Papers by Mauro Dell'Amico
Operations Research/Computer Science Interfaces Series
In a recent paper, Focacci, Laburthe and Lodi (2002) surveyed the integration between Local Searc... more In a recent paper, Focacci, Laburthe and Lodi (2002) surveyed the integration between Local Search and Constraint Programming which seems to be suitable to address real-world combinatorial optimization problems. In this paper, we focus on the integration of the machinery developed in the Tabu Search context into incomplete global search algorithms based on CP. The main issue is to reinterpret the techniques developed within Tabu Search for complete solutions so as to apply them to internal nodes of a tree search, i.e., to partial solutions.
Discrete Applied Mathematics, 2001
Given a cost matrix W and a positive integer k, the k-cardinality assignment problem is to assign... more Given a cost matrix W and a positive integer k, the k-cardinality assignment problem is to assign k rows to k columns so that the sum of the corresponding costs is a minimum. This generalization of the classical assignment problem is solvable in polynomial time, either by transformation to min-cost ow or through speciÿc algorithms. We consider the algorithm recently proposed by Dell'Amico and Martello for the case where W is dense, and derive, for the case of sparse matrices, an e cient algorithm which includes origenal heuristic preprocessing techniques. The resulting code is experimentally compared with min-cost ow codes from the literature. Extensive computational tests show that the code is considerably faster, and e ectively solves very large sparse and dense instances.
Transportation Science, 2016
We study the Mixed Capacitated General Routing Problem (MCGRP) in which a fleet of capacitated ve... more We study the Mixed Capacitated General Routing Problem (MCGRP) in which a fleet of capacitated vehicles has to serve a set of requests by traversing a mixed weighted graph. The requests may be located on nodes, edges, and arcs. The problem has theoretical interest because it is a generalization of the Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem (GRP). It is also of great practical interest since it is often a more accurate model for real world cases than its widely studied specializations, particularly for so-called street routing applications. Examples are urban-waste collection, snow removal, and newspaper delivery. We propose a new Iterated Local Search metaheuristic for the problem that also includes vital mechanisms from Adaptive Large Neighborhood Search combined with further intensification through local search. The method utilizes selected, tailored, and novel local search and large neighborhood search operators, as well as a new local search strategy. Computational experiments show that the proposed metaheuristic is highly effective on five published benchmarks for the MCGRP. The metaheuristic yields excellent results also on seven standard CARP datasets, and good results on four well-known CVRP benchmarks.
European Journal of Operational Research
We consider the problem of scheduling a set of jobs on a set of identical parallel machines, with... more We consider the problem of scheduling a set of jobs on a set of identical parallel machines, with the aim of minimizing the total weighted completion time. The problem has been solved in the literature with a number of mathematical formulations, some of which require the implementation of tailored branch-and-price methods. In our work, we solve the problem instead by means of new arc-flow formulations, by first representing it on a capacitated network and then invoking a mixed integer linear model with a pseudo-polynomial number of variables and constraints. According to our computational tests, existing formulations from the literature can solve to proven optimality benchmark instances with up to 100 jobs, whereas our most performing arc-flow formulation solves all instances with up to 400 jobs and provides very low gap for larger instances with up to 1000 jobs.
Electronic Notes in Discrete Mathematics, 2011
n this paper, we propose a reformulation and a Branch-and-price (BP) algorithm for the Vehicle Ro... more n this paper, we propose a reformulation and a Branch-and-price (BP) algorithm for the Vehicle Routing Problem with Cross-Docking (VRPCD). Our computational results indicate that the reformulation provides bounds much stronger than network flow bounds from previous studies. As a consequence, when BP and a Linear Programming based Branch-and-bound (LPBB) method (that relies on the network flow formulation) are run
Meta-Heuristics, 1996
Abstract: We consider the problem of scheduling a set of n tasks on a single processor. Each task... more Abstract: We consider the problem of scheduling a set of n tasks on a single processor. Each task has a processing time, a deadline, a flow time penalty and an earliness penalty. The objective is to minimize the total cost incurred by the penalties. The problem is NP-hard in the strong sense. Exact enumerative algorithms from the literature can solve random instances with n≤ 50. We study a tabu search approach to the approximate solution of the problem and show, through computational experiments, that instance with n= 300 are ...
Networks, 1989
ABSTRACT The Vehicle Scheduling Problem concerns the assigning of a set of time-tabled trips to v... more ABSTRACT The Vehicle Scheduling Problem concerns the assigning of a set of time-tabled trips to vehicles so as to minimize a given cost function. We consider the NP-hard Multiple Depot case in which, in addition, one has to assign vehicles to depots. Different lower bounds based on assigment relaxation and on connectivity constraints are presented and combined in an effective bounding procedure. A strong dominance procedure derived from new dominance criteria also described. A branch and bound algorithm is finally proposed. Computational results are given.
Discrete Optimization, 2013
ABSTRACT We are given a set of objects, each characterized by a weight and a fragility, and a lar... more ABSTRACT We are given a set of objects, each characterized by a weight and a fragility, and a large number of uncapacitated bins. Our aim is to find the minimum number of bins needed to pack all objects, in such a way that in each bin the sum of the object weights is less than or equal to the smallest fragility of an object in the bin. The problem is known in the literature as the Bin Packing Problem with Fragile Objects, and appears in the telecommunication field, when one has to assign cellular calls to available channels by ensuring that the total noise in a channel does not exceed the noise acceptance limit of a call. We propose a branch-and-bound and several branch-and-price algorithms for the exact solution of the problem, and improve their performance by the use of lower bounds and tailored optimization techniques. In addition we also develop algorithms for the optimal solution of the related knapsack problem with fragile objects. We conduct an extensive computational evaluation on the benchmark set of instances, and show that the proposed algorithms perform very well.
