基于改进蚁群算法的应急物流中心选址问题研究

针对灾情等级不确定情况下应急物流的特点,本文利用不确定理论的相关方法,就应急物流中转站选址问题进行了相关分析和研究,构建了适应于应急物流特性的中心选址模型。最后进行仿真实例,利用改进蚁群算法求解模型,并得出相关结论。     

基于Lingo语言求解物流配送中心选址模型

首先针对物流配送中心选址的一般要求,以配送中心最小配送费用为目标,构造了混合整数规划选址模型。其次,结合物流配送中心选址实例,运用所建立的混合整数规划模型确定物流配送中心选址最佳方案。最后,借助优化建模软件LINGO,通过对实际问题的抽象建模,编写求解程序,成功求解了该模型。     

多品种多周期再利用逆向物流网络设计

指出了不确定环境下研究多品种、多周期再利用逆向物流网络优化问题的重要性,建立了再制造逆向物流网络随机规划模型,并给出了期望值方法求解模型的步骤。通过实例数值仿真,证明了该模型的可行性,并揭示了回收率的波动对最优选址策略的影响。     

动态需求下的多目标配送中心选址研究

为弥补现有选址模型未考虑需求变化的不足,研究了非确定性需求环境下的配送中心选址问题;运用随机机会约束规划为基本建模工具,建立了配送中心动态选址模型;模型不仅考虑最小化物流成本,而且考虑了最大满意度;运用主要目标法将多目标选址模型转化为单目标选址模型,并构造了相应的贪婪启发式求解算法,算例结果表明了模型和算法的有效性。     

动态需求下的多目标配送中心选址研究

为弥补现有选址模型未考虑需求变化的不足,研究了非确定性需求环境下的配送中心选址问题;运用随机机会约束规划为基本建模工具,建立了配送中心动态选址模型;模型不仅考虑最小化物流成本,而且考虑了最大满意度:运用主要目标法将多目标选址模型转化为单目标选址模型,并构造了相应的贪婪启发式求解算法,算例结果表明了模型和算法的有效性.     

不确定性物流中心选址问题研究

结合物流中心选址过程中的不确定因素,从仓储和运输管理的角度出发,建立基于库存策略的物流中心选址问题随机期望值规划模型。模型以综合成本(包括库存成本、运输成本和建设成本)最小为优化目标函数,运用混合智能算法对选址问题的随机期望值模型进行求解,结合比较算例与既有文献的计算结果,得出建设成本在时间段影响下综合成本的结论。     

应急物流服务网点选址模型研究

应急物流是应对突发事件的重要保障。应急物流可以降低灾害影响程度,缩短受害持续时间,使突发性事件造成损失最小化。而在进行应急物流决策时,首先会面临应急物流服务网点的选址问题。应急物流服务网点置于合理的位置,不仅可以降低成本,而且还能够保证提供应急物资的时效性,从而避免可能导致的更大损失。在给定限制条件下应急服务网点选址模型基础上,综合考虑应急需求的随机性和由灾害导致应急配送时间模糊的不确定情况,研究新的模糊和随机混合机会约束规划选址模型。     

Matlab应用于物流配送中心选址问题的研究

基于Matlab软件优化工具箱与带时效性的物流配送中心选址模型,通过优化工具箱中的有约束非线性规划函数对模型进行求解。并通过实例所分析出的结果,体现出模型与工具对物流企业或供应链优化选址具有积极意义。     

基于随机需求的物流配送网络规划决策方法

本文对不确定环境下的选址建模及求解进行了研究，建立了在模拟随机需求下的相关机会优化模型，提出一种将优化算法、随机模拟方法和遗传算法合成在一起，设计出的混合智能算法，为科学的网络决策规划提供帮助，最后进行数值演算。     

城市蔬菜批发市场配送中心选址模型及算法

针对城市蔬菜物流配送中心选址特点及要求,以实现配送中心配送费用最小为目标,建立混合整数选址优化模型,同时结合滨州市六街蔬菜批发市场配送中心选址实例,运用所构建的混合整数规划模型确定该蔬菜物流配送中心选址最佳方案.设计了基于Lingo软件的算法程序,成功求解了模型.     

基于随机需求的军事物流中心选址研究

军事物流中心的合理选择,对于提高后勤与装备保障水平,降低军事物流成本具有重要意义。文章针对军用装备的特点,当需求量为随机变量时,建立了军事物流中心选址的随机数学模型。通过等价变换。将该随机数学模型转换成确定性等价问题。结果表明：在随机需求条件下,该方法在保障军事目标实现的同时,能够有效降低军事物流成本,实现了军事效益和经济效益的和谐统一。     

企业内部二级库存系统中库存策略比较研究

针对随机需求下企业内部由中心仓库和下设的地区仓库组成的二级库存系统,运用推动式库存模型通过对相同服务水平下有中心仓库和无中心仓库两种情况的成本进行比较分析,来确定在企业内部二级库存系统中是否也设立中心仓库,以供决策者对库存策略进行合理规划。     

A multi-stage stochastic programming approach for pre-positioning of relief supplies considering returns

A dynamic pre-positioning problem is proposed to efficiently respond to victims’ need for relief supplies under uncertain and dynamic demand in humanitarian relief. The problem is formulated as a multi-stage stochastic programming model that considers pre-positioning with the dynamic procurement and return decisions about relief supplies over a time horizon. To validate the advantages of dynamic pre-positioning, three additional pre-positioning strategies are presented: pre-positioning with one-time procurement and without returns, pre-positioning with one-time procurement and returns, and pre-positioning with dynamic procurement and without returns. Using data from real-world disasters in the United States in the Emergency Events Database, we present a numerical analysis to study the applicability of the proposed models. We develop a sample average approximation approach to solving the proposed model in large-scale cases. Our main contribution is that we integrate dynamic procurement and return strategies into pre-positioning to decrease both costs and shortage risks in uncertain and dynamic contexts. The results illustrate that dynamic pre-positioning outperforms the other three strategies in cost savings. It also indicates that a higher return price is particularly helpful for decreasing unmet demand. The proposed models can help relief agencies evaluate and choose the solutions that will have the greatest overall effectiveness in the context of different relief practices.     

