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With increasing urbanization and the demand for efficient, flexible transportation solutions, demand-responsive transportation services (DTRS) has emerged as a viable alternative to traditional public transit. However, determining the optimal fleet size to balance the investment and operational revenue remains a significant challenge for service providers. In this article, we address the optimization of fleet size in point-to-point shared demand DRTS, which widely operates within many cities. To capture the uncertain passenger demands in the future when planning the fleet size currently, we model this problem with a framework of two-stage stochastic programming with recourse. Fleet sizing decisions are made in the first stage before the uncertain demands are revealed. After the uncertainty is revealed, the second stage involves making additional decisions to maximize operational revenue. The objective is to optimize the total revenue of the first-stage decisions and the expected revenue of the recourse actions. To solve this practical problem, we resort to the Model Predictive Control method (MPC) and propose a network decomposition approach that first converts the transportation network to a nodal tree structure and then develops a Nodal Tree Recourse with Dependent Arc Capacities (NTRDAC) algorithm to obtain the exact value of the expected recourse functions. In the experiments, NTRDAC is able to produce results within seconds for transportation networks with over 30 nodes. In contrast, a commercial solver is only capable of solving networks with up to five nodes. The stability tests show that NTRDAC remains robust as the problem size varies. Lastly, the value of the stochastic solution (VSS) was evaluated, and the results indicate that it consistently outperforms the expected value solutions. Numerical experiments show that the performance of the NTRDAC algorithm is quite encouraging and fit for large-scale practical problems. [ABSTRACT FROM AUTHOR]

Urgensi Electric Vehicle Routing Problems (EVRP) terletak pada kebutuhan untuk mengoptimalkan rute kendaraan listrik guna mengurangi emisi karbon dan dampak lingkungan. Tantangan EVRP meliputi kompleksitas perhitungan rute dengan mempertimbangkan batasan daya baterai dan infrastruktur pengisian daya yang terbatas. Di artikel ini diformulasikan masalah perutean kendaraan listrik dengan permintaan antar-jemput barang dalam bentuk pemrograman linear bilangan bulat. Model yang diajukan memiliki fitur penggunaan armada kendaraan dengan kapasitas angkut dan kapasitas baterai heterogen. Implementasi model meliputi penyelesaian masalah antar-jemput barang menggunakan armada kendaraan listrik homogen dan heterogen dengan depot, pelanggan, kendaraan listrik, dan stasiun pengisian kendaraan listrik umum. Metode eksak digunakan dalam pencarian solusi optimum dengan bantuan Lingo 17.0.

Using a parsimonious model, this paper analyzes a dockless bike-sharing (DLB) service that competes with walking and a generic motorized mode. The DLB operator chooses a fleet size and a fare schedule that dictate the level of service (LOS) as measured by the access time or the walking time taken to reach the nearest bike location. The market equilibrium is formulated as a solution to a nonlinear equation system over which three counterfactual design problems are defined to maximize (i) profit, (ii) ridership, or (iii) social welfare. The model is calibrated with empirical data collected in Chengdu, China, and all three counterfactual designs are tested against the status quo. We show the LOS of a DLB system is subject to rapidly diminishing returns to the investment on the fleet. Thus, under the monopoly setting considered herein, the current fleet cap set by Chengdu can be cut by up to three quarters even when the DLB operator aims to maximize ridership. This indicates the city's fleet cap decision might have been misguided by the prevailing conditions of a competitive yet highly inefficient market. For a regulator seeking to influence the DLB operator for social good, the choice of policy instruments depends on the operator's objective. When the operator focuses on profit, limiting fare is much more effective than limiting fleet size. If, instead, it aims to grow market share, then setting a limit on fleet size becomes a dominant strategy. We also show, both analytically and numerically, that the ability to achieve a stable LOS with a low rebalancing frequency is critical to profitability. A lower rebalancing frequency always rewards users with cheaper fares and better LOS even for a profit-maximizing operator. Funding: This research was partially supported by the U.S. National Science Foundation [Grant CMMI 1922665] and the National Natural Science Foundation of China [Grant 71971044]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0304. [ABSTRACT FROM AUTHOR]

