4,169 research outputs found
A concise guide to existing and emerging vehicle routing problem variants
Vehicle routing problems have been the focus of extensive research over the
past sixty years, driven by their economic importance and their theoretical
interest. The diversity of applications has motivated the study of a myriad of
problem variants with different attributes. In this article, we provide a
concise overview of existing and emerging problem variants. Models are
typically refined along three lines: considering more relevant objectives and
performance metrics, integrating vehicle routing evaluations with other
tactical decisions, and capturing fine-grained yet essential aspects of modern
supply chains. We organize the main problem attributes within this structured
framework. We discuss recent research directions and pinpoint current
shortcomings, recent successes, and emerging challenges
Constraint Propagation for the Dial-a-Ride Problem with Split Loads
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
A concise guide to existing and emerging vehicle routing problem variants
Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges.</p
Modeling and solving the multimodal car- and ride-sharing problem
We introduce the multimodal car- and ride-sharing problem (MMCRP), in which a
pool of cars is used to cover a set of ride requests, while uncovered requests
are assigned to other modes of transport (MOT). A car's route consists of one
or more trips. Each trip must have a specific but non-predetermined driver,
start in a depot and finish in a (possibly different) depot. Ride-sharing
between users is allowed, even when two rides do not have the same origin
and/or destination. A user has always the option of using other modes of
transport according to an individual list of preferences.
The problem can be formulated as a vehicle scheduling problem. In order to
solve the problem, an auxiliary graph is constructed in which each trip
starting and ending in a depot, and covering possible ride-shares, is modeled
as an edge in a time-space graph. We propose a two-layer decomposition
algorithm based on column generation, where the master problem ensures that
each request can only be covered at most once, and the pricing problem
generates new promising routes by solving a kind of shortest path problem in a
time-space network. Computational experiments based on realistic instances are
reported. The benchmark instances are based on demographic, spatial, and
economic data of Vienna, Austria. We solve large instances with the column
generation based approach to near optimality in reasonable time, and we further
investigate various exact and heuristic pricing schemes
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