Dynamic Vehicle Allocation Research Articles

Developing an Agent-Based Simulator Combining Mesoscopic Traffic Simulator with Dynamic Vehicle Allocation System to Evaluate a Ride-Sharing Service in Urban Area

Developing an Agent-Based Simulator Combining Mesoscopic Traffic Simulator with Dynamic Vehicle Allocation System to Evaluate a Ride-Sharing Service in Urban Area

Dynamic Vehicle Allocation Policies for Shared Autonomous Electric Fleets

In the future, vehicle sharing platforms for passenger transport will be unmanned, autonomous, and electric. These platforms must decide which vehicle should pick up which type of customer based on the vehicle’s battery level and customer’s travel distance. We design dynamic vehicle allocation policies for matching appropriate vehicles to customers using a Markov decision process model. To obtain the model parameters, we first model the system as a semi-open queuing network (SOQN) with multiple synchronization stations. At these stations, customers with varied battery demands are matched with semi-shared vehicles that hold sufficient remaining battery levels. If a vehicle’s battery level drops below a threshold, it is routed probabilistically to a nearby charging station for charging. We solve the analytical model of the SOQN and obtain approximate system performance measures, which are validated using simulation. With inputs from the SOQN model, the Markov decision process minimizes both customer waiting cost and lost demand and finds a good heuristic vehicle allocation policy. The experiments show that the heuristic policy is near optimal in small-scale networks and outperforms benchmark policies in large-scale realistic scenarios. An interesting finding is that reserving idle vehicles to wait for future short-distance customer arrivals can be beneficial even when long-distance customers are waiting.

Dynamic Vehicle Allocation and Charging Policies for Autonomous Electric Vehicle Sharing Systems

Platforms sharing autonomous electric vehicles have to decide which vehicle should pick up which type of customer and when vehicles should be recharged, based on remaining battery level and proximity to the charging station. We model and analyze this problem. To guarantee maximum service and cost performance, in the future, vehicle sharing platforms will be unmanned, autonomous, and electric. Methods are needed to control the vehicles in such systems. To evaluate vehicle control policies, we first model the system as a semi-open queueing network (SOQN) with multiple synchronization stations for matching customer battery-demand classes with vehicles holding ample remaining battery capacity. If a vehicle's battery level drops below a threshold, it is routed to a nearby charging station for partial or full charging. We solve the analytical model of the SOQN and obtain approximate system performance with known vehicle routings. Then the SOQN is used in a Markov decision process (MDP) to minimize total cost, and to find good heuristic policies. Simulation results indicate that the performance of the approximate SOQN network model is accurate under given vehicle routings. We then test the performance of different policies for a small-scale network. The experiments show that a state-dependent policy is near optimal. The policies are also tested in a real case with shared vehicles. Partial charging can effectively increase customer throughput when servicing large demand with a limited number of vehicles. Dynamic vehicle allocation and charging can reduce system cost substantially. An interesting finding of our research is that reserving some idle vehicles to wait for future short-distance customers even when long-distance customers are waiting for vehicles can be beneficial.

Dynamic Vehicle Allocation and Charging Policies for Autonomous Electric Vehicle Sharing Systems

Dynamic Vehicle Allocation and Charging Policies for Autonomous Electric Vehicle Sharing Systems

Cotas para el problema de asignación de vehículos

El problema de asignación dinámica de vehículos consiste en asignar una flota de vehículos para atender la demanda prevista por transporte de carga entre terminales, durante un horizonte de tiempo finito y con múltiples periodos, cuyo objetivo es maximizar el lucro generado por los servicios completados. Dada la dispersión geográfica por demanda de servicios de transporte de carga, es común que se acumulen vehículos vacíos en lugares donde no son necesarios o se genere una escasez de vehículos donde son necesitados a lo largo del horizonte de planeación, por tanto, es importante balancear el suministro de vehículos y la demanda por servicios a lo largo del horizonte de planeación. El tamaño de los problemas prácticos enfrentados por transportadores logísticos es considerablemente grande para resolver en tiempos computacionales razonables, especialmente en transporte de carga por carre-tera. Consecuentemente, se recurre a métodos heurísticos para obtener soluciones factibles, pese a que no se tiene certificado de calidad de la solución. Por esta razón, en este trabajo se propone estudiar dos métodos de decomposición, que proporcionan certificados de calidad, para obtener cotas para el PADV. Los métodos utilizados son relajación lagranjeana junto con método de subgradiente de optimización y decomposición de Dantzig-Wolfe junto con generación de columnas. Experimentos compu-tacionales son realizados con instancias realistas, y los resultados de las cotas se muestran promisorios en términos de eficiencia computacional para el segundo método.

