With the most recent releases of MD-JEEP, new relevant features have been included to our software tool.MD-JEEP solves instances of the class of Discretizable Distance Geometry Problems (DDGPs), which ask to find possible realizations, in a Euclidean space, of a simple weighted undirected graph for which distance constraints between vertices are given, and for which a discretization of the search space can be supplied.Since its version 0.3.0,MD-JEEP is able to deal with instances containing interval data.We focus in this short paper on the most recent release MD-JEEP 0.3.2:among the new implemented features, we will focus our attention on three features: (i) an improved procedure for the generation and update of the boxes used in the coarse-grained representation (necessary to deal with instances containing interval data); (ii) a new procedure for the selection of the so-called discretization vertices (necessary to perform the discretization of the search space); (iii) the implementation of a general parser which allows the user to easily load DDGP instances in a given specified format.The source code of MD-JEEP 0.3.2 is available on GitHub, where the reader can find all additional details about the implementation of such new features, as well as verify the effectiveness of such features by comparing MD-JEEP 0.3.2 with its previous releases.

We experiment with an alternative routing scheme for the robust network loading problem with demand uncertainty. Named k‐adaptive, it is based on the fact that the decision‐maker chooses k second‐stage solutions and then commits to one of them only after realization of the uncertainty. This routing scheme, with its corresponding k‐partition of the uncertainty set, is dynamically defined under an iterative method to sequentially improve the solution. The method has an inherent characteristic of multiplying the number of variables and constraints after each iteration, so that additional measures are introduced in the solution strategy in order to control time performance. We compare our k‐adaptive results with the ones obtained through other routing schemes and also verify the effectiveness of the methods utilized using several realistic networks from SNDlib and other sources.

We present a new iterative algorithm for the molecular distance geometry problem with inaccurate and sparse data, which is based on the solution of linear systems, maximum cliques, and a minimization of nonlinear least-squares function. Computational results with real protein structures are presented in order to validate our approach.

Abstract Let G = (V, E, Q) be a undirected graph, where V is the set of vertices, E is the set of edges, and Q = {Q1,…,Qq} is a partition of V into q subsets. We refer to Q1,…,Qq as the components of the partition. The partition coloring problem (PCP) consists of finding a subset V′ of V with exactly one vertex from each component Q1,…,Qq and such that the chromatic number of the graph induced in G by V′ is minimum. This problem is a generalization of the graph coloring problem. This work presents a branch‐and‐cut algorithm proposed for PCP. An integer programing formulation and valid inequalities are proposed. A tabu search heuristic is used for providing primal bounds. Computational experiments are reported for random graphs and for PCP instances originating from the problem of routing and wavelength assignment in all‐optical WDM networks. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010

We consider the Discretizable Molecular Distance Geometry Problem (DMDGP), which consists in a subclass of instances of the distance geometry problem related to molecular conformations for which a combinatorial reformulation can be supplied. We investigate the performances of two different algorithms for solving the DMDGP. The first one is the Branch and Prune (BP) algorithm, an exact algorithm that is strongly based on the structure of the combinatorial problem. The second one is the Monkey Search (MS) algorithm, a meta-heuristic algorithm that is inspired by the behavior of a monkey climbing trees in search for food supplies, and that exploits ideas and strategies from other meta-heuristic searches, such Genetic Algorithms, Differential Evolution, and so on. The comparison between the two algorithms is performed on a set of instances related to protein conformations. The used instances simulate data obtained from the Nuclear Magnetic Resonance (NMR), because the typical distances provided by NMR are considered and a predetermined number of wrong distances are included.

In this article we consider the SONET ring assignment problem (SRAP) presented in 7. The authors pointed out the inadequacy of solving SRAP instances using their integer programming formulation and commercial linear programming solvers. Similar experiences with IP models for SRAP are reported in 1. In this article we reformulate SRAP as a set partitioning model with an additional knapsack constraint. This new formulation has an exponential number of columns and, to solve it, we implemented a branch-and-price/column generation algorithm. Extensive computational experiments showed that the new algorithm is orders of magnitude faster than standard branch-and-bound codes running on compact IP models introduced earlier. Instances taken from 1, 7, which could not be solved there in hours of computation were solved here to optimality in just a few seconds. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 47(3), 157–171 2006