众包模式下考虑不同交付方式的配送路径优化研究

针对城市末端配送需求井喷情况下出现的物流运力短缺和社会及企业资源利用效率低下的问题;将众包模式引入具有个性化服务需求的城市末端配送中;通过社会闲置资源共享以缓解末端配送压力和提高配送效率与质量。建立了以自营车辆使用成本和众包配送的补偿成本最小化为目标函数的混合整数规划模型;并设计具有多种优化算子的自适应大邻域搜索算法对该模型进行求解。基于重庆市南岸区某物流公司的配送案例进行实例仿真分析;验证了该模型和算法的适用性和有效性;其实验结果表明在个性化交付场景下众包配送模式优于传统配送模式;可以实现18.1%的成本节约。     

客户订单合成配送问题的建模

为实现客户订单合成配送的自动化,以便节省人力、物力和运输时间,实现最优或近优合成配送,构造了客户订单合成配送问题的数学模型,此模型能够广泛应用于大中型企业、商场等产品配送系统,为实现客户订单合成配送问题的自动化提供了一个基本方法.     

客户成套订单配送策略混合遗传算法研究

针对第三方物流企业如何安排成套订单的合成运输问题,提出了客户成套订单配送决策问题,通过增加虚拟节点的方式,使此问题模型巧妙地转化为有时间窗车辆路径问题VRPTW模型。同时提出了一种混合遗传算法,成功地解决了在车辆数最小条件下,行车路程最短的关键问题。算例结果以及与其他文献算法的比较结果证明算法是有效的。     

客户订单合成配送问题的建模与启发式算法

在一些模型假设的基础上,建立了客户订单合成配送问题的数学模型;然后提出了求解该问题的以最小化运输费用为目标函数的两阶段启发式算法;最后给出一个用此算法求解该问题的实例。实验结果表明,该算法对于2×30×2规模能够在较短时间内有效地求得问题的近优解,算法的运行时间取决于源批发商数、订单数和交货组数。此算法为实现客户订单合成配送问题的自动化提供了一个基本方法。     

客户订单合成配送问题建模与遗传算法研究

为了降低成本,取得企业竞争优势,合理进行配送成为实现物流规模经济、降低物流成本的重要手段。在一些模型假设的基础上,建立了目标函数为最小化运距的客户订单合成配送问题的数学模型,提出并实现了解决该问题的遗传算法。大量仿真结果表明：该算法能够在较短的时间内有效求得问题的近优解,该算法的运行时间主要取决于订单数和交货组数,为实现物流配送提供了一个基本方法。     

顺丰大规模布局电商配送：已接易迅30％订单

原本定位高端商务快递市场的顺丰速递,正在想办法分食电商快递这一高速增长的市场,除了自建电商做经验探索、推出电商类客户特惠产品,顺丰还悄然与腾讯旗下电商易迅达成深度合作,目前顺丰已经接下易迅30％订单量。     

考虑客户满意度的网络零售配送时隙定价策略

为研究网络零售配送时隙的定价问题,结合客户满意度和收益双重目标,根据预期不确认理论和层次分析法给出了基于价格、交付期长度和宽度的客户满意度模型。基于收益管理理论,引入0-1变量,给出了考虑机会成本及额外成本的收益模型。根据零售商不同的收益偏好给出了双目标条件下的时隙定价模型。通过算例模拟分析了不同的零售商收益偏好对于时隙定价以及收益的影响;根据客户在线时隙选择随机性的特点,分析了在不同交付期长度及敏感度下的时隙定价策略,以及不同的客户到达率对于收益、客户满意度的影响。分析结果得出了考虑客户满意度和不同影响因素下的时隙定价策略,以及一系列比较有指导意义的性质和方法。     

考虑具有不同交付选择的物流配送路径问题研究

为了满足城市配送中顾客对交付方式及特定时间的个性化需求,引入顾客偏好概念刻画送货上门与自提服务的交付需求,以总运营成本最小化为优化目标建立了具有不同交付选择的车辆路径优化模型。考虑到模型的复杂性引入多种算子,设计并改进自适应大邻域搜索算法对模型进行求解。最后结合重庆市南岸区某配送案例进行实例分析,验证了该模型与算法的有效性。     

基于聚类的城市共同配送海量订单调度问题研究

针对云物流环境下城市共同配送海量订单调度难的问题,本文提出基于订单聚类的调度算法.首先针对单中心多车辆调度问题,提出基于单亲遗传的优化调度算法;在此基础上综合考虑城市配送中心的地理位置、车辆及配送点的地理位置、货物的种类、需求量,提出采用蚁群算法构建基于配送中心的海量订单聚类、优化调度算法.     

多门店协同下的订单拆分与配送联合优化方法

新零售带动传统企业转型,加速了以实体门店作为前置仓的线上订单履行模式的发展。针对订单需求不确定导致的就近门店无法满足订单需求的情况,提出多门店协同下的订单拆分与配送的联合优化问题。通过引入拆单数量限制,缩减问题求解空间,同时为了减少单独配送导致的路径重叠,采用协同配送的模式整合路径,并通过订单拆分与配送之间的调整优化降低订单履行成本。集成广度优先搜索和局部搜索算法,构造TNILS(top-N&improved local search)混合启发式算法求解问题。在合成数据集的基础上,通过协同配送与单独配送的结果对比,证明了协同配送的有效性及提出算法的可行性。通过与其他算法的实验结果对比,验证TNILS算法的有效性和稳定性。     

Towards solving 2-TBSG efficiently

Two-player turn-based stochastic game (2-TBSG) is a two-player game model which aims to find Nash equilibriums and is widely utilized in reinforcement learning and AI. Inspired by the fact that the simplex method for solving the deterministic discounted Markov decision processes is strongly polynomial independent of the discount factor, we are trying to answer an open problem whether there is a similar algorithm for 2-TBSG. We develop a simplex strategy iteration where one player updates its strategy with a simplex step while the other player finds an optimal counterstrategy in turn, and a modified simplex strategy iteration. Both of them belong to a class of geometrically converging algorithms. We establish the strongly polynomial property of these algorithms by considering a strategy combined from the current strategy and the equilibrium strategy. Moreover, we present a method to transform general 2-TBSGs into special 2-TBSGs where each state has exactly two actions.     