The effect of cooperation among shipping lines on transport costs and pollutant emissions

This study explores the effect of cooperation among shipping lines on transport costs and pollutant emissions. The quantitative benefits of the cooperation were measured via a simulation-based model that (i) optimized inland empty container reuse and (ii) considered a two-dimensional capacity (weight and size) for vehicle types and demands. Inland empty container optimization was integrated with a dynamic vehicle allocation and routing problem with time-window constraints, while the two-dimensional capacity considered minimising total transport costs in a time-varying network with road segment usage constraints by truck type. The simulation model was used to evaluate the status quo and the cooperation scenarios by analysing two weeks of import and export container movements for the port of Brisbane (Australia). The major findings from the study are: (i) the cooperation among shipping companies avoids a significant number of unnecessary truck movements and of storage days for empty containers; (ii) the cooperation translates into truck-sharing and utilisation of larger trucks, which are more environmentally friendly and cost-efficient choices when compared to smaller trucks; (iii) the introduction of a decision support system provides solutions to the freight actors regarding optimal routing and vehicle allocation, based on real-world constraints and dynamics. Remarkably, the savings in the cooperation scenario are substantial, yielding a 40% reduction of fuel consumption and pollutant emissions.

The dynamic vehicle allocation problem with application in trucking companies in Brazil

This paper deals with the dynamic vehicle allocation problem (DVAP) in road transportation of full truckloads between terminals. The DVAP involves multi-period resource allocation and consists of defining the “movements” of a fleet of vehicles that transport goods between terminals with a wide geographical distribution. These movements may be of fully laden vehicles, unladen vehicles for repositioning or vehicles held at a terminal to meet future demands. Emphasis is given to the characterization of the problem in real situations, the mathematical modeling of the problem and the use of exact and heuristic methods to solve it, including GRASP and simulated annealing metaheuristics. Results based on a case study of a transportation company in Brazil are presented and analyzed, showing that the approach can be effective in supporting practical decisions.

Dimensionamento e alocação dinâmica de veículos no transporte rodoviário de cargas completas entre terminais

Resumo Este artigo trata do Problema da Alocação Dinâmica de Veículos (PADV) no transporte rodoviário de cargas completas entre terminais, incluindo o dimensionamento da frota adicional necessária para o atendimento de demandas em um horizonte de planejamento multi-períodos e finito. O PADV consiste em definir “movimentos” de uma frota de veículos que realiza viagens entre terminais geograficamente dispersos que interagem entre si. Estes movimentos podem ser: veículos carregados com carga completa, vazios para reposicionamento, ou mantidos em um terminal de um período para outro como provisão para o atendimento de demandas futuras. A ênfase é dada na caracterização do problema em situações reais, na modelagem matemática do problema por meio de programação linear inteira e na solução deste utilizando um pacote comercial de otimização. Os experimentos computacionais realizados indicam que essa abordagem pode ser útil para a solução de problemas encontrados no dia a dia de uma empresa transportadora de carga parcelada no Brasil.

Otimização na alocação dinâmica de veículos no transporte rodoviário de cargas completas entre terminais

Este artigo trata do problema da alocação dinâmica (multiperíodos) de veículos (PADV) no transporte rodoviário de cargas completas entre terminais. O PADV pertence à classe de problemas de alocação de recursos multiperíodos e consiste em definir "movimentos" de uma frota de veículos que realiza viagens entre terminais geograficamente dispersos que interagem entre si. Esses movimentos podem ser: veículos carregados com carga completa, vazios para reposicionamento ou mantidos em um terminal de um período para outro como provisão para o atendimento de demandas futuras. A ênfase é dada na caracterização do problema em situações reais, na modelagem matemática do problema e na sua solução utilizando um método exato e métodos aproximados, incluindo uma heurística GRASP. Resultados baseados em um estudo de caso de uma transportadora brasileira são apresentados e analisados mostrando que a abordagem pode ser efetiva para apoiar decisões na prática.

Dynamic vehicle allocation control for automated material handling system in semiconductor manufacturing

The current study examines the dynamic vehicle allocation problems of the automated material handling system (AMHS) in semiconductor manufacturing. With the uncertainty involved in wafer lot movement, dynamically allocating vehicles to each intrabay is very difficult. The cycle time and overall tool productivity of the wafer lots are affected when a vehicle takes too long to arrive. In the current study, a Markov decision model is developed to study the vehicle allocation control problem in the AMHS. The objective is to minimize the sum of the expected long-run average transport job waiting cost. An interesting exhaustive structure in the optimal vehicle allocation control is found in accordance with the Markov decision model. Based on this exhaustive structure, an efficient algorithm is then developed to solve the vehicle allocation control problem numerically. The performance of the proposed method is verified by a simulation study. Compared with other methods, the proposed method can significantly reduce the waiting cost of wafer lots for AMHS vehicle transportation.

