Vehicle Allocation Problem Research Articles

Sustainable vehicle allocation decisions under a vertical logistics collaboration setting

Enhancing vehicle utilization through a collaborative logistics approach allows businesses to contribute to the three pillars of sustainability by reducing fuel consumption and emissions, enhancing profit margins, and increasing customer satisfaction. This paper aims to introduce an Integer Linear Programming model for solving a vehicle allocation problem within a proposed vertical logistics collaboration setting. In this collaboration setup, production capacity allocation decisions are jointly made by collaborating customers and the logistics service provider, in order to create a more balanced and profitable logistics plan for all involved parties. Through the application of the proposed mathematical model, our numerical analyses illustrate the effects of the vertical logistics collaboration on the performance of transportation operations, including improved vehicle utilization rate and increased satisfied demand amount. Besides, we analyze the outputs of this operation related to the economic (by focusing on profit growth), environmental (by measuring the reduction in emissions), and social (by assessing the discounted transportation costs for customers) aspects of sustainability. To the best of our knowledge, no current study in the literature has established the vehicle allocation problem in the context of vertical logistics collaboration across all three sustainability pillars at the same time. The model can also serve as a tool for evaluating the potential economic, environmental, and social benefits arising from vertical collaboration and joint projects with customers, along with the support offered to logistics decisions.

The Fleet and Berth Selection Model to Improve Energy and Environment Sustainability

This research aims to improve energy sustainability in transportation management. The case was derived from port-to-port coal transportation problem commonly faced by third-party logistic (3PL) company. During planning, they must determine which shipsets to be used and assign them to the loading/unloading berths. Each berth varies in terms of its loading/unloading speed and each shipset varies in terms of its capacity, sail speed, and fuel consumption rate. The selection of shipset impacts the auxiliary and main engine fuel consumption while the selection of berth impacts only the auxiliary engine fuel consumption. The target is to minimize the total fuel consumed by both engines for the whole shipset. We modelled the case through Multiple Vehicle Allocation Problem (MVAP) framework and proposed a heuristic algorithm to find the solutions. The heuristic algorithm is proven to be able to reach an optimal solution for small cases and near-optimal for medium to large cases.

Distributed energy trading for electric vehicle charging stations equipped with distributed PV considering transportation network

As distributed renewable energy (DRE) generation technologies advance and transportation electrification deepens, electric vehicle charging stations (EVCSs) equipped with DRE will quickly proliferate in the near future. This paper, thus, proposes a distributed energy trading framework for EVCSs equipped with distributed photovoltaic (PV) system considering transportation network (TN). The framework contains the electric vehicle allocation problem (EAP) in TN and the distributed energy trading (DET) problem among EVCSs. The target of EAP is minimizing the whole time cost of TN. Therefore, the EV quantities in each EVCS can be obtained after solving the EAP problem by introducing the time cost coefficient. As for DET, this paper develops a distributed trading method among EVCSs derived from the Nash bargaining (NB) theory. The method considers not only the benefit of each participant itself, but also the fair distribution of the mutual profits among the participants at the same time. Numerical simulations are performed to validate the effectiveness and viability of the mentioned framework and models. The numerical results show that the EAP model can reasonably allocate the electric vehicles (EVs) that need to be charged. The operating profits of each EVCS can be significantly promoted by the DET among EVCSs. In addition, the proposed distributed solution methodology can efficiently address DET problems and protect the business privacy of participants.

Rescue vehicle allocation problem based on optimal reliable path under uncertainty

Rescue vehicle allocation problem based on optimal reliable path under uncertainty

Analyses on the Effects of Time Windows Choices on Sustainable Vehicle Allocation Problems

This study aims to analyze the effects of the time windows choices in Vehicle Allocation Problems with respect to economic, social, and environmental indicators. As far as we know, such an attempt does not exist in the related literature. For this purpose, we first present a Mixed Integer Programming model for a generic Vehicle Allocation Problem with profit maximization objective under time windows constraints. This model is used to conduct analyses for demonstrating the applicability of the model in practice and to reveal potential effects of the time windows choices on economic, social, and environmental outcomes of allocation decisions in Vehicle Allocation Problems. The numerical results show that loosened time windows have the potential to provide economic and environmental benefits in the addressed problem. We also propose a collaboration policy attractive to both the transportation company and its customers through price discounts, which improves economic and social performances of the addressed transportation logistics network.

The vehicle allocation problem: Alternative formulation and branch-and-price method

The Vehicle Allocation Problem (VAP) consists of repositioning empty vehicles across a set of terminals over a given planning horizon so as to maximize the profits generated from serving demand for transportation of goods between pair of terminals. This problem has been classically modeled using an extended space–time network which captures the staging of the decision-making process. The present paper proposes a new integer linear programming (ILP) model based on the idea of representing the demands to be met as nodes on a graph. We also derive a Dantzig–Wolfe reformulation which is solved with a branch-and-price (BP) method. The proposed BP uses a stabilized interior-point column generation approach and a branching procedure that imposes constraints in the master problem, thus not damaging the structure of the subproblems. Additionally, we show that these subproblems can be solved efficiently using a shortest path algorithm on directed acyclic graphs (DAG). Computational experiments are carried out on realistic-sized benchmark instances commonly used in the literature. The results show the efficacy of the proposed strategies. In particular, the BP method solved the whole set of instances to proven optimality for the first time and in faster competitive times.