This paper studies the joint fleet sizing and charging system planning problem for a company operating a fleet of autonomous electric vehicles (AEVs) for passenger and goods transportation. Most of the relevant published papers focus on intracity scenarios and adopt heuristic approaches, e.g., agent based simulation, which do not guarantee optimality. In contrast, we propose a mixed integer linear programming model for intercity scenarios. This model incorporates comprehensive considerations of 1) limited AEV driving range; 2) optimal AEV routing and relocating operations; 3) time-varying origin-destination transport demands; and 4) differentiated operation cost structure of passenger and goods transportation. The proposed model can be computational expensive when the scale of the transportation network is large. We then exploit the structure of this program to expedite its solution. Numerical experiments are conducted to validate the proposed method. Our experimental results show that AEVs in passenger and goods transportation have remarkable planning and operation differences. We also demonstrate that intelligent routing and relocating operations, charging system and vehicle parameters, e.g., charging power, battery capacity, driving speed etc., can significantly affect the economic efficiency and the planning results of an AEV fleet.

Optimizing job shop scheduling in modern factories demands flexibility and adaptability to handle unexpected events and Unmanned Ground Vehicles (UGVs) limitations. This paper addresses these challenges by introducing a novel multi-agent simulator for the Job Shop Scheduling Problem (JSSP) with UGVs handling transportation tasks. The simulator, designed with Netlogo, incorporates real-world constraints, such as collision avoidance, UGV fleet size, and battery limitations, often overlooked in prior studies. By comparing the two categories of UGVs, namely, Autonomous Guided Vehicles (AGVs) and Autonomous Intelligent Vehicles (AIVs), under different scheduling methods (static vs dynamic), we evaluate their performance in constrained manufacturing environments. Our findings highlight the superior performance of AIVs in terms of overall makespan and resilience to additional constraints. Among other results, we found that schedules with a fleet of 2 AIVs produced similar makespans than schedules with a fleet of 4 AGVs. Consequently, the simulator and the conducted study provide valuable insights for optimizing JSSP within constrained environments and making informed decisions regarding AIVs adoption. [ABSTRACT FROM AUTHOR]

A study about the requirements and cargo transportation demand in the Black Sea as part of a multimodal transportation frame is performed, estimating the potential need of a ship fleet of multipurpose ships. The study performs conceptual multipurpose vessel design and fleet sizing using the long-time experience and statistics in defining main dimensions of the ship and her hull form, resistance and propulsion, weights, stability, free-board, seakeeping and manoeuvrability, capital, operational and decommissioning expenditure, where the optimal design solution is obtained based on the energy efficiency, shipbuilding, operation, and resale costs at the end of the service life. A discussion about possible applications of a different fleet of ship sizes in improving the cargo transportation efficiency considers the vessel's typical operational profile in such a way to maximise the economic impact conditional of the unsteady cargo flow and environmental impact.

This paper investigates an optimization problem regarding feeder bus network design for metro stations, based on the wide use of battery-powered electric buses. To characterize the problem, a mixed integer programming model is formulated with the objective of minimizing the total cost of the electric feeder bus system, including the user and operator investment costs. The model considers the constraints of route length, bus stop coverage, vehicle capacity, and depot size. The solution incorporates a multiserver queuing submodel with limited system capacity to explore the relationship among the bus headway, the fleet size, and the number of depot-located chargers. Then a costoptimized planning methodology is developed to solve these three variables for a given network. To solve the network optimization problem, a genetic algorithm that includes customized operators is developed to guide the solution evolution process. Synthetic case studies are conducted to test the model and the algorithm, and a sensitivity analysis on the important input parameters follows. [ABSTRACT FROM AUTHOR]

