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2024 | Book

Optimization in Green Sustainability and Ecological Transition

ODS, Ischia, Italy, September 4–7, 2023

Editors: Maurizio Bruglieri, Paola Festa, Giusy Macrina, Ornella Pisacane

Publisher: Springer Nature Switzerland

Book Series : AIRO Springer Series

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About this book

This book collects selected contributions of the “Optimization and Decision Science - ODS2023” international conference on the theme of optimization in green sustainability and ecological transition. ODS2023 was held in Ischia, 4–7 September 2023, and was organized by AIRO, the Italian Operations Research Society. The book offers new and original contributions on operations research, optimization, decision science, and prescriptive analytics from both a methodological and applied perspectives with a special focus on SDG related topics.

It provides a state-of-the art on problem models and solving methods to address a widely class of real-world problems, arising in different application areas such as logistics, transportation, manufacturing, health, ICT and mobile networks, and emergency/disaster management. In addition, the scientific works collected in this book aim at providing significant contributions in the themes of sustainability, traffic and pollution reductions, and energy management.

This book is aimed primarily at researchers and Ph.D. students in the Operations Research community. However, due to its interdisciplinary contents, this book is of high interest also for students and researchers from other disciplines, including artificial intelligence, computer sciences, finance, mathematics, and engineering as well as for practitioners facing complex decision-making problems in logistics, manufacturing production, and services.

Table of Contents

Frontmatter
Optimising Italian Electricity and Gas Sector Coupling in a 2030 Decarbonized Energy System

Power-to-X (PtX) technology is the production of hydrogen by electrolysis and, from this, of other gaseous fuels such as biomethane. In decarbonized energy systems, PtX plants can convert excess renewable energy production into gas that can either be injected into the gas grid, stored temporarily, or used to meet gas demand from other sectors, such as transportation. Due to its flexibility, PtX technology is expected to contribute to the integration of large shares of renewable energy into energy systems. Given the relevance of PtX plants for decarbonized energy systems, this paper provides a comprehensive formulation for the operational planning of integrated systems with bidirectional energy conversion. We then focus on the Italian energy system and present results on the challenges for Italy in exploring decarbonization pathways.

Giovanni Micheli, Maria Teresa Vespucci, Maria Gaeta, Fabio Lanati, Dario Siface, Laura Tagliabue
Lagrangian Approaches for QoS Scheduling in Computer Networks

We study a routing problem arising in computer networks where stringent Quality of Service (QoS) scheduling requirements ask for a routing of the packets with controlled worst-case “end-to-end” delay. With widely used delay formulæ, this is a shortest-path-type problem with a nonlinear constraint depending in a complex way on the reserved rates on the chosen arcs. However, when the minimum reserved rate in the path is fixed, the Lagrangian problem obtained by relaxing the delay constraint presents a special structure and can be solved efficiently. We exploit this property and present an effective method that provides both upper and lower bounds of very good quality in extremely short computing times.

Antonio Frangioni, Laura Galli, Enrico Sorbera
Some Remarks on Network Games with Local Average

We investigate a class of network games with bounded strategy space, where each player’s action is influenced by her neighbors’ average action. We provide a variational inequality formulation of the model and derive a Katz–Bonacich representation formula for the case where some components of the solution lay on the boundary. Furthermore, we propose algorithms based on the best response dynamics which converge to the unique Nash equilibrium, and study the price of anarchy for a small test problem.

Fabio Raciti
MIP Models for Flow Shop Scheduling with Inter-stage Flexibility and Blocking Constraints

We investigate a scheduling problem inspired by a material handling problem arising at a production line of an Austrian company building prefabricated house walls. The addressed problem is a permutation flow shop with blocking constraint in which some machines are flexible, that is, there are a number of operations that can be processed on any of two successive machines of the system. This situation is usually referred to as multi-task or inter-stage flexibility. We propose four different MIP Models and test their efficiency and effectiveness on a number of randomly generated instances similar to those of the real-life application.