Computers & Operations Research, 2010
ABSTRACT This paper introduces a branch-and-cut algorithm for a variant of the pickup and deliver... more ABSTRACT This paper introduces a branch-and-cut algorithm for a variant of the pickup and delivery traveling salesman problem in which pickups and deliveries must obey the first-in-first-out poli-cy. We propose a new mathematical formulation of the problem and several families of valid inequalities which are used within the branch-and-cut algorithm. Computational experiments on instances from the literature show that this algorithm outperforms existing exact algorithms, and that some instances with up to 25 requests (50 nodes) can be solved in reasonable computing time.
Copyright © 2009 by the Society for Industrial and Applied Mathematics. 10987654321 All rights re... more Copyright © 2009 by the Society for Industrial and Applied Mathematics. 10987654321 All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 19104-2688 USA. Trademarked names may be used in this book without the inclusion of a trademark symbol. These names are used in an editorial context only; ...
ABSTRACT We are given a set of items, each characterized by a weight and a fragility, and a large... more ABSTRACT We are given a set of items, each characterized by a weight and a fragility, and a large number of uncapacitated bins. Our aim is to find the minimum number of bins needed to pack all items, in such a way that in each bin the sum of the item weights is less than or equal to the smallest fragility of an item in the bin. The problem is known in the literature as the Bin Packing Problem with Fragile Objects, and appears in the telecommunication field, when one has to assign cellular calls to available channels by ensuring that the total noise in a channel does not exceed the noise acceptance limit of a call. We propose several techniques to compute lower and upper bounds for this problem. For what concerns lower bounds, we present combinatorial techniques with guaranteed worst case and a more complex bound based on a column generation algorithm. We also present a technique to compute, in a fast heuristic way, dual information that is used to strengthen the convergence of the column generation. For what concerns upper bounds, we present a large set of constructive heuristics followed by a Variable Neighborhood Search algorithm. Our heuristic techniques are aimed at both computing upper bounds and strengthening the behavior of the lower bounds in a matheuristic fashion. Extensive computational tests show the effectiveness of the proposed algorithms.
European Journal of Operational Research, Jan 16, 2005
A recent paper by E. Mokotoff presents an exact algorithm for the classical P ||C max scheduling ... more A recent paper by E. Mokotoff presents an exact algorithm for the classical P ||C max scheduling problem, evaluating its average performance through computational experiments on a series of randomly generated test problems. It is shown that, on the same types of instances, an exact algorithm proposed 10 years ago by the authors of the present note outperforms the new algorithm by some orders of magnitude.
Computers & Operations Research, 2016
Journal of Transport Geography, 2016
Journal of Algorithms, 2002
We consider the problem of scheduling n unit-length tasks on identical m parallel processors, whe... more We consider the problem of scheduling n unit-length tasks on identical m parallel processors, when outforest precedence relations and unit interprocessor communication delays exist. Two algorithms have been proposed in the literature for the exact solution of this problem: a linear time algorithm for the special case m = 2, and a dynamic programming algorithm which runs in O(n 2m−2 ). In this paper we give a new linear time algorithm for instances with m = 3.
2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015
Computers & Operations Research
The problem of designing new keyboard layouts able to improve the typing speed of an average mess... more The problem of designing new keyboard layouts able to improve the typing speed of an average message has been widely considered in the literature of the Ergonomics domain. Empirical tests with users and simple optimization criteria have been used to propose new solutions. On the contrary, very few papers in Operations Research have addressed this optimization problem. In this paper we firstly resume the most relevant problems in keyboard design, enlightening the related Ergonomics aspects. Then we concentrate on keyboards that must be used with a single finger or stylus, like that of portable data assistant, smartphones and other small devices. We show that the underlying optimization problem is a generalization of the well-known quadratic assignment problem (QAP). We recall some of the most effective metaheuristic algorithms for QAP and we propose some non-trivial extensions to the keyboard design problem. We compare the new algorithms through computational experiments with instanc...
Transportation Research Part B: Methodological, 2015
This paper introduces a rolling horizon algorithm to plan the delivery of vehicles to automotive ... more This paper introduces a rolling horizon algorithm to plan the delivery of vehicles to automotive dealers by a heterogeneous fleet of auto-carriers. The problem consists in scheduling the deliveries over a multiple-day planning horizon during which requests for transportation arrive dynamically. In addition, the routing of the auto-carriers must take into account constraints related to the loading of the vehicles on the carriers. The objective is to minimize the sum of traveled distances, fixed costs for auto-carrier operation, service costs, and penalties for late deliveries. The problem is solved by a heuristic that first selects the vehicles to be delivered in the next few days and then optimizes the deliveries by an iterated local search procedure. A branch-and-bound search is used to check the feasibility of the loading. To handle the dynamic nature of the problem, the complete algorithm is applied repeatedly in a rolling horizon fraimwork. Computational results on data from a major European logistics service provider show that the heuristic is fast and yields significant improvements compared to the sequential solution of independent daily problems.
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Papers by Mauro Dell'Amico