A study about the requirements and cargo transportation demand in the Black Sea as part of a multimodal transportation frame is performed, estimating the potential need of a ship fleet of multipurpose ships. The study performs conceptual multipurpose vessel design and fleet sizing using the long-time experience and statistics in defining main dimensions of the ship and her hull form, resistance and propulsion, weights, stability, free-board, seakeeping and manoeuvrability, capital, operational and decommissioning expenditure, where the optimal design solution is obtained based on the energy efficiency, shipbuilding, operation, and resale costs at the end of the service life. A discussion about possible applications of a different fleet of ship sizes in improving the cargo transportation efficiency considers the vessel's typical operational profile in such a way to maximise the economic impact conditional of the unsteady cargo flow and environmental impact. [ABSTRACT FROM AUTHOR]

A vehicle routing problem (VRP), using a mixed fleet of vehicles, with sequencebased pallet loading and axle weight constraints is introduced. The effect of the integration of axle weight constraints in a Fleet Size and Mix VRP is analyzed by comparing the problem with and without axle weight constraints. A vehicle fleet of 30-foot and 45-foot trucks, consisting of a tractor and a semi-trailer, is considered. Two scenarios are analyzed with different objective functions. In the first scenario, the objective aims to minimize total distance while in the second scenario the objective aims the minimization of total transport costs. An Iterated Local Search metaheuristic algorithm is used to solve the problem. The results indicate that the impact of axle weight constraints on the solution cost of a vehicle routing problem depends on the fleet composition. Therefore, decisions on the deployment of a mixed-size fleet may be influenced by the integration of axle weight constraints. [ABSTRACT FROM AUTHOR]

As public perception of sharing economy in transportation has changed, mobilephone-hailed ridesharing is gaining prominence. The key aspect of capitalizing and promoting better shared-mobility systems depends on the matching rate between the supply and demand for rides. Peer-to-peer (P2P) ridesharing systems devise higher matching rate than pure ridesharing systems by attracting more drivers. Even relaxing the spatiotemporal constraints for participants could increase the chances to be matched. However, we notice that sole P2P ridesharing systems still do not guarantee matching when the number of drivers is limited. We propose the utilization of a fleet service to cover the unmatched riders in P2P ridesharing. While it can be any type of fleet services such as taxis, Uber/Lyft, or paratransit, we explore the idea of utilizing shared autonomous vehicles as a fleet, as they can be dispatched without labor. We model an integrated system for P2P ridesharing and shared autonomous fleet vehicles (SAFVs). The proposed algorithm is designed to maximize matching ratio while optimizing the number of required SAFVs. Based on a simulated study on the northern Los Angeles, the integrated shared-mobility system is shown to have high potential to serve a high fraction of riders. [ABSTRACT FROM AUTHOR]

• Optimize the battery electric bus charging facility location and fleet size problem. • Consider stochastic charging demand, and time of use electricity tariffs. • Develop a stochastic integer program and a customized Lagrangian Relaxation algorithm. The electrification of city bus systems is an increasing trend, with many cities replacing their diesel buses with battery electric buses (BEBs). Due to limited battery capacities, and to random battery discharge rates—which are affected by weather, road and traffic conditions—BEBs often need daytime charging to support their operation for a whole day. The deployment of charging infrastructures, as well as the number of stand-by buses available, has a significant effect on the operational efficiency of electric bus systems. In this work, a stochastic integer program has been developed to jointly optimise charging station locations and bus fleet size under random bus charging demand, considering time-of-use electricity tariffs. The stochastic program is first approximated by its sample average and is solved by a customised Lagrangian relaxation approach. The applicability of the model and solution algorithm is demonstrated by applications to a series of hypothetical grid networks and to a real-world Melbourne City bus network. Managerial insights are also presented. [ABSTRACT FROM AUTHOR]

