供应网络中越库转运中心仓门分配问题研究

本文探讨一种带有时间窗口的仓门分配问题———车辆在转运中心进行货物装卸作业时如何在其时间窗口限制内有效的分配有限的仓门资源,以达到最佳运作效率。以往的研究结果表明该问题是强NP难题,因此本文针对该问题的特殊结构,提出一种新颖的整合了贪婪算法、遗传算法以及禁忌算法思想的混合启发式算法来有效的解决该问题。我们并将该混合启发式算法与遗传算法、禁忌算法以及CPLEX这三种方式的求解效果进行对比,其数值实验结果表明混合启发式算法在求解效果上有明显的优势

越库转运问题的自适应遗传算法研究

探讨一种固定运输模式下的越库转运问题——采用运输量不可拆分的单次运送方式以最小费用通过选择固定的运输路径将货物经过越库转运到目的地,其货物将可能在越库中停留甚至无法运到目的地,这将会导致库存成本和惩罚成本.文中证明了此类越库转运问题是强NP难题,因此本文针对该问题的特殊结构,提出一种采用了邻域搜索技术的自适应遗传算法(AGA with NS)来有效的解决该类问题,数值试验结果表明该算法比CPLEX求解更加高效.此外文中还分别比较了在不采用邻域搜索或者自适应策略的情况下的三种遗传算法,其数值实验结果表明邻域搜索策略以及自适应策略对提高算法的效率有显著的影响

碳税制度下企业产品升级及信息披露策略研究

在实行碳税制度的背景下，本文构建了包含两家制造商的双寡头模型，以其中一家制造商是否投资产品升级减排作为关键信息，分别研究信息不公开（模型1）和信息公开（模型2）时两家制造商的均衡定价与最优利润以及制造商产品升级的条件,并且进一步研究当制造商同时掌握技术决策权和信息披露权时选择四种不同决策方案的条件。理论结果表明，如果升级后单位实际生产成本变大，制造商选择不升级且不公开；如果升级后单位实际成本变小，当升级后利润增值大于固定成本投入时，制造商选择升级并不公开，反之，选择不升级并公开。无论升级前后单位实际成本大小关系如何，制造商都不会选择升级且公开信息。数值试验部分表明，当制造商同时掌握技术选择权和信息披露权时，碳税是驱动制造商进行升级减排的主要因素，随着碳税的提高，制造商会依次从不升级不公开，不升级公开到升级不公开做决策。当不升级的概率越大时，制造商会在较低的碳税点越早开始升级；当减排努力越大时，制造商会在较高的碳税点才开始升级。国家自然科学基金资助项目（71671151、71371158、71711530046

基于组合算法的电子产品回收预测系统研究

对第三方逆向物流服务商而言，电子产品回收数量具有少样本、不确定性及模糊性的特点，电子产品回收量预测的精度直接影响到企业的运营成本以及服务水平。在单个预测模型中，GM(1,1)模型具有适应少样本预测的特点，对近期数据具有较好的逼近效果，但是对序列的趋势性比较敏感；FTS模型能够处理不确定性数据中因模糊性而产生的噪声，但是对序列趋势的把握具有滞后性。本文设计了GM(1,1)模型与FTS模型相结合的组合预测模型（FTS＿GM(1,1)模型），通过利用两个模型的优势以提高电子产品回收预测的准确性和可靠性。本文根据企业的真实回收数据进行预测，实验结果表明组合预测法比单个预测法具有更好的预测效果。在此基础上，本文提出了以FTS＿GM(1,1)组合模型为主，其他预测模型为辅的回收预测系统原型，为企业在实践中选取合适的预测模型提供建议。国家自然科学基金资助项目（71671151、71371158、71711530046

Supply Chain Management Based on Corporate Social Responsibility——Evaluation System and Performance Testing

我国企业承担社会责任,是参与国际竞争、顺应社会责任国际标准化趋势的必然要求。本文从供应链利益相关群体的角度,对社会责任的组成成分进行计量,并对企业社会责任与供应链管理绩效的关系进行了实证检验。研究发现,企业社会责任包括供应商责任、客户责任、环保责任、员工权益保障、社会道义责任5个组成成分;企业承担社会责任,会对供应链管理绩效(客户服务、内部效率和经济效益)产生积极的影响。In order to compete in global market and meet the international standardized trend of social responsibility,Chinese enterprises are required to take social responsibility of supply chain.Based on the view of stakeholder theory,we conduct an empirical study to explore the dimensions of logistics social responsibility (LSR),and investigate the relationship between LSR and supply chain performance.The results of data analysis identify five dimensions of LSR,namely supplier,customer,environment,employee and philanthropy.Besides,LSR is found conductive to the supply chain performance(customer service, internal efficiency and economic benefits).国家自然科学基金项目“基于企业社会责任的绿色供应链实证分析与运作研究”(70802052);教育部人文社会科学项目“绿色物漉系统设计与优化策略研究”(07JC630047)

