Open vehicle routing problem with driver nodes and time deadlines

In this paper, we consider a variant of the open vehicle routing problem in which vehicles depart from the depot, visit a set of customers, and end their routes at special nodes called driver nodes. A driver node can be the home of the driver or a parking lot where the vehicle will stay overnight. The resulting problem is referred to as the open vehicle routing problem with driver nodes (OVRP-d). We consider three classes of OVRP-d: with no time constraints, with a maximum route duration, and with both a maximum route duration as well as time deadlines for visiting customers. For the solution of these problems, which are not addressed previously in the literature, we develop a new tabu search heuristic. Computational results on randomly generated instances indicate that the new heuristic exhibits a good performance both in terms of the solution quality and computation time.     

An alternative tactic to deal with the contingency of driver absenteeism

On any given workday, unexpected absence of drivers calls for emergency operational tactics to ensure completion of planned deliveries of merchandise to customers. A typical tactic in the workforce scheduling literature is to rely exclusively on a pool of workers who were not scheduled for work; that is, full-time off-duty drivers and part-time drivers. Concerns about the expense of maintaining a staff of part-timers prompt consideration of alternative tactics. This study proposes one such alternative. It minimizes the part-time pool by reassigning routes among drivers who show for work. In multi-route vehicle routing problems, the prospect of this alternative arises because cost-minimizing patterns of travel distances across routes often allow one driver to do extra work by handling more than one route (subject to time-window and workload constraints). By considering costs such as overtime rates when drivers perform extra (emergency) driving duties and the cost to maintain part-time staff, this study tests the efficacy of the aforementioned alternative. Its efficacy is confirmed by the fact that it is less costly than the typical tactic: while the typical tactic adds just over 22% to driver payroll, the proposed alternative adds a noticeably smaller cost of just under 15%.     

驾驶者行为选择对十字路口通行能力的影响

考虑通过十字路口时左转车辆和直行车辆发生路权冲突时的不同驾驶者行为,将驾驶者分为跟随型驾驶者和谦让型驾驶者,并建立了左转车辆通过十字路口的模型,研究不同的驾驶者行为选择对十字路口通行能力的影响。结果表明,随着交通流量逐渐增加,驾驶者的行为越趋向于跟随时,十字路口的通行能力将会急剧降低;而当驾驶者的行为趋向于谦让时,十字路口的通行能力甚至优于有交警指挥的情形。该结果很好地解释了在行车高峰期中国十字路口堵塞的原因,即驾驶者的行为选择,可以通过改变驾驶者的行为选择和在交通流量达到一定的饱和度时派出交警来改善十字路口的通行能力。     

The efficacy of exclusive territory assignments to delivery vehicle drivers

A supporting logic for having a vehicle driver exclusively assigned to serve the same territory on every delivery trip is the deepening of the driver’s knowledge of the territory and the customers therein. This contributes to the driver’s proficiency in serving that territory. However, in situations of randomness in day-to-day customer demands, the choice of exclusive territory assignments entails the sacrifice of sub-optimal route configuration. This study quantifies the extent of that sacrifice in order to depict the cost implications of exclusive territory assignments vis-à-vis tactics that keep pace with day-to-day demand fluctuations by allowing for flexibility in the assignments. The study’s analysis of exclusive territory assignments covers those that involve territory sharing among a team of drivers.     

Viscosity solutions for second order integro-differential equations without monotonicity condition: the probabilistic approach

In this paper, we establish a new existence and uniqueness result of a continuous viscosity solution for integro-partial differential equation (IPDE in short). The novelty is that we relax the so-called monotonicity assumption on the driver which is classically assumed in the literature of viscosity solutions of equation with non-local terms. Our method strongly relies on the link between IPDEs and backward stochastic differential equations with jumps for which we already know that the solution exists and is unique for general drivers. In the second part of the paper, we deal with the IPDE with obstacle and we obtain similar results.     

Optimal static pricing for a service facility with holding costs

We study a service facility modelled as a single-server queueing system with Poisson arrivals and limited or unlimited buffer size. In systems with unlimited buffer size, the service times have general distributions, whereas in finite buffered systems service times are exponentially distributed. Arriving customers enter if there is room in the facility and if they are willing to pay the posted price. The same price is charged to all customers at all times (static pricing). The service provider is charged a holding cost proportional to the time that the customers spend in the system. We demonstrate that there is a unique optimal price that maximizes the long-run average profit per unit time. We also investigate how optimal prices vary as system parameters change. Finally, we consider buffer size as an additional decision variable and show that there is an optimal buffer size level that maximizes profit.     