Gaia Nicosia, Andrea Pacifici, Ulrich Pferschy, Cecilia Salvatore
Game Theory Models of International Agreements on Adaptation to Climate Change

This note deals with the formation of coalitions of countries to jointly fight the adverse effects of climate change. We propose two game theory models of international agreements and compare them with the situation where each country individually develops new means to adapt to climate change.

Mauro Passacantando
Model-Predictive Control of Traffic Emissions in Port-City Environments

In this paper, we present a method, based on model predictive control (MPC), to reduce the impact of pollutant emissions in contexts where a port is located within a city. To this purpose, we first introduce a dynamic model of the interactions between truck flows generated by the port and general mobility traffic in the shared urban infrastructure at the port-city interface. In order to keep track of the multiclass and complex nature of the system, the model takes advantage of microsimulation and deep learning for the prediction of road network traffic and related pollutant emissions. Then, we define a MPC control scheme exploiting the proposed model, to be used in real time to maintain the emissions levels below a certain threshold by appropriately adjusting traffic inflows from the port to the city, which represent the controls optimized by the MPC procedure. A simulation case study, involving the port of Genova in north-west Italy, is presented to showcase the ability of the proposed MPC scheme to control emissions in the shared area, also in complex situations such as transitions to mobility rush hours.

Cristiano Cervellera, Danilo Macciò
MWU 2.0 with Approximation Guarantee for the Distance Geometry Problem

In this short paper, we define an approximation guaranteed algorithm for Distance Geometry Problem (DGP), by straightforwardly extending the framework proposed by Plotkin, Shmoys, and Tardos for fractional packing and covering problems. In particular, following the approach in Arora et al. [1], we adapt the Multiplicative Weights Update (MWU) framework to define an approximated algorithm which calls an oracle solving the surrogate relaxation of the feasibility problem a polynomial number of times. We implemented the algorithm and we present promising computational results in terms of mean and largest error of the produced solutions. We compare the new algorithm with the previous version of MWU for DGP introduced in Mencarelli et al. [9].

Luca Mencarelli
Risk Measures in Energy Markets

Energy market players face a litany of risks and sources of uncertainty. It is therefore desirable that stochastic programming models describing and informing their decisions allow for risk-averse behaviour, with players sacrificing the prospect of extreme profits to mitigate against potential losses. Risk measures ranging from Value at Risk (VaR) and Conditional Value at Risk (CVaR) to expected shortfall, convex utility functions, and stochastic dominance constraints have been incorporated into energy market models to achieve this. This discussion provides an overview of these measures and a review of their implementation in existing energy literature. Alongside this is a discussion of Arrow–Debreu securities and their role in the hedging market. It additionally encompasses a sample optimisation problem comparing the distribution of profits caused by these risk measures for a player faced with stochastic costs. Furthermore, a sensitivity analysis of the parameters controlling these risk measures is provided. The discussion aims to illustrate the behaviour of the risk measures and elucidate those situations in which each would be applicable. This is valuable to those seeking to model energy markets credibly, particularly given the increasing uncertainty faced by such markets due to the transition to green and sustainable energy.

Dáire Byrne, Mel T. Devine
On the Use of the SYMMBK Algorithm for Computing Negative Curvature Directions Within Newton–Krylov Methods

In this paper, we consider the issue of computing negative curvature directions, for nonconvex functions, within Newton–Krylov methods for large scale unconstrained optimization. This issue has been widely investigated in the literature, and different approaches have been proposed. We focus on the well known SYMMBK method proposed for solving large scale symmetric possibly indefinite linear systems [3, 5, 7, 20], and show how to exploit it to yield an effective negative curvature direction. The distinguishing feature of our proposal is that the computation of such negative curvature direction is iteratively carried out, without storing no more than a couple of additional vectors. The results of a preliminary numerical experience are reported showing the reliability of the novel approach we propose.

