arXiv subjects

Manuel Iori

Publications and source records attributed to Manuel Iori.

At least 19 recordsLinked to original sources

arXiv2026

Polynomial and Pseudopolynomial Algorithms for Two Classes of Bin Packing Instances

The Cutting Stock Problem (CSP) and Bin Packing Problem (BPP) are classical combinatorial optimization problems extensively studied since the 1960s. State-of-the-art exact algorithms are based on set-cover and arc-flow models whose linear relaxation, rounded up, matches the integer optimum for most benchmark instances, a condition known as the Integer Round-up Property (IRUP). In 2016, Delorme et al. showed that all existing instances could be solved within ten minutes by approaches exploiting this property. This motivated them to introduce two new classes, Augmented IRUP (AI) and Augmented Non-IRUP (ANI), designed to make IRUP less evident to state-of-the-art methods. Although these classes have motivated significant advances over the past decade, 13 out of 500 AI and ANI instances remain unsolved within standard time limits from the literature. In this paper, we show that while AI and ANI are particularly hard for MIP-based methods, the BPP restricted to these classes is not strongly NP-hard. We present polynomial-time algorithms for the AI class and pseudopolynomial-time algorithms for the ANI class, which solve all such instances orders of magnitude faster than previous approaches. They are also straightforward to adapt to the Skiving Stock Problem, the dual counterpart of the CSP. In addition, they can be used as preprocessing routines in exact methods, as their runtime is independent of the instance class, although they are guaranteed to return an optimality status only for instances belonging to the class for which they were designed.

cs.DS↗

arXiv2026

Bilevel optimization with sustainability perspective: A survey on applications

Bilevel optimization, a well-established field for modeling hierarchical decision-making problems, has recently intersected with sustainability studies and practices, resulting in a series of works focusing on bilevel optimization problems involving multiple decision makers with diverse economic, environmental, and social objectives. This survey offers a comprehensive overview of sustainable bilevel optimization applications. First, we introduce the main concepts related to the nature of bilevel optimization problems and present some typical mathematical formulations for bilevel pricing problems that cover many of the collected applications. Then, we review the most relevant works published in sustainable bilevel optimization, giving a classification based on the application domains and their association with well-known operations research problems, while briefly discussing the proposed solution methodologies. We survey applications on transportation and logistics, production planning and manufacturing, water, waste, and agriculture management, supply chains, and disaster prevention and response. Finally, we outline a list of open questions and opportunities for future research in this domain.

math.OC↗

arXiv2026

The Quadratic Bin Packing Problem: Exact Formulations and Algorithm

In this article, we introduce and study the Quadratic Bin Packing Problem (QBPP), which generalizes the classical bin packing problem by introducing a fixed cost for each used bin and a pairwise cost (or profit) incurred whenever two items are packed together. Beyond its theoretical relevance, the QBPP is of practical interest due to its numerous real-world applications, mainly related to cluster analysis. To address the QBPP, we propose three compact mixed-integer linear programming (MILP) formulations, along with a set-partitioning formulation. For each compact model, we present an enhanced version with a strengthened continuous relaxation, while, for the set-partitioning formulation, we develop a tailored Branch-and-Price algorithm. Computational experiments on benchmark instances demonstrated that, while the enhanced compact formulations can be effectively solved by a standard MILP solver for small-sized instances, the Branch-and-Price approach delivered superior performance overall, especially on larger and more challenging instances.

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arXiv2024

On incorporating variable consumption functions within energy-efficient parallel machine scheduling

The increase in non-renewable energy consumption and CO2 emissions, especially in the manufacturing sector, is moving radical shifts in energy supply policies and production models. Renewable energy integration and regulated pricing policies require new and effective scheduling strategies, as highlighted by the emerging field of energy-efficient scheduling. In this paper, we aim to contribute to this field by addressing a scheduling problem where a set of jobs must be allocated to a set of machines over a discrete finite horizon and variable energy consumptions are required for job execution. Energy can be obtained by a renewable source or through transactions on the market. The goal is to minimize the total energy costs from the grid while scheduling all the jobs within the time horizon and adhering to an energy limit per time period. We introduce a novel time-indexed Mixed Integer Linear Programming (MILP) formulation capable of handling variable energy consumption functions, surpassing traditional models that assume constant energy usage of jobs. We then develop a matheuristic algorithm based on an Iterated Local Search (ILS) framework that exploits the MILP formulation for large neighborhood searches. We tested more than 200 instances with up to 200 jobs, 35 machines, and 120 time slots. The results show a good performance of both our methods and highlight the advantage of using the ILS when jobs are characterized by variable consumption functions.

