The Simultaneous Vehicle Scheduling and Passenger Service Problem

This paper investigates the application of a new class of neighborhood search algorithms—cyclic transfers—to multivehicle routing and scheduling problems. These algorithms exploit the two-faceted decision structure inherent to this problem class: First, assigning demands to vehicles and, second, routing each vehicle through its assigned demand stops. We describe the application of cyclic transfers to vehicle routing and scheduling problems. Then we determine the worst-case performance of these algorithms for several classes of vehicle routing and scheduling problems. Next, we develop computationally efficient methods for finding negative cost cyclic transfers. Finally, we present computational results for three diverse vehicle routing and scheduling problems, which collectively incorporate a variety of constraint and objective function structures. Our results show that cyclic transfer methods are either comparable to or better than the best published heuristic algorithms for several complex and important vehicle routing and scheduling problems. Most importantly, they represent a novel approach to solution improvement which holds promise in many vehicle routing and scheduling problem domains.

In this paper, the ant colony algorithms is studied, and improve the shortcomings of the algorithm, And the improved algorithm is introduced into the field of logistics transportation.Aiming at the complexity and uncertainty of logistics transportation vehicle scheduling problem, a new algorithm is designed.The experimental results show that the improved algorithm can choose the transport route, speed up the transportation speed, improve the service quality, reduce the transportation cost and increase economic benefits.

This paper examines time windows for scheduled trips in multiple depot vehicle and crew scheduling problems that arise in public bus transportation. In practice, the two planning tasks vehicle scheduling and crew scheduling are traditionally solved sequentially with the implicit understanding that the scheduled time for timetabled trips remains fixed. In order to improve cost efficiency two concepts have been developed over the last years: In order to obtain better flexibility when scheduling crews, vehicle and crew scheduling problems are tackled simultaneously. In order to extend flexibility while scheduling vehicles, variable trip departure and arrival times are considered. Obviously the combination of both concepts promises the largest savings, but probably leads to bursting computational times due to growing problem complexity. In this paper we combine both concepts by extending the integrated vehicle and crew scheduling problem with the possibility to shift scheduled trips within defined time windows. We examine the tradeoffs between solution quality and computational time for different solution approaches.

This work addresses the multiple-depot vehicle and crew scheduling problem (MDVCSP). In MDVCSP, we deal with two NP-hard problems in an integrated way: the multiple-depot vehicle scheduling problem (MDVSP) and the crew scheduling problem (CSP). For solving the MDVCSP, we define the vehicles’ operational routine and the workdays of the crews of a public bus transport system with multiple depots. Given the difficulty of solving real-world instances of the MDVCSP using exact mathematical methods, we propose a matheuristic algorithm for solving it. This matheuristic algorithm combines two strategies into an iterated local search (ILS) based framework: a branch-and-bound algorithm for solving the MDVSP and a variable neighborhood descent (VND) based algorithm for treating the associated CSPs. We compared the proposed ILS-MDVCSP with five approaches in the literature that use the same benchmark test instances. We also solved a real-world problem of one of Brazil’s largest cities. For this problem, we proposed a formulation based on a time-space network to address the MDVSP subproblem. The results obtained showed the effectiveness of ILS-MDVCSP, mainly to deal with real-world and large-scale problems. The algorithm was able to solve the largest instances from the literature, for which there was no reported solution. Regarding the running time, as the instances’ size increases, our approach becomes substantially less costly than the others from the literature. For the Brazilian instances, the ILS-MDVCSP saved, on average, the use of 12 vehicles per day and reduced by up to 15% the daily operational time of the vehicles.

One of the problems in public transportation is the vehicle scheduling problem (VSP), which can reduce the bus company cost and meet the demand of passengers’ minimum waiting time. This paper proposes an ensemble differential algorithm based on particle swarm optimization (abbreviated as PSOEDE) to solve the VSP. In PSOEDE algorithm, the mutation process is designed by dividing the original process into two parts: the first part combines the PSO operator with the improved mutation strategy to enhance the global search ability, while the second part is to randomly select two mutation strategies (i.e. random learning and optimal learning) to improve the diversity of population. In addition, the random selection methods of the parameters and crossover strategies are proposed and applied in the total PSOEDE algorithm. The effectiveness and superiority of the proposed PSOEDE algorithm in dealing with the VSP are verified using the simulation experiments and six comparison algorithms.

The demand-responsive public transport system with multi-vehicles has the potential to efficiently meet real-time and high-volume transportation needs through effective scheduling. This paper focuses on studying the real-time vehicle scheduling problem, which involves dispatching and controlling different model vehicles uniformly based on generated vehicle number tasks at a given point in time. By considering the immediacy of real-time itinerary tasks, this paper optimizes the vehicle scheduling problem at a single time point. The objective function is to minimize the total operating cost of the system while satisfying constraints such as passenger capacity and vehicle transfer time. To achieve this, a vehicle scheduling optimization model is constructed, and a solution approach is proposed by integrating bipartite graph optimal matching theory and the Kuhn–Munkres algorithm. The effectiveness of the proposed approach is demonstrated by comparing it with a traditional greedy algorithm using the same calculation example. The results show that the optimization method has higher solution efficiency and can generate a scheduling scheme that effectively reduces operating costs, improves transportation efficiency, and optimizes the operation organization process for demand-responsive buses.

