Mara Servilio

A competitive scheduling problem and its relevance to UMTS channel assignment

This article investigates a two-user competitive scheduling problem. The problem arises in a Universal Mobile Telecommunication System (UMTS) developed within the European IST project FUTURE: given two mobile terminals, one wants to maximize the on-time data packets transmitted to one user, while guaranteeing a certain amount of on-time data packets to the other. We show that the problem is NP-hard, despite peculiar properties of data and solutions. We propose a fast lagrangian heuristic able to cope with a severe real-time requirement, and compare it to a greedy-like heuristic on a set of practical instances.

The directed profitable location Rural Postman Problem

In this paper we introduce an extension of the well known Rural Postman Problem, which combines arc routing with profits and facility location. Profitable arcs must be selected, facilities located at both end-points of the selected arcs, and a tour identified so as to maximize the difference between the profit collected along the arcs and the cost of traversing the arcs and installing the facilities. We analyze properties of the problem, present a mathematical programming formulation and a branch-and-cut algorithm. In an extensive computational experience the algorithm could solve instances with up to 140 vertices and 190 arcs and up to 50 vertices and 203 arcs.

Sorting common operations to minimize the number of tardy jobs

We study an operation scheduling problem where a finite set of jobs with due dates must be completed by one machine: each job is completed as soon as a specific subset of unit operations is done. Distinct jobs may share operations, and when an operation is done, it is done for all the jobs that share it. The goal is to schedule operations so that the weighted number of tardy jobs is minimized. We reformulate the problem as maximum stable set problem on a special graph and study its structure. Valid inequalities and optimality cuts are derived, separated, and tested in a computational experience that identifies some features of hard instances and the potential contribution of the addition, at root, of various cut classes.

Robust Shift Scheduling in Call Centers

We propose a robust optimization model for shift scheduling in call centers. The model is designed to react to the deviations that often occur between the planned staffing levels and the actual number of employees that would be necessary to guarantee the desired level of service. Different perturbation patterns are considered giving rise to different uncertainty sets, and the corresponding algorithmic implications are discussed. A case study from an Italian Public Agency is finally presented, which shows how the proposed methodology improves the quality of the schedules. Interestingly, although the methodology is fairly sophisticated, it perfectly fits in a quite common managers current practice.

Scheduling two chains of unit jobs on one machine: A polyhedral study

We investigate polyhedral properties of the following scheduling problem: given two sets of unit, indivisible jobs and revenue functions of the jobs completion times, find a one-machine schedule maximizing the total revenue under the constraint that the schedule of each job set respects a prescribed chain-like precedence relation. A solution to this problem is an order preserving assignment of the jobs to a set of time-slots. We study the convex hull of the feasible assignments and provide families of facet-defining inequalities in two cases: (i) each job must be assigned to a time-slot and (ii) a job does not need to be assigned to any time-slot.

Optimal power control in OFDMA cellular networks

This article addresses the problem of allocating users to radio resources in the downlink of an OFDMA cellular system. We consider a classical multicellular environment with a realistic interference model and a margin adaptive approach, i.e., we aim at minimizing total transmission power while maintaining a certain given rate for each user. We discuss computational complexity issues of the resulting model and present a heuristic approach that finds optima under suitable conditions or reasonably good solutions in the general case. Computational experiments show the effectiveness of the proposed heuristic in a comparison with both a commercial state-of-the-art optimization solver and other approaches from the literature.

An improved integer linear programming formulation for the closest 0-1 string problem

Abstract The Closest String Problem (CSP) calls for finding an n -string that minimizes its maximum Hamming distance from m given n -strings. Recently, integer linear programs (ILP) have been successfully applied within heuristics to improve efficiency and effectiveness. We consider an ILP for the binary case (0-1 CSP) that updates the previous formulations and solve it by branch-and-cut. The method separates in polynomial time the first closure of { 0 , 1 2 } -Chvatal-Gomory cuts and can either be used stand-alone to find optimal solutions, or as a plug-in to improve heuristics based on the exact solution of reduced problems. Due to the parity structure of the right-hand side, the impressive performances obtained with this method in the binary case cannot be directly replicated in the general case.

Optimum Solution of the Closest String Problem via Rank Distance

The Closest String Problem (CSP) calls for finding an n-string that minimizes its maximum distance from m given n-strings. Integer linear programming (ILP) proved to be able to solve large CSPs under the Hamming distance, whereas for the Levenshtein distance, preferred in computational biology, no ILP formulation has so far be investigated. Recent research has however demonstrated that another metric, rank distance, can provide interesting results with genomic sequences. Moreover, CSP under rank distance can easily be modeled via ILP: optimal solutions can then be certified, or information on approximation obtained via dual gap. In this work we test this ILP formulation on random and biological data. Our experiments, conducted on strings with up to 600 nucleotides, show that the approach outperforms literature heuristics. We also enforce the formulation by cover inequalities. Interestingly, due to the special structure of the rank distance between two strings, cover separation can be done in polynomial time.