math.OC↗

arXiv2022

Solution of a Practical Vehicle Routing Problem for Monitoring Water Distribution Networks

In this work, we introduce a generalization of the well-known Vehicle Routing Problem for a specific application in the monitoring of a Water Distribution Network (WDN). In this problem, multiple technicians must visit a sequence of nodes in the WDN and perform a series of tests to check the quality of water. Some special nodes (i.e., wells) require technicians to first collect a key from a key center. The key must then be returned to the same key center after the test has been performed, thus introducing precedence constraints and multiple visits in the routes. To solve the problem, three mathematical models and an Iterated Local Search have been implemented. The efficiency of the proposed methods is demonstrated by means of extensive computational tests on randomly created instances, as well as on instances derived from a real-world case study.

math.OC↗

arXiv2021

Exact solution of network flow models with strong relaxations

We address the solution of Mixed Integer Linear Programming (MILP) models with strong relaxations that are derived from Dantzig-Wolfe decompositions and allow a pseudo-polynomial pricing algorithm. We exploit their network-flow characterization and provide a framework based on column generation, reduced-cost variable-fixing, and a highly asymmetric branching scheme that allows us to take advantage of the potential of the current MILP solvers. We apply our framework to a variety of cutting and packing problems from the literature. The efficiency of the framework is proved by extensive computational experiments, in which a significant number of open instances could be solved to proven optimality for the first time.

math.OC↗

arXiv2021

Arc Flow Formulations Based on Dynamic Programming: Theoretical Foundations and Applications

Network flow formulations are among the most successful tools to solve optimization problems. Such formulations correspond to determining an optimal flow in a network. One particular class of network flow formulations is the arc flow, where variables represent flows on individual arcs of the network. For $\mathcal{NP}$-hard problems, polynomial-sized arc flow models typically provide weak linear relaxations and may have too much symmetry to be efficient in practice. Instead, arc flow models with a pseudo-polynomial size usually provide strong relaxations and are efficient in practice. The interest in pseudo-polynomial arc flow formulations has grown considerably in the last twenty years, in which they have been used to solve many open instances of hard problems. A remarkable advantage of pseudo-polynomial arc flow models is the possibility to solve practical-sized instances directly by a Mixed Integer Linear Programming solver, avoiding the implementation of complex methods based on column generation. In this survey, we present theoretical foundations of pseudo-polynomial arc flow formulations, by showing a relation between their network and Dynamic Programming (DP). This relation allows a better understanding of the strength of these formulations, through a link with models obtained by Dantzig-Wolfe decomposition. The relation with DP also allows a new perspective to relate state-space relaxation methods for DP with arc flow models. We also present a dual point of view to contrast the linear relaxation of arc flow models with that of models based on paths and cycles. To conclude, we review the main solution methods and applications of arc flow models based on DP in several domains such as cutting, packing, scheduling, and routing.

math.OC↗

arXiv2020

An Iterated Dual Substitution Approach for Binary Integer Programming Problems under the Min-Max Regret Criterion

We consider binary integer programming problems with the min-max regret objective function under interval objective coefficients. We propose a new heuristic framework, which we call the iterated dual substitution (iDS) algorithm. The iDS algorithm iteratively invokes a dual substitution heuristic and excludes from the search space any solution already checked in previous iterations. In iDS, we use a best-scenario-based lemma to improve performance. We apply iDS to four typical combinatorial optimization problems: the knapsack problem, the multidimensional knapsack problem, the generalized assignment problem, and the set covering problem. For the multidimensional knapsack problem, we compare the iDS approach with two algorithms widely used for problems with the min-max regret criterion: a fixed-scenario approach, and a branch-and-cut approach. The results of computational experiments on a broad set of benchmark instances show that the proposed iDS approach performs best on most tested instances. For the knapsack problem, the generalized assignment problem, and the set covering problem, we compare iDS with state-of-the-art results. The iDS algorithm successfully updates best known records for a number of benchmark instances.

