A Reactive Tabu Search algorithm with variable clustering for the Unicost Set Covering Problem (original) (raw)

An effective and simple heuristic for the set covering problem

European Journal of Operational Research, 2007

This paper investigates the development of an effective heuristic to solve the set covering problem (SCP) by applying the meta-heuristic Meta-RaPS (Meta-heuristic for Randomized Priority Search). In Meta-RaPS, a feasible solution is generated by introducing random factors into a construction method. Then the feasible solutions can be improved by an improvement heuristic. In addition to applying the basic Meta-RaPS, the heuristic developed herein integrates the elements of randomizing the selection of priority rules, penalizing the worst columns when the searching space is highly condensed, and defining the core problem to speedup the algorithm. This heuristic has been tested on 80 SCP instances from the OR-Library. The sizes of the problems are up to 1000 rows • 10,000 columns for non-unicost SCP, and 28,160 rows • 11,264 columns for the unicost SCP. This heuristic is only one of two known SCP heuristics to find all optimal/ best known solutions for those non-unicost instances. In addition, this heuristic is the best for unicost problems among the heuristics in terms of solution quality. Furthermore, evolving from a simple greedy heuristic, it is simple and easy to code. This heuristic enriches the options of practitioners in the optimization area.

A New Formulation of the Set Covering Problem for Metaheuristic Approaches

ISRN Operations Research, 2013

Two difficulties arise when solving the set covering problem (SCP) with metaheuristic approaches: solution infeasibility and set redundancy. In this paper, we first present a review and analysis of the heuristic approaches that have been used in the literature to address these difficulties. We then present a new formulation that can be used to solve the SCP as an unconstrained optimization problem and that eliminates the need to address the infeasibility and set redundancy issues. We show that all local optimums with respect to the new formulation and a 1-flip neighbourhood structure are feasible and free of redundant sets. In addition, we adapt an existing greedy heuristic for the SCP to the new formulation and compare the adapted heuristic to the original heuristic using 88 known test problems for the SCP. Computational results show that the adapted heuristic finds better results than the original heuristic on most of the test problems in shorter computation times.

A Heuristic Method for the Set Covering Problem

Operations Research, 1999

We present a Lagrangian-based heuristic for the well-known Set Covering Problem (SCP). The algorithm was initially designed for solving very large scale SCP instances, involving up to 5,000 rows and 1,000,000 columns, arising from crew scheduling in the Italian Railway Company, Ferrovie dello Stato SpA. In 1994 Ferrovie dello Stato SpA, jointly with the Italian Operational Research Society, organized a competition, called FASTER, intended to promote the development of algorithms capable of producing good solutions for these instances, since the classical approaches meet with considerable di culties in tackling them. The main characteristics of the algorithm we propose are (1) a dynamic pricing scheme for the variables, akin to that used for solving large-scale LP's, to be coupled with subgradient optimization and greedy algorithms, and (2) the systematic use of column xing to obtain improved solutions. Moreover, we propose a number of improvements on the standard way of de ning the step-size and the ascent direction within the subgradient optimization procedure, and the scores within the greedy algorithms. Finally, an e ective re ning procedure is proposed. Our code won the rst prize in the FASTER competition, giving the best solution value for all the proposed instances. The algorithm was also tested on the test instances from the literature: in 92 out of the 94 instances in our test bed we found, within short computing time, the optimal (or the best known) solution. Moreover, among the 18 instances for which the optimum is not known, in 6 cases our solution is better than any other solution found by previous techniques.

A two-phase heuristic for set covering

International Journal of Mathematics in Operational Research, 2018

The set covering problem (SCP) is a well-known computationally intractable problem. We suggest here a two-phase heuristic to solve it. The first phase reduces substantially the size of the given SCP by removing some variables; the second phase applies a simple Lagrangian heuristic applied to the reduced problem. Construction and improvement heuristics are embedded in the Lagrangian solution approach. The construction heuristic provides good covers by solving small SCPs. The improvement heuristic inserts these covers into larger ones from which better covers are extracted, again by solving different but also small SCPs. The novelty lies in the reduction of the problem size by an effective variable-fixing heuristic, which, in practice, eliminates up to 95% of the variables of the problem without sacrificing the solution quality. Extensive computational and comparative results are presented.

