A simple greedy algorithm for a class of shuttle transportation problems

Greedy algorithms for combinatorial optimization problems are typically direct and efficient, but hard to prove optimality. The paper presents a special class of transportation problems where a supplier sends goods to a set of customers, returning to the source after each delivery. We show that these problems with different objective functions share a common structural property, and therefore a simple but powerful generic greedy algorithm yields optimal solutions for all of them.     

Maximally Disjoint Solutions of the Set Covering Problem

This paper is concerned with finding two solutions of a set covering problem that have a minimum number of variables in common. We show that this problem is NP-complete, even in the case where we are only interested in completely disjoint solutions. We describe three heuristic methods based on the standard greedy algorithm for set covering problems. Two of these algorithms find the solutions sequentially, while the third finds them simultaneously. A local search method for reducing the overlap of the two given solutions is then described. This method involves the solution of a reduced set covering problem. Finally, extensive computational tests are given demonstrating the nature of these algorithms. These tests are carried out both on randomly generated problems and on problems found in the literature.     

Speeding up continuous GRASP

Continuous GRASP (C-GRASP) is a stochastic local search metaheuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints (Hirsch et al., 2007). Like a greedy randomized adaptive search procedure (GRASP), a C-GRASP is a multi-start procedure where a starting solution for local improvement is constructed in a greedy randomized fashion. In this paper, we describe several improvements that speed up the original C-GRASP and make it more robust. We compare the new C-GRASP with the original version as well as with other algorithms from the recent literature on a set of benchmark multimodal test functions whose global minima are known. Hart’s sequential stopping rule (1998) is implemented and C-GRASP is shown to converge on all test problems.     

Performance guarantees of greedy algorithms for the problems on hereditary systems

Under study are the problems of maximization and minimization of additive functions on hereditary systems which generalize many computationally hard combinatorial optimization problems. A performance guarantee of the greedy algorithm is proven in terms of the parameters of a feasible set and the objective function of the maximization problem. This bound improves the well-known Jenkyns—Korte—Hausmann bound. An analogous result is obtained for the minimization problem of an additive function on a hereditary system.     

Global optimization by continuous grasp

We introduce a novel global optimization method called Continuous GRASP (C-GRASP) which extends Feo and Resende’s greedy randomized adaptive search procedure (GRASP) from the domain of discrete optimization to that of continuous global optimization. This stochastic local search method is simple to implement, is widely applicable, and does not make use of derivative information, thus making it a well-suited approach for solving global optimization problems. We illustrate the effectiveness of the procedure on a set of standard test problems as well as two hard global optimization problems.     

Quasi-concave functions on meet-semilattices

This paper deals with maximization of set functions defined as minimum values of monotone linkage functions. In previous research, it has been shown that such a set function can be maximized by a greedy type algorithm over a family of all subsets of a finite set. In this paper, we extend this finding to meet-semilattices.We show that the class of functions defined as minimum values of monotone linkage functions coincides with the class of quasi-concave set functions. Quasi-concave functions determine a chain of upper level sets each of which is a meet-semilattice. This structure allows development of a polynomial algorithm that finds a minimal set on which the value of a quasi-concave function is maximum. One of the critical steps of this algorithm is a set closure. Some examples of closure computation, in particular, a closure operator for convex geometries, are considered.     

Column generation extensions of set covering greedy heuristics

Large-scale set covering problems are often approached by constructive greedy heuristics, and many selection criteria for such heuristics have been considered. These criteria are typically based on measures of the cost of setting an additional variable to one in relation to the number of yet unfulfilled constraints that it will satisfy. We show how such greedy selections can be performed on column-oriented set covering models, by using a fractional optimization formulation and solving sequences of ordinary column generation problems for the application at hand.     