The proportion of spot trading and short-term contracts has gradually increased in the rapidly growing LNG market, leading to more uncertainties in LNG demand and prices that significantly challenge LNG shipping decisions. In this paper, a mathematical model is developed to minimize transportation costs from multiple exporting countries to multiple importing countries under demand and price uncertainty, both of which are results of LNG market liberalization. A Gaussian process metamodel based simulation optimization method is proposed to solve the fleet planning problem, accounting for various uncertainties. A case study is given to illustrate the effects of uncertainties on the optimal solutions. The results demonstrate that shipping companies may purchase fewer ships and charter more ships to hedge against the risk of uncertainty. LNG market liberalization can significantly reduce its transportation costs and carbon emissions. The results suggest the need for further LNG market liberalization and measures to mitigate uncertainties for shipping companies, such as removing destination clauses. • More price and demand uncertainties stemming from LNG market liberalization challenge fleet planning decisions. • Consider a fleet size and composition optimization problem with a mathematical model. • A Gaussian process based simulation optimization method solves the fleet planning problem. • Shipping companies may purchase fewer ships and charter more ships to hedge against the risk of uncertainty. • LNG market liberalization can significantly reduce transportation costs and carbon emissions. [ABSTRACT FROM AUTHOR]

The first commercial fleets of Robo-Taxis will be on the road soon. Today important efforts are made to anticipate future Robo-Taxi services. Fleet size is one of the key parameters considered in the planning phase of service design and configuration. Based on multi-agent approaches, the fleet size can be explored using dynamic demand response simulations. Time and cost are the most common variables considered in such simulation approaches. However, personal taste variation can affect the demand and consequently the required fleet size. In this paper, we explore the impact of user trust and willingness-to-use on the Robo-Taxi fleet size. This research is based upon simulating the transportation system of the Rouen-Normandie metropolitan area in France using MATSim, a multi-agent activity-based simulator. A local survey is made in order to explore the variation of user trust and their willingness-to-use future Robo-Taxis according to the sociodemographic attributes. Integrating survey data in the model shows the significant importance of traveler trust and willingness-to-use varying the Robo-Taxi use and the required fleet size. [ABSTRACT FROM AUTHOR]

• A set partitioning model is developed for the EVFS problem. • The dynamic vehicle relocation and nonlinear charging profile is incorporated. • A tailored B&P approach is proposed to solve the set partitioning model. • Heuristics are developed to reinforce B&P approach and solve large-scale problems. This study aims to determine the electric vehicle fleet size for one-way carsharing services by maximizing the profit of carsharing operators while taking into account the vehicle relocation operations and nonlinear electric vehicle charging profile. We formulate a set partitioning model for the considered problem. A tailored branch-and-price (B&P) approach is proposed to find the exact optimal solution of the model. In particular, an effective multi-label correcting method is developed to solve the pricing problem (i.e., generate columns) within the B&P approach. A novel non-dominated charging strategy is put up to avoid the exponential growth of labels caused by the allowance of partial charging with a nonlinear charging profile. In addition to the B&P approach, two heuristic methods are put forward for solving the large-scale problems or reinforcing the B&P approach. Numerical experiments with randomly generated instances and a case study based on a one-way carsharing operator in Singapore are conducted to further assess the efficiency and applicability of the proposed solution methods. The effects of several key parameters, i.e., the fixed cost of EV, relocation cost, electricity cost, service charge, EV driving range, the charging efficiency, and the number of rentals on the performance of a one-way electric carsharing system are also examined. [ABSTRACT FROM AUTHOR]

Highlights • Introduction of new methodology for autonomous shuttle bus service (ASBS). • Determine of optimal public transport (PT) timetable integrated with vehicle scheduling. • Analysis based on the deficit function (DF) graphical concept. • Treating a single-depot bidirectional circle ASBS route using the DF as a multi-decision choice model. • Use of binary-variable iteration method for genetic algorithm. Abstract The development of new technologies that allow us to know more precisely real-time public transport (PT) passenger demand enables the introduction of new methodologies to improve autonomous shuttle bus service (ASBS) from the perspective of both the user and the operator. This work develops a new methodology to determine the optimized PT timetable integrated with vehicle scheduling, including the possibility of using the skip-stop tactic based on real-time passenger demand. The consideration of a skip-stop operation allows for a reduction in the passengers' total travel time and in the number of autonomous shuttle vehicles in use. The analysis is based on the deficit function (DF) graphical concept and considers the constraints of the vehicle with regard to capacity. The methodology is developed for a single-depot bidirectional circle ASBS route using the DF as a multi-decision choice model. Because of the complexity involved in large-size genetic algorithm problems, a binary-variable iteration method is applied to attain efficient solutions. A case study in Auckland, New Zealand, is employed to assess the new model. The case study shows a reduction of 1.83% in total passenger travel time and 8.11% in the number of vehicles as compared to solutions that do not consider the skip-stop tactic. Moreover, it is shown that the advanced DF-based methodology could be used to develop fully autonomous PT in a network in the future. [ABSTRACT FROM AUTHOR]

