基于SEM的城市公交服务质量-满意度-忠诚度研究

将城市公交服务质量简化为乘客便利感知质量、乘车环境质量、运营服务质量三个维度,运用结构方程模型(SEM)建立了常规公交乘客服务质量-满意度-忠诚度模型,并结合南昌市实际调查数据,通过服务质量指标因子定量分析各变量对乘客满意度-忠诚度的影响。研究结果表明,乘客便利感知质量、乘车环境质量、运营服务质量对乘客满意度均有直接正向相关性,而对乘客忠诚度则无直接正向相关性;影响城市公交服务质量的重要因素依次是公交路线的换乘便利情况、公交车况、公交卫生状况和公交运行安全性。     

回收量不确定的垃圾回收网络优化模型及算法

考虑到实际垃圾回收网络中回收中心日回收量的不确定性特征,以及经城市垃圾处理中心处理后的垃圾被再利用的可能性,建立了一类新的日回收量不确定的垃圾回收处理网络优化模型,分别就全部或部分垃圾能够被再利用两种情形,在假定日回收量为三角模糊量的条件下,利用模糊机会约束规划的方法推导了所建立的不确定性优化模型的确定型等价类.对给定的置信水平,利用LINGO软件能够直接求解所得到的确定性模型.案例研究证实了模型和方法的有效性,应用参数的灵敏度分析得到:目标和不同约束的置信水平对最优运输方案和最小运输成本均具有不同影响.     

公交网络时刻表再协同设计的多目标优化模型

为了改善公交服务质量,公交运营者试图调整现有时刻表的发车时间,使不同线路的车次协同到达换乘站点以方便乘客换乘。针对此场景,研究了公交时刻表重新协同设计问题,提出了求解该问题的多目标模型。模型考虑了对发车间隔灵敏的乘客需求、灵活的车次协同到站方式和发车时间的规则性,分析了该多目标模型的特征和计算复杂性,表明本文研究的问题是NP-hard问题,且它的帕累托最优前沿是非凸的,设计了基于非支配排序的遗传算法求解模型。算例表明,与枚举算法相比,提出的求解算法在较短的时间内可获得高质量的帕累托解。     

技术进步视角下经济政策不确定性对经济增长的影响研究

文章首次将经济政策不确定性、技术进步和经济增长纳入统一分析框架,在技术进步视角下分析了经济政策不确定性对经济增长的非线性影响.通过利用非线性格兰杰因果检验方法识别出中国经济政策不确定性,技术进步和经济增长之间存在的非线性信息,进一步运用半参数全局向量自回归模型进行实证研究.结果表明:1)经济政策不确定性对技术进步和经济...     

城市公交查询系统的设计与实现

针对含有“公汽、地铁、步行”的复杂公交网络环境,首先对公交问题所提供的数据进行分析,并优化数据的存储结构;其次充分考虑到公交网络客流分配的主要因素一换乘次数、票价、时间,提出了公交网中这三个目标的加权平均最优路径模型及其算法;最后对模型的算法用Matlab软件实现.通过测试,结果显示本系统能快速响应出满足乘客不同需求的公交出行路径。     

模糊环境下的城市生活垃圾逆向物流网络设计

在垃圾回收逆向物流网络中,回收的数量具有不确定性,根据这一特点,将各消费区域垃圾回收量看成模糊参数,提出了逆向物流网络的模糊约束机会规划模型,通过把模型中模糊机会约束清晰化,将模型转化为确定性的混合整数规划模型,通过算例对模型进行了求解,为逆向物流网络设计提供了依据.     

一种改进的公交配流模型与算法

对两种经典的公交配流模型进行了对比分析,指出了在考虑拥挤影响时两种模型进行公交配流各自存在的缺点．随后对两种模型存在的不足进行了原因分析,并在此基础上对Spiess和Florian提出的线性规划模型及算法进行了改进．改进的模型运用了“最优策略”和“有效频率”的思想, 考虑了拥挤对站点乘客等车时间的影响．模型用MSA（相继平均法）算法进行求解,最后在一个简单网络上进行对比分析,表明改进后的模型能够较合理地求解考虑站点拥挤的公交配流问题．     

不确定性最小二乘估计及其统计性质

在不确定性线性模型中,参数通常带有不确定的先验信息,它们影响着参数估计及其精度.本文利用椭球集合对含有先验信息的参数进行描述,建立基于椭球不确定性的线性模型,并提出相应的估计算法.本文主要采用直接迭代算法解决参数估计中的权值分配问题,给出参数估计可容许性的充要条件.最后,通过实证分析算法的有效性,解决权值分配对平差解的影响.     

