Vehicle routing with soft time windows and Erlang travel times

This paper investigates vehicle-routing problems in which the travel times are random variables, and deliveries are made subject to soft time-window constraints. In particular, we model the travel time using a shifted gamma distribution. Penalties are incurred for deviations from the customers' time windows—early or late—and are developed using a fixed cost, a linear cost penalty, and/or a quadratic loss penalty. Alternatively, specifying a given probability of meeting the time-window constraints is considered. A tabu-search metaheuristic is developed, and computational results on test problems from the literature are reported.     

Decentralized communication, trail connectivity and emergent benefits of ant pheromone trail networks

Communication improves decision-making for group-living animals, especially during foraging, facilitating exploitation of resources. Here we model the trail-based foraging strategy of Pharaoh’s ants to understand the limits and constraints of a specific group foraging strategy. To minimise assumptions we used model parameters acquired through behavioural study. Pharaoh’s ants (Monomorium pharaonis) exploit the geometry of trail networks bifurcations to make U-turns, if they are walking the wrong way. However, 7% of foragers perform apparently incorrect U-turns. These seemingly maladaptive U-turns are performed by a consistent minority of specialist U-turners that make frequent U-turns on trails and lay trail pheromones much more frequently compared to the rest of the colony. Our study shows a key role for U-turning ants in maintaining the connectivity of pheromone trails. We produced an agent-based model of a heterogeneous ant community where 7% of agents were specialised frequent U-turners whilst the remaining 93% rarely U-turned. Simulations showed that heterogeneous colonies enjoyed significantly greater success at foraging for distant food resources compared to behaviourally homogeneous colonies. The presence of a cohort of specialised trail-layers maintains a well-connected network of trails which ensures that food discoveries are rapidly linked back to the nest. This decentralised information transfer might ensure that foragers can respond to dynamic changes in food distribution, thereby allowing more individuals in a group to benefit by successfully locating food finds.     

基于鲁棒优化的随机时变网络最优路径研究

交通事故、恶劣天气以及偶发的交通拥堵等都会导致道路交通网络中行程时间的不确定性,极大地影响了道路交通系统的可靠性,同时给日常生活中出行计划的制定以及出行路径的选择带来了不便。因此,本次研究将综合考虑道路交通网络中由于交通流量的全天变化所导致的路径行程时间的时变特征,以及由于事故、天气等不确定因素所导致的路径行程时间的随机特征,并以此作为路网环境的假设条件,对出行路径选择问题进行研究。具体地,首先建立行程时间的动态随机变量,并在此基础上模拟构建了随机时变网络。随后,定义了该网络环境下路径选择过程中所考虑的成本费用,并通过鲁棒优化的方法,将成本费用鲁棒性最强的路径视为最优路径。随后,在随机一致性条件下,通过数学推导证明了该模型可以简化为解决一个确定性时变网络中的最短路径问题。最终,具有多项式时间计算复杂度的改进Dijkstra算法被应用到模型的求解中,并通过小型算例验证模型及算法的有效性。结果表明,本研究中所提出的方法可以被高效率算法所求解,并且不依赖于先验行程时间概率分布的获取,因此对后续的大规模实际城市道路网络应用提供了良好的理论基础。此外,由于具有行程时间随机时变特征的交通网络更接近实际道路情况,因此本次研究的研究成果具有较高的实际意义和应用价值。     

Proprietor and customer costs in the incomplete hub location-routing network topology

The hub location problem finds the location of hubs and allocates the other nodes to them. It is widely supposed the network created with the hub nodes is complete in the extensive literature. Relaxation of this basic supposition forms the present work. The model minimizes the cost of the proprietor, including the fixed costs of hubs, hub links and spoke links. Costs of hub and spoke links are contemplated as fixed cost or maintenance cost. Moreover, the model considers routing costs of customers who want to travel from origins to destinations. In this study, we offer a model to the multiple allocations of the hub location problems, under the incomplete hub location-routing network design. This model is easily transformed to other hub location problems using one or more constraints. No network format is dictated on the hub network. We suggest a set of valid inequalities for the formulation. Some lower bounds are developed using a Lagrangian relaxation approach and the valid inequalities. Computational analyses evaluate the performances of the lower bounding implementations and valid inequalities. Furthermore, we explore the effects of several factors on the design and solution time of the problem formulation.     