Giovanni Fasano, Christian Piermarini, Massimo Roma
Optimization and Simulation for the Daily Operation of Renewable Energy Communities

Renewable Energy Communities (RECs) are an important building block for the decarbonization of the energy sector. The concept of RECs allows individual consumers to join together in local communities to generate, store, consume and sell renewable energy. A major benefit of this collective approach is a better match between supply and demand profiles, and thus, an increase in local self-consumption. The optimal exploitation of locally produced electricity raises many operational questions. In this context, we introduce a Mixed Integer Linear Program (MILP) that optimizes the energy flows within a REC. It employs the following instruments relevant for local self-consumption: (a) stationary batteries, (b) batteries of electric vehicles and (c) load shifting (i.e. moving the use of electric appliances from one time period to another). To handle the uncertainty of the involved planning parameters, we use a Model Predictive Control (MPC) approach and solve the optimization model in an iterative manner. The introduced planning framework can be applied to generate realistic performance measures of specific community configurations and to evaluate strategic investment decisions.

Nathalie Frieß, Elias Feiner, Ulrich Pferschy, Joachim Schauer, Thomas Strametz
A Recycling Heuristic Capable of Generating Initial Solutions for Use in Vehicle Routing Metaheuristics

Instances of the vehicle routing problem (VRP) and its variants are notoriously difficult to solve. Moreover, when combining characteristics of multiple VRP variants, the underlying complexities of the variants proliferate. This has led to a variety of metaheuristics being proposed for solving VRP instances with side constraints. Metaheuristics, however, require the generation of an appropriate initial solution (trajectory-based metaheuristics) or a population of initial solutions (population-based metaheuristics), the quality of which affects the performance of the solution approach. When computing VRP solutions for a depot and its assigned customers, historical solutions generated for the same depot may be adapted to form the initial solution or part of the initial population for the metaheuristic employed, thereby effectively recycling historical solutions. In this paper, such a recycling heuristic is proposed. A genetic algorithm is applied to solve instances of the periodic VRP with time-windows (PVRPTW) and the effect of the recycling heuristic on the run time of the metaheuristic is evaluated. It is shown that a decrease in run time of up to 26% may be achieved by employing the recycling heuristic to generate initial solutions, without affecting the quality of the solutions returned.

Jacobus King, Jan van Vuuren
An Integer Programming Approach for a 2D Bin Packing Problem with Precedence Constraints in the Sheet Metal Industry

We consider an optimization problem of practical relevance arising in Salvagnini Italia, a multinational corporation in the sheet metal industry. The problem falls into the well-know area of Two-Dimensional Bin Packing Problems, and aims at determining efficient item-to-sheet assignments by minimizing the material waste and by keeping into account several technological constraints involving, in particular, hard and soft precedence relations among groups of items. We devise two Mixed Integer Linear Programming (MILP) formulations able to address the different practical aspects of the problem. Based on the MILP models, we propose an exact approach and a matheuristic. The two methods have been applied to instances of practical relevance, and we report computational results and a comparison with the current company’s procedure.

Luigi De Giovanni, Nicola Gastaldon, Chiara Turbian
Optimal Drone Routing for Seal Pup Counts

We introduce the seal pup count using drones problem. Drones, also known as Unmanned Aerial Vehicles (UAVs), are used to fly over a marine archipelago during the birthing season to estimate the number of harbour seal pups. Pup counts are used as an environmental indicator of ecosystem health and as inputs to national wildlife management policies. We determine minimum cost routes for the UAVs to collect data for the seal pup count, subject to drone battery capacity. The Drone Routing Problem (DRP) can be formulated as a variant of the Capacitated Vehicle Routing Problem (CVRP). We demonstrate the DRP on a real case study in the Kosterhavet National Park area in Sweden.