math.OC↗

arXiv2020

The double traveling salesman problem with partial last-in-first-out loading constraints

In this paper, we introduce the Double Traveling Salesman Problem with Partial Last-In-First-Out Loading Constraints (DTSPPL). It is a pickup-and-delivery single-vehicle routing problem, where all pickup operations must be performed before any delivery one because the pickup and delivery areas are geographically separated. The vehicle collects items in the pickup area and loads them into its container, a horizontal stack. After performing all pickup operations, the vehicle begins delivering the items in the delivery area. Loading and unloading operations must obey a partial Last-In-First-Out (LIFO) policy, i.e., a version of the LIFO policy that may be violated within a given reloading depth. The objective of the DTSPPL is to minimize the total cost, which involves the total distance traveled by the vehicle and the number of items that are unloaded and then reloaded due to violations of the standard LIFO policy. We formally describe the DTSPPL through two Integer Linear Programming (ILP) formulations and propose a heuristic algorithm based on the Biased Random-Key Genetic Algorithm (BRKGA) to find high-quality solutions. The performance of the proposed solution approaches is assessed over a broad set of instances. Computational results have shown that both ILP formulations have been able to solve only the smaller instances, whereas the BRKGA obtained good quality solutions for almost all instances, requiring short computational times.

cs.AI↗

arXiv2020

Scheduling jobs with release dates on identical parallel machines by minimizing the total weighted completion time

This paper addresses the problem of scheduling a set of jobs that are released over the time on a set of identical parallel machines, aiming at the minimization of the total weighted completion time. This problem, referred to as $P|r_j|\sum w_jC_j$, is of great importance in practice, because it models a variety of real-life applications. Despite its importance, the $P|r_j|\sum w_jC_j$ has not received much attention in the recent literature. In this work, we fill this gap by proposing mixed integer linear programs and a tailored branch-and-price algorithm. Our {branch-and-price} relies on the decomposition of an arc-flow formulation and on the use of efficient exact and heuristic methods for solving the pricing subproblem. Computational experiments carried out on a set of randomly generated instances prove that the proposed methods can solve to the proven optimality instances with up to 200 jobs and 10 machines, and provide very low gaps for larger instances.

math.OC↗

arXiv2020

Successful implementation of discrete event simulation: the case of an Italian emergency department

This paper focuses on the study of a practical management problem faced by a healthcare {\it emergency department} (ED) located in the north of Italy. The objective of our study was to propose organisational changes in the selected ED, which admits approximately 7000 patients per month, aiming at improving key performance indicators related to patient satisfaction, such as the waiting time. Our study is based on a design thinking process that adopts a {\it discrete event simulation} (DES) model as the main tool for proposing changes. We used the DES model to propose and evaluate the impact of different improving scenarios. The model is based on historical data, on the observation of the current ED situation, and information obtained from the ED staff. The results obtained by the DES model have been compared with those related to the existing ED setting, and then validated by the ED managers. Based on the results we obtained, one of the tested scenarios was selected by the ED for implementation.

cs.CY↗

arXiv2020

Integer Linear Programming for the Tutor Allocation Problem: A Practical Case in a British University

In the Tutor Allocation Problem, the objective is to assign a set of tutors to a set of workshops in order to maximize tutors' preferences. The problem is solved every year by many universities, each having its own specific set of constraints. In this work, we study the tutor allocation in the School of Mathematics at the University of Edinburgh, and solve it with an integer linear programming model. We tested the model on the 2019/2020 case, obtaining a significant improvement with respect to the manual assignment in use. Further tests on randomly created instances show that the model can be used to address cases of broad interest. We also provide meaningful insights on how input parameters, such as the number of workshop locations and the length of the tutors' preference list, might affect the performance of the model and the average number of preferences satisfied.

cs.CY↗

arXiv2020

Exact and heuristic solutions for the assembly line balancing problem with hierarchical worker assignment