A formal framework to analyze cost and performance in Map-Reduce based applications

Satisfying the global throughput targets of scientific applications is an important challenge in high performance computing (HPC) systems. The main difficulty lies in the high number of parameters having an important impact on the overall system performance. These include the number of storage servers, features of communication links, and the number of CPU cores per node, among many others.In this paper we present a model that computes a performance/cost ratio using different hardware configurations and focusing on scientific computing. The main goal of this approach is to balance the trade-off between cost and performance using different combinations of components for building the entire system. The main advantage of our approach is that we simulate different configurations in a complex simulation platform. Therefore, it is not necessary to make an investment until the system computes the different alternatives and the best solutions are suggested. In order to achieve this goal, both the system's architecture and Map-Reduce applications are modeled. The proposed model has been evaluated by building complex systems in a simulated environment using the SIMCAN simulation platform.     

Perturbation and Asymptotic Solutions for Problems in the Theory of Urban Land Rent

Simple but accurate approximate solutions for R. M. Solow's theory of urban land rent with congest cost of transportation are obtained by perturbation and asymptotic methods. Only the case of an absentee landlord is discussed though the techniques used can be modified in a straightforward manner to handle the more interesting (and mathematically more complicated) case of municipal ownership. Solow's model is also extended to have expenditures for housing and space as separate items in the budget equation. This extension eliminates the ambiguity in the value for the fraction of income after travel cost spent on ground rent, without affecting the structure of the mathematical problems associated with Solow's original theory.     

Using fuzzy integral for evaluating subjectively perceived travel costs in a traffic assignment model

A good traffic assignment model can be a powerful tool to describe the characteristics of traffic behavior in a road network. The traffic assignment results often play an important role in transportation planning, e.g., an optimal and economical network design. Many traditional traffic assignment models rely heavily on the travel cost function established by Wardrop’s principles; however, the Wardrop’s travel cost function has been proven to be weak for explaining the uncertainty and interactivity of traffic among links. This study tries to construct a traffic assignment model that is different from Wardrop’s in many aspects. First, it considers the cross-effect among the links. Second, a fuzzy travel cost function is established based on the possibility concept instead of precise calculation of traffic volumes. Third, the techniques of fuzzy measure and fuzzy integral are applied to calculate the subjectively perceived travel costs during traffic assignment. Furthermore, in order to validate our model, a detailed network with 22 nodes and 36 links is used to illustrate it. Study results show that our model explains more interactivity and uncertainty of traffic among links when compared with the traditional model of Wardrop’s.     

Dynamic system optimal performances of shared autonomous and human vehicle system for heterogeneous travellers

ABSTRACT

Autonomous vehicles (AV) can solve vehicle relocation problems faced by traditional one-way vehicle-sharing systems. This paper explores the deterministic time-dependent system optimum of mixed shared AVs (SAV) and human vehicles (SHV) system to provide the benchmark for the situation of mixed vehicle flows. In such a system, the system planner determines vehicle-traveller assignment and optimal vehicle routing in transportation networks to serve predetermined travel demand of heterogeneous travellers. Due to large number of vehicles involved, travel time is considered endogenous with congestion. Using link transmission model (LTM) as a traffic flow model, the deterministic time-dependent system optimum is formulated as linear programming (LP) model to minimize the comprehensive cost including travellers’ travel time cost, waiting time cost and empty vehicle repositioning time cost. Numerical examples are conducted to show system performances and model effectiveness.     

Team assignments and scheduling for the NCAA basketball tournament

The National Collegiate Athletic Association (NCAA) organizes a men's basketball tournament every March to determine the national champion for the current season. In organizing the tournament, the emphasis is typically on selection of the most deserving teams to participate and providing a fair, equitable environment in which to play that result in a true, undisputed champion for the season. However, there are growing concerns of dwindling actual attendance at tournament games and increasing financial burden on the NCAA related to reimbursable team travel expenses. In this paper, we describe the development of an integer program designed to optimize team assignments in the sense of minimizing the distance travelled by teams to game sites and the corresponding travel costs. The goal is to increase tournament accessibility to fans as well as lessen the financial impact to the NCAA while maintaining the integrity of the tournament. We test our model against actual tournament assignments from the past 5 years. Results show consistent and significant cost savings and reductions in distance travelled without compromising the fairness and structure of the tournament. Overall, we demonstrate the usefulness of the model in both operational and strategic business decisions.     