Performance analysis of probabilistic action systems

Formal notations like B or action systems support a notion of refinement. Refinement relates an abstract specification A to a concrete specification C that is as least as deterministic. Knowing A and C one proves that C refines, or implements, specification A. In this study we consider specification A as given and concern ourselves with a way to find a good candidate for implementation C. To this end we classify all implementations of an abstract specification according to their performance. We distinguish performance from correctness. Concrete systems that do not meet the abstract specification correctly are excluded. Only the remaining correct implementations C are considered with respect to their performance. A good implementation of a specification is identified by having some optimal behaviour in common with it. In other words, a good refinement corresponds to a reduction of non-optimal behaviour. This also means that the abstract specification sets a boundary for the performance of any implementation. We introduce the probabilistic action system formalism which combines refinement with performance. In our current study we measure performance in terms of long-run expected average-cost. Performance is expressed by means of probability and expected costs. Probability is needed to express uncertainty present in physical environments. Expected costs express physical or abstract quantities that describe a system. They encode the performance objective. The behaviour of probabilistic action systems is described by traces of expected costs. A corresponding notion of refinement and simulation-based proof rules are introduced. Probabilistic action systems are based on discrete-time Markov decision processes. Numerical methods solving the optimisation problems posed by Markov decision processes are well-known, and used in a software tool that we have developed. The tool computes an optimal behaviour of a specification A thus assisting in the search for a good implementation C.Received September 2002 Accepted in revised form January 2004 by E.C.R. Hehner     

激励学习的最优判据研究

激励学习智能体通过最优策略的学习与规划来求解序贯决策问题,因此如何定义策略的最优判所是激励学习研究的核心问题之一,本文讨论了一系列来自动态规划的最优判据,通过实例检验了各种判据对激励学习的适用性和优缺点,分析了设计各种判据的激励学习算法的必要性。     

基于马尔可夫决策过程的接纳控制模型研究

针对异构网络中系统容量有限、资源利用率低的问题,在分析对比传统呼叫接纳控制模型的基础上,提出一种基于马尔可夫决策过程理论的接纳控制模型。理论采用定义五元组的方式来描述建模过程,推导出目标评价函数,并通过求解具有QoS约束条件下的方程进行数值分析。仿真结果表明,该模型能满足网络动态实时性,解决系统容量有限情况下的最优接纳控制问题,从而能够在一定程度上降低各类呼叫业务的阻塞概率,达到提高不同用户服务质量的体验性、网络的系统收益最大化的目的。     

连续时间Markov决策过程互模拟等价及逻辑保持

模型检测中,Markov决策过程可以建模具有不确定性的系统,然而状态空间爆炸问题将会影响系统验证的成败与效率,互模拟等价可以用于系统状态的简约.在强互模拟关系的基础上,给出Markov决策过程模型弱互模拟等价关系的概念,导出了连续时间Markov决策过程及其内嵌离散时间Markov决策过程互模拟等价关系的内在联系;在强互模拟等价关系逻辑特征保持的基础上,给出弱互模拟等价关系下的逻辑保持性质,证明了弱互模拟等价的两个状态,同时满足除下一步算子外的连续随机逻辑公式,从而可以将原模型中的验证问题转换为简约后模型的验证问题,提高验证的效率.     

Impact of demand correlation and information sharing in a capacity constrained supply chain with multiple-retailers

This paper considers a single product, two-echelon capacity constrained supply chain consisting of a supplier and two retailers facing correlated end-item demand. We use a decentralized Markov decision process with restricted observations to represent this system and conduct a numerical study to quantify the benefits of information sharing to the retailers under varying levels of supplier capacity and supply allocation mechanisms. Our results show an inverse relationship between capacity and information and indicate the retailers can achieve significant benefits as a result of the information sharing partnership.     

On the use of MTBDDs for performability analysis and verification of stochastic systems

This paper describes how to employ multi-terminal binary decision diagrams (MTBDDs) for the construction and analysis of a general class of models that exhibit stochastic, probabilistic and non-deterministic behaviour. It is shown how the notorious problem of state space explosion can be circumvented by compositionally constructing symbolic (i.e. MTBDD-based) representations of complex systems from small-scale components. We emphasise, however, that compactness of the representation can only be achieved if heuristics are applied with insight into the structure of the system under investigation. We report on our experiences concerning compact representation, performance analysis and verification of performability properties.     

The impact of problem size on decision processes: an experimental investigation on very large choice problems with support of decision support systems

Abstract: Choice problems as a class of decision problems have attracted great attention for the last couple of decades. Among the frameworks and supporting theories used in their study, two have had the greatest impact: bounded rationality and cost–benefit. Both theories could find support from past empirical studies under different conditions or problem environments. In the past studies, problem size has been shown to play an important role in decision‐making. As problem size increases, a decision process may be detoured and the decision outcome may be different. In this paper we investigate the impact of problem size on three important aspects of the computer‐aided decision process – strategy selection, decision time/effort, and decision quality – through very large choice problems.