Decision models for closed-loop supply chains with trade-ins

构建了3种基于以旧换新策略的闭环供应链决策模型,包括集中决策模型(C)、制造商销售第三方回收商回收模型(M3P)、零售商销售第三方回收商回收模型(R3P),并分别讨论了模型的最优定价与回收策略.通过理论与数值分析表明:制造商和整个供应链系统所获得的利润方面,模型C优于模型M3P,而模型M3P优于模型R3P;而第三方回收商所获得的利润方面,模型M3P也优于模型R3P;此外,根据产品全生命周期评估方法进行环境绩效分析,结果表明在环境绩效的表现上不存在具有绝对优势的模型.This paper develops three decision models for closed-loop supply chains( CLSC) with trade-ins,including the centralized decision model( model C),the manufacturer selling and the third-party collecting model( model M3P),and the retailer selling and the third-party collecting model( model R3P),and investigates the optimal pricing and collection strategies for each model. The theoretical analysis and numerical experiments show that,in terms of the profits obtained by the manufacturer and the whole supply chain,model C dominates model M3 P and model M3 P dominates model R3P; in terms of the third-party collector's profits,model M3 P dominates model R3 P. However,in terms of environmental performance,none of them dominates the others according to the life-cycle assessment methodology.国家自然科学基金资助项目(71371158;71671151

An Adaptive Genetic Algorithm for the Truck Scheduling Problem in the Crossdock Distribution Center

本文研究的是带有车辆容量限制以及时间窗口约束的越库配送车辆调度问题,该问题旨在通过车辆与仓门的合理分配来实现越库内部货物的最优调度从而达到高效的运作目标。由于该问题是强NP难的问题,本文基于遗传算法的思想,设计了单点交叉算子和两点交叉算子,并采用"交叉行为自适应选择机制"设计了一种自适应遗传算法来进行求解。在数值实验中,本文将该自适应遗传算法与分别采用单种交叉算子的遗传算法进行算法性能的比较,通过大量不同规模的数值算例的结果对比发现在这三种算法中,采用自适应机制的遗传算法在最终解的质量上总体表现最好,表明该算法对于求解此类问题具有良好的性能,同时也表明该自适应机制对于提升算法性能上具有显著的促进作用。As a just-in-time(JIT) logistics technology, crossdock refers to the operation of pickup-delivery and order-dealt activities at any intermediate points between upstream suppliers and downstream customers. Those intermediate points include transshipment warehouse or distribution center. They are used to achieve the elimination of goods storage, which can be temporarily stored and generally not more than one or two days, and can greatly reduce response time and inventory cost as well. Crossdock is increasingly being used to optimize the distribution network of supply chain, which can significantly reduce company's inventory levels by integrating inventory management strategy and distribution strategy. Thus, crossdock can reduce not only inventory management cost and cargo loss rate, but also speed up cash flow. It is reported that many well-known multi-national companies, such as Wal-Mart, Unilever, Dell, and Cisco, have implemented this technique successfully. The truck scheduling strategy plays a very important role in operations within the crossdock distribution center, which has a significant impact on the operational efficiency of crossdock. A good truck scheduling strategy can increase operational efficiency, improve customer service level, and reduce the total cost. Because crossdock-related problems are a hot topic in both academic and industrial areas nowadays, there is a lot of related research works in the previous literatures, which can be mainly divided into five categories:(1) the facility location problem of crossdocks;(2) the network flow problem in a multiple-crossdock distribution system;(3) the layout design problem within a crossdock;(4) the resource allocation problem within a crossdock;(5) the scheduling problem within a crossdock. In this paper, we try to extend the truck scheduling problem in a crossdock and consider the capacity constraint of outbound trucks as well as that of the crossdock. In reality, the number of inbound/outbound docks is limited, and the distance between inbound and outbound docks is different, and the amount of transshipment cargos and the time window for each inbound/outbound truck is different. Therefore, it is important to schedule these trucks to guarantee the number of trucks assigned to a door as many as possible so that cargos can be transshipped as many as possible and the transfer distance inside the crossdock can be minimum. These considerations will have a great impact on the efficiency and cost of crossdock operations. This paper tries to find a truck scheduling with a minimum cost under the time windows and capacity constraints of outbound truck and crossdock in order to increase the efficiency of crossdock operations. In the first part, we introduce this truck scheduling problem including constraints, assumptions, notations as well as decision variables, by which we propose a 0-1 Integer Programming Model. This kind of optimization problem is NP-hard in the strong sense. As a result, we try to develop a genetic algorithm by adopting single-point crossover(SPCO) and double-point crossover(DPCO) adaptively to solve it. In the second part, we introduce the steps of designing our adaptive genetic algorithm. We design the chromosome and adopt heuristic method to obtain a group of initial solutions at first rather than generate them randomly in order to obtain good quality ones because the quality of the initial solutions will influence the efficiency of the genetic algorithm greatly. SPCO and DPCO are developed as well as probability mutation operator. After that, an adaptive scheme is proposed to adopt the two crossover operators adaptively. The third part is numerical experiments, where we compare the performance of the adaptive genetic algorithm with those adopting SPCO and DPCO, respectively. We not only show the generation procedure of those parameters, but also develop three categories experiments including large, medium and small scale instances respectively. The results show that the adaptive genetic algorithm has the best performance in terms of final near-optimal solution quality in all scenarios. These findings mean that the proposed adaptive genetic algorithm is a good way to solve this kind of truck scheduling problem. Moreover, it shows that this adaptive scheme can improve the efficiency of the genetic algorithm for this problem. In summary, this paper tries to resolve the truck scheduling problem with the time windows and capacity constraints of outbound truck and crossdock, and develops an adaptive genetic algorithm to solve it. The numerical experiments show that our algorithm outperforms the others with a single crossover operator. This suggests that this adaptive genetic algorithm is an efficient way to solve this kind of problem.国家自然科学基金资助项目(71371158,71301032);; 教育部“新世纪优秀人才支持计划”资助项目(NCET-10-0712);; 中央高校基本科研业务费资金资助项目(2012221011

