Typical values of extremal-weight combinatorial structures with independent symmetric weights

Suppose that the edges of a complete graph are assigned weights independently at random and we ask for the weight of the minimal-weight spanning tree, or perfect matching, or Hamiltonian cycle. For these and several other common optimisation problems, we establish asymptotically tight bounds when the weights are independent copies of a symmetric random variable (satisfying a mild condition on tail probabilities), in particular when the weights are Gaussian.Comment: Final version. To appear in Electron. J. Combi

Approximation algorithms for the shortest common superstring problem

AbstractThe object of the shortest common superstring problem (SCS) is to find the shortest possible string that contains every string in a given set as substrings. As the problem is NP-complete, approximation algorithms are of interest. The value of an aproximate solution to SCS is normally taken to be its length, and we seek algorithms that make the length as small as possible. A different measure is given by the sum of the overlaps between consecutive strings in a candidate solution. When considering this measure, the object is to find solutions that make it as large as possible. These two measures offer different ways of viewing the problem. While the two viewpoints are equivalent with respect to optimal solutions, they differ with respect to approximate solutions. We describe several approximation algorithms that produce solutions that are always within a factor of two of optimum with respect to the overlap measure. We also describe an efficient implementation of one of these, using McCreight's compact suffix tree construction algorithm. The worstcase running time is O(m log n) for small alphabets, where m is the sum of the lengths of all the strings in the set and n is the number of strings. For large alphabets, the algorithm can be implemented in O(m log m) time by using Sleator and Tarjan's lexicographic splay tree data structure

Phase Transitions and Backbones of the Asymmetric Traveling Salesman Problem

In recent years, there has been much interest in phase transitions of combinatorial problems. Phase transitions have been successfully used to analyze combinatorial optimization problems, characterize their typical-case features and locate the hardest problem instances. In this paper, we study phase transitions of the asymmetric Traveling Salesman Problem (ATSP), an NP-hard combinatorial optimization problem that has many real-world applications. Using random instances of up to 1,500 cities in which intercity distances are uniformly distributed, we empirically show that many properties of the problem, including the optimal tour cost and backbone size, experience sharp transitions as the precision of intercity distances increases across a critical value. Our experimental results on the costs of the ATSP tours and assignment problem agree with the theoretical result that the asymptotic cost of assignment problem is pi ^2 /6 the number of cities goes to infinity. In addition, we show that the average computational cost of the well-known branch-and-bound subtour elimination algorithm for the problem also exhibits a thrashing behavior, transitioning from easy to difficult as the distance precision increases. These results answer positively an open question regarding the existence of phase transitions in the ATSP, and provide guidance on how difficult ATSP problem instances should be generated

Alternative Mathematical Models and Solution Approaches for Lot-Sizing and Scheduling Problems in the Brewery Industry: Analyzing Two Different Situations

This research proposes new approaches to deal with the production planning and scheduling problem in brewery facilities. Two real situations found in factories are addressed, which differ by considering (or not) the setup operations in tanks that provide liquid for bottling lines. Depending on the technology involved in the production process, the number of tank swaps is relevant (Case A) or it can be neglected (Case B). For both scenarios, new MIP (Mixed Integer Programming) formulations and heuristic solution methods based on these formulations are proposed. In order to evaluate the approach for Case A, we compare the results of a previous study with the results obtained in this paper. For the solution methods and the result analysis of Case B, we propose adaptations of Case A approaches yielding an alternative MIP formulation to represent it. Therefore, the main contributions of this article are twofold: (i) to propose alternative MIP models and solution methods for the problem in Case A, providing better results than previously reported, and (ii) to propose new MIP models and solution methods for Case B, analyzing and comparing the results and benefits for Case B considering the technology investment required

Combinação de abordagens GLSP e ATSP para o problema de dimensionamento e sequenciamento de lotes de produção de suplementos para nutrição animal

In this paper we study the combination of GLSP (General Lot Sizing and Scheduling Problem) and ATSP (Asymmetric Travelling Salesman Problem) approaches with sub-tour elimination and patching to a lot sizing and sequencing problem in the animal nutrition industry. This problem consists of deciding the lots size for each product as well the production sequence of the lots, while meeting demand without backlogs and minimizing production and inventory costs. The coordination of these decisions is a challenge for production scheduling in this industry as the setup times are sequence dependent. The ATSP approaches are compared with relax-and-fix approaches applied to the GLSP (General Lot-sizing and Scheduling Problem) formulated in previous research, using real data from an animal nutrition plant in Sao Paulo state. Portuguese: Neste artigo estudamos a combinação de abordagens GLSP (General Lot Sizing and Scheduling Problem) e ATSP (Asymmetric Travelling Salesman Problem) para o problema de dimensionamento e sequenciamento de lotes na indústria de nutrição animal. Este problema consiste em determinar o tamanho de cada lote de produção para cada produto, assim como a sequência de produção destes lotes, de forma a satisfazer a demanda sem atrasos e minimizar os custos de produção e estoques. Uma dificuldade para a programação da produção nesta indústria é integrar estas decisões, pois os tempos de preparação da linha de produção são dependentes da sequência produtiva e não obedecem a desigualdade triangular. A abordagem proposta é comparada com abordagens relax-and-fix para o modelo GLSP (General Lot-sizing and Scheduling Problem) estudadas em trabalhos anteriores, utilizando dados reais de um estudo de caso de uma fábrica de nutrição animal localizada no interior de São Paulo