弹性需求下城市公交网络服务的优化

对具有弹性需求的城市公交网络系统进行了票价结构与发车频率组合的优化。考虑到公交定价和发车频率会影响乘客需求以及乘客对路径的选择行为,将这一问题描述为一个双层规划问题,上层是寻求社会福利最大的优化问题;下层考虑了乘客的出行选择行为,为弹性需求下乘客在城市公交网络上流量分布的随机用户平衡分配模型。鉴于双层规划问题的非凸性,运用模拟退火算法对模型进行求解,并给出一个仿真算例说明提出的模型和算法的合理性。     

具有现实约束的均值-下半方差模糊投资组合优化研究

在实际投资中,不确定性和现实约束影响着投资决策。为了度量实际投资中的不确定性,本文引入模糊变量,并采用下半方差度量投资组合的风险,考虑一个具有上下界限制、交易成本以及无风险资产借贷约束的均值-下半方差模糊投资组合模型。该模型是一个具有不等式约束的二次规划问题,使用不等式的旋转算法对该模型进行求解。最后,通过“滚动样本”方法,对上述模型与最小方差模型以及等比例模型进行对比与评价。结果表明,本文考虑的模型的夏普比率表现更优。     

Advances on measuring the operational complexity of supplier–customer systems

Even structurally simple supplier–customer systems can be operationally complex. This operational complexity can be colloquially defined as the uncertainty associated with managing the dynamic variations, in time or quantity, across information and material flows at the supplier–customer interface. This paper proposes a means of measuring the information demands placed on supplier–customer systems, as a result of this uncertainty.This paper mathematically models the operational complexity of supplier–customer systems from an information-theoretic perspective. A unique feature of this measure is that it captures, in relative terms, the expected amount of information required to describe the state of the system. The measure provides flexibility in the scope and detail of analysis, while at the same time allowing a systematic hierarchical approach.The application of the measure allows valuable insights to be obtained in terms of the degree of uncertainty, level of control and the detail of monitoring required to manage the operational complexity of supplier–customer systems.     

Robust vehicle routing problem with deadlines and travel time/demand uncertainty

In this article, we investigate the vehicle routing problem with deadlines, whose goal is to satisfy the requirements of a given number of customers with minimum travel distances while respecting both of the deadlines of the customers and vehicle capacity. It is assumed that the travel time between any two customers and the demands of the customer are uncertain. Two types of uncertainty sets with adjustable parameters are considered for the possible realizations of travel time and demand. The robustness of a solution against the uncertain data can be achieved by making the solution feasible for any travel time and demand defined in the uncertainty sets. We propose a Dantzig-Wolfe decomposition approach, which enables the uncertainty of the data to be encapsulated in the column generation subproblem. A dynamic programming algorithm is proposed to solve the subproblem with data uncertainty. The results of computational experiments involving two well-known test problems show that the robustness of the solution can be greatly improved.     

MRP performance effects due to forecast bias and demand uncertainty

The effects of forecast bias and demand uncertainty in a batch production environment are investigated using an integrated MRP planning and execution test bed. The use of inflated planned lead time and safety stock to compensate for forecast error is evaluated. Analysis is performed in terms of meeting both the MPS due dates and customer delivery requirements. Forecast bias and demand uncertainty are shown to affect MPS and delivery performance quite differently. Results also show that increasing either planned lead times or safety stock is effective in improving delivery performance. If demand uncertainty dominates completion time variability, the use of safety stock will achieve delivery objectives with less finished goods inventory.     

Accurate response by postponement

Accurate response and postponement are two strategies for a firm to deal with demand uncertainty and better match supply with demand. Under accurate response, reactive capacity could be used for customized make-to-order (MTO) production according to customer demands. On the other hand, using postponement a firm could perform the non-postponed activities speculatively and use reactive capacity only to customize the final product thereby enhancing the profitability of accurate response. Surprisingly existing research in the areas of accurate response and postponement does not address this potential. In this paper we aim to fill this gap by proposing a stylized model for accurate response by postponement.     

Simple Heuristics for the Vehicle Routeing Problem with Soft Time Windows

We describe three simple heuristics for the vehicle routeing problem with customer time windows that can be violated by paying appropriate penalties. The customer demands are known, and a homogeneous fleet of vehicles stationed at a single depot is considered. The penalty payable to a customer is assumed to be a linear function of the amount of time window violation. Upper limits are imposed on both the penalty payable and the waiting time allowed at any customer. At each customer in a route, the PC-based heuristics simultaneously determine the actual time to begin service, and the next customer to serve. To achieve this, each heuristic uses different measures to compare the cost of waiting and penalty payable, with the benefit obtained by leaving immediately for the next customer. Computational results on a set of benchmark problems show that the procedure is efficient and enables significant reduction in the number of vehicles required and/or the total route distances while controlling both customer penalties and waiting times.     