Quantum mechanics from cosmic fluctuations

We show that it is possible to derive Quantum Mechanics from cosmic fluctuations in the light of Santamato's geometric approach and a simple model in the Robertson–Walker geometry considered in that context.     

双瓶颈路段家庭出行者早高峰出行问题研究

针对一次连续出行中两个路段各存在一个瓶颈的双瓶颈路段,研究了家庭出行者早高峰期间先后经过双瓶颈到达学校和工作地的出行行为。首先建立了无收费下的用户均衡模型,接着考虑了拥挤收费模型,并就学校工作地开始时间差值大小的不同展开讨论,分析得出对应系统总出行成本最优时的收费值和收费时窗。研究发现收费管理能够有效降低系统总成本,并且学校和工作地开始时间差较小时的系统总成本更低,最后,通过数值算例验证了高峰时长保持不变,且得出了使得系统出行成本最优时的收费方案。     

Entropy maximization model for the trip distribution problem with fuzzy and random parameters

Many trip distribution problems can be modeled as entropy maximization models with quadratic cost constraints. In this paper, the travel costs per unit flow between different zones are assumed to be given fuzzy variables and the trip productions at origins and trip attractions at destinations are assumed to be given random variables. For this case, an entropy maximization model with chance constraint is proposed, and is proved to be convex. In order to solve this model, fuzzy simulation, stochastic simulation and a genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, a numerical example is presented to demonstrate the application of the model and the algorithm.     

多目的地多停车场系统动态停车分配模型

在一个多目的地多停车场系统中,每个目的地附近有多个停车场可供选择,每个停车场也可供多个目的地的停车需求停车。每个目的地的停车需求在各停车场的停车量即为停车分配问题. 本文定义了停车量分配均衡原则:各目的地的驾驶员总是首选最低费用(包括在停车场内的停车费用以及停车时间、步行时间转换后的货币成本,其中停车费用及在停车场内的车位找寻时间都正相关于该停车场当前时刻停车饱和度)的停车场停车;由于停车场具有容量限制,当费用最低的停车场已满则再选择费用次低的停车场,以此类推;所有没被使用的停车场或者比被使用的停车场具有更高的费用或者停车位已满。考虑停车需求和各停车场内空余泊位数的实时动态特性,提出了与该均衡原则等价的数学规划模型,证明了其解的唯一性并设计了求解算法。通过两个算例对模型进行了验证,并再现了目的地附近各停车场内车位占用变化规律,从而为动态停车收费、停车选址规划等提供依据。     

中国股票市场的收益-风险关系和惯性分析

本文采用非对称 EGARCH-M模型刻画了中国股票市场的个股风险 ,并对全市场的收益 -风险特征进行了横向分析 ,结果表明 ,从长期的角度来讲 ,在正常的市场条件下 ,中国股票市场存在着一定的高风险 -高收益、低风险 -低收益的关系 ,但从短期来看 ,由于不确定性因素较多 ,这种风险收益关系并不显著 .其次 ,本文分别采用每只股票历史 3个月的累积收益率、6个月的累积收益率以及 1 2个月的累积收益率分析了中国股票市场的惯性 ,我们发现 ,无论是从短期还是从比较长的时间来看 ,中国股票市场都不存在市场惯性 ,而表现出一定的反转现象 ,但反转现象会随着时间的推移逐渐减弱     

A Particle Migrating Randomly on a Sphere

Consider a particle moving on the surface of the unit sphere in R ³ and heading towards a specific destination with a constant average speed, but subject to random deviations. The motion is modeled as a diffusion with drift restricted to the surface of the sphere. Expressions are set down for various characteristics of the process including expected travel time to a cap, the limiting distribution, the likelihood ratio and some estimates for parameters appearing in the model.     

Study of the Human Body Vibrations for the Seated Vehicle Driver

The paper presents some aspects about the dynamic response of the human body subjected to the vertical vibration in the travel inside the auto vehicle. There is a model presented in the specialized literature. This is transformed for the given study because was necessary to realize the vertical vibration along the human body. There are two different studies: using a linear lumped parameter systems seat - human body model; using a nonlinear lumped parameter systems seat-human body model. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

动态最小费用路在L1模下的逆问题讨论

考察动态最小费用路在L_1模下的逆问题,其中在弧费用的定义中,将弧(i,j)上的运行时间d_(ij)(t)分成最小可能运行时间d_(ij)~*和超出的运行时间(excess time)e_(ij)(t)两部分,弧(i,j)上费用即为两者赋权之和.在逆问题的讨论中考虑先将动态网络中的问题通过时间扩张网络G~T转化为静态问题,然后再利用解线性规划的逆问题的方法来解该动态最短路问题的逆问题.     