Lavinia Amorosi, Dáire Carroll, Paula Carroll, Annunziata Esposito Amideo
Scheduling Automated Guided Vehicles: Challenges and Opportunities

Automated Guided Vehicles (AGVs) play a fundamental role in different logistic systems, being widely used for the automatic handling of materials, goods, and containers. The management of AGVs requires the solution of several optimization problems, such as task allocation/scheduling, routing, and path planning, which are often enriched by additional attributes, such as multi-load, battery constraints, and conflict avoidance. Many of these problems are faced in the real-world context of the Italian company E80 Group, one of the world leaders in the production of AGV systems. The literature is huge for all the aforementioned problems, and hence we focus only on the problem of scheduling AGVs, modeled as a Pickup and Delivery Problem (PDP). In particular, we propose a PDP formulation, discuss real-world and literature scheduling applications, and indicate challenges and research opportunities providing a guide for future researches.

Francesco Gallesi, Rafael Praxedes, Manuel Iori, Marco Locatelli, Anand Subramanian
Multi-neighborhood Simulated Annealing for Nurse Rostering

We consider the Nurse Rostering problem, in the real-world formulation proposed by Curtois and Qu [8]. For this formulation, we propose a local search approach based on a combination of four neighborhoods guided by a Simulated Annealing metaheuristic, and we test it on the publicly available dataset. This research is still ongoing and the preliminary results show that we are able to obtain results in line with the state-of-the-art ones on a few instances (notably the largest ones), but currently fail to reach the optimal solutions.

Eugenia Zanazzo, Andrea Schaerf
Dealing with Inexactness in Hierarchical Multi-portfolio Selection

We address the multi-portfolio selection problem where two decision-making levels are considered: account owners and managers play different Nash Equilibrium Problems. We rely on a Tikhonov approach allowing for inexactness to obtain approximate solutions. We corroborate our analysis with numerical results.

Lorenzo Lampariello, Simone Sagratella, Valerio Giuseppe Sasso
On the Bayes Risk Induced by Alternative Design Priors for Sample Size Choice

In a decision-theoretic framework, criteria for selecting the optimal sample size for an experiment can be based on the Bayes risk of a decision function, i.e. the expected value of the risk function with respect to a prior distribution that describes a design scenario. In the presence of uncertainty on such a scenario, an entire class of parametric distributions can be taken into account. The resulting robust optimal sample size is the one yielding a sufficiently small value for the largest risk over the class. In this article we illustrate this robust sample size determination approach for a one-sided testing problem on a normal mean, that is the typical set-up of a superiority clinical trial with continuous endpoints.

Fulvio De Santis, Stefania Gubbiotti, Francesco Mariani
Ensemble Aggregation Approaches for Functional Optimization

In this work we investigate the use of ensemble methods, consisting in the aggregation of several approximating models, in the context of functional optimization. In fact, while ensemble techniques are routinely employed in the machine learning literature for classification and regression, there is little research on their application to general optimization problems. Here we consider two strategies to aggregate different solutions to a functional optimization problem, based on optimized weighted averaging and aggregation over the minimum, the latter also in approximate version. A theoretical analysis of approximate functional optimization in the context of ensemble aggregation is provided. Then, simulation results are reported to showcase the advantages of ensembles for functional optimization, in terms of better accuracy and improved robustness with respect to single solutions.

Cristiano Cervellera, Danilo Macciò, Marcello Sanguineti
A Robust Nonlinear Support Vector Machine Approach for Vehicles Smog Rating Classification

Nowadays all new vehicles are labelled in terms of their emissions thanks to ad hoc legislation. However, from a practical perspective, it is difficult to rank all of them. This paper considers the problem of classifying vehicles in terms of smog rating emissions by adopting a Machine Learning technique. Specifically, a new Support Vector Machine approach is considered, designed for nonlinear separating decision boundaries. To protect the model against uncertainty arising in the measurement procedure, a robust optimization model with spherical uncertainty sets is formulated. Numerical results are performed on both synthetic and real-world datasets, showing the good performance of the proposed formulation.

