Vehicle Travel Cost Research Articles

Optimization Model for Relief Distribution After Flood Disaster

Logistics planning is critical and a key component in meeting initial emergency needs in the aftermath of a disaster. The rapid and efficient distribution of logistical aid becomes critically important. In such situations, the construction of temporary depots in strategic locations and the determination of optimal distribution routes play an important role in ensuring that logistics aid can be distributed to the affected areas evenly. In this study, the Multi Depot Vehicle Routing Problem (MDVRP) is used which aims to minimize the total cost of distributing logistics aid which includes shipping costs, vehicle usage costs, temporary depot construction costs, and vehicle travel costs from distribution centers to temporary depots, while still meeting constraints such as logistics aid demand, vehicle capacity, area visits, maximum mileage, and depot construction. This model uses two types of vehicles where vehicle is tasked with carrying logistics aid from the distribution center to the temporary depot and vehicle is tasked with delivering logistics aid directly to the point of demand.

Branch-and-price for routing with probabilistic customers

We study the Vehicle Routing Problem with Probabilistic Customers (VRP-PC), a two-stage optimization model, which is a fundamental building block within the broad family of stochastic routing problems. This problem is mainly motivated by logistics distribution networks in which customers receive frequent delivery services, and by the last mile problem faced by companies such as UPS and FedEx. In a first stage before customer service requests realize, a dispatcher determines a set of vehicle routes serving all potential customer locations. In a second stage occurring after observing all customer requests, vehicles execute planned routes skipping all locations of customers not requiring service. The objective is to minimize the expected vehicle travel cost assuming known customer realization probabilities. We propose a column generation framework to solve the VRP-PC to a given optimality tolerance. Specifically, we present two novel algorithms, one that under-approximates a solution’s expected cost, and another that uses its exact expected cost. Each algorithm is equipped with a route pricing mechanism that iteratively improves the approximation precision of a route’s reduced cost; this produces fast route insertions at the start of the algorithm and reaches termination conditions at the end of the execution. Compared to branch-and-cut algorithms for arc-based formulations, our framework can more readily incorporate sequence-dependent constraints, which are typically required in routing problems. We provide a priori and a posteriori performance guarantees for these algorithms, and demonstrate their effectiveness via a computational study on instances with realization probabilities ranging from 0.5 to 0.9.

Dial-a-ride problem with modular platooning and en-route transfers

Modular vehicles (MV) possess the ability to physically connect/disconnect with each other and travel in platoon with less energy consumption. A fleet of demand-responsive transit vehicles with such technology can serve passengers door to door or have vehicles deviate to platoon with each other to travel at lower cost and allow for en-route passenger transfers before splitting. A mixed integer linear programming (MILP) model is formulated to solve this “modular dial-a-ride problem” (MDARP). A heuristic algorithm based on Steiner-tree-inspired large neighborhood search is developed to solve the MDARP for practical scenarios. A set of small-scale synthetic numerical experiments are tested to evaluate the optimality gap and computation time between exact solutions of the MDARP using commercial software and the proposed heuristic. Large-scale experiments are conducted on the Anaheim network with up to 75 vehicles and 150 requests considering 378 candidate join/split nodes to further explore the potentials and identify the ideal operation scenarios of MVs. The results show that MV technology can save up to 52% in vehicle travel cost, 41% in passenger service time, and 29% in total cost against existing on-demand mobility services in the scenarios tested. Results suggest that MVs best benefit from platooning by serving “enclave pairs” as a hub-and-spoke service.

