Search Results (16)

Innovative demand-responsive transport services are spreading in most urban areas, allowing dynamic matching between demand and supply and enabling travellers to request shared rides in real-time via mobile applications. They are used both as an alternative to public transport and as an access/egress leg to mass transit stations, i.e., acting as a feeder service. In low-demand areas and small-sized cities, it is often difficult to provide effective and cost-efficient public transport, thus resulting in an extensive use of private vehicles. Using an agent-based modelling approach, this study compares the performance of fixed-route transit (FRT) and demand-responsive transit (DRT), where optional stops can be activated on demand. The aim is to identify the conditions allowing DRT to become more advantageous than FRT in small-sized cities, both for travellers and the transport operator. A real-time matching algorithm identifies optimal trip chains (i.e., public transport lines; pick-up, drop-off and transfer stops; and time windows) for travel requests, dynamically updating vehicles’ routes and schedules. The model is applied to the city of Caltanissetta, Italy, where a transit service with six FRT urban lines is currently operating. Travel patterns were reconstructed from thousands of travel requests collected by a Mobility-as-a-Service platform within one-year. The main findings demonstrate the benefits of DRT in providing a higher quality of service, reducing riding times for passengers, and enhancing service efficiency without burdening operating costs. The DRT reduced the vehicle-kilometres travelled by up to 5% compared to FRT while decreasing passenger ride times by approximately 10%. An economic analysis showed reductions in operator unit costs of up to 3.4% for low-demand rates, confirming the advantages of flexible operations in small-sized cities. Full article

Demand-responsive transit (DRT) is a kind of new public transit tailored to passenger needs that can provide passengers with fast, convenient, and diversified travel services. This paper proposes a scheduling model for demand-responsive transit based on reservations applicable to multi-vehicle task dispatching during the time period. It uses an ant colony algorithm for a solution. The model uses vehicle size and mileage as the optimization objectives while considering practical constraints like multi-vehicle operation, maximum pick-up intervals, etc. The feasibility of the model and the algorithm’s effectiveness are verified using the Shanghai Huyi Highway Demonstration Line as a case study. The results indicate that the model can effectively generate the optimal scheduling plan for DRT, significantly reduce the system’s operating cost, and improve resource utilization efficiency. Full article

This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach. Full article

Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park. Full article

In the present paper, the online valet driving problem (OVDP) is studied. In this problem, customers request a valet driving service through the platform, then the valets arrive on e-bikes at the designated pickup location and drive the vehicle to the destination. The key task is to assign the valets effectively for driving orders to minimize the overall cost. To serve that purpose, we first propose a new online scheduling strategy that divides the planning horizon into several rounds with fixed length of time, and each round consists of pooling time and scheduling time. By including the features of online scheduling and the power level of e-bikes, this OVDP becomes more practical but nevertheless challenging. To solve the OVDP, we formulate it into a set partitioning model and design a branch-and-price (B&P) algorithm. To improve the computation efficiency, a label setting algorithm is incorporated to address the pricing subproblem, which is accelerated via a heuristic pricing method. As an essential part of the algorithm design, an artificial column technique and a greedy-based constructive heuristic are implemented to obtain the initial solution. Based on the numerical analysis of various scaled instances, it is verified that the proposed B&P algorithm is not only effective in optimum seeking, but also shows a high level of efficiency in comparison with the off-the-shelf commercial solvers. Furthermore, we also explore the impact of pooling and scheduling time on the OVDP and discover a bowl-shaped trend of the objective value with respect to the two time lengths. Full article

Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational costs in order to make profits in an extremely competitive environment. Therefore, the efficiency of logistics management in the supply chain and the quick response to customers’ demands are treated as an additional source of profit. One of the warehouse operations for intelligent logistics network design, called cross-docking (CD) operations, is used to reduce inventory levels and improve responsiveness to meet customers’ requirements. Accordingly, the optimization of a vehicle dispatch schedule is imperative in order to produce a routing plan with the minimum transport cost while meeting demand allocation. Methods: This paper developed a two-phase algorithm, called sAIS, to solve the vehicle routing problem (VRP) with the CD facilities and systems in the logistics operations. The sAIS algorithm is based on a clustering-first and routing-later approach. The sweep method is used to cluster trucks as the initial solution for the second phase: optimizing routing by the Artificial Immune System. Results: In order to examine the performance of the proposed sAIS approach, we compared the proposed model with the Genetic Algorithm (GA) on the VRP with pickup and delivery benchmark problems, showing average improvements of 7.26%. Conclusions: In this study, we proposed a novel sAIS algorithm for solving VRP with CD problems by simulating human body immune reactions. The experimental results showed that the proposed sAIS algorithm is robustly competitive with the GA on the criterion of average solution quality as measured by the two-sample t-test. Full article