As public perception of sharing economy in transportation has changed, mobilephone-hailed ridesharing is gaining prominence. The key aspect of capitalizing and promoting better shared-mobility systems depends on the matching rate between the supply and demand for rides. Peer-to-peer (P2P) ridesharing systems devise higher matching rate than pure ridesharing systems by attracting more drivers. Even relaxing the spatiotemporal constraints for participants could increase the chances to be matched. However, we notice that sole P2P ridesharing systems still do not guarantee matching when the number of drivers is limited. We propose the utilization of a fleet service to cover the unmatched riders in P2P ridesharing. While it can be any type of fleet services such as taxis, Uber/Lyft, or paratransit, we explore the idea of utilizing shared autonomous vehicles as a fleet, as they can be dispatched without labor. We model an integrated system for P2P ridesharing and shared autonomous fleet vehicles (SAFVs). The proposed algorithm is designed to maximize matching ratio while optimizing the number of required SAFVs. Based on a simulated study on the northern Los Angeles, the integrated shared-mobility system is shown to have high potential to serve a high fraction of riders. [ABSTRACT FROM AUTHOR]

The shipping route network design is a strategic decision issue for shipping lines because liner ships operate along routes and timetables published months ahead. This paper considers an Integrated Planning Problem (IPP) of determining the speed, route, and number of ships deployed (fleet size) of a ship type. The objective is to maximize a carrier's profit, which is the revenue minus Cargo Handling Charge (CHC), bunker consumption cost, port charge, cargo inventory cost and ship time cost. We present a nonlinear shortest-path model to represent the considered problem and suggest a Simulated Annealing (SA) algorithm with some analytic methods. Performance evaluation experiments demonstrate that the proposed algorithm works well for small test problems. A number of scenario analyses are further performed with the heuristic algorithm to investigate the effect of exogenous factors, including bunker price, chartering cost, revenue (sea freight rate) and ship size. The analysis results show that strategic decisions for determining the route, fleet size and ship speed can be affected by changes in the factors. The performance tests and scenario analyses show that the heuristic algorithm can be utilized as an efficient and effective planning tool for the liner service in both the current environment and feasible future situations. [ABSTRACT FROM AUTHOR]

Rail wagon planning is an important type of planning in the rail transport network since its costs are high. Therefore, enhancing the accuracy of fleet planning could result in cost efficiency. In this research, we aim to model and optimize the fleet size and allocation problem in a rail transportation network. We proposed a novel two-step algorithm composed of two meta-heuristic steps to optimize the allocation of the fleet between origins and destinations and to determine the optimal number of wagons. Besides, we provided a case study of Iran's minerals rail transport network. The results demonstrate that 4925 wagons are required to transport approximately 17.7 million tons of minerals per year, roughly 10% less than the current proportion. The validity of the results was also analyzed by using a comparison between our algorithm and CEPLEX for a test dataset. It is shown that our approach outperforms CEPLEX in terms of speed while retaining the same performance. [ABSTRACT FROM AUTHOR]

Recently, tradespace analysis and exploration has emerged as an important focus area within the Department of Defense Engineered Resilient System initiative, which draws upon engineering concepts, science, and design tools to produce trusted and effective solutions for a wide range of operational contexts. Most of the previous research on tradespace analysis, including those developed for rotorcraft, emphasize performance. However, non-functional requirements such as reliability, availability, and maintainability have received minimal consideration, despite their direct influence on program level concerns such as operation and support as well as affordability. This paper proposes a strategy to incorporate reliability engineering into tradespace analysis. We also develop a subsystem-level reliability investment model that is illustrated through a simplified example. Our results suggest that reliability investment could achieve significant savings over a systems lifecycle, thereby enabling improved fleet availability and a larger fleet size. [ABSTRACT FROM AUTHOR]