Exploring Further Advantages in an Alternative Formulation for the Set Covering Problem

Mathematical Problems in Engineering, 2020

The set covering problem (SCP) is an NP-complete optimization problem, fitting with many problems in engineering. The traditional SCP formulation does not directly address both solution unsatisfiability and set redundancy aspects. As a result, the solving methods have to control these aspects to avoid getting unfeasible and nonoptimized in cost solutions. In the last years, an alternative SCP formulation was proposed, directly covering both aspects. This alternative formulation received limited attention because managing both aspects is considered straightforward at this time. This paper questions whether there is some advantage in the alternative formulation, beyond addressing the two issues. Thus, two studies based on a metaheuristic approach are proposed to identify if there is any concept in the alternative formulation, which could be considered for enhancing a solving method considering the traditional SCP formulation. As a result, the authors conclude that there are concepts f...

Experiments with LAGRASP heuristic for set k-covering

Optimization Letters, 2011

The set k-covering problem is a variant of the classical set covering problem, in which each object is required to be covered at least k times. We describe a hybrid Lagrangean heuristic, named LAGRASP, which combines subgradient optimization and GRASP with path-relinking to solve the set kcovering problem. Computational experiments carried out on 135 test instances show experimentally that by properly tuning the parameters of LAGRASP, it is possible to obtain a good trade-off between solution quality and running times. Furthermore, LAGRASP makes better use of the dual information provided by subgradient optimization and is able to discover better solutions and to escape from locally optimal solutions even after the stabilization of the lower bounds, whereas other strategies fail to find new improving solutions. Keywords GRASP • hybrid heuristics • metaheuristics • path-relinking • Lagrangean relaxation • Lagrangean heuristics • local search • set covering • set multicovering • set k-covering.

Two Neighbourhood-based Approaches for the Set Covering Problem

U.Porto Journal of Engineering, 2019

The Set Covering Problem is a well-known NP-complete problem which we address in this work. Due to its combinatorial nature heuristic methods, namely neighbourhood-based meta-heuristics, were used.Based on the well-known algorithms GRASP, Simulated Annealing and Variable Neighbourhood Descend, along with a constructive heuristic based on a dynamic dispatching rule to generate initial feasible solutions, two approaches to the problem were formulated. The performance of both methods was assessed in 42 instances of the problem. Our best approach has an average deviation from the best-known solution of 0.23% and reached 0% for 26 instances under 40 minutes.

An OR Practitioner's Solution Approach for the Set Covering Problem

International Journal of Applied Metaheuristic Computing, 2015

The set covering problem (SCP) is an NP-complete problem that has many important industrial applications. Since industrial applications are typically large in scale, exact solution algorithms are not feasible for operations research (OR) practitioners to use when called on to solve real-world problems involving SCPs. However, the best performing heuristics for the SCP reported in the literature are not usually straightforward to implement. Additionally, these heuristics usually require the fine-tuning of several parameters. In contrast, simple greedy or even randomized greedy heuristics typically do not give as good results as the more sophisticated heuristics. In this paper, the authors present a compromise; a straightforward to implement, population-based solution approach for the SCP. It uses a randomized greedy approach to generate an initial population and then uses a genetic-based two phase approach to improve the population solutions. This two-phase approach uses transformation equations based on a Teaching-Learning based optimization approach developed by Rao, Savsani and Vakharia (2011, 2012) for continuous nonlinear optimization problems. Empirical results using set covering problems from Beasley's OR-library demonstrate the competitiveness of this approach both in terms of solution quality and execution time. The advantage to this approach is its relative simplicity for the practitioner to implement.

Algorithms for large scale set covering problems

Annals of Operations Research, 1993

This paper is concerned with the set covering problem (SCP), and in particular with the development of a new algorithm capable of solving large-scale SCPs of the size found in real-life situations. The set covering problem has a wide variety of practical applications which, in general, yield large and sparse instances, normally with hundreds of rows and thousands of columns. In this paper, we present an algorithm capable of solving problems of this size and test problems up to 400 rows and 4000 columns are solved. The method developed in this paper consists of a tree-search procedure based on a combination of decomposition and state space relaxation which is a technique developed for obtaining lower bounds on the dynamic program associated with a combinatorial optimization problem. The large size SCPs are decomposed into many smaller SCPs which are then solved or bounded by state space relaxation (SSR). Before using the decomposition and SSR, reductions both in the number of columns and the number of rows of the problem are made by applying Lagrangian relaxation, linear programming and heuristic methods.