In order to solve the problem that the existing low-carbon vehicle scheduling model ignores the economic benefits of enterprises and cannot fully reflect the fuzzy needs of customers, the green simultaneous pick-up and delivery vehicle scheduling problem is studied here. With the goal of minimizing the total cost composed of service cost, fuel consumption cost, and carbon emission cost, a multi-objective comprehensive model of green simultaneous pick-up and delivery under fuzzy demand is established. In order to fully consider the objective uncertainty of customer demand and customer service time, triangular fuzzy numbers are introduced and simultaneous delivery demand is considered. An improved genetic tabu search algorithm is proposed to solve this problem. In the improved GA-TS algorithm, the penalty factor is introduced into the fitness function, the selection operator combined with elite strategy is adopted, and a mutation operator combined with tabu search algorithm is proposed. The Taguchi analysis method is used to obtain reasonable parameter settings of the GA-TS algorithm. Finally, a case study is used to verify the effectiveness of the model and hybrid algorithm. The experimental results show that the proposed comprehensive model can effectively optimize the scheduling of low-carbon simultaneous pick-up and delivery vehicles under fuzzy demand, and the effectiveness and feasibility of genetic tabu search algorithm are verified by comparing the experimental results of different algorithms and different case sizes. Full article

Background: The logistics network design with cross-docking operations enables shipping service providers to integrate the physical flow of products between vendors and dealers in logistics management. The collective goal is to synchronize the goods in both pickup and delivery operations concurrently to reduce the handling cost, inventory cost, and operation cost generated. Therefore, the optimal vehicle routing plan is crucial to generate a truck routing schedule with minimal total cost, fulfilling the purchasing requirements and the distribution demand. Global warming and climate change are important topics due to increasing greenhouse gas emissions. Sustainable logistics management with optimized routes for trucks can assist in reducing greenhouse gas emissions and easing the effects of temperature increases on our living environment. Methods: A heuristic approach based on Particle Swarm Optimization, called ePSO, was proposed and implemented in this paper to solve the vehicle routing problems with cross-docking and carbon emissions reduction at the same time. Results: Performance comparisons were made with the Genetic Algorithm (GA) through the experiments of several vehicle routing problems with pickup and delivery benchmark problems to validate the performance of the ePSO procedure. Conclusions: Experimental results showed that the proposed ePSO approach was better than the GA for most cases by statistical hypothesis testing. Full article

Many real-life combinatorial optimization problems are subject to a high degree of dynamism, while, simultaneously, a certain level of synchronization among agents and events is required. Thus, for instance, in ride-sharing operations, the arrival of vehicles at pick-up points needs to be synchronized with the times at which users reach these locations so that waiting times do not represent an issue. Likewise, in warehouse logistics, the availability of automated guided vehicles at an entry point needs to be synchronized with the arrival of new items to be stored. In many cases, as operational decisions are made, a series of interdependent events are scheduled for the future, thus making the synchronization task one that traditional optimization methods cannot handle easily. On the contrary, discrete-event simulation allows for processing a complex list of scheduled events in a natural way, although the optimization component is missing here. This paper discusses a hybrid approach in which a heuristic is driven by a list of discrete events and then extended into a biased-randomized algorithm. As the paper discusses in detail, the proposed hybrid approach allows us to efficiently tackle optimization problems with complex synchronization issues. Full article

Nowadays, robust and efficient solid waste collection is crucial to motivate citizens to participate in the circular economy by sorting recyclable solid waste. Vocational vehicles, including garbage trucks, contribute significantly to CO${}_2$ emissions; therefore, it is strongly recommended, and in the European Union it is mandatory, to replace conventional-fuel-based garbage trucks with electric ones. For providing sustainable and energy-efficient solid waste collection with a heterogeneous fleet, in-depth mathematical computations are needed to support solving complex decision-making problems, including crew rostering and vehicle routing, because the distance and capacity of electric garbage trucks differ from conventional-fuel-based ones. However, the literature on solid waste collection using electric garbage trucks is still relatively scarce. The main contribution of this paper is developing an optimization problem for balancing travel distance assigned to each garbage truck of a heterogeneous fleet. The problem is based on specific requirements of the Municipal Solid Waste Management in Cracow, Poland, where the working time of routes is balanced and the total time of collection service can be minimized. For the problem, an MIP program was developed to generate optimal crew schedules, so that the hitherto network of segregated solid waste pickup nodes can be served using a heterogeneous fleet in which the share of electric garbage trucks is up to 30%. We study the impact of the changed composition of the fleet on modifications in crew rostering due to the shorter range of an electric vehicle compared to a conventional-fuel-based one. Full article