This paper proposes new algorithms for the assembly line balancing problem with hierarchical worker assignment (ALBHW). The ALBHW appears in real industrial contexts where companies deal with a multi-skilled workforce. It considers task execution times that vary depending on the worker type to whom the task is assigned. Qualification levels among workers are ranked hierarchically, where a lower qualified worker costs less but requires larger execution times. The aim is to assign workers and tasks to the stations of an assembly line, in such a way that cycle time and precedence constraints are satisfied, and the total cost is minimized. In this paper, we first present a mathematical model and improve it with preprocessing techniques. Then, we propose a constructive heuristic and a variable neighborhood descent that are useful to solve large instances. Extensive computational experiments on benchmark instances prove the effectiveness of the algorithms.

math.OC↗

arXiv2020

Exact Solution Techniques for Two-dimensional Cutting and Packing

We survey the main formulations and solution methods for two-dimensional orthogonal cutting and packing problems, where both items and bins are rectangles. We focus on exact methods and relaxations for the four main problems from the literature: finding a packing with minimum height, packing the items into the minimum number of bins, finding a packing of maximum value, and determining the existence of a feasible packing.

math.OC↗

arXiv2019

Optimizing the nozzle path in the 3D Printing Process

In this paper, we define the 3D printing routing problem, the problem of finding the optimal path of the nozzle in a fused deposition modeling 3D printing system, so as to minimize the time required to create on object. We formally model the problem with an integer linear programming formulation and then solve it via heuristic algorithms. We test the algorithms on a set of large-size real-life instances, comparing them with one of the most widely used open source software for the problem. We show that large time reductions can be obtained. We finally propose a set of interesting directions for future research.

math.OC↗

arXiv2019

A Primal Decomposition Algorithm for the Two-dimensional Bin Packing Problem

The Two-dimensional Bin Packing Problem calls for packing a set of rectangular items into a minimal set of larger rectangular bins. Items must be packed with their edges parallel to the borders of the bins, cannot be rotated and cannot overlap among them. The problem is of interest because it models many real-world applications, including production, warehouse management and transportation. It is, unfortunately, very difficult, and instances with just 40 items are unsolved to proven optimality, despite many attempts, since the 1990s. In this paper, we solve the problem with a combinatorial Benders decomposition that is based on a simple model in which the two-dimensional items and bins are just represented by their areas, and infeasible packings are imposed by means of exponentially-many no-good cuts. The basic decomposition scheme is quite naive, but we enrich it with a number of preprocessing techniques, valid inequalities, lower bounding methods, and enhanced algorithms to produce the strongest possible cuts. The resulting algorithm behaved very well on the benchmark sets of instances, improving on average upon previous algorithms from the literature and solving for the first time a number of open instances.

math.OC↗

arXiv2019

Branch-and-cut and iterated local search for the weighted $k$-traveling repairman problem: an application to the maintenance of speed cameras

Private enterprises and governments around the world use speed cameras to control traffic flow and limit speed excess. Cameras may be exposed to difficult weather conditions and typically require frequent maintenance. When deciding the order in which maintenance should be performed, one has to consider both the traveling times between the cameras and the traffic flow that each camera is supposed to monitor. In this paper, we study the problem of routing a set of technicians to repair cameras by minimizing the total weighted latency, that is, the sum of the weighted waiting times of each camera, where the weight is a parameter proportional to the monitored traffic. The resulting problem, called weighted k-traveling repairman problem (wkTRP), is a generalization of the well-known traveling repairman problem and can be used to model a variety of real-world applications. To solve the wkTRP, we propose an iterated local search heuristic and an exact branch-and-cut algorithm enriched with valid inequalities. The effectiveness of the two methods is proved by extensive computational experiments performed both on instances derived from a real-world case study, as well as on benchmark instances from the literature on the wkTRP and on related problems.

cs.DS↗

arXiv2019

Solution of minimum spanning forest problems with reliability constraints

We propose the reliability constrained k-rooted minimum spanning forest, a relevant optimization problem whose aim is to find a k-rooted minimum cost forest that connects given customers to a number of supply vertices, in such a way that a minimum required reliability on each path between a customer and a supply vertex is satisfied and the cost is a minimum. The reliability of an edge is the probability that no failure occurs on that edge, whereas the reliability of a path is the product of the reliabilities of the edges in such path. The problem has relevant applications in the design of networks, in fields such as telecommunications, electricity and transports. For its solution, we propose a mixed integer linear programming model, and an adaptive large neighborhood search metaheuristic which invokes several shaking and local search operators. Extensive computational tests prove that the metaheuristic can provide good quality solutions in very short computing times.

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