基于新老司机道路选择行为的交通流均衡研究

针对新上路司机数量的爆发式增长带来的交通流均衡变化问题,本文在对司机进行分类和道路选择行为分析的基础上,应用决策树方法构建了基于新老司机道路选择行为的交通流均衡模型,并用实际案例研究了主要参数的敏感性。研究发现:路况通过联合系统车流量的分配情况、车速预期和司机学习过程等因素共同对司机比例产生影响;当某一条道路达到均衡时,车流量对司机比例的敏感程度受到路况差异的影响;司机在驾驶经验、对道路的熟悉程度以及学习能力等方面存在差异,但这种差异仅存在于对交通信息的感知方面。该研究有助于加深对城市交通流变化规律的认识和理解,对于发展出有效的交通管制措施有积极的理论价值和现实意义。     

AHP灰色综合评价的新训驾驶员训练评估

随着汽车行业的不断发展,我军车辆装备的高信息程度逐渐增强,要求部队新训驾驶员的培训方法不断更新,寻求科学有效的训练方法必须依据训练实践的评估.依据部队新训驾驶员的考核大纲,对其训练科目进行的分析,建立科学的指标体系,利用层次分析法(Analytic Hierarchy Process,简称AHP)确定了指标权重,根据灰色综合评价法原理,建立了新训驾驶员训练评估模型,对提高驾驶员训练质量效益有着重要的现实意义.     

A tabu search algorithm for the multi-trip vehicle routing and scheduling problem

This paper describes a novel tabu search heuristic for the multi-trip vehicle routing and scheduling problem (MTVRSP). The method was developed to tackle real distribution problems, taking into account most of the constraints that appear in practice. In the MTVRSP, besides the constraints that are common to the basic vehicle routing problem, the following ones are present: during each day a vehicle can make more than one trip; the customers impose delivery time windows; the vehicles have different capacities considered in terms of both volume and weight; the access to some customers is restricted to some vehicles; the drivers' schedules must respect the maximum legal driving time per day and the legal time breaks; the unloading times are considered.     

Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach

We study a vehicle routing problem with soft time windows and stochastic travel times. In this problem, we consider stochastic travel times to obtain routes which are both efficient and reliable. In our problem setting, soft time windows allow early and late servicing at customers by incurring some penalty costs. The objective is to minimize the sum of transportation costs and service costs. Transportation costs result from three elements which are the total distance traveled, the number of vehicles used and the total expected overtime of the drivers. Service costs are incurred for early and late arrivals; these correspond to time-window violations at the customers. We apply a column generation procedure to solve this problem. The master problem can be modeled as a classical set partitioning problem. The pricing subproblem, for each vehicle, corresponds to an elementary shortest path problem with resource constraints. To generate an integer solution, we embed our column generation procedure within a branch-and-price method. Computational results obtained by experimenting with well-known problem instances are reported.     

考虑交叉网络外部性的网约车平台市场定价研究

随着移动互联网的发展,具有双边市场特征的以网约车平台为载体的多种新型出行方式相继出现,但同时也带来了新的交通管理问题。基于双边市场理论,考虑了交叉网络外部性,从用户、司机均为单归属以及用户多归属、司机单归属两个方面研究了网约车平台的市场定价问题。同时,引入了时间敏感系数和司机提成比例表征网约车行业特征。最终得到了网约车平台对用户和司机的定价结构公式、用户和司机规模及平台收益。研究显示平台定价及收益与交叉网络外部性及时间敏感系数的关系与用户归属结构相关;平台定价及收益与差异化程度正相关,且在用户多归属情况下提高差异化程度会导致多归属方用户规模降低;在双边均为单归属情况下,提高司机提成比例会降低平台对出行用户的定价且增加对司机方的定价,最终导致平台均衡收益降低;但多归属下平台对司机的定价与司机提成比例的关系还与双边交叉网络外部性的相对大小有关。     

Vehicle routing in the 1-skip collection problem

In this paper, we consider a real problem, which we call the 1-skip collection problem, where a fleet of vehicles must collect a number of skips situated in different locations and transport them to one among different plants chosen on the basis of the kind of waste contained in the skip. Each vehicle has a capacity of one skip and it starts and ends its tour at the depot. Each time a vehicle collects a skip, it has to go to a plant and empty it. A number of constraints are imposed, which involve time windows for the customers and the plants, shift-time, different kinds of skips, number of drivers available to carry out the service and priorities assigned to the customers who have to be served. The objective is to minimize the total cost of the service given by the fixed cost of the drivers engaged to carry out the service, the cost of the extra time and the penalty cost paid if a customer is not served. A heuristic algorithm to solve the real problem is presented. The algorithm first constructs a feasible solution by means of the nearest-neighbour algorithm. Then, if it finds a feasible solution, it improves it. The computational results show that the solution of the algorithm is much better than the solution applied by the firm that carries out the service since it serves a higher number of skips with a smaller number of drivers.     