A general model for matroids and the greedy algorithm

We present a general model for set systems to be independence families with respect to set families which determine classes of proper weight functions on a ground set. Within this model, matroids arise from a natural subclass and can be characterized by the optimality of the greedy algorithm. This model includes and extends many of the models for generalized matroid-type greedy algorithms proposed in the literature and, in particular, integral polymatroids. We discuss the relationship between these general matroids and classical matroids and provide a Dilworth embedding that allows us to represent matroids with underlying partial order structures within classical matroids. Whether a similar representation is possible for matroids on convex geometries is an open question. S. Fujishige’s research was supported by a Grant-in-Aid for Scientific Research of the Ministry of Education, Culture, Sports, Science and Technology of Japan.     

Mixed integer programming models for dispatching vehicles at a container terminal

This paper presents scheduling models for dispatching vehicles to accomplish a sequence of container jobs at the container terminal, in which the starting times as well as the order of vehicles for carrying out these jobs need to be determined. To deal with this scheduling problem, three mixed 0–1 integer programming models, Model I, Model II and Model III are provided. We present interesting techniques to reformulate the two mixed integer programming models, Model I and Model II, as pure 0–1 integer programming problems with simple constraint sets and present a lower bound for the optimal value of Model I. Model III is a complicated mixed integer programming model because it involves a set of non-smooth constraints, but it can be proved that its solutions may be obtained by the so-called greedy algorithm. We present numerical results showing that Model III is the best among these three models and the greedy algorithm is capable of solving large scale problems.     

Performance Bounds with Curvature for Batched Greedy Optimization

The batched greedy strategy is an approximation algorithm to maximize a set function subject to a matroid constraint. Starting with the empty set, the batched greedy strategy iteratively adds to the current solution set a batch of elements that results in the largest gain in the objective function while satisfying the matroid constraints. In this paper, we develop bounds on the performance of the batched greedy strategy relative to the optimal strategy in terms of a parameter called the total batched curvature. We show that when the objective function is a polymatroid set function, the batched greedy strategy satisfies a harmonic bound for a general matroid constraint and an exponential bound for a uniform matroid constraint, both in terms of the total batched curvature. We also study the behavior of the bounds as functions of the batch size. Specifically, we prove that the harmonic bound for a general matroid is nondecreasing in the batch size and the exponential bound for a uniform matroid is nondecreasing in the batch size under the condition that the batch size divides the rank of the uniform matroid. Finally, we illustrate our results by considering a task scheduling problem and an adaptive sensing problem.     

A theoretical justification of the set covering greedy heuristic of Caprara et al.

Large scale set covering problems have often been approached by constructive greedy heuristics, and much research has been devoted to the design and evaluation of various greedy criteria for such heuristics. A criterion proposed by Caprara et al. (1999) is based on reduced costs with respect to the yet unfulfilled constraints, and the resulting greedy heuristic is reported to be superior to those based on original costs or ordinary reduced costs.We give a theoretical justification of the greedy criterion proposed by Caprara et al. by deriving it from a global optimality condition for general non-convex optimisation problems. It is shown that this criterion is in fact greedy with respect to incremental contributions to a quantity which at termination coincides with the deviation between a Lagrangian dual bound and the objective value of the feasible solution found.     

Asymptotic Analysis of a Greedy Heuristic for the Multi-Period Single-Sourcing Problem: The Acyclic Case

The multi-period single-sourcing problem that we address in this paper can be used as a tactical tool for evaluating logistics network designs in a dynamic environment. In particular, our objective is to find an assignment of customers to facilities, as well as the location, timing and size of production and inventory levels, that minimizes total assignment, production, and inventory costs. We propose a greedy heuristic, and prove that this greedy heuristic is asymptotically optimal in a probabilistic sense for the subclass of problems where the assignment of customers to facilities is allowed to vary over time. In addition, we prove a similar result for the subclass of problems where each customer needs to be assigned to the same facility over the planning horizon, and where the demand for each customer exhibits the same seasonality pattern. We illustrate the behavior of the greedy heuristic, as well as some improvements where the greedy heuristic is used as the starting point of a local interchange procedure, on a set of randomly generated test problems. These results suggest that the greedy heuristic may be asymptotically optimal even for the cases that we were unable to analyze theoretically.     