Modeling Heterogeneous Fleet Vehicle Allocation Problem with Emissions Considerations

Aims: This study proposes a bi-objective linear integer programming model for heterogeneous fleet VAP with emissions considerations. Profit maximization and emissions minimization objectives are employed to handle economic and environmental sustainability purposes. Background: Our literature survey shows that there is no model for the heterogeneous fleet VAP with emissions considerations that simultaneously consider vehicle heterogeneity, penalty costs for unmet demands, and emissions from transportation operations. Objective: The model is employed to also make several scenario analyses on sustainable freight logistics management to understand the trade-offs among economic and environmental objectives. In freight transportation problems, decision-makers need to be able to maintain profitability and to reduce emissions. Methods: In this study, a bi-objective linear integer programming model is proposed for a heterogeneous fleet Vehicle Allocation Problem (VAP) with emissions considerations encountered in the field of sustainable freight transportation. Results: In the numerical analyses, various practical assumptions that can be confronted by decision-makers in real life are discussed. In each analysis, total profit and emissions amounts are revealed along with several other KPIs. The results of the analyses provided in this study could also be useful in terms of understanding the relations among pillars of sustainability in VAPs. Conclusion: It is thought that the proposed model has the potential to aid decision-making processes in sustainable logistics management. In the base case analyses, the total profit obtained under profit maximization is about nine times higher than that obtained under emissions minimization. When the aim is to minimize emissions, the total emissions are found to be nearly one-tenth of that of profit maximization. Supported by also additional scenario analyses, it can be concluded that it might not economically viable to be environmentally-friendly for companies. Therefore, companies have to be encouraged or forced to take environmentally and socially responsible actions through legislation. The analyses demonstrated that various legislative policies on emissions may affect the transportation plans differently in such vehicle allocation systems.

A branch-and-price method for the vehicle allocation problem

The Vehicle Allocation Problem (VAP) consists of allocating a fleet of vehicles to attend to the expected demand for freight transportation between terminals along a finite multiperiod planning horizon. The objective is to maximize the profits generated for the completed services. The previous deterministic and stochastic approaches used heuristic procedures and approximations for solving large-scale instances of this problem. This paper proposes a Branch-and-Price method which is the first tailored exact solution approach for the VAP. This method provides proven optimal solutions within reasonable computational times, even for large-scale problem instances, and it is based on reformulating a compact Integer Linear Programming model of the VAP through the Dantzig–Wolfe decomposition and using efficient procedures for solving each component of the reformulation. The Primal–Dual Column Generation Method (PDCGM) is used to solve the master problem, while the subproblem is modeled as a Maximum Cost Flow Problem and is solved using the aggregation of the optimal longest paths on Directed Acyclic Graphs (DAG). Finally, we use three branching procedures (branching on a set of arcs, on the original variables and on the demand constraints) to obtain the optimal integer solution of the VAP. Computational experiments with 30 instances from a case study and 200 random realistic-sized instances are presented and analyzed, which show that the method has superior performance with respect to other exact approaches in solving large-scale VAP instances.

A Novel Emergent Intelligence Technique for Public Transport Vehicle Allocation Problem in a Dynamic Transportation System

Public transport systems in a metropolitan area experiences several complex issues, like resource scarcity, resource allocation, congestion, resource reliability and so on, due to the dynamic arrivals of heterogeneous commuter and exceptional occurrence of unforeseen events. The progress of these issues may lead to economic losses, under-utilization of transport resources, and commuters’ queuing delay. In this paper, we propose a novel dynamic public transport vehicle allocation scheme based on Emergent Intelligence (EI) technique in a metropolitan area. In addition, we demonstrate the EI technique’s capability for solving public transport system problems. To do so, the EI technique maintains historical information, commuters’ arrival rates, resource avaialability, deficit resources and surplus resources of neighbor depots’s agent. In the proposed scheme, the EI technique is utilized to collect, analyze, share and optimally allocate transport resources effectively. The proposed EI technique provides reliable services (allocation and scheduling) by coordinating with a reliable neighborhood depot’s agent. We have build mathematical models for estimation of resources, utilization and reliability parameters. The proposed scheme is exhaustively tested by simulation and analyzed with varying commuters’ arrival rates, number of vehicles, number of requests, and different values of reliability parameters. The proposed scheme’s results (analytical, simulation and comparison) show the reliabiltiy, accuracy and real time deployability.

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.

