R. Spliet (Remy)
http://repub.eur.nl/ppl/20819/
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http://repub.eur.nl/
RePub, Erasmus University RepositoryPreference inference with general additive value models and holistic pair-wise statements
http://repub.eur.nl/pub/41546/
Sat, 01 Feb 2014 00:00:01 GMT<div>R. Spliet</div><div>T. Tervonen</div>
Additive multi-attribute value models and additive utility models with discrete outcome sets are widely applied in both descriptive and normative decision analysis. Their non-parametric application allows preference inference by analyzing sets of general additive value functions compatible with the observed or elicited holistic pair-wise preference statements. In this paper, we provide necessary and sufficient conditions for the preference inference based on a single preference statement, and sufficient conditions for the inference based on multiple preference statements. In our computational experiments all inferences could be made with these conditions. Moreover, our analysis suggests that the non-parametric analyses of general additive value models are unlikely to be useful by themselves for decision support in contexts where the decision maker preferences are elicited in the form of holistic pair-wise statements. Vehicle Routing with Uncertain Demand
http://repub.eur.nl/pub/41513/
Fri, 18 Oct 2013 00:00:01 GMT<div>R. Spliet</div>
In distribution networks a supplier transports goods from a distribution center to customers by means of vehicles with limited capacity. Drivers will drive routes on which they visit multiple customers to make deliveries. Typically, deliveries are made regularly and a fixed schedule is maintained. A fixed schedule is beneficial for many operational purposes, as it for instance allows for easy planning of the packing of the vehicles at the distribution center, or it allows the customer to roster the delivery handling personnel. A fixed schedule is often reused to make weekly deliveries for a period of a year or longer.
However, at the moment of designing a schedule, the demand of the customers is usually unknown. Moreover, in most cases, demand of a customer will be different for each delivery. Therefore, it will be necessary to construct or adapt vehicle routes for each day of delivery, without deviating too much from the fixed schedule.
In this thesis several different views on a fixed schedule are explored. It addresses the need from practice to incorporate the uncertainty of demand in transportation models to increase the efficiency of transport. Innovative vehicle routing models are presented taking uncertain or varying demand into account. New algorithms using state-of-the-art methods are presented based on these models, to construct fixed schedules and vehicle routes. The algorithms make use of recent scientific advances in mathematical programming, specifically in the domain of vehicle routing.The Time Window Assignment Vehicle Routing Problem
http://repub.eur.nl/pub/32175/
Sun, 01 Apr 2012 00:00:01 GMT<div>R. Spliet</div><div>A.F. Gabor</div>
In many distribution networks, it is vital that time windows in which deliveries are made are assigned to customers for the long term. However, at the moment of assigning time windows demand is not known. In this paper we introduce the time window assignment vehicle routing problem, the TWAVRP. In this problem time windows have to be assigned before demand is known. Next the realization of demand is revealed and an optimal vehicle routing schedule has to be made that satisfies the time window constraints. We assume that different scenarios of demand realizations are known, as well as their probability distribution. The TWAVRP is the problem of assigning time windows such that the expected traveling costs are minimized. We propose a formulation of the TWAVRP and develop two variants of a column generation algorithm to solve the LP relaxation of this formulation. Numerical experiments show that these algorithms provide us with very tight LP-bounds to instances of moderate size in reasonable computation time. We incorporate the column generation algorithm in a branch and price algorithm and find optimal integer solutions to small instances of the TWAVRP. In our numerical experiments, the branch and price algorithm typically finds the optimal solution very early in the branching procedure and spends most time on proving optimality.A Branch-and-Price Approach for a Ship Routing Problem with Multiple Products and Inventory Constraints
http://repub.eur.nl/pub/18255/
Tue, 23 Feb 2010 00:00:01 GMT<div>R. de Mare</div><div>R. Spliet</div><div>D. Huisman</div>
In the oil industry, different oil components are blended in a
refinery to fuel products. These products are transported to different
harbors by ship. Due to the limited storage capacity at the harbors
and the undesirability of a stock-out, inventory levels at the
harbors have to be taken into account during the construction of the
ship routes. In this paper, we give a detailed description of this
problem, which we call the ship routing problem with multiple
products and inventory constraints. Furthermore, we formulate this
problem as a generalized set-covering problem, and we present a
Branch-and-Price algorithm to solve it. The pricing problems have a
very complex nature. We discuss a dynamic programming algorithm to
solve them to optimality.The Vehicle Rescheduling Problem
http://repub.eur.nl/pub/17350/
Thu, 26 Nov 2009 00:00:01 GMT<div>R. Spliet</div><div>A.F. Gabor</div><div>R. Dekker</div>
The capacitated vehicle routing problem is to find a routing schedule describing the order in which geographically dispersed customers are visited to satisfy demand by supplying goods stored at the depot, such that the traveling costs are minimized. In many practical applications, a long term routing schedule has to be made for operational purposes, often based on average demand estimates. When demand substantially differs, constructing a new schedule is beneficial. The vehicle rescheduling problem is to find a new schedule that not only minimizes the total traveling costs but also minimizes the costs of deviating from the original schedule. In this paper two mathematical programming formulations of the rescheduling problem are presented as well as two heuristic methods, a two-phase heuristic and a modified savings heuristic. Computational and analytical results show that for sufficiently high deviation costs, the two-phase heuristic generates a schedule that is on average close to optimal or even guaranteed optimal, for all considered problem instances. The modified savings heuristic generates schedules of constant quality, however the two-phase heuristic produces schedules that are on average closer to the optimum.