Francesca Maggioni, Andrea Spinelli
A Threshold Recourse Policy for the Electric Vehicle Routing Problem with Stochastic Energy Consumption

The Electric Vehicle Routing Problem (EVRP) aims at routing Electric Vehicles (EVs) while planning their stops at Charging Stations (CSs), due to the limited autonomy of their batteries. The majority of studies on the EVRP and its variants have considered deterministic energy consumption. However, energy consumption is subject to a great deal of uncertainty, which if ignored can lead the EV to run out of battery mid-route. In this paper, we develop a two-stage stochastic programming formulation for the electric vehicle routing problem with stochastic energy consumption. In particular, we propose a threshold recourse policy which entails that the EV will head to a charging station after a certain energy level is reached. We show the added value of the extensive formulation of our model on a set of small instances derived from the deterministic literature.

Dario Bezzi, Ola Jabali, Francesca Maggioni
Strengthened Integer Programming Formulations for the Fleet Quickest Routing Problem on Grids

This paper is concerned with the problem of finding collision-free, nonstop Manhattan paths for a set of vehicles that move on a grid, each from a node on the bottom row to the top of the destination column; in particular, we are interested in minimising the number of rows that allow such routing. This problem is known as the Fleet Quickest Routing Problem on Grids. We propose an Integer Linear Programming formulation, introduce some valid inequalities and present a reduced-size model, based on the analysis of vehicle movements on the grid. Computational tests, performed on random benchmarks, show the impact of inequalities on the proposed formulation and that reducing the size of the formulation results in better performances for some classes of instances.

Carla De Francesco, Luigi De Giovanni, Martina Galeazzo
Optimal Design of a Vaccination Clinic: The Trade-Off Between Costs and QoS

Vaccination clinics are an essential tool to fight pandemics and can be set up temporarily in hospitals. Though typically hosted in permanent institutions, they need to be sized for effective use of resources (mainly space and staff) while maintaining an acceptable level of service, embodied by the waiting time for patients. In this paper, we employ an in-house developed simulation tool to size vaccination clinics and show how trade-offs can be achieved in the search for the optimal solution. We analyse two cases of low and high attendance and show that properly implementing batch processing can help reduce staffing levels (namely, the number of nurses) without sacrificing the level of service.

Ludovica Adacher, Marta Flamini, Maurizio Naldi
A Computational Journey in Job Scheduling with Time-of-Use Costs

We present recent advances on both exact and heuristic algorithms for the bi-objective identical parallel machine scheduling with time-of-use costs problem. This problem belongs to the field of energy-efficient scheduling, which has received large attention during the last years in the literature on sustainable manufacturing. As a novel contribution, we investigate how multi-threaded computation is able to improve the performances of the current state-of-the-art approaches over a set of problem instances characterized by different sizes, ranging from small to large.

Mauro Gaggero, Massimo Paolucci, Roberto Ronco
A Continuous Time Physical Graph Based Formulation to Scheduled Service Network Design

Scheduled Service Network Design supports consolidation-based freight carriers in setting up a transportation network by selecting the transportation services to operate, with their schedules, and the itineraries of the commodities to move. We propose a new formulation to the problem that represents time in its continuous nature, directly over the physical graph, thus mitigating the drawbacks that a traditional formulation, relying on a time-space network, may have for large scale instances, due to the increase in its dimensions and the consequent intractability in solving the problem exactly. Preliminary numerical experiments comparing the new and traditional formulations on a set of randomly generated instances are performed. Results highlight that the proposed formulation is a valuable tool to solve large scale instances with a long schedule length.