Evaluation of three production planning procedures for the use of recipe flexibility

Process industries often obtain their raw materials from mining or agricultural industries. These raw materials usually have variations in quality, which often lead to variations in the recipes used for manufacturing a product. Another reason for varying the recipe is to minimize production costs by using the cheapest materials that still lead to a satisfactory quality in the product. A third reason for using recipe flexibility is that it may occur that at the time of production not all materials for the standard recipe are available. In earlier research we showed under what conditions the use of this type of recipe flexibility should be preferred to the use of high materials stock to avoid materials shortages. We also showed that the use of recipe flexibility to account for material shortages can be justified if the material replenishment leadtime is long, the demand uncertainty is high and the required service level is high. In this paper we assume that these conditions are satisfied and we investigate three different production planning procedures that make use of recipe flexibility to cope with the uncertainty in demand and supply. We assume that the customer order leadtime is much smaller than the material replenishment leadtime, and therefore demand uncertainty is high. The optimal procedure optimizes material use over a planning horizon equal to the material replenishment leadtime, taking into account the customers orders and knowledge of the distribution function of future demand. The deterministic procedure also optimizes the material use over the material replenishment leadtime, but it assumes a deterministic demand level for unknown orders. The simplest, myopic procedure optimizes material use over only the accepted customer orders. These three procedures are investigated via an experimental design of computer simulations of an elementary small scale model of the production planning situation. The results show that the optimal procedure outperforms the other two procedures. Furthermore, for a realistic cost structure in feed industry under certain circumstances the use of the optimal procedure may lead to a 4% increase in profit. However, this improvement must be weighted against the cost incurred by the operational use of this complex procedure. Based on these considerations and the numerical results in this paper, we may expect that for some situations in practice the use of the simplest myopic procedure, optimizing material use only over the available customer orders, will be justified from an overall cost point of view.     

Time-dependent and independent control rules for coordinated production and pricing under demand uncertainty and finite planning horizons

We address the effect of uncertainty on a manufacturer’s dynamic production and pricing decisions over a finite planning horizon. The demand for products, which depends on their price, is characterized by two stochastic processes: potential demand and customer price sensitivity. An optimal policy for coordinating production and pricing is a time-dependent feedback rule with respect to the state of the manufacturer’s inventories. We show that when the volatility of customer sensitivity to the product price is negligible, the optimal policy can be obtained analytically. Moreover, our simulations demonstrate that the volatility of stochastic customer price sensitivity does not have a strong effect on the manufacturer’s expected profit. Therefore, the solution derived for the case of customer price sensitivity with zero volatility can serve as a good approximation heuristic for the optimal policy if the true volatility of customer price sensitivity is within 40 % of its mean and the volatility of potential demand is within 25 % of its mean. Moreover, under these conditions, a simplified, time-independent control rule deteriorates expected profits by only 1.5 %.     

大规模定制模式下基于三角直觉模糊信息的生产指派问题研究

针对大规模定制模式下生产指派面临的模糊不确定性等问题,探讨如何运用三角直觉模糊数构建生产指派模型进而确定最满意的生产指派方案。首先,运用三角直觉模糊数刻画、设计基于顾客个性化需求与企业产出的相对偏差、产品价格与交货期等影响因素的顾客满意度函数和企业利润函数。其次,建立了兼顾企业利润和顾客满意度的调和函数,据此构建了生产指派模型,并证明该模型有可行解和最优解,然后说明模型的求解方法。最后,通过具体实例说明生产指派模型的求解过程及其有效性、合理性。     

Coordinated control of distribution supply chains in the presence of fuzzy customer demand

This paper considers a single product inventory control in a Distribution Supply Chain (DSC). The DSC operates in the presence of uncertainty in customer demands. The demands are described by imprecise linguistic expressions that are modelled by discrete fuzzy sets. Inventories at each facility within the DSC are replenished by applying periodic review policies with optimal order up-to-quantities. Fuzzy customer demands imply fuzziness in inventory positions at the end of review intervals and in incurred relevant costs per unit time interval. The determination of the minimum of defuzzified total cost of the DSC is a complex problem which is solved by applying decomposition; the original problem is decomposed into a number of simpler independent optimisation subproblems, where each retailer and the warehouse determine their optimum periodic reviews and order up-to-quantities. An iterative coordination mechanism is proposed for changing the review periods and order up-to-quantities for each retailer and the warehouse in such a way that all parties within the DSC are satisfied with respect to total incurred costs per unit time interval. Coordination is performed by introducing fuzzy constraints on review periods and fuzzy tolerances on retailers and warehouse costs in local optimisation subproblems.     

A priori orienteering with time windows and stochastic wait times at customers

In the pharmaceutical industry, sales representatives visit doctors to inform them of their products and encourage them to become an active prescriber. On a daily basis, pharmaceutical sales representatives must decide which doctors to visit and the order to visit them. This situation motivates a problem we more generally refer to as a stochastic orienteering problem with time windows (SOPTW), in which a time window is associated with each customer and an uncertain wait time at a customer results from a queue of competing sales representatives. We develop a priori routes with the objective of maximizing expected sales. We operationalize the sales representative’s execution of the a priori route with relevant recourse actions and derive an analytical formula to compute the expected sales from an a priori tour. We tailor a variable neighborhood search heuristic to solve the problem. We demonstrate the value of modeling uncertainty by comparing the solutions to our model to solutions of a deterministic version using expected values of the associated random variables. We also compute an empirical upper bound on our solutions by solving deterministic instances corresponding to perfect information.