20.00 66.00 119.00 0.85 0.93 0.84 0.95 0.63 5 8 1.50 11 1.20 40 2.20 49 3.90 8.80 12.00 15.00 24.00 66.00 117.00 0.84 0.93 0.78 0.95 0.58 6 8 1.50 11 1.20 4 2.70 50 4.80 10.20 12.00 15.00 20.00 66.00 113.00 0.84 0.93 0.84 0.96 0.63 
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