707 research outputs found

    Constraint Propagation for the Dial-a-Ride Problem with Split Loads

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    International audienceAbstract. This paper deals with a new problem: the Dial and Ride Problem with Split Loads (DARPSL), while using randomized greedy insertion techniques together with constraint propagation techniques. Though it focuses here on the static versions of Dial and Ride, it takes into account the fact that practical DARP has to be handled according to a dynamical point of view, and even, in some case, in real time contexts. So, the kind of algorithmic solution which is proposed here, aim at making easier to bridge both points of view. First, we propose the general framework of the model and discuss the link with dynamical DARP, second, we describe the two algorithms (DARP and DARPSL), and lastly, show numerical experiments for both

    Insertion techniques and constraint propagation for the DARP

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    International audienceThis paper deals with the Dial and Ride Problem (DARP), while using randomized greedy insertion techniques together with constraint propagation techniques. Though it focuses here on the static version of Dial and Ride, it takes into account the fact that practical DARP has to be handled according to a dynamical point of view, and even, in some case, in real time contexts. So, the kind of algorithmic solution which is proposed here, aim at making easier to bridge both points of view. The model is a classical one, and considers a performance criterion which is a mix between Quality of Service (QoS) and economical cost. We first propose the general framework of the model and discuss the link with dynamical DARP, next describe the algorithm and end with numerical experiments

    Transfers in the on-demand transportation: the DARPT Dial-a-Ride Problem with transfers allowed

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    International audienceToday, the on-demand transportation is used for elderly and disabled people for short distances. Each user provides a specific demand: a particular ride from an origin to a destination with hard time constraints like time windows, maximum user ride time, maximum route duration limits and precedence. This paper deals with the resolution of these problems (Dial-a-Ride Problems - DARP), including the possibility of one transshipment from a transfer point by request. We propose an algorithm based on insertion techniques and constraints propagation

    Evaluation of Anticipatory Decision-Making in Ride-Sharing Services

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    In recent years, innovative ride-sharing services have gained significant attention. Such services require dynamic decisions on the acceptance of arriving trip requests and vehicle routing to ensure the fulfillment of requests. Decision support for acceptance and routing must be made under uncertainty of future requests. In this paper, we highlight that state-of-the-art approaches focus on anticipatory decision-making for either acceptance or routing decisions. Our aim is to evaluate the potential of different levels of anticipation in ride-sharing services. Up to now, it is unclear how the value of information differs between none, partial, or fully anticipatory decision-making processes. To this end, we define and solve variants of the underlying dial-a-ride problem, which differ in the information available about future requests. Using a large neighborhood search, our experimental results demonstrate that ride-sharing services can highly benefit from anticipatory decision-making, while the favorable level of anticipation depends on particular characteristics of the service, esp. the demand-to-service ratio

    Optimization of a city logistics transportation system with mixed passengers and goods

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    International audienceIn this paper, we propose a mathematical model and an adaptive large neighborhood search to solve a two{tiered transportation problem arising in the distribution of goods in congested city cores. In the rst tier, goods are transported in city buses from a consolidation and distribution center to a set of bus stops. The main idea is to use the buses spare capacity to drive the goods in the city core. In the second tier, nal customers are distributed by a eet of near{zero emissions city freighters. This system requires transferring the goods from buses to city freighters at the bus stops. We model the corresponding optimization problem as a variant of the pickup and delivery problem with transfers and solve it with an adaptive large neighborhood search. To evaluate its results, lower bounds are calculated with a column generation approach. The algorithm is assessed on data sets derived from a eld study in the medium-sized city of La Rochelle in France

    Solving the Dynamic Dial-a-Ride Problem Using a Rolling-Horizon Event-Based Graph

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    In many ridepooling applications transportation requests arrive throughout the day and have to be answered and integrated into the existing (and operated) vehicle routing. To solve this dynamic dial-a-ride problem we present a rolling-horizon algorithm that dynamically updates the current solution by solving an MILP formulation. The MILP model is based on an event-based graph with nodes representing pick-up and drop-off events associated with feasible user allocations in the vehicles. The proposed solution approach is validated on a set of real-word instances with more than 500 requests. In 99.5% of all iterations the rolling-horizon algorithm returned optimal insertion positions w.r.t. the current schedule in a time-limit of 30 seconds. On average, incoming requests are answered within 2.8 seconds

    Workforce scheduling and planning : a combinatorial approach

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    This thesis investigates solution methodologies for concrete combinatorial problems in scheduling and planning. In all considered problems, it is assumed that the available information does not change over time; hence these problems have a deterministic structure. The problems studied in this thesis are divided into two groups; \workforce scheduling" and \planning". In workforce scheduling, the center problem is to build a schedule of tasks and technicians. It is assumed that the time line is split into workdays. In every workday, tasks must be grouped as sequences, each being performed by a team of technicians. Skill requirements of every task in a sequence must be met by the assigned team. This scheduling problem with some other aspects is di??cult to solve quickly and e??ciently. We developed a Mixed Integer Programming (MIP) based heuristic approach to tackle this complex scheduling problem. Our MIP model is basically a formulation of the matching problem on bipartite graphs and it enabled us to have a global way of assigning technicians to tasks. It is capable of revising technician-task allocations and performs very well, especially in the case of rare skills. A workday schedule of the aforementioned problem includes many-to-one type workforce assignments. As the second problem in workforce scheduling, stability of these workforce assignments is investigated. The stability de??nition of Gale-Shapley on the Marriage model is extended to the setting of multi-skill workforce assignments. It is shown that ??nding stable assignments is NP-hard. In some special cases stable assignments can be constructed in polynomial time. For the general case, we give linear inequalities of binary variables that describe the set of stable assignments. We propose a MIP model including these linear inequalities. To the best of our knowledge, the Gale-Shapley stability is not studied under the multi-skill workforce scheduling framework so far in the literature. The closed form description of stable assignments is also the ??rst embedding of the Gale-Shapley stability concept into an NP-complete problem. In the second problem group, two vehicle related problems are studied; the "dial a ride problem" and the "vehicle refueling problem". In the former, the goal is to check whether a list of pick-up and delivery tasks can be achieved under several timing constraints. It is shown this feasibility testing can be done in linear time using interval graphs. In the vehicle refueling problem, the goal is to make refueling decisions to reach a destination such that the cost of the travel is minimized. A greedy algorithm is presented to ??nd optimal refueling decisions. Moreover, it is shown that the vehicle refueling problem is equivalent to a ow problem on a special network
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