Vehicular multihop intelligent transportation framework for effective communication in vehicular ad‐hoc networks

AbstractIn the smart city, the presence of intelligent transportation systems (ITS) is much essential. The emerging vehicular ad‐hoc networks (VANET) technology offers a wide range of applications in transportation, mobility, connectivity, and safety applications. In this decade, vehicular traffic is a prime hassle. The actual‐instant traffic data to drivers along with navigation system would facilitate vehicles to select an excellent path. This article proposes hybrid VANET architecture for effective communication between vehicles. The proposed hybrid architecture uses the multihop routing algorithm to permit vehicles to enlarge their driving performance and road safety. The proposed hybrid vehicular multihop routing algorithm with intelligent transportation system (Hybrid VMR‐ITS) allows the transportation system for instantaneous communications between vehicles and road‐side units (RSUs) and vehicles‐traffic servers effectively. The proposed work enhanced the whole spatial usage of a road network by reducing average vehicle travel costs with a reduced traffic overhead of 4%–9% compared with existing works.

Multi-objective optimization design and control of plug-in hybrid electric vehicle powertrain for minimization of energy consumption, exhaust emissions and battery degradation

Plug-in hybrid electric vehicles (PHEV) offer an attractive alternative to achieve the ambitious goals set by strong policies focused on near-term air quality and fuel-efficient road transportation. This paper provides a comprehensive study regarding the PHEV’s optimum powertrain design, by means of a multi-criteria analysis carried out by the interactive adaptive-weight genetic algorithm approach. The optimization aims to simultaneously minimize the PHEV’s fuel consumption, exhaust emissions, electric powertrain size, battery state of health, charging time and costs. To achieve these objectives, several PHEV’s design parameters are optimized such as in-wheel electric motors’ torque curves, battery voltage and capacity. The drivetrain is also optimized according to the determination of the best configuration of gearbox and differential gear ratios, taking into account constructive constraints. Furthermore, the fuzzy logic controllers responsible for the engine/electric motors power-split management and gear shifting control are included in the multi-objective optimization in order to define the best membership functions, rules and respective weights. To guarantee robust solutions, the PHEV is optimized under different driving conditions, which is given by the combination of the FTP-75, HWFET and US06 driving cycles. To evaluate the optimum PHEV performance, it is also simulated under the WLTC driving cycle and a real-world driving cycle based on the Campinas city. The best trade-off configuration results in 39.57% decrease in vehicle travel cost along with 43.39% carbon monoxide (CO), 45.13% unburned hydrocarbons (HC) and 72.64% nitrogen oxides (NOx) emissions reduction under the combined driving cycle.

Effect of Routing Constraints on Learning Efficiency of Destination Recommender Systems in Mobility-on-Demand Services

With Mobility-as-a-Service platforms moving toward vertical service expansion, we propose a destination recommender system for Mobility-on-Demand (MOD) services that explicitly considers dynamic vehicle routing constraints as a form of a “physical internet search engine”. It incorporates a routing algorithm to build vehicle routes and an upper confidence bound based algorithm for a generalized linear contextual bandit algorithm to identify alternatives which are acceptable to passengers. As a contextual bandit algorithm, the added context from the routing subproblem makes it unclear how effective learning is under such circumstances. We propose a new simulation experimental framework to evaluate the impact of adding the routing constraints to the destination recommender algorithm. The proposed algorithm is first tested on a 7 by 7 grid network and performs better than benchmarks that include random alternatives, selecting the highest rating, or selecting the destination with the smallest vehicle routing cost increase. The RecoMOD algorithm also reduces average increases in vehicle travel costs compared to using random or highest rating recommendation. Its application to Manhattan dataset with ratings for 1,012 destinations reveals that a higher customer arrival rate and faster vehicle speeds lead to better acceptance rates. While these two results sound contradictory, they provide important managerial insights for MOD operators.