Recovery management for a dial-a-ride system with real-time disruptions

The problem considered in this work stems from a non-profit organization in charge of door-to-door passenger transportation for medical appointments. Patients are picked up at home by a driver and are then dropped at their appointment location. They may also be driven back home at the end of their appointment. Some patients have specific requirements, e.g., they may require an accompanying person or a wheelchair. Planning such activities gives rise to a so-called dial-a-ride problem. In the present work, it is assumed that the requests assigned to the drivers have been selected, and the transportation plan has been established for the next day. However, in practice, appointment durations may vary due to unforeseen circumstances, and some transportation requests may be modified, delayed or canceled during the day. The aim of this work is to propose a reactive algorithm which can adapt the initial plan in order to manage the disruptions and to take care of as many patients as possible in real-time. The plan should be modified quickly when a perturbation is observed, without resorting to major changes which may confuse the drivers and the patients. Several recourse procedures are defined for this purpose. They allow the dispatcher to temporarily delete a request, to insert a previously deleted request, or to permanently cancel a request. Simulation techniques are used to test the approach on randomly generated scenarios. Several key performance indicators are introduced in order to measure the impact of the disruptions and the quality of the solutions.     

Solving a multi-objective simulation model using a hybrid response surface method and lexicographical goal programming approach—a case study on integrated circuit ink-marking machines

In an integrated circuit (IC) packaging plant, the ink-marking machine has a significantly higher throughput than the other processing machines. When periodic demand surges result in backlog orders or in lost customers, there is a need to increase system throughput. To resolve this problem, the purchase of a new machine often results in excess capacity in addition to added operation and acquisition costs. Therefore, the productivity improvement effort has priority over the machine purchase decision. This paper seeks to optimize both throughput and cycle time performance for IC ink-marking machines. While throughput increase is the primary objective, there is an acceptable cycle time limit for a feasible solution. It is a multi-objective problem. The proposed solution methodology constructed a simulation metamodel for the ink-marking operation by using a fractional factorial experimental design and regression analysis. It is then solved by a hybrid response surface method and lexicographical goal programming approach. Solution results illustrated a successful application.     

Service contracts: A stochastic model

There is a growing trend to outsource maintenance where equipment failures are rectified by an external agent under a service contract. The agent's profit is influenced by many factors—the terms of the contract, equipment reliability, and the number of customers being serviced. The paper develops a stochastic model to study the impact of these on the agent's expected profit and the agent's optimal strategies using a game theoretic formulation.     

Analysis of the M [X]/G/1 queues with second multi-optional service and unreliable server

A bulk-arrival single server queueing system with second multi-optional service and unreliable server is studied in this paper. Customers arrive in batches according to a homogeneous Poisson process, all customers demand the first ＂essential＂ service, whereas only some of them demand the second ＂multi-optional＂ service. The first service time and the second service all have general distribution and they are independent. We assume that the server has a service-phase dependent, exponentially distributed life time as well as a servicephase dependent, generally distributed repair time. Using a supplementary variable method, we obtain the transient and the steady-state solutions for both queueing and reliability measures of interest.     

The Analysis of a General Input Queue with N Policy and Exponential Vacations

This paper studies a single removable server in a G/M/1 queueing system with finite capacity where the server applies an N policy and takes multiple vacations when the system is empty. We provide a recursive method, using the supplementary variable technique and treating the supplementary variable as the remaining interarrival time, to develop the steady-state probability distributions of the number of customers in the system. The method is illustrated analytically for exponential and deterministic interarrival time distributions. We establish the distributions of the number of customers in the queue at pre-arrival epochs and at arbitrary epochs, as well as the distributions of the waiting time and the busy period.     

Multi-Path Approach for Reliability-Redundancy Allocation Using a Scaling Method

The reliability-redundancy allocation problem is an optimization problem that achieves better system reliability by determining levels of component redundancies and reliabilities simultaneously. The problem is classified with the hardest problems in the reliability optimization field because the decision variables are mixed-integer and the system reliability function is nonlinear, non-separable, and non-convex. Thus, iterative heuristics are highly recommended for solving the problem due to their reasonable solution quality and relatively short computation time. At present, most iterative heuristics use sensitivity factors to select an appropriate variable which significantly improves the system reliability. The sensitivity factor represents the impact amount of each variable to the system reliability at a designated iteration. However, these heuristics are inefficient in terms of solution quality and computation time because the sensitivity factor calculations are performed only at integer variables. It results in degradation of the exploration and growth in the number of subsequent continuous nonlinear programming (NLP) subproblems. To overcome the drawbacks of existing iterative heuristics, we propose a new scaling method based on the multi-path iterative heuristics introduced by Ha (2004). The scaling method is able to compute sensitivity factors for all decision variables and results in a decreased number of NLP subproblems. In addition, the approximation heuristic for NLP subproblems helps to avoid redundant computation of NLP subproblems caused by outlined solution candidates. Numerical experimental results show that the proposed heuristic is superior to the best existing heuristic in terms of solution quality and computation time.