Truck dock assignment problem with operational time constraint within crossdocks

In this paper, we consider a truck dock assignment problem with an operational time constraint in crossdocks where the number of trucks exceeds the number of docks available. The problem feasibility is affected by three factors: the arrival and departure time window of each truck, the operational time for cargo shipment among the docks, and the total capacity available to the crossdock. The objective is to find an optimal assignment of trucks that minimizes the operational cost of the cargo shipments and the total number of unfulfilled shipments at the same time. We combine the above two objectives into one term: the total cost, a sum of the total dock operational cost and the penalty cost for all the unfulfilled shipments. The problem is then formulated as an integer programming (IP) model. We find that as the problem size grows, the IP model size quickly expands to an extent that the ILOG CPLEX Solver can hardly manage. Therefore, two meta-heuristic approaches, Tabu Search (TS) and genetic algorithm (GA), are proposed. Computational experiments are conducted, showing that meta-heuristics, especially the Tabu search, dominate the CPLEX Solver in nearly all test cases adapted from industrial applications. (C) 2007 Elsevier B.V. All rights reserved

ON PLANAR MEDIANOID COMPETITIVE LOCATION PROBLEMS WITH MANHATTAN DISTANCE

National Natural Science Foundation of China [71222105, 70701039, 70802052, 71072090]; Humanities and Social Science [09YJC630237]; National Ministry of Education; Fundamental Research Funds for the Central UniversitiesA medianoid problem is a competitive location problem that determines the locations of a number of new service facilities that are competing with existing facilities for service to customers. This paper studies the medianoid problem on the plane with Manhattan distance. For the medianoid problem with binary customer preferences, i.e., a case where customers choose the closest facility to satisfy their entire demand, we show that the general problem is NP-hard and present solution methods to solve various special cases in polynomial time. We also show that the problem with partially binary customer preferences can be solved with a similar approach we develop for the model with binary customer preferences

A Hybrid Genetic Algorithm for the Multiple Crossdocks Problem

We study a multiple crossdocks problem with supplier and customer time windows, where any violation of time windows will incur a penalty cost and the flows through the crossdock are constrained by fixed transportation schedules and crossdock capacities. We prove this problem to be NP-hard in the strong sense and therefore focus on developing efficient heuristics. Based on the problem structure, we propose a hybrid genetic algorithm (HGA) integrating greedy technique and variable neighborhood search method to solve the problem. Extensive experiments under different scenarios were conducted, and results show that HGA outperforms CPLEX solver, providing solutions in realistic timescales