Request acceptance in same-day delivery

We study request acceptance dynamics in same-day delivery systems by formulating the Dynamic Dispatch Waves Problem with Immediate Acceptance, which models integrated management and distribution for dynamically arriving customer requests. We consider an e-commerce platform that attempts to serve most customers with same-day delivery service. When a shopper attempts to place an order, a decision is made immediately to offer same-day delivery service (accept the request) or to deny service. Accepted requests are not available for immediate dispatch; they must be processed (picked and packed) before they are loaded for delivery. Vehicle routes are updated dynamically and serve each accepted delivery request no later than the end of the service day. In this work, we limit the study to the case of a single vehicle serving requests, potentially using multiple trips from the distribution center. The objective is to make request acceptance and distribution decisions that minimize the expected sum of vehicle travel costs and penalties for service denials. We develop a framework for dynamic decision policies over continuous time for such systems, where a feasible vehicle dispatch plan is redesigned and used to guide decisions over time. We design methods for determining an initial optimal a priori plan and for updating the plan using a heuristic roll-out procedure. Our methods are tested on a family of simulated instances against two common-sense benchmarks and an infeasible relaxed policy that allows the dispatcher to delay the acceptance or rejection of a request until the end of the service day. We demonstrate in our computational study that the cost-per-request of the best benchmark policy is on average 9.7% higher than our proposed dynamic policy and furthermore that the dynamic policy leads to only a 4.4% cost increase over the infeasible relaxed policy lower bound.

The Dynamic Dispatch Waves Problem for same-day delivery

We study same-day delivery (SDD) systems by formulating the Dynamic Dispatch Waves Problem (DDWP). The DDWP models an order dispatching problem faced by a distribution center, where orders arise dynamically throughout a service day and must be delivered by day’s end. At each decision epoch (wave), the system’s operator chooses whether or not to dispatch a single vehicle loaded with orders ready for service in order to minimize vehicle travel costs and penalties for unserved requests. We formulate an arc-based integer programming model and design local search heuristics to solve a deterministic DDWP where order arrival times are known in advance. We use the deterministic variant to design an a priori solution approach, and provide two approaches to obtain dynamic policies using the a priori solution. We test and compare solution approaches on two sets of instances with different geography scenarios, size, information dynamism, and order timing variability. The computational results suggest that our best dynamic policy can reduce the average cost of an a priori policy by 9.1% and substantially improves the fraction of orders delivered (order coverage), demonstrating the importance of reactive optimization for dynamic SDD services. We also analyze the tradeoff between two common SDD objectives: total cost minimization versus order coverage maximization. We find structural differences in the dispatch frequency and route duration of solutions for the two different objectives, and demonstrate empirically that small increases in order coverage may require substantial increases in vehicle travel cost.

Solving the location-routing problem with simultaneous pickup and delivery by simulated annealing

The study concerns the location-routing problem with simultaneous pickup and delivery (LRPSPD) in which the pickup and delivery take place at the same time for each customer. The goal is to determine the facility locations and vehicle routes in order to minimise the total system cost as a sum of facility opening cost, vehicle fixed cost and vehicle travel cost. A simulated annealing (SA) heuristic is proposed for the problem and extensive computational experiments are conducted. The results show that the proposed SA effectively solves LRPSPD and outperforms existing exact approaches in terms of solution quality.

Real-Time Path Planning Based on Hybrid-VANET-Enhanced Transportation System

Real-time path planning can efficiently relieve traffic congestion in urban scenarios. However, how to design an efficient path-planning algorithm to achieve a globally optimal vehicle-traffic control still remains a challenging problem, particularly when we take drivers' individual preferences into consideration. In this paper, we first establish a hybrid intelligent transportation system (ITS), i.e., a hybrid-VANET-enhanced ITS, which utilizes both vehicular ad hoc networks (VANETs) and cellular systems of the public transportation system to enable real-time communications among vehicles, roadside units (RSUs), and a vehicle-traffic server in an efficient way. Then, we propose a real-time path-planning algorithm, which not only improves the overall spatial utilization of a road network but reduces average vehicle travel cost for avoiding vehicles from getting stuck in congestion as well. A stochastic Lyapunov optimization technique is exploited to address the globally optimal path-planning problem. Finally, the transmission delay of the hybrid-VANET-enhanced ITS is evaluated in VISSIM to show the timeliness of the proposed communication framework. Moreover, system-level simulations conducted in Java demonstrate that the proposed path-planning algorithm outperforms the traditional distributed path planning in terms of balancing the spatial utilization and drivers' travel cost.

