Igor Vasilyev

A computational study of exact knapsack separation for the generalized assignment problem

The Generalized Assignment Problem is a well-known NP-hard combinatorial optimization problem which consists of minimizing the assignment costs of a set of jobs to a set of machines satisfying capacity constraints. Most of the existing algorithms are of a Branch-and-Price type, with lower bounds computed through Dantzig–Wolfe reformulation and column generation.In this paper we propose a cutting plane algorithm working in the space of the variables of the basic formulation, whose core is an exact separation procedure for the knapsack polytopes induced by the capacity constraints. We show that an efficient implementation of the exact separation procedure allows to deal with large-scale instances and to solve to optimality several previously unsolved instances.

A Cut and Branch Approach for the Capacitated p-Median Problem Based on Fenchel Cutting Planes

The capacitated p-median problem (CPMP) consists of finding p nodes (the median nodes) minimizing the total distance to the other nodes of the graph, with the constraint that the total demand of the nodes assigned to each median does not exceed its given capacity. In this paper we propose a cutting plane algorithm, based on Fenchel cuts, which allows us to considerably reduce the integrality gap of hard CPMP instances. The formulation strengthened with Fenchel cuts is solved by a commercial MIP solver. Computational results show that this approach is effective in solving hard instances or considerably reducing their integrality gap.

The dispatching problem on multitrack territories: Heuristic approaches based on mixed integer linear programming

Trains running through railway lines often accumulate some delay. When this happens, rescheduling and rerouting decisions must be quickly taken in real time. Despite the fact that even a single wrong decision may deteriorate the performance of the whole railway network, this complex optimization task is still basically performed by human operators. In very recent years, the interest of train operators to implement automated decision systems has grown. Not incidentally, the railway application section (RAS) of INFORMS has issued a challenge devoted to this problem concomitantly with the INFORMS Annual Meeting 2012. In this article, we describe two heuristic approaches to solve the RAS problem based on a mixed integer linear programming formulation, and we report computational results on the three RAS instances and on an additional set of instances defined on a more congested network. Computational results on the challenge test bed show that our algorithms positively compare with other approaches to the RAS problem.

A computational study of a nonlinear minsum facility location problem

A discrete location problem with nonlinear objective is addressed. A set of p plants is to be open to serve a given set of clients. Together with the locations, the number p of facilities is also a decision variable. The objective is to minimize the total cost, represented as the transportation cost between clients and plants, plus an increasing nonlinear function of p. Two Lagrangean relaxations are considered to derive lower bounds. Dual information is also used to design a core heuristic. Computational results are given, showing that nearly optimal solutions are obtained in short running times.

Computational Testing of a Separation Procedure for the Knapsack Set with a Single Continuous Variable

We study an exact separation procedure---SEP- MK ---for the knapsack set with a single continuous variable X MK . Then, we address the question of whether SEP- MK can be of practical use in tightening mixed-integer programming (MIP) formulations when using standard (floating-point) MIP solvers. To this purpose, we present a separation procedure for MIP problems---SEP- MIP MK ---where we derive knapsack sets of the form X MK by aggregating the continuous variables in the mixed knapsack inequalities of the formulation. Then, we use SEP- MK to generate cutting planes. Before the continuous variables are aggregated, the mixed knapsack inequalities are modified through the use of a bound substitution procedure to take into account fixed and variable bounds on the continuous variables. Bound substitution is made according to some heuristic rules, so even if its basic component SEP- MK is “exact,” the overall separation procedure for MIP problems, SEP- MIP MK , is heuristic. We perform a computational study on a wide set of mixed-integer programming instances from the MIPLIB 2003 [Achterberg, T., T. Koch, A. Martin. 2006. Mixed Integer Problem Library (MIPLIB) 2003. Konrad-Zuse-Zentrum fur Informationstechnik Berlin, Berlin. http://miplib.zib.de] and Mittelmann [Mittelmann, H. 2010. MILP testcases. http://plato.asu.edu/ftp/milp] benchmark sets. Computational experiments confirm that lifted cover and mixed-integer rounding (MIR) inequalities are effective from a computational viewpoint. Nevertheless, there are several instances where SEP- MIP MK is able to significantly raise the lower bounds given by lifted cover and MIR inequalities.

A Branch-and-Cut Algorithm for the Multilevel Generalized Assignment Problem

The multilevel generalized assignment problem (MGAP) consists of minimizing the assignment cost of a set of jobs to machines, each having associated therewith a capacity constraint. Each machine can perform a job with different efficiency levels that entail different costs and amount of resources required. The MGAP was introduced in the context of large manufacturing systems as a more general variant of the well-known generalized assignment problem, where a single efficiency level is associated with each machine. In this paper, we propose a branch-and-cut algorithm whose core is an exact separation procedure for the multiple-choice knapsack polytope induced by the capacity constraints and single-level execution constraints. A computational experience on a set of benchmark instances is reported, showing the effectiveness of the proposed approach.

Polyhedral study of simple plant location problem with order

Abstract This paper is addressed to the generalization of simple plant location problem where customer’s preferences are taken into account. Some basic polyhedral studies and a new family of facet-defining inequalities are given. The effectiveness of the proposed approach is illustrated by the computational experience.

Bi-level and Bi-objective p-Median Type Problems for Integrative Clustering: Application to Analysis of Cancer Gene-Expression and Drug-Response Data

Recent advances in high-throughput technologies have given rise to collecting large amounts of multidimensional heterogeneous data that provide diverse information on the same biological samples. Integrative analysis of such multisource datasets may reveal new biological insights into complex biological mechanisms and therefore remains an important research field in systems biology. Most of the modern integrative clustering approaches rely on independent analysis of each dataset and consensus clustering, probabilistic or statistical modeling, while flexible distance-based integrative clustering techniques are sparsely covered. We propose two distance-based integrative clustering frameworks based on bi-level and bi-objective extensions of the p-median problem. A hybrid branch-and-cut method is developed to find global optimal solutions to the bi-level p-median model. As to the bi-objective problem, an

An implementation of exact knapsack separation

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Automatic defects classification with p-median clustering technique

-constraint algorithm is proposed to generate an approximation to the Pareto optimal set. Every solution found by any of the frameworks corresponds to an integrative clustering. We present an application of our approaches to integrative analysis of NCI-60 human tumor cell lines characterized by gene expression and drug activity profiles. We demonstrate that the proposed mathematical optimization-based approaches outperform some state-of-the-art and traditional distance-based integrative and non-integrative clustering techniques.