Giacomo Lanza, Teodor Gabriel Crainic, Mauro Passacantando, Maria Grazia Scutellà
Teaching Mathematics Through Problem Modelling and Solving

The teaching of mathematics in high school and university and its relationship with problem modelling and solving is at the centre of debate in many countries, with a rich scientific literature. The theme has to be viewed in a broader framework. The definition of educational chain is preliminary given, starting from the content of a discipline and covering teaching strategies, forms of learning, evaluation and assessment. On this basis the mathematical language and the role of mathematics is discussed. Then the experience of OPS4Math (Optimization and Problem Solving for Teaching of Mathematics), a training project developed at Federico II University of Naples and aimed at high school teachers, is described, including motivations and objectives, and implementation phases. Finally, the proposed teaching strategy is presented through a classical optimization problem.

Antonio Sforza, Claudio Sterle, Adriano Masone, Maurizio Boccia, Andrea Mancuso, Angela Orabona
Multi-objective Optimization for the Security of Water-Energy-Food Nexus

Climate change, population growth, and rapid urbanization are seriously threatening the security of water, energy, and food resources, which are fundamental for the human survival and sustainable development. These resources are inextricably interrelated and the term Water-Energy-Food Nexus, or other permutations of the three words, refers to the synergies and trade-offs among the three sectors. Nexus planning and management for the overall security of the system are very complicated, because of conflicting objectives among sectors and, at the same time, unavoidable. For this reason, much of the scientific research, that studies sustainability issues and the nexus system, focuses on multi-objective optimization problems. The present work aims at finding several solutions for a Nexus system, by developing a linear mixed-integer multi-objective problem. The weighted Global Criterion Method was adopted as solution method and real-world Water-Energy-Food Nexus scenarios are used to assess the behaviour of the considered approach.

Chiara Maragò, Rosita Guido, Francesca Guerriero
Generating Informative Scenarios via Active Learning

Scenario generation is a crucial task in Stochastic Programming (SP). It involves a trade-off between keeping the scenario set small while making it representative for the target problem. While most state-of-the-art methods focus on matching the uncertainty of the stochastic process using distribution-driven approaches, problem-driven methodologies have been proposed in recent years to exploit the structure of the target problem during the scenario generation process. In order to represent uncertainties in a more concise way, we propose a novel approach based on Active Learning that sequentially generates a set of scenarios by including a new promising scenario at each iteration. Searching for the most promising scenario is a black-box global optimization problem, efficiently solved via Bayesian Optimization. Preliminary experimental results are presented on a classical newsvendor problem, providing empirical evidence that the proposed method can both identify the smallest and most informative scenario set for the problem. Our method can also efficiently and effectively handle multi-modal and fat-tailed distributions, analogously to the most recent problem-driven methods.

Antonio Candelieri, Xiaochen Chou, Francesco A. Archetti, Enza Messina
An Efficient Approach for Pancreas Segmentation in Computer Tomography Scans

Pancreatic cancer presents a significant challenge in detection and treatment, with a shallow five-year survival rate. The small size and variable shape of the pancreas make it challenging to identify the presence of cancer in its early stages. To address this problem, we propose a novel method for the semantic segmentation of the pancreas in Computer Tomography (CT) scans, utilizing Convolutional Neural Networks (CNNs). Our approach involves training an encoder-decoder neural network to segment precisely the pancreas in CT images. We conducted experiments on a publicly available dataset, achieving results comparable to state-of-the-art methods based on the average Dice score. Furthermore, we evaluated the impact of different backbone models, providing valuable insights for future optimization. Our findings demonstrate that our approach effectively segments the pancreas in CT scans, potentially improving early detection and treatment planning for pancreatic cancer. This success validates the necessity of developing computer-aided diagnosis tools based on deep learning methods for pancreatic cancer, which are essential to enhancing patient outcomes. In summary, our work provides a solid foundation for developing computer-aided diagnosis tools for pancreatic cancer. Using CNNs for semantic segmentation of the pancreas in CT scans is a promising approach that could significantly improve the early detection and treatment of this deadly disease.