Management of Dynamic Vehicle Allocation for Carsharing Systems

Carsharing offers innovative mobility solutions and has been gaining popularity around the world as an environmentally sustainable, socially responsible, and economically feasible form of mobility. Carsharing allows members to benefit from private vehicle use without the costs and responsibilities of ownership and provides individuals with access to a fleet of shared-use vehicles in a network of locations on a short-term, as-needed basis. This paper seeks to develop a stochastic optimization framework to address the dynamic vehicle allocation problem for carsharing systems, in which the service operator needs to manage and determine the optimal vehicle allocation in both time and space to maximize profits. A multistage stochastic linear programming model with recourse, which can account for system uncertainties such as car-sharing demand variation, is formulated and solved. Numerical results are discussed and computational insights are presented on the basis of a seven-stage experimental network pilot study.

Carsharing

Carsharing provides members access to a fleet of shared-use vehicles in a network of locations on a short-term, as-needed basis. It allows individuals to gain the benefits of private vehicle use without the costs and responsibilities of ownership. The dynamic vehicle allocation problem is addressed in a carsharing context, that is, as a decision-making problem for vehicle fleet management in both time and space to maximize profits for the carsharing service operator. A multistage stochastic linear integer model with recourse is formulated that can account for system uncertainties such as carsharing demand variation. A stochastic optimization method based on Monte Carlo sampling is proposed to solve the carsharing dynamic vehicle allocation problem. Preliminary results are discussed and related insights are presented on the basis of a five-stage experimental network pilot study.

Selected Abstracts for the 1990–1991 Transportation Science Section Dissertation Prize Competition

Fourteen dissertations were submitted to the 1990–1991 Transportation Science Section dissertation prize competition. The first prize was awarded by Dr. Linos F. Frantzeskakis for “Dynamic Networks with Random Arc Capacities, with Application to the Stochastic Dynamic Vehicle Allocation Problem.” Three honorable mentions were also awarded. The recipients are listed in alphabetical order: Dr. Saad A. H. Al-Gadhi for “Characterization of Crowd Behavior and Movement,” Dr. Rina Schneur for “Scaling Algorithms for Multicommodity Flow Problems and Network Flow Problems with Side Constraints,” and Dr. Garrett van Ryzin for “Stochastic and Dynamic Vehicle Routing in Euclidean Service Regions.” The abstracts of all 14 theses submitted are reproduced.

A Successive Linear Approximation Procedure for Stochastic, Dynamic Vehicle Allocation Problems

The Stochastic Dynamic Vehicle Allocation problem involves managing a fleet of vehicles over time in an uncertain demand environment to maximize expected total profits. The problem is formulated as a Stochastic Programming problem. A new heuristic algorithm is developed and is contrasted to various deterministic approximations. The paper presents computational results that were obtained by employing a Rolling Horizon Procedure to simulate the operation of the truckload carrier. Results indicate the superiority of the new algorithm over other approaches tested.

Mitigating End Effects in the Dynamic Vehicle Allocation Model

A class of network models developed for optimizing the use of a fleet of vehicles can be viewed as time-staged optimization models. Such models which have linear objective functions suffer particularly from end effects, the distortions introduced by employing a finite planning horizon. This paper brings together the end effects literature and the vehicle allocation literature to find a finite linear programming problem that gives an approximation to the first stage optimal solution of the infinite model. In addition, we develop another method of approximation and show that both methods can be formulated as generalized networks. Numerical results are presented for each method for comparison.

An operational planning model for the dynamic vehicle allocation problem with uncertain demands

The dynamic vehicle allocation problem arises when a motor carrier must simultaneously and in real time coordinate the dispatching of vehicles from one region to the next all over the country. The decision to send a vehicle loaded or empty from one region to the next, arriving in the destination region at some point in the future, must anticipate the downstream impacts of the decision. The consequences of another load in a particular region at some point in the future, however, are highly uncertain. A simple methodology is proposed which calculates approximately the marginal value of an additional vehicle in each region in the future. This information is then used to generate a standard pure network which can be efficiently optimized to give dispatching decisions for today.

A Stochastic Model of the Dynamic Vehicle Allocation Problem

The stochastic vehicle allocation problem addresses the movement of vehicles between locations over a given planning horizon. The demand for vehicles to carry loads between locations is uncertain, and vehicles are assumed to be able to handle several loads over the course of the planning horizon. This requires tracking the movement of both loaded and empty vehicles, resulting in a network with stochastic flows. The methodology represents the flows of vehicles over the network explicitly as random variables, taking advantage of the acyclic structure of the time space network. The decision variables are formulated in terms of sending a certain fraction of the supply of vehicles at a node (which is random) over each of the outbound links. The result is a nonseparable objective function with a very simple constraint structure which lends itself readily to the Frank-Wolfe algorithm. Numerical experiments suggest very good computational efficiency.