409 research outputs found

    Model and algorithm for solving real time dial-a-ride problem

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    This research studies a static and real-time dial-a-ride problem with time varying travel times, soft time windows, and multiple depots. First, a static DARP model is formulated as a mixed integer programming and in order to validate the model, several random small network problems are solved using commercial optimization package, CPLEX. Three heuristic algorithms based on sequential insertion, parallel insertion, and clustering first-routing second are proposed to solve static DARP within a reasonable time for implementation in a real-world situation. Also, the results of three heuristic methods are compared with the results obtained from exact solution by CPLEX to validate and evaluate three heuristic algorithms. Computational results show that three heuristic algorithms are superior compared to the exact algorithm in terms of the calculation time as the problem size (in terms of the number of demands) increases. Also among the three heuristic algorithms, the heuristic algorithm based on sequential insertion is more efficient than other heuristic algorithms that are based on parallel insertion and clustering first-routing second. For the case study, Maryland Transit Administration (MTA)'s real operation of Dial-a-ride service is introduced and compared with the results of developed heuristic. The objective function values from heuristic based on clustering first- routing second are better than those from MTA's operation for all cases when waiting cost, delay cost, and excess ride cost are not included in the objective function values. Also, the algorithm for real-time DARP considering dynamic events such as customer no shows, accidents, cancellations, and new requests is developed based on static DARP. The algorithm is tested in a simulation framework. In the simulation test, we compared the results of cases according to degree of gap between expected link speeds and real link speeds. Also for competitive analysis, the results of dynamic case are compared with the results of static case, where all requests are known in advance. The simulation test shows that the heuristic method could save cost as the uncertainty in new requests increases

    Parallelization of Dial-a-Ride Using Tabu Search

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    Dial-A-Ride is a transport system heavily constrained by following fleet size, vehicle capacity, and a fixed number of requests (pickup and drop-off points) with time windows. It is often modelled as Integer Programming, various solutions are proposed using heuristics. One such heuristic is Tabu Search . Tabu Search is very CPU intensive with its process of search, therefore many modern computing techniques like using GPUs have been employed to make it efficient. As with many other greedy algorithms, the local optima is not always the same as the global optima, so it is not possible to go past the local optima using greedy techniques for this problem. It is often modelled as Integer Programming, with the search space being very big, there are proven to not be so efficient. So, many heuristics have been proposed in the past, one such heuristic is Tabu Search . The local search of this heuristic uses memory to keep track of recent moves made and tries to avoid them for specified iterations (marks as Tabu) and also continues to explore the neighbourhood search space even with the degradation optimization function value, thus helping the algorithm to go past the local optima towards global optima. This thesis focuses on limitations of parallelizing DARP-TS for multi-core CPU, discussing major factors like (i) number of good moves in the neighbourhood and how we can estimate a value for N\_SIZE (number of parallel moves to make in each iteration), (ii) difference between a CPU core and a GPU core in the context of thread scheduling, memory layout and memory limitations, (iii) proposes few data-structures to keep the memory allocations low thus reducing the time for garbage collection and (iv) proposes an incremental way of calculating the value of optimization function in the local search phase which helps in mapping the execution and evaluation of N\_SIZE moves in each iteration onto the multiple CPU cores

    On green routing and scheduling problem

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    The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

    Dynamic vehicle routing problems: Three decades and counting

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    Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

    The dynamic nearest neighbor policy for the multi-vehicle pick-up and delivery problem

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    In this paper, a dynamic nearest neighbor (DNN) policy is proposed for operating a fleet of vehicles to serve customers, who place calls in a Euclidean service area according to a Poisson process. Each vehicle serves one customer at a time, who has a distinct origin and destination independently and uniformly distributed within the service area. The new DNN policy is a refined version of the nearest neighbor (NN) policy that is well known to perform sub-optimally when the frequency of customer requests is high. The DNN policy maintains geographically closest customer-to-vehicle assignments, due to its ability to divert/re-assign vehicles that may be already en-route to pick up other customers, when another vehicle becomes available or a new customer call arrives. Two other pertinent issues addressed include: the pro-active deployment of the vehicles by anticipating in which regions of the service area future calls are more likely to arise; and, imposition of limits to avoid prohibitively long customer wait times. The paper also presents accurate approximations for all the policies compared. Extensive simulations, some of which are included herein, clearly show the DNN policy to be tangibly superior to the first-comefirst-served (FCFS) and NN policies

    A Study on the application of genetic algorithms on the Dial-A-Ride Problem

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    [[abstract]]The Dial-a-Ride Problem (DARP) is a vehicle routing problem faced in arranging Dial-a-Ride services. The DARP has been proven a NP-Hard problem; therefore, most research has used heuristic solution methods to address this issue. The purpose of this study is to evaluate of the application of a Diversity Control Adaptive Genetic Algorithm (DCAGA) and Family Competition Genetic Algorithm (FCGA) on the DARP. This study proposed two solution procedures, which were integrated approach and cluster approach. A series of case studies with different characteristics, such as demand density and demand size, were used to test the solution capability of the proposed algorithms. Based on the results of the case studies, the Diversity Control Adaptive Genetic Algorithm is identified as the best algorithm in solution quality. Overall, the solution of the integrated procedure is better than, those of the two-phase procedure.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]US

    A tabu search heuristic for a dynamic transportation problem of patients between care units

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    29 pagesThe ambulance central station of the Hospital Complex of Tours (France) has to plan the transportation demands between care units which require a vehicle. Some demands are known in advance and the others arrive dynamically. Each demand requires a specific type of vehicle and a vehicle can transport only one person at a time. Moreover, transportations are subject to particular constraints: priority of urgent demands, disinfection of a vehicle after the transportation of a patient with contagious disease, respect of the type of vehicle, etc. This problem is related to the \emph{Dial A Ride Problem}. For solving the dynamic version of this problem, we propose a tabu search algorithm inspired by \cite{Gendreau99}. This method supports an adaptive memory which stores routes and consists in running a tabu search algorithm several times: one for improving the set of initial solutions, one for the neighborhood exploration and finally for improving the final solution. Computational experiments show that the method can provide high-quality solutions for this dynamic transportation problem

    Clustered tabu search optimization for reservation-based shared autonomous vehicles

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    This paper investigates the optimization of Reservation-based Autonomous Car Sharing (RACS) systems, aiming at minimizing the total vehicle travel time and customer waiting time. Thus, the RACS system and its routing are formulated with a consideration for system efficiency and passengers’ concerns. A meta-heuristic Tabu search method is investigated as a solution approach, in combination with K–Means (KMN–Tabu) or K–Medoids (KMD–Tabu) clustering algorithms. The proposed solution algorithms are tested in two different networks of varying complexity, and the performance of the algorithms is evaluated. The evaluation results show that the TS method is more suitable for small-scale problems, while KMD–Tabu is suitable for large-scale problems. However, KMN-Tabu has the least computation time, although the solution quality is lower
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