The design of steel logistics parks acts as fundamental infrastructure supporting the operations of storage, allocation, and distribution of steel products in the steel logistics industry, which actually lags behind the development of other logistics industries, such as e-commerce logistics, due to its large lot bulk storage, low turnover rate, and costly transportation and operations. This research proposes a data-driven approach for a specific steel logistics park, aiming to improve its operational efficiency in terms of product layout and allocation in multiple yards. The entry and delivery order data are analyzed comprehensively so as to determine the products with high operational frequency and the corresponding relevancy among them. Experimental results show that, among the 69 steel specifications, 14 high-frequency products are identified, and the correlation among the 14 identified high-frequency products possesses evident distribution characteristics concerning their brands and specifications. The identified frequency and correlation among various products can not only facilitate the product layout and allocation in steel logistics parks, but also advance the vehicle scheduling efficiency for product pick-up and delivery. Moreover, the research methodology and framework can provide managerial insights for other industries with mass data processing requirements. Full article

This paper deals with the scheduling of paratransit vehicles. The current scheduling method utilized by paratransit providers is to provide a door-to-door ride for those customers who have made reservations. Thus, the paratransit providers know in advance the pickup and drop-off locations of each customer. Using this information, they are able to determine a route for each vehicle to minimize the total operating costs. In the current scheduling method, vehicles are not allowed to pick up unscheduled customers. This practice often leads to low seat utilization. To address this shortcoming, this paper explores the idea of allowing vehicles to pick up unscheduled customers who are in close proximity to the prescheduled stops (referred to as the dynamic response area or DRA). To this end, this paper develops an agent-based simulation model to evaluate the effectiveness of this strategy. The model was tested using the Chicago network. The results of the simulation experiments indicate that (1) the proposed strategy is able to serve more customers using the same fleet size, and (2) the proposed strategy will not significantly affect the scheduled customers’ in-vehicle travel time. Full article

Timeliness of steel distribution centers can effectively ensure the smooth progress of ship construction, but the carbon emissions of vehicles in the distribution process are also a major source of pollution. Therefore, when considering the common cost of vehicle distribution, taking the carbon emissions of vehicles into account, this paper establishes a Mixed Integer Linear Programming (MILP) model called green vehicle routing and scheduling problem with simultaneous pickups and deliveries and time windows (GVRSP-SPDTW). An intelligent water drop algorithm is designed and improved, and compared with the genetic algorithm and traditional intelligent water drop algorithm. The applicability of the improved intelligent water drop algorithm is proven. Finally, it is applied to a specific example to prove that the improved intelligent water drop algorithm can effectively reduce the cost of such problems, thereby reducing the carbon emissions of vehicles in the distribution process, achieving the goals of reducing environmental pollution and green shipbuilding. Full article

In this paper, a home service problem is studied, where a capacitated vehicle collects customers’ parcels in one pick-up tour. We consider a situation where customers, who have scheduled their services in advance, may call to cancel their appointments, and customers, who do not have appointments, also need to be visited if they request for services as long as the capacity is allowed. To handle those changes that occurred over the tour, a dynamic strategy will be needed to guide the vehicle to visit customers in an efficient way. Aimed at minimizing the vehicle’s total expected travel distance, we model this problem as a multi-dimensional Markov Decision Process (MDP) with finite exponential scale state space. We exactly solve this MDP via dynamic programming, where the computing complexity is exponential. In order to avoid complexity continually increasing, we aim to develop a fast looking-up method for one already-examined state’s record. Although generally this will result in a huge waste of memory, by exploiting critical structural properties of the state space, we obtain an $O(1)$ looking-up method without any waste of memory. Computational experiments demonstrate the effectiveness of our model and the developed solution method. For larger instances, two well-performed heuristics are proposed. Full article

The transportation of elderly and impaired people is commonly solved as a Dial-A-Ride Problem (DARP). The DARP aims to design pick-up and delivery vehicle routing schedules. Its main objective is to accommodate as many users as possible with a minimum operation cost. It adds realistic precedence and transit time constraints on the pairing of vehicles and customers. This paper tackles the DARP with time windows (DARPTW) from a new and innovative angle as it combines hybridization techniques with an adaptive large neighborhood search heuristic algorithm. The main objective is to improve the overall real-life performance of vehicle routing operations. Real-life data are refined and fed to a hybrid adaptive large neighborhood search (Hybrid-ALNS) algorithm which provides a near-optimal routing solution. The computational results on real-life instances, in the Canadian city of Vancouver and its region, and DARPTW benchmark instances show the potential improvements achieved by the proposed heuristic and its adaptability. Full article