Optimal technology choice in a dynamic-stochastic environment

The issue of technology management has become particularly relevant to operations planners as a result of the introduction of new, computer-aided process technologies in recent years. At the same time, the fields of economics and management science have developed a number of models and paradigms for technology management and equipment replacement. In this article we present a model structure that builds on this theory and that is directed toward providing insights into the particular issues associated with fixed versus variable cost trade-offs.A fairly general, dynamic, stochastic model is presented. This model links production planning with technology selection in a direct manner. The model computes optimal production plans in the face of uncertain demand in the course of evaluating the costs and benefits of each technology alternative. The key result of the article concerns conditions for the optimality of moves to lower variable cost technologies.     

Simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand

To ensure a timely response to emergencies, governments are obliged to implement effective ambulance allocation plans. In practice, an emergency medical service (EMS) system works in an uncertain environment, with stochastic demand, response-times, and travel-times. This uncertainty significantly affects ambulance allocation planning. However, few studies in this field adequately consider the effect of spatiotemporal uncertainty in demand, because it is difficult to measure it quantitatively. As a result, few analytic models capture the dynamic nature of an EMS system and, thus, the allocation plans they generate are not efficient in practice. Therefore, this study proposes a simulation-based optimization method for ambulance allocation. A simulation model is constructed to mimic the operational processes of an EMS system, and to evaluate the performance of an ambulance allocation plan in an uncertain environment. Gaussian mixture model clustering is used to derive the uncertain spatial demand. Then, the simulation generates emergency demand based on the obtained spatial distribution. A Gaussian-process-based search algorithm is used together with the simulation model to identify optimal solutions. To validate the proposed method, a case study is conducted using data on emergency patients in the Shanghai Songjiang District. Compared with the current plan adopted in Songjiang, the experimental results demonstrate that the delay time and frequency of the EMS system can be reduced significantly by employing the proposed methods. Furthermore, nearly 41% of the allocation cost can be saved.     

A transshipment model for distribution and inventory relocation under uncertainty in humanitarian operations

The number of disasters and humanitarian crises which trigger humanitarian operations is ever-expanding. Unforeseen incidents frequently occur in the aftermath of a disaster, when humanitarian organizations are already in action. These incidents can lead to sudden changes in demand. As fast delivery of relief items to the affected regions is crucial, the obvious reaction would be to deliver them from neighbouring regions. Yet, this may incur future shortages in those regions as well. Hence, an integrated relocation and distribution planning approach is required, considering current demand and possible future developments.For this situation, a mixed-integer programming model is developed containing two objectives: minimization of unsatisfied demand and minimization of operational costs. The model is solved by a rolling horizon solution method. To model uncertainty, demand is split into certain demand which is known, and uncertain demand which occurs with a specific probability. Periodically increasing penalty costs are introduced for the unsatisfied certain and uncertain demand. A sensitivity analysis of the penalty costs for unsatisfied uncertain demand is accomplished to study the trade-off between demand satisfaction and logistical costs. The results for an example case show that unsatisfied demand can be significantly reduced, while operational costs increase only slightly.     

Quantity and price controls by the firm under demand uncertainty and welfare implications

A monopoly facing an uncertain demand can affect its profit distribution through the choice of ex ante controls. This paper compares two modes of behavior - price-setting and quantity-setting - in the context of a mean-variance model. The main results are: (a) With nonlinear cost, the monopoly will not be indifferent between the two modes. In the particular case of quadratic cost, conditions for the dominance of price-setting over quantity-setting behavior are derived. (b) Whereas it is well-known that the risk averse, quantity-setting monopoly will produce less under uncertainty than under certainty (or risk neutrality), the price-setting monopoly increases its expected output when faced by uncertain demand, possibly exceeding even the competitive output under uncertainty. (c) Using expected social surplus as a welfare criterion, price-setting emerges as the welfare-dominant behavior when there is a conflict between the privately and the socially preferred modes. (d) Finally, there exist conditions where price-setting monopolies welfare-dominate a competitive industry facing the same random demand.     

战时导弹保障路径的随机规划优化模型

针对战时导弹保障路径规划这一组合优化问题,在分析导弹保障路径优化过程中的不确定性因素的基础上,以时间、可靠度、损耗为置信度,运用网络图及不确定机会约束理论构建了导弹保障路径优化的随机规划模型,并通过具体案例来模拟验证模型的合理性和有效性。     

Integrated manufacturing/distribution planning decisions with multiple imprecise goals in an uncertain environment

This work presents a possibilistic linear programming (PLP) method for solving the integrated manufacturing/distribution planning decision (MDPD) problems with multiple imprecise goals in supply chains under an uncertain environment. The imprecise PLP model designed here aims to simultaneously minimize total net costs and total delivery time with reference to available supply, capacities, labor levels, quota flexibility and cost budget constraints at each source, as well as forecast demand and warehouse space at each destination. The proposed method achieves greater computational efficiency by employing the simplified triangular distribution to represent imprecise numbers. An industrial case is used to demonstrate the feasibility of applying the proposed method to a real MDPD problem. Overall, the proposed PLP method provides a practical means of solving the multi-objective MDPD problems in an uncertain environment, and can effectively improve manufacturer/ distributor relationships in a supply chain.