Highlights • A hub-and-spoke logistics network with consideration of road congestion is proposed. • A multiple assignment model is proposed for the design of the logistics network. • Two types of trucks are considered to model economies of scale in logistics networks. • The asymptotic analysis reveals bottleneck routes restrain the system throughput. • A math-heuristic algorithm is developed to solve our problem. Abstract This paper describes third-party logistics (3PL) systems with consolidation hubs as a hub-and-spoke network. We propose a multiple assignment (MA) model for the joint design of the fleet size and the number, locations and capacities of hubs. The objective is to minimize the total costs, including congestion costs. We explicitly model the road congestion by formulating the route travel time as an increasing function of the number of trucks on the route. We consider two types of trucks to model economies of scale. Similar to a truck booking system, we control the node and road congestions by delaying some of the overfull demand at the origin nodes. We derive the asymptotic behavior as design variables increase and demonstrate how the 3PL system throughput is bounded by the bottleneck routes. When the existing road congestion on the bottleneck routes is heavy, excessively large fleets and hub capacities will worsen the traffic conditions and downgrade the system throughput. We develop a math-heuristic algorithm to solve our problem, which is decomposed into two subproblems. We prove that the two subproblems can be linearized. The numerical experiments reveal some interesting findings: (1) heavier congestion on an existing road network (without 3PL trucks) generates a design with higher requirements (a larger number of hubs, a larger hub capacity and a larger fleet size) and larger economies of scale; (2) neglecting the road congestion constraints optimistically estimates the system performance and generate a design with fewer hubs and a larger number of large trucks; and (3) decreasing the fixed costs of establishing hubs or the large truck-related costs can significantly increase the economies of scale and reduce the total number of trucks. [ABSTRACT FROM AUTHOR]

This study investigates the cost competitiveness of different types of charging infrastructure, including charging stations, charging lanes (via charging-while-driving technologies) and battery swapping stations, in support of an electric public transit system. To this end, we first establish mathematical models to investigate the optimal deployment of various charging facilities along the transit line and determine the optimal size of the electric bus fleet, as well as their batteries, to minimize total infrastructure and fleet costs while guaranteeing service frequency and satisfying the charging needs of the transit system. We then conduct an empirical analysis utilizing available real-world data. The results suggest that: (1) the service frequency, circulation length, and operating speed of a transit system may have a great impact on the cost competitiveness of different charging infrastructure; (2) charging lanes enabled by currently available inductive wireless charging technology are cost competitive for most of the existing bus rapid transit corridors; (3) swapping stations can yield a lower total cost than charging lanes and charging stations for transit systems with high operating speed and low service frequency; (4) charging stations are cost competitive only for transit systems with very low service frequency and short circulation; and (5) the key to making charging lanes more competitive for transit systems with low service frequency and high operating speed is to reduce their unit-length construction cost or enhance their charging power. [ABSTRACT FROM AUTHOR]

This study proposes an interesting electric vehicle fleet size and trip pricing (EVFS&TP) problem for one-way carsharing services by taking into account the necessary practical requirements of vehicle relocation and personnel assignment. The EVFS&TP problem aims to maximize the profit of one-way carsharing operators by determining the electric vehicle fleet size, trip pricing, and strategies of vehicle relocation and personnel assignment subject to the elastic demand for the one-way carsharing services. A mixed-integer nonlinear and nonconvex programming model is first built for the EVFS&TP problem. By exploiting the unique structure of the original built model, a mixed-integer convex programming model is subsequently developed. An effective global optimization method with several outer-approximation schemes is put up to find the global optimal or ε-optimal solution to the EVFS&TP problem. A case study based on a one-way carsharing operator in Singapore is conducted to demonstrate the efficiency of the proposed model and solution method and further analyse the impact of demand, the degree of demand variation, the fixed operational cost of the vehicles as well as payment for personnel on the performance of the one-way carsharing services. [ABSTRACT FROM AUTHOR]