Vehicle Allocation Research Articles

Optimal Network Design for Municipal Waste Management: Application to the Metropolitan City of Rome

This work is part of the regional research project PIPER—Intelligent Platform for the Optimization of Recycling Operations, which aims to develop a network design model for waste collection in the metropolitan city of Rome, Italy. The goal is to enhance the strategic planning of treatment and disposal facilities by incorporating transshipment locations to improve the efficiency of waste collection. The motivation for this study stems from the stringent targets set by the European Green Deal to achieve near-zero emissions and the critical role of waste management in realizing circular economy practices. Methods: The problem is formulated as a mixed integer linear program (MILP) that includes constraints on vehicle allocation to shifts and driver requirements. An additional feature of the model is its ability to account for vehicle maintenance issues when deployed on consecutive shifts. Results: The model was tested against a real-world case study in Rome and demonstrated significant potential savings, with a reduction in total traveled distance exceeding 10%. The mathematical model was also used for evaluating strategic scenarios in a “what-if” analysis, allowing the municipal collection company to assess location options for depots, waste treatment facilities, and transshipment points, as well as to optimize the fleet composition and driver allocation during work shifts. Conclusions: This study presents a robust tool for strategic planning in waste collection operations, highlighting the benefits of using transshipment locations to improve efficiency. The findings indicate substantial potential for cost savings and operational improvements, making the model a valuable asset for municipal waste management planning.

Food Distribution Models to Determine Distribution Routes, Distances and Costs

The agricultural sector is one of the sectors that has an important role in supporting the Indonesian economy. The agricultural sector in Indonesia needs to continue to be developed in line with population growth and technological developments to increase agricultural production. The problem that occurs is handling so that deficit areas will get supplies from surplus areas, so good food distribution is needed to aggregate and distribute these supplies as needed so that the basic needs of the community are met fairly and equitably. To overcome this, a food distribution model is needed to be able to accumulate the process of distributing food products, especially rice from commodity warehouses to 9 warehouses spread across Serang Regency, as a solution to the problems that occur and aims to achieve low cost efficiency by determining efficient and optimal routes, distances, and distribution costs using the Saving Matrix method and Nearest Neighbor. Based on this method, 3 routes were formed with a total distribution distance of 294.5 km. The allocation of vehicles used is 3 units of tronton trucks with a capacity of 45 tons. The distribution cost needed is Rp.1.934.866,- every day or Rp.58.045.980,- per month in other words the cost requirement every year is Rp.696.551.760,- Keywords: Agricultural Sector, Distribution Model, Saving Matrix and Nearest Neighbor

Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming

In view of recent developments in urban transport systems, there has been a significant increase in the presence of electric vehicles on the market. One of the major challenges faced is the effective allocation of these electric vehicles to appropriate charging stations. The aim of this study is to tackle this challenge by leveraging various input parameter models within a smart city context. The primary objective is to assign electric vehicles to the most suitable charging stations, taking into account four objectives: maximizing energy requested by electric vehicles, minimizing total response time, reducing charging costs, and minimizing battery degradation. Several factors, such as travel distance, state of charge, grid-to-vehicle energy trading, time, road traffic, driver priorities, and charging station capacity, are taken into account to address this problem. In addition, we propose a novel methodology that combines the analytic hierarchy process with a multi-objective goal programming model to aid decision-making regarding the allocation of electric vehicles–charging stations. The aim of this methodology is to assist electric vehicle owners in selecting effective charging stations within a complex decision-making environment. In addition, practical examples of how these methodologies can be used to solve a real-life problem have been highlighted. We validated the effectiveness of the proposed method in terms of enhancing the satisfaction factor of electric vehicles by updating their energy levels and reducing range anxiety through simulation results using SUMO and GAMS with the BARON solver. These results confirm the efficacy of the proposed method.