Minimum cost flow problem formulation for the static vehicle allocation problem with stochastic lane demand in truckload strategic planning

ABSTRACTThis paper studies the complex strategic planning problem faced by truckload (TL) companies related to finding the allocation of vehicles serving lanes with stochastic demand within their networks. Lanes are origin–destination pairs associated with volumes of TLs per unit of time that follow discrete probability distributions. Finding strategic vehicle allocations that anticipate demand variability is critical because unrealized demands increase repositioning costs and reduce perceived revenues. The allocation problem is formulated as a two-stage stochastic program. The paper proposes a novel procedure based on network transformations that formulate the complex stochastic problem as a minimum cost-flow problem (MCFP), which can be efficiently solved with classic MCFP algorithms. Numerical experiments demonstrate the computational efficiencies of the method (instances with 1404 nodes and 98,904 arcs solved in 20.74 seconds) and reinforce the necessity of incorporating stochastic effects to avoid overestimation from deterministic approaches.

A double standard model for allocating limited emergency medical service vehicle resources ensuring service reliability

This paper introduces a new double standard model (DSM), along with a genetic algorithm (GA), for solving the emergency medical service (EMS) vehicle allocation problem that ensures acceptable service reliability with limited vehicle resources. Without loss of generality, the model is formulated to address emergency services to human injuries caused by vehicle crashes at intersections within an urban street network. The EMS fleet consists of basic life support (BLS) and advanced life support (ALS) vehicles suited for treating crashes with different severity levels within primary and secondary service coverage standards corresponding to extended response times. The model ensures that all demand sites are covered by at least one EMS vehicle within the secondary standard and a portion of which also meets the service reliability requirement. In addition, a portion of demand sites can be covered by at least one of each type of EMS vehicles within the primary standard. Meanwhile, it aims to achieve maximized coverage of demand sites within the primary standard that complies with the required service reliability. A computational experiment is conducted using 2004–2010 data on top two hundred high crash intersections in the city of Chicago as demand sites for model application. With an EMS fleet size of 15 BLS and 60 ALS ambulances maintained by the Chicago Fire Department, at best 92.4–95.5% of demand could be covered within the secondary standard at 90% of service reliability; and 65.5–68.4% of high severity demand and 50.2–54.5 low severity demand could be covered within the primary standard at 90% of service reliability. The model can help optimize EMS vehicle allocation in urban areas.

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.

A priority based genetic algorithm for nonlinear transportation costs problems

In this manuscript, a vehicle allocation problem involving a heterogeneous fleet of vehicles for delivering products from a manufacturing firm to a set of depots is considered. Each depot has a specific order quantity and transportation costs consist of fixed and variable transportation cost. The objective is to assign the proper type and number of vehicle to each depot route to minimize the total transportation costs. It is assumed that the number of chartering vehicle types is limited. It is also assumed that a discount mechanism is applied to the vehicles renting cost. The discount mechanism is applied to the fixed cost, based on the number of vehicles to be rented. A mathematical programming model is proposed which is then converted to a mixed 0–1 integer programming model. Due to the computational complexity of the proposed mathematical model, a priority based genetic algorithm capable of solving the real world size problems was proposed. A computational experiment is conducted through which, the performance of the proposed algorithm is evaluated. The results reveal that the proposed algorithm is capable of providing the astonishing solutions with minimal computational effort, comparing with the CPLEX solutions.

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.

Small Quantities of Cargo Transportation Route Selection Base on Minimum Spanning Tree Algorithm

This paper mainly studies the single distribution center in the circumstances, transport path selection and vehicle allocation problem of small batch of goods. Using the theory of minimum spanning tree theory to the entire logistics network path selection research, while determining the nodes need to configure the transport vehicle for shunt transport, further establish the logistic distribution routing out several cars choice model, but also provides a direction for future research on this type of problem.

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.

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.

Optimal vehicle allocation for an Automated Materials Handling System using simulation optimisation

The Automated Materials Handling System (AMHS) in the semiconductor industry plays a vital role in reducing wafer cycle times and enhancing fabrication facility (fab) productivity. Due to the complexity of the manufacturing process and the stochasticity introduced by the inherent variability of processing times, the vehicle allocation for the AMHS is a challenging task, especially in 300 mm wafer fabs where the AMHS comprises both the interbay and intrabay systems to perform the timely deliveries. This paper studied the vehicle allocation problem in a typical 300 mm wafer fab. We formulated it as a simulation optimisation problem and proposed a conceptual framework to handle the problem. A discrete event simulation model was developed to characterise the AMHS, and the technique of simulation optimisation was applied to obtain the optimal vehicle allocation for both the interbay and intrabay systems. To demonstrate the feasibility and advantages of the simulation optimisation approach, a photobay example was used to compare the solution derived from the analytical model and simulation optimisation model. Finally, an empirical problem based on real data was conducted to show the viability of the proposed framework in practice.