The transportation problem with exclusionary side constraints.

We consider the so-called Transportation Problem with Exclusionary Side Con- straints (TPESC), which is a generalization of the ordinary transportation problem. We determine the complexity status for each of two special cases of this problem, by proving NP-completeness, and by exhibiting a pseudo-polynomial time algorithm. For the general problem, we show that it cannot be approximated with a constant perfor- mance ratio in polynomial time (unless P=NP). These results settle the complexity status of the TPESC.

Profit-based latency problems on the line.

The latency problem with profits is a generalization of the minimum latency problem. In this generalization it is not necessary to visit all clients, however, visiting a client may bring a certain revenue. More precisely, in the latency problem with profits, a server and a set of n clients, each with corresponding profit p_i (1 ≤ i ≤ n), are given. The single server is positioned at the origin at time t = 0 and travels with unit speed. When visiting a client, the server receives a revenue of p_i - t, with t the time at which the server reaches client i (1 ≤ i ≤ n). The goal is to select clients and find a route for the server such that total collected revenue is maximized. We formulate a dynamic programming algorithm to solve this problem when all clients are located on a line. We also consider the problem on the line with k servers and prove NP-completeness for the latency problem on the line with k non-identical servers and release dates. In this proof we also settle the complexity of an open problem in de Paepe et al. [4].Minimum latency; Traveling repairman; Dynamic programming; Complexity;

Approximation algorithms for rectangle stabbing and interval stabbing problems.

Inthe weighted rectangle stabbing problem we are given a grid in R2 consisting of columns and rows each having a positive integral weight, and a set of closed axis-parallel rectangles each having a positive integral demand. The rectangles are placed arbitrarily in the grid with the only assumption that each rectangle is intersected by at least one column and at least one row. The objective is to find a minimum-weight (multi)set of columns and rows of the grid so that for each rectangle the total multiplicity of selected columns and rows stabbing it is at least its demand. A special case of this problem arises when each rectangle is intersected by exactly one row. We describe two algorithms, called STAB and ROUND, that are shown to be constant-factor approximation algorithms for different variants of this stabbing problem.Research; Approximation; Algorithms; Problems; Demand;

Partitioning a permutation graph: algorithms and an application.

In this paper we discuss the problem of partitioning a permutation graph into cliques of bounded size, and describe a real-life application of this problem encountered at a manufacturing company. We formulate the problem as an integer program, and present two exact algorithms for solving it. The first algorithm is a branch-and-price algorithm based on the integer programming formulation; the second one is an algorithm based on the concept of bounded clique-width. The latter algorithm was motivated by the structure present in the real-life instances. Test results are given, both for real-life instances and randomly generated instances. As far as we are aware, this is the first implementation of an algorithm based on bounded clique-width.Algorithms; Analysis of algorithms; Branch-and-price; Companies; Integer programming; Manufacturing; Real life; Size; Structure;

Partitioning a weighted partial order.

The problem of partitioning a partially ordered set into a minimum number of chains is a well-known problem. In this paper we study a generalization of this problem, where we not only assume that the chains have bounded size, but also that a weight wi is given for each element i in the partial order such that wiOrder; Studies; Size; Lower bounds;

Exact algorithms for the matrix bid auction.

In a combinatorial auction, multiple items are for sale simultaneously to a set of buyers. These buyers are allowed to place bids on subsets of the available items. A special kind of combinatorial auction is the so-called matrix bid auction, which was developed by Day (2004). The matrix bid auction imposes restrictions on what a bidder can bid for a subsets of the items. This paper focusses on the winner determination problem, i.e. deciding which bidders should get what items. The winner determination problem of a general combinatorial auction is NP-hard and inapproximable. We discuss the computational complexity of the winner determination problem for a special case of the matrix bid auction. We present two mathematical programming formulations for the general matrix bid auction winner determination problem. Based on one of these formulations, we develop two branch-and-price algorithms to solve the winner determination problem. Finally, we present computational results for these algorithms and compare them with results from a branch-and-cut approach based on Day & Raghavan (2006).Algorithms; Bids; Branch-and-price; Combinatorial auction; Complexity; Computational complexity; Exact algorithm; Mathematical programming; Matrix; Matrix bids; Research; Winner determination;

An approximation algorithm for a generalized assignment problem with small resource requirements.

We investigate a generalized assignment problem where the resource requirements are either 1 or 2. This problem is motivated by a question that arises when data blocks are to be retrieved from parallel disks as efficiently as possible. The resulting problem is to assign jobs to machines with a given capacity, where each job takes either one or two units of machine capacity, and must satisfy certain assignment restrictions, such that total weight of the assigned jobs is maximized. We derive a 2/3-approximation result for this problem based on relaxing a formulation of the problem so that the resulting constraint matrix is totally unimodular. Further, we prove that the LP-relaxation of a special case of the problem is half-integral, and we derive a weak persistency property.Assignment; Constraint; Data; Matrix; Requirements;

Counting aggregate classifiers.

There are many methods to design classifiers for the supervised classification problem. In this paper, we study the problem of aggregating classifiers. We construct an algorithm to count the number of distinct aggregate classifiers. This leads to a new way of finding a best aggregate classifier. When there are only two classes, we explore the link between aggregating classifiers and n-bit boolean functions. Further, the sequence of the number of distinct aggregated classifiers appears to be new.Boolean function; Classification; Classifiers; Design; Functions; Methods; Studies; Supervised classification; Weighted majority vote;

The periodic vehicle routing problem: a case study.

This paper deals with a case study which is a variant of the Periodic Vehicle Routing Problem (PVRP). As in the traditional Vehicle Routing Problem (VRP), customer locations each with a certain daily demand are given, as well as a set of capacitated vehicles. In addition, the PVRP has a horizon, say T days, and there is a frequency for each customer stating how often within this T-day period this customer must be visited. A solution to the PVRP consists of T sets of routes that jointly satisfy the demand constraints and the frequency constraints. The objective is to minimize the sum of the costs of all routes over the planning horizon. We develop different algorithms solving the instances of the case studied. Using these algorithms we are able to realize considerable cost reductions compared to the current situation.Periodic vehicle routing; Case study;

Local search heuristics for multi-index assignment problems with decomposable costs.

The multi-index assignment problem (MIAP) with decomposable costs is a natural generalization of the well-known assignment problem. Applications of the MIAP arise for instance in the field of multi-target multi-sensor tracking. We describe an (exponentially sized) neighborhood for a solution of the MIAP with decomposable costs, and show that one can find a best solution in this neighborhood in polynomial time. Based on this neighborhood, we propose a local search algorithm. We empirically test the performance of published constructive heuristics and the local search algorithm on random instances; a straightforward tabu search is also tested. Finally, we compute lower bounds to our problem, which enable us to assess the quality of the solutions found.Assignment; Costs; Heuristics; Problems; Applications; Performance;