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 Activity-based model ABIT: Modeling 24 hours, 7 days a week

The paper introduces the activity-based model ABIT as a novel approach to modeling travel demand. Traditional aggregate transport models are limited in their ability to assess certain transport policies, such as ride-pooling services and autonomous cars, due to their inability to accurately represent complex travel behaviors. ABIT generates weekly activity patterns for individuals, forming the basis for understanding habitual and incremental travel behavior. Unlike traditional models, ABIT can distinguish between day-to-day variations in travel behavior and more significant year-to-year changes. The model's base-year structure is described in detail, including steps for assigning habitual modes, mandatory activities, discretionary activities, subtours, duration, start times, destination choices, and vehicle allocation. The paper emphasizes the importance of habitual mode choice, especially in modeling commute modes. The results of ABIT show variations in activity frequency across different days of the week, with weekdays dominated by work and education activities, while weekends exhibit a higher proportion of discretionary activities. The paper acknowledges longer runtimes and random variations as potential limitations, suggesting caution in analyzing results at a fine- grained level.

Solving Relay Task Allocation of Unmanned Aerial Vehicles: A Solution Using Group Multirole Assignment

Solving Relay Task Allocation of Unmanned Aerial Vehicles: A Solution Using Group Multirole Assignment

Rolling Time Domain Charging Allocation of Electric Vehicles Under Time Varying Demand

Rolling Time Domain Charging Allocation of Electric Vehicles Under Time Varying Demand

Application of simulated annealing in logistics vehicle allocation System: A case study from Vietnamese FMCG company

Application of simulated annealing in logistics vehicle allocation System: A case study from Vietnamese FMCG company

Research on VRP Model Optimization of Cold Chain Logistics Under Low-Carbon Constraints

The research in this article aims to consider low-carbon factors, through reasonable vehicle allocation and optimization of distribution routes, to achieve high satisfaction and low total cost, and to provide an optimized solution for fresh food distribution companies. In this article, cargo damage cost, energy cost, and carbon emission cost are added to the total cost, and customer satisfaction constraints based on time and quality are added, respectively, to construct a multi-vehicle cold chain VRP model under the low-carbon perspective. In order to obtain a good initial path method, a good chromosome is generated and added to the initial chromosome population according to the constraints of the vehicle type and time window, and the local elite retention strategy is combined to speed up the population convergence. Finally, taking the data of A Fresh Food Company as an example, the MATLAB software is used to realize the programming, which verifies the validity and superiority of the multi-vehicle cold chain VRP model under the low-carbon perspective.

Sustainable management of EOL tyres: A remanufacturing supply chain network design

End-of-life (EOL) objects such as tyres have been categorised as hazardous to the environment due to heavy metals and chemicals. To manage the recovery and remanufacturing operations of these EOL items, an effective network design is necessary. In this paper, we developed a mixed integer linear programming (MILP) model to minimize the total cost of the proposed EOL tyre remanufacturing supply chain network. Since, the problem is Non-deterministic Polynomial time hardness (NP-hard) in nature, we propose a prediction model that evaluates the operational feasibility of vehicle allocation and an evolutionary algorithm-based technique is employed to identify optimal facility locations, material flows among facilities, and demand distribution to individual automobiles for near-optimal solutions. Numerical experiments were conducted on a leading tyre remanufacturing company as a case study, and the results were compared with several evolutionary algorithms found that the Modified Genetic Algorithm (MGA) outperforms other algorithms. We demonstrate how, in each case, an optimal cost-effective location for a factory is found based on the cost and demand of the products. The study findings can assist governments and industry stakeholders in developing successful EOL tyre management strategies.

Optimal allocation of fast charging stations for large-scale transportation systems

The modern quest for sustainable cities increasingly relies on using distributed energy resources (DERs), which requires new planning practices. This paper proposes an optimisation strategy to solve the fast charging station (FCS) allocation of electric vehicles (EVs). A mixed-integer programming (MIP) model minimises investment and operation costs, considering the building of FCSs with photovoltaic (PV) systems over carports and battery energy storage systems (BESSs) as planning alternatives. The embedded set covering problem has special aspects that allow the development of a novel approach to evaluate candidate sites to accommodate FCSs. A preprocessing strategy is developed to fine-tune the entire solution space. A multiobjective approach is used to obtain an optimal compromise solution for the MIP model when it is required to serve the maximum number of EV owners at the lowest possible cost. The combined strategies reduce the computational burden, allowing full-scale studies of EV charging system planning. The results of studies using a real-world Brazilian case certify the benefits of the proposed strategy in the FCS allocation problem and in optimising the operation when considering renewable alternatives.