Research on Optimal Vehicle Path in Intelligent Transportation

The rapid development of economy and science and technology is quite obvious, which makes cars becoming more and more widely into the general family life. With the rapid development of economics and technology; the number of vehicles has largely increased. In this paper, there is the basic organizational framework for intelligent transportation, intelligent transportation network proposed model and its data storage structure, and the important influence on optimal path trajectory intelligent transportation planning. This paper analyzes intersection road network in the distribution based on computer graphics language, type and grade roads. The dynamic mathematical model of single vehicle is used vehicle planning algorithm based on period, effectively avoided the traffic road, reduce vehicle travel cost, improve the real-time effect and accuracy of the vehicle dynamic path. We hope the results and researches could combine with reality in order to reduce traffic congestion.

Development of Decision Tool for Strategies to Reduce Greenhouse Gas Emissions

The National Household Travel Survey (NHTS) provides important information for the development of local and regional models to support decision making related to climate change and sustainability goals. This paper documents the use of NHTS data in the development of the Greenhouse Gas Statewide Transportation Emissions Planning (GreenSTEP) model, which forecasts estimates of greenhouse gas emissions at county and urban area levels. The model was developed to be sensitive to a broad number of policy variables and other factors that were not addressed in existing models. Because there was a lack of local and current sources of information about individuals, households, and their vehicle ownership patterns and travel behavior, GreenSTEP made use of the information in the national sample of the 2001 NHTS to estimate several model modules. The NHTS data were useful specifically in the development of modules on (a) land use characteristics, (b) vehicle ownership, (c) vehicle use [daily vehicle miles traveled (DVMT)], (d) impacts of vehicle travel costs on DVMT, (e) lightweight vehicles (bicycles, mopeds, electric bicycles, etc.), and (f) vehicle fleets (type and age). The NHTS data were particularly important for modeling the adoption and use of limited-range electric vehicles, as the data enabled estimates of trip length distributions to be made. This paper highlights the utility of the NHTS data for this modeling framework, the modifications and augmentations that were necessary, the limitations that were encountered, and the potential for the wider dissemination and use of the GreenSTEP tool because the initial estimation was made with a national sample.

Estimating Farm Vehicle Travel Costs on Local Rural Roads

The local rural road and bridge system is under physical stress from heavy and wide farm vehicle travel. It is also under financial stress from the prospect of declining revenues for maintaining and rebuilding the system. There are significant research opportunities for agricultural economists to evaluate alternative investment strategies to reduce the net cost of the local rural road system. A major input in the evaluation of alternative investment strategies on these road systems is the change in vehicle travel costs if maintenance levels are changed or the road systems themselves are changed. Registered vehicle variable travel costs -automobiles and trucks -can be obtained from previous studies or are easily generated from raw data. However, only one analysis has been found that estimates the costs of farm tractor and combine travel on paved and gravel roads. This paper presents a procedure and a set of estimates of farm tractor and combine travel costs on these roads.

A Computer Simulation Model for Determining Fleet Size and Composition

A heterogeneous vehicle fleet is one that is composed of several types of vehicles. The number of each type of vehicle in the fleet is called the fleet’s composition. The problem of determining the best fleet size and composition for an in-house heterogeneous company fleet having a known demand was solved in this paper. A computer model was developed which tied a fleet simulation model to two different search algorithms. One of the search algorithms is a complete factorial nonsequential search and the other is a combination of a partial factorial nonsequential search and a heuristic sequential hill-climbing search. The objective of both searches is to select the fleet size and composition which provides the lowest total vehicle travel costs to the company. Several examples were used to demonstrate the use of the model.