Application of improved ant colony algorithm in vehicle scheduling 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.

Economic lot sizes and vehicle scheduling

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.

The vehicle scheduling problem with intermittent customer demands

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.

Optimisation of vehicle schedule with time windows is discussed in this paper. The problem is based on the description and data of a real-life problem. Simulation-based optimisation is used in the search of an optimal solution in this paper. A simulation model is developed for the reviewed vehicles scheduling problem. Two scenarios of optimisation are described. The first scenario involves the use of OptQuest software for the optimisation. In the second scenario, the optimisation uses an evolutionary algorithm and preliminary analysis of the problem complexity. Fitness landscape analysis methods are proposed to enhance vehicle schedule optimisation. The fitness landscape analysis is performed for the vehicle scheduling problem with time windows. The procedure of simulation-based fitness landscape analysis is described.

Heuristic approaches for solving transit vehicle scheduling problem with route and fueling time constraints

A probabilistic model for vehicle scheduling based on stochastic trip times

The ant system (AS) is a metaheuristic approach originally developed for solving the traveling salesman problem. AS has been successfully applied to various hard combinatorial optimization problems and different variants have been proposed in the literature. In this paper, we introduce a time-based pheromone approach for AS (TbAS). Due to this nature TbAS is applicable to routing problems involving time-windows. The novelty in TbAS is the multi-layer pheromone network structure which implicitly utilizes the service time information associated with the customers as a heuristic information. To investigate the performance of TbAS, we use the well-known vehicle routing problem with time-windows as our testbed and we conduct an extensive computational study using the Solomon (Algorithms for the vehicle routing and scheduling problems with time window constraints 35:254–265, 1987) instances. Our results reveal that the proposed time-based pheromone approach is effective in obtaining good quality solutions.

Multi-depot vehicle scheduling with multiple vehicle types on overlapped bus routes

The vehicle scheduling problem is specified in terms of a set of tasks to be executed with a fleet of multiple vehicle types. The purpose of this paper is to formulate the problem and to show that the heuristic and exact methods developed for the vehicle scheduling problem with time windows and with a single type of vehicle can be extended in a straightforward fashion to the multiple-vehicle-types problem.

Optimal Multi-Vehicle Type Transit Timetabling and Vehicle Scheduling

Contributors

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.

Passengers using public transport systems often experience waiting times when transferring between two scheduled services. In this paper we propose a planning approach that seeks to obtain a favourable trade-off between the two contrasting objectives, passenger service and operating cost, by modifying the timetable. The planning approach is referred to as the simultaneous vehicle scheduling and passenger service problem (SVSPSP). The SVSPSP is modelled as an integer programming problem and solved using a large neighborhood search metaheuristic. The proposed framework is tested on data inspired by the express-bus network in the Greater Copenhagen area. The results are encouraging and indicate a potential decrease of passenger transfer waiting times in the network of up to 20%, with the vehicle scheduling costs remaining mostly unaffected.