Competition and Cooperation for Multiple Solar Powered Unmanned Aerial Vehicles under Static Soaring

This work examines the competition and allocation of multiple solar-powered unmanned aerial vehicles (SUAVs) to a single thermal since multiple SUAVs often demonstrate superior mission performance compared to a single SUAV. Additionally, they can harvest extra energy from thermal updrafts. This work considers two conditions, a non-cooperative competition and a cooperative allocation of thermal. In each case, corresponding objective functions and constraints are established, and assignment schemes are derived by solving these objective functions. The allocation results are simulated and integrated with the dynamics and solar energy model. The numerical results show that, in the non-cooperative mode, the first vehicle to reach the thermal can occupy it for soaring, while the remaining SUAVs will fly towards the destination directly. But in the cooperative mode, the multiple SUAVs will allocate the thermal to the SUAV with the highest energy gain through soaring, to maximize the overall electric energy storage of the SUAV group.

Computation of vehicle allocation, scheduling and number of commuters in selected Nigerian routes

The research is aimed at developing a transport management algorithm that will be used to determine the number of commuters and vehicular allocation with high numerical accuracy. Many research works have proposed either using machine learning algorithms for clustering, classification, determination of frequent item-set in datasets (databases) or traditional algorithms to effectively design transportation systems. In this research a non-machine learning algorithm that does not require any training time, which will require less computation power and increase process execution time is developed. The scheduling algorithm is designed taking note of two most important variables: the number of commuters is the independent variable and the number of buses assigned or schedule to a designated route is the dependent variable. Some decision assumptions are made which include all vehicle used in the research have same carrying capacity for all the routes. The algorithm numerically computes the number of commuters, the number of assigned vehicles and the number of scheduled vehicles for each route. So, the research will assist in making useful deductions such; number of resources required to effectively service a route without bottle-necks and more accurately the size of input per route and approximate output for the corresponding routes. The results show the total number of vehicles assigned, scheduled and number of commuters on a text file (document).

Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm

The work deals with the issue of assigning vehicles to tasks in transport companies, taking into account the minimization of the risk of dangerous events on the route of vehicles performing the assigned transport tasks. The proposed risk management procedure based on a heuristic algorithm reduces the risk to a minimum. The ant algorithm reduces it in the event of exceeding the limit, which differs from the classic methods of risk management, which are dedicated only to risk assessment. A decision model has been developed for risk management. The decision model considers the limitations typical of the classic model of assigning vehicles to tasks, e.g. window limits and additionally contains limitations on the acceptable risk on the route of vehicles' travel. The criterion function minimizes the probability of an accident occurring along the entire assignment route. The probability of the occurrence of dangerous events on the routes of vehicles was determined based on known theoretical distributions. The random variable of the distributions was defined as the moment of the vehicle's appearance at a given route point. Theoretical probability distributions were determined based on empirical data using the STATISTICA 13 package. The decision model takes into account such constraints as the time of task completion and limiting the acceptable risk. The criterion function minimizes the probability of dangerous events occurring in the routes of vehicles. The ant algorithm has been validated on accurate input data. The proposed ant algorithm was 95% effective in assessing the risk of adverse events in assigning vehicles to tasks. The algorithm was run 100 times. The designated routes were compared with the actual hours of the accident at the bottom of the measurement points. The graphical interpretation of the results is shown in the PTV Visum software. Verification of the algorithm confirmed its effectiveness. The work presents the process of building the algorithm along with its calibration.