Terminable and interminable paths and trails in infinite graphs

The paper is concerned with certain kinds of random processes in infinite graphs. A finite trail of a graph which cannot be continued from either end is called terminated, and a finite trail is called terminable of it is a segment of a finite terminated trail; analogously for 1 - ∞ trails, finite paths, and 1 - ∞ paths.For k = 1,2,3,…, there exist graphs which contain 2 - ∞ paths and have node-connectivity k and in which no finite path and no 1 - ∞ path is terminable, and also such graphs in which every finite path and every 1 - ∞ path is terminable. In any graph with infinite node-connectivity every node of valency N₀ is the end-node of terminated 1 - ∞ paths. There exist graphs with node-connectivity N₀ in which every 1 - ∞ path is terminable. For λ = 1,2,3,…, there exist graphs which contain 2 - ∞ paths and have edge-connectivity λ and in which no finite trail and no 1 - ∞ trail is terminable, and also such graphs in which every finite trail and every 1 - ∞ trail is terminable. In contrast to the situation for 1 - ∞ paths, every connected infinite graph in which every 1 - ∞ trail is terminable contains at least one node of odd edge-degree and if in addition every finite trail is terminable, then there are at least two nodes of odd edge-degree.     

An Experimental Comparison of MRP Purchase Discount Methods

This paper addresses the managerial issue of how best to order purchased materials in MRP environments when discounts are available from vendors. The least unit cost, least period cost, McLaren's order moment, revised part-period balancing, incremental part-period balancing, traditional discount order quantity, and an optimal algorithm are experimentally investigated under a variety of simulated scenarios. Other experimental factors include the coefficient of variation in demand, forecast uncertainty beyond the current period, the average time between orders, the ratio of the discount quantity to the EOQ, the attractiveness of the discount, the length of the planning horizon, inventory holding costs, and the autocorrelation of demand. All factors tested in this comprehensive experiment significantly affected the performance of the discount ordering procedures. Furthermore, the results from this study suggest that the least unit cost, McLaren's order moment, the traditional discount order quantity, and the optimal procedures significantly out-perform the others. A further choice among these alternative methods was found to be a function of the operating environment and limitations that may exist on available computing time.     

Learning-by-doing may not be enough to sustain competitiveness in a market

The economic order quantity model and its variants are the oldest reported scientific inventory models in the literature. They are mathematically simple to use and understand. This characteristic contributed to their popularity. However, they have limitations that restrict their application in practice. Researchers have extended those models by relaxing some of their restrictive assumptions. One of the main limitations of the EOQ model is the estimation of its cost parameters, such as setup and holding costs, which include some hidden (or difficult to estimate) components. Emerging research proposes using the second law of thermodynamics when to measure the hidden (entropy) costs of inventory systems. Another research stream focuses on learning-by-doing, which reduces the unit cost of a product, thus reducing its price and enhancing a firm's competitiveness. This paper assumes that a buyer receives a shipment from its vendor following the EOQ model. The buyer invests in the vendor's process to accelerate learning (mainly workers training, among other things). The buyer's unit purchase cost reduces because of learning at the vendor's side. The buyer's competitors also learn, increasing competitiveness. For this purpose, we develop an EOQ model where price-dependent demand is analogous to a heat transfer equation, which is used to determine the entropy cost. It accounts for improvement by learning as a result of the buyer's investment to compete in a market. The implementation and management of improvement programs are not usually smooth, resulting in disorder quantified by an entropy cost function. The developed EOQ model is investigated with and without entropy costs. Numerical results representing different scenarios are provided to illustrate the behavior of the models and identify the factors affecting the decision variables and, subsequently, the buyer's profitability. The results showed that in a competitive market, the buyer has to maintain a learning rate faster than those of its competitors to ensure a prolonged, but indefinite, profitability. The results also showed that investment in learning is meaningful when the competition is fierce.