Cristian Tommasino, Andrea Mancuso, Cristiano Russo, Adriano Masone, Antonio Maria Rinaldi, Claudio Sterle, Giuseppina Dell’Aversano Orabona, Marco Di Serafino, Roberto Ronza, Raffaele La Mura, Francesco Verde, Luigia Romano
Forecasting and Modeling the Dynamics of Large-Scale Energy Networks Under the Supply and Demand Balance Constraint

With the emergence of “Big Data” the analysis of large data sets of high-dimensional energy time series in network structures have become feasible. However, building large-scale data-driven and computationally efficient models to accurately capture the underlying spatial and temporal dynamics and forecast the multivariate time series data remains a great challenge. Additional constraints make the problem more challenging to solve with conventional methods. For example, to ensure the security of supply, energy networks require the demand and supply to be balanced. This paper introduces a novel large-scale Hierarchical Network Regression model with Relaxed Balance constraint (HNR-RB) to investigate the network dynamics and predict multistep-ahead flows in the natural gas transmission network, where the total in- and out-flows of the network have to be balanced over a period of time. We concurrently address three main challenges: high dimensionality of networks with more than 100 nodes, unknown network dynamics, and constraint of balanced supply and demand in the network. The effectiveness of the proposed model is demonstrated through a real-world case study of forecasting demand and supply in a large-scale natural gas transmission network. The results demonstrate that HNR-RB outperforms alternative models for short- and mid-term horizons.

Milena Petkovic, Janina Zittel
Demand and Capacity Management in a Stochastic Dynamic Pickup and Delivery Problem with Crowdsourced Resources

In this study we examine the situation of a consortium of brick-and-mortar businesses operating an e-portal to collect customer orders and provide same-day delivery as a dynamic and stochastic pickup-and-delivery problem in which deliveries are performed with a mixed fleet of vehicles composed of dedicated vehicles and crowdsourced resources. Forms of delivery capacity vary with respect to the level of information and control that the dispatcher has on the availability and behavior of the courier. Taking into account the specific nature of the different types of delivery capacity, we present an approximate dynamic programming approach to manage the same-day delivery. We present computational results for different scenarios of demand.

Sara Stoia, Demetrio Laganá, Jeffrey W. Ohlmann
A New Classification Schema for Literature Reviews on the Applications of Machine Learning and Optimization Methods in Maritime Terminals: A Focus on the Seaside Area

Maritime terminals play a crucial role in the supply chain networks, transporting containerized cargo from sea to hinterland and vice versa. With the impact of naval gigantism, maritime terminals have experienced a significant increase in daily throughput, resulting in new challenges to enhance operational efficiency. There is a necessity for updated reviews on new methods applied to maritime terminal operations. The idea of this paper is to furnish a new classification schema for organizing literature reviews on machine learning and optimization applied to maritime terminals. An example of the proposed schema for revising papers related to operations performed in the seaside area of a terminal is presented.

Daniela Ambrosino, Haoqi Xie
Application of Linear Algebra in the Data Mining: A Proposal of New Matrix Decompositions

In the datafication era, huge amount of data can be collected and then analyzed. In all the contexts, very large data sets may be analyzed not only by using statistical approach, but may be viewed as very large size matrices. The linear algebra approach may be very useful, for example, in the data mining phase, with the advantage that all the information is maintained in the data set. In this work, two new matrix decompositions are presented. Given the characteristics of the new decompositions, their use in the data analysis may be certainly very attractive for the practitioners.

Valentina Minnetti
Backmatter
Metadata
Title
Optimization in Green Sustainability and Ecological Transition
Editors
Maurizio Bruglieri
Paola Festa
Giusy Macrina
Ornella Pisacane
Copyright Year
2024
Electronic ISBN
978-3-031-47686-0
Print ISBN
978-3-031-47685-3
DOI
https://doi.org/10.1007/978-3-031-47686-0