Joint Location–Allocation Model for Multi-Level Maintenance Service Network in Agriculture

The maintenance service network is always designed as a multi-level service network to provide timely maintenance service for failed machinery, and is rarely studied in agriculture. Thus, this paper focuses on a three-level maintenance service network location–allocation problem in agriculture, which contains several spare part centres, service stations, and service units. This research aims to obtain the optimal location of spare part centres and service stations while determining service vehicle allocation results for service stations, and the problem can be called a multi-level facility location and allocation problem (MLFLAP). Considering contiguity constraints and hierarchical relationships, the proposed MLFLAP is formulated as a mixed-integer linear programming (MILP) model integrating with P-region and set covering location problems to minimize total service costs, including spare part centre construction costs, service vehicle usage costs, and service mileage costs of service stations. The Benders decomposition-based solution method with several improvements is then applied to decompose the original MLFLAP into master problem and subproblems to find the optimal solutions effectively. Finally, a real-world case in China is proposed to evaluate the performance of the model and algorithm in agriculture, and sensitivity analysis is also conducted to demonstrate the impact of several parameters.

On Using Metaheuristics for the Allocation of Electric Vehicles to Charging Stations

In this article, we present a comprehensive study regarding the problem of allocating a fleet of electric vehicles to charging stations according to charging time and battery constraints. Each charging station’s capacity as well as the necessary charging time is known in advance while each vehicle’s arrival time is provided by a GPS device. We provide an integer programming model solved with an exact method to effectively handle this combinatorial problem along with a set of metaheuristic algorithms. To evaluate the performance of this solution framework, computational experiments are conducted on large-scale randomly generated instances simulating a real-world scenario.

Multi-objective Optimization Approach for Allocation of Electric Vehicles Parking Lots and Smart Charging with Distributed Energy Resource

Multi-objective Optimization Approach for Allocation of Electric Vehicles Parking Lots and Smart Charging with Distributed Energy Resource

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.

A variable neighborhood search algorithm for airport ferry vehicle scheduling problem

This paper formulates the airport ferry vehicle scheduling problem as unrelated parallel machine scheduling (UPMS) problem to achieve a better reduction in the weighted sum of flight tardiness. In the UPMS model, the departure time and the estimated end time of each ferry vehicle for each ferry task is simultaneously planned with the allocation of ferry vehicles, and the specific journey of each ferry vehicle for each task is arranged. A variable neighborhood search (VNS) algorithm is proposed to address the model. Three scheduling rules including random allocation, service time in turn (STT), and minimum maximum completion time (MCT) are designed to combine with VNS as comparisons, together with five neighborhood structures suited for the airport ferry vehicle scheduling problem. Comparative experiments are carried out by using two weeks of real flight data from a major international airport in China. Experimental results show the superiority of the proposed MCT initialization combined with VNS (MCT_VNS) for the airport ferry vehicle scheduling problem. The solutions obtained by MCT_VNS can provide the allocation of the airport ferry vehicles to flights that need ferry services.

Optimal planning and allocation of Plug-in Hybrid Electric Vehicles charging stations using a novel hybrid optimization technique.

India's expanding population has necessitated the development of alternate transportation methods with electric vehicles (EVs) being the most indigenous and need for the current scenario. The major hindrance is the undue influence on the power distribution system caused by incorrect charging station setup. Renewable Energy Sources (RES) have a lower environmental impact than the non-renewable sources of energy and due to which Plug-in Hybrid Electric Vehicles (PHEV) charging stations are installed in the highest-ranking buses to facilitate their effective placements. Based on meta-heuristic optimization, this study offers an effective PHEV charging stations allocation approach for RES applications. The primary objective of the developed system is to create a charging network at a reasonable cost while maintaining the operational features of the distribution network. These troublesare handled by applying meta-heuristic algorithms and optimum planning based on renewable energy systems to satisfy the outcomes of the variables. As a result, by adding charging station parameters, this research proposes to conceptualize the distribution of optimal charging stationsas multiple-objectives of the problem. Furthermore, the PHEV RES and charging station location problem is handled in this study by deploying a novel hybrid algorithm termed as Atom Search Woven Aquila Optimization Algorithm (AT-AQ) that includes the ideas of both Aquila Optimizer (AO) and Atom Search Optimization (ASO) Algorithms. In reality, Aquila Optimizer is a unique population-based optimization approach energized by Aquila's behaviour when seeking prey and it solves the problems of slow convergence and local optimum trapping. According to the findings of the experiments, the proposed model outperformed the other methods in terms of minimized cost function.