Random sampling and greedy sparsification for matroid optimization problems

Random sampling is a powerful tool for gathering information about a group by considering only a small part of it. We discuss some broadly applicable paradigms for using random sampling in combinatorial optimization, and demonstrate the effectiveness of these paradigms for two optimization problems on matroids: finding an optimum matroid basis and packing disjoint matroid bases. Application of these ideas to the graphic matroid led to fast algorithms for minimum spanning trees and minimum cuts. An optimum matroid basis is typically found by agreedy algorithm that grows an independent set into an optimum basis one element at a time. This continuous change in the independent set can make it hard to perform the independence tests needed by the greedy algorithm. We simplify matters by using sampling to reduce the problem of finding an optimum matroid basis to the problem of verifying that a givenfixed basis is optimum, showing that the two problems can be solved in roughly the same time. Another application of sampling is to packing matroid bases, also known as matroid partitioning. Sampling reduces the number of bases that must be packed. We combine sampling with a greedy packing strategy that reduces the size of the matroid. Together, these techniques give accelerated packing algorithms. We give particular attention to the problem of packing spanning trees in graphs, which has applications in network reliability analysis. Our results can be seen as generalizing certain results from random graph theory. The techniques have also been effective for other packing problems. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.Some of this work done at Stanford University, supported by National Science Foundation and Hertz Foundation Graduate Fellowships, and NSF Young Investigator Award CCR-9357849, with matching funds from IBM, Schlumberger Foundation, Shell Foundation and Xerox Corporation. Also supported by NSF award 962-4239.     

Gossip algorithms for heterogeneous multi-vehicle routing problems

In this paper we address a class of heterogeneous multi-vehicle task assignment and routing problems. We propose two distributed algorithms based on gossip communication: the first algorithm is based on a local exact optimization and the second is based on a local approximate greedy heuristic. We consider the case where a set of heterogeneous tasks arbitrarily distributed in a plane has to be serviced by a set of mobile robots, each with a given movement speed and task execution speed. Our goal is to minimize the maximum execution time of robots.     

The linking set problem: a polynomial special case of the multiple-choice knapsack problem

We consider the linking set problem, which can be seen as a particular case of the multiple-choice knapsack problem. This problem occurs as a subproblem in a decomposition procedure for solving large-scale p-median problems such as the optimal diversity management problem. We show that if a non-increasing diference property of the costs in the linking set problem holds, then the problem can be solved by a greedy algorithm and the corresponding linear gap is null.     

NP-Completeness results concerning greedy and super greedy linear extensions

Various problems concerning greedy and super greedy linear extensions are shown to be NP-complete. In particular, the problem, due to Cogis, of determining that an ordered set is not greedy is NP-complete, as is the problem, due to Rival and Zaguia, of determining whether an ordered set has a greedy linear extension, which satisfies certain additional constraints. The author was supported in part by ONR grant N00014-85K-0494 and NSERC grants 69-3378. 69-0259, and 69-1325.     

A probabilistic greedy search algorithm for combinatorial optimisation with application to the set covering problem

We present a probabilistic greedy search method for combinatorial optimisation problems. This approach is implemented and evaluated for the Set Covering Problem (SCP) and shown to yield a simple, robust, and quite fast heuristic. Tests performed on a large set of benchmark instances with up to 1000 rows and 10?000 columns show that the algorithm consistently yields near-optimal solutions.     

The -digraph optimization problem and the greedy algorithm

In this paper we present a new optimization problem and a general class of objective functions for this problem. We show that optimal solutions to this problem with these objective functions are found with a simple greedy algorithm. Special cases include matroids, Huffman's data compression problem, a special class of greedoids, a special class of min cost max flow problems (related to Monge sequences), a special class of weighted f-factor problems, and some new problems.     

A greedy-algorithm characterization of valuated Δ-matroids

We study a variant of the greedy algorithm for weight functions defined on the system of subsets of a given finite set E and show that this algorithm works exactly for “valuated Δ-matroids.” Examples come from valuation theory.     

Worst case analysis of a class of set covering heuristics

In [2], Chvatal provided the tight worst case bound of the set covering greedy heuristic. We considered a general class of greedy type set covering heuristics. Their worst case bounds are dominated by that of the greedy heuristic.