Eric Angel

A Dynasearch Neighborhood for the Bicriteria Traveling Salesman Problem

In this paper we extend the dynasearch approach proposed by Congram, Potts and van de Velde [4, 17] in the context of multicriteria optimization for the bicriteria traveling salesman problem. The idea is to use local search with an exponential sized neighborhood which can be searched in polynomial time using dynamic programming and a rounding technique. Experimental results are presented to verify the quality of the proposed approach to obtain approximate Pareto curves for the bicriteria traveling salesman problem.

On the approximate tradeoff for bicriteria batching and parallel machine scheduling problems

We consider multiobjective scheduling problems, i.e. scheduling problems that are evaluated with respect to many cost criteria, and we are interested in determining a trade-off (Pareto curve) among these criteria. We study two types of bicriteria scheduling problems: single-machine batching problems and parallel machine scheduling problems. Instead of proceeding in a problem-by-problem basis, we identify a class of multiobjective optimization problems possessing a fully polynomial time approximation scheme (FPTAS) for computing an ?-approximate Pareto curve. This class contains a set of problems whose Pareto curve can be computed via a simple pseudo-polynomial dynamic program for which the objective and transition functions satisfy some, easy to verify, arithmetical conditions. Our study is based on a recent work of Woeginger (Electronic Colloquium on Computational Complexity, Report 84 (short version appeared in SODA?99, pp. 820?829)) for the single criteria optimization ex-benevolent problems. We show how our general result can be applied to the considered scheduling problems.

On the Hardness of the Quadratic Assignment Problem with Metaheuristics

Meta-heuristics are a powerful way to approximately solve hard combinatorial optimization problems. However, for a problem, the quality of results can vary considerably from one instance to another. Understanding such a behaviour is important from a theoretical point of view, but also has practical applications such as for the generation of instances during the evaluation stage of a heuristic.In this paper we propose a new complexity measure for the Quadratic Assignment Problem in the context of metaheuristics based on local search, e.g. simulated annealing. We show how the ruggedness coefficient previously introduced by the authors, in conjunction with the well known concept of dominance, provides important features of the search space explored during a local search algorithm, and gives a rather precise idea of the complexity of an instance for these heuristics. We comment previous experimental studies concerning tabu search methods and genetic algorithms with local search in the light of our complexity measure. New computational results with simulated annealing and taboo search are presented.

A FPTAS for Approximating the Unrelated Parallel Machines Scheduling Problem with Costs

We consider the classical problem of scheduling a set of independent jobs on a set of unrelated machines with costs. We are given a set of n monoprocessor jobs and m machines where each job is to be processed without preemptions. Executing job j on machine i requires time pij ≥ 0 and incurs cost cij . Our objective is to find a schedule obtaining a tradeoff between the makespan and the total cost. We focus on the case where the number of machines is a fixed constant, and we propose a simple FPTAS that computes for any Ɛ > 0 a schedule with makespan at most (1+Ɛ)T and cost at most Copt(T), in time O(n(n/Ɛ)m), given that there exists a schedule of makespan T, where Copt(T) is the cost of the minimum cost schedule which achieves a makespan of T. We show that the optimal makespan-cost trade-off (Pareto) curve can be approximated by an efficient polynomial time algorithm within any desired accuracy. Our results can also be applied to the scheduling problem where the rejection of jobs is allowed. Each job has a penalty associated to it, and one is allowed to schedule any subset of jobs. In this case the goal is the minimization of the makespan of the scheduled jobs and the total penalty of the rejected jobs.

(Non)-approximability for the multi-criteria TSP (1, 2)

Many papers deal with the approximability of multi-criteria optimization problems but only a small number of non-approximability results, which rely on NP-hardness, exist in the literature. In this paper, we provide a new way of proving non-approximability results which relies on the existence of a small size good approximating set (i.e. it holds even in the unlikely event of P=NP). This method may be used for several problems but here we illustrate it for a multi-criteria version of the traveling salesman problem with distances one and two (TSP(1,2)). Following the article of Angel et al. (FCT 2003) who presented an approximation algorithm for the bi-criteria TSP(1,2), we extend and improve the result to any number k of criteria.

A multi-start dynasearch algorithm for the time dependent single-machine total weighted tardiness scheduling problem

We extend the dynasearch technique, recently proposed by Congram et al., in the context of time-dependent combinatorial optimization problems. As an application we consider a general time-dependent (idleness) version of the well known single-machine total weighted tardiness scheduling problem, in which the processing time of a job depends on its starting time of execution. We develop a multi-start local search algorithm and present experimental results on several types of instances showing the superiority of the dynasearch neighborhood over the traditional one.

Approximation algorithms for the bi-criteria weighted MAX-CUT problem

We consider a generalization of the classical MAX-CUT problem where two objective functions are simultaneously considered. We derive some theorems on the existence and the non-existence of feasible cuts that are at the same time near optimal for both criteria. Furthermore, two approximation algorithms with performance guarantee are presented. The first one is deterministic while the second one is randomized. A generalization of these results is given for the bi-criteria MAX-k-CUT problem.

A survey of approximation results for local search algorithms

In this chapter we review the main results known on local search algorithms with worst case guarantees. We consider classical combinatorial optimization problems: satisfiability problems, traveling salesman and quadratic assignment problems, set packing and set covering problems, maximum independent set, maximum cut, several facility location related problems and finally several scheduling problems. A replica placement problem in a distributed file systems is also considered as an example of the use of a local search algorithm in a distributed environment. For each problem we have provided the neighborhoods used along with approximation results. Proofs when too technical are omitted, but often sketch of proofs are provided.

Low complexity scheduling algorithms minimizing the energy for tasks with agreeable deadlines

Power management aims in reducing the energy consumed by computer systems while maintaining a good level of performance. One of the mechanisms used to save energy is the shut-down mechanism which puts the system into a sleep state when it is idle. No energy is consumed in this state, but a fixed amount of energy is required for a transition from the sleep state to the active state which is equal to L times the energy required for the execution of a unit-time task. In this paper, we focus on the off-line version of this problem where a set of unit-time tasks with release dates and deadlines have to be scheduled in order to minimize the overall consumed energy during the idle periods of the schedule. Here we focus on the case where the tasks have agreeable deadlines. For the single processor case, an O(n^3) algorithm has been proposed in Gururaj et al. (2010) for unit-time tasks and arbitrary L. We improve this result by introducing a new O(n^2) polynomial-time algorithm for tasks with arbitrary processing times and arbitrary L. For the multiprocessor case we also improve the complexity from O(n^3m^2) Gururaj et al. (2010) to O(n^2m) in the case of unit-time tasks and unit L.

Energy Aware Scheduling for Unrelated Parallel Machines

We consider the problem of energy aware scheduling of a set of jobs on a set of unrelated parallel machines with the average weighted completion time plus energy objective. The processing time and the energy consumption of the jobs are machine and speed dependent. Also, every job is subject to a machine-dependent release date. Firstly, we aim to find a non-preemptive schedule of the jobs minimizing the average weighted completion time plus energy, and we propose a randomized approximation algorithm that we derandomize obtaining a deterministic approximation algorithm. We then consider the budget variant of the problem where the objective is to minimize the average completion time while the total energy consumption does not exceed a given budget.