This thesis goes deep into the vehicle scheduling problem (VSP), which is the key problem for the distribution center.This paper analyzes and optimizes the mathematical model of vehicle scheduling problem. The problem of dynamic vehicle scheduling with time windows is described in great details in the thesis, which also gives an arithmetic solution aiming at the scheduling problem.Finally, based on the research results and under the background of logistics distribution enterprises, the vehicle scheduling algorithm is exposed to experiment.

This paper describes an integer programming formulation of the vehicle scheduling problem and illustrates how such a formulation can be extended to incorporate restrictions on work load, coverage and service that occur in real world vehicle scheduling problems. The integer programme is solved using the Revised Simplex method, additional constraints being introduced to retain integrality during convergence. The feasible region of this integer programme is initially restricted so that only routes constructed through sets of radially contiguous locations are considered. The effect of relaxing these over-constraints is explored. The method is demonstrated on fifteen problems ranging in size from 21 to 100 locations and the results generally show an improvement on previously published results. This is particularly true of the larger problems. This method compares favourably with other methods in computational efficiency.

Insertion heuristics have proven to be popular methods for solving a variety of vehicle routing and scheduling problems. In this paper, we focus on the impact of incorporating complicating constraints on the efficiency of insertion heuristics. The basic insertion heuristic for the standard vehicle routing problem has a time complexity of O(n3). However, straightforward implementations of handling complicating constraints lead to an undesirable time complexity of O(n4). We demonstrate that with careful implementation it is possible, in most cases, to maintain the O(n3) complexity or, in a few cases, increase the time complexity to O(n3 log n). The complicating constraints we consider in this paper are time windows, shift time limits, variable delivery quantities, fixed and variable delivery times, and multiple routes per vehicle. Little attention has been given to some of these complexities (with time windows being the notable exception), which are common in practice and have a significant impact on the feasibility of a schedule as well as the efficiency of insertion heuristics.

Purpose Autism is characterised by differences in social skills, limited communication abilities and repetitive behaviour, which often result in increased reliance on other people. Transportation is but one task that is commonly burdened on family members. Public transport is an inexpensive and widely available form of travel which facilitates independence. However, it presents unique challenges for individuals on the spectrum, as it requires complex skills including, but not limited to, understanding abstract information (e.g., maps, service schedules, etc.), problem-solving unexpected situations and timely management of transfers. As such, most individuals on the autism spectrum do not use public transport and have never considered using it. Here we evaluate the effectiveness of an autism-specific public transport app, OrienTrip, with autistic individuals and allied health professionals. Methods A total of 16 individuals on the autism spectrum (eight male and eight female participants) and 22 allied health professionals (19 females and three male participants) were recruited for the pilot study. Results We found that OrienTrip is effective in facilitating public transport use for autistic individuals. Individuals on the autism spectrum expressed their satisfaction with the app and agreed that it makes public transport easy to use. Similarly, allied health professionals also indicated that OrienTrip is helpful in assisting autistic individuals use public transport safely. Conclusion Our findings demonstrate that OrienTrip can be used to facilitate independent travel for individuals on the autism spectrum using public transport. This can improve community participation opportunities for autistic individuals, including enhanced education, employment and social outcomes. Implications for rehabilitation Individuals on the autism spectrum heavily rely on other people, namely family members, for their transportation needs. Public transport is an inexpensive and widely available form of travel which facilitates independence; however, it presents unique challenges for autistic individuals, as such, most individuals do not use it or consider using it. In this research, we have developed and evaluated one of the first autism-specific public transport mobile apps that facilitates independent public transport use. This tool can improve community participation opportunities for autistic individuals, including enhanced education, employment and social outcomes.

Economic lot sizes and vehicle scheduling

To meet the operational requirements and to ensure safe operations, train units of a passenger railway operator require inspection and/or cleaning at the maintenance depots after running a specified mileage or time period. Accordingly, these inspection and cleaning activities are called depot services. In this paper, we propose an integer linear programming (ILP) model for the integrated optimization problem of depot service scheduling, train parking, and train routing. Both short trains (8 marshaling) and long trains (16 marshaling) are considered in the ILP model which is a combinational optimization problem in the dead-end tracks scenario. Moreover, a real-world case study of the Shanghai South Depot, China is carried out to further examine the effectiveness and efficiency of the proposed methodology. Computational results indicate that our method is able to generate optimized shunting plans for the linearized INLP within reasonable solution time for real-life applications, outperforming the manual method in terms of the solution quality and the computation speed.

A probabilistic model for vehicle scheduling based on stochastic trip times

Contributors

In industrial applications, many systems present serious productivity problems due to limited resources. Generally, the dynamics of resource allocation are inherently discrete-event driven, such as the buffer allocation in production line systems. In this paper, we develop a discrete-event mathematical model for resource allocation optimization. In this work, we consider two crucial optimization objectives, e.g., deadlock-free and efficiency, that originate from the customer’s actual requirements. The main aim is to develop a resource allocation scheme for fulfilling the production process (without deadlock) while ensuring that the cost of the process is minimized. As a case study, we consider the vehicle scheduling problem in a signal-free intersection. The intersection is divided into several disjoint spatial traffic resources, and vehicles need to occupy different traffic resources for passing through the intersection. Thus, the traffic control problem at the signal-free intersection is transformed into a scheduling problem with limited resource constraints. An online control approach is developed to schedule vehicles to go through the intersection safely and efficiently by optimizing the resource allocation order. Simulation results demonstrate the efficiency and robustness of the proposed model and optimization approach.