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Über dieses Buch

This book presents expert descriptions of the successful application of operations research in both the private and the public sector, including in logistics, transportation, product design, production planning and scheduling, and areas of social interest. Each chapter is based on fruitful collaboration between researchers and companies, and company representatives are among the co-authors. The book derives from a 2017 call by the Italian Operations Research Society (AIRO) for information from members on their activities in promoting the use of quantitative techniques, and in particular operations research techniques, in society and industry. A booklet based on this call was issued for the annual AIRO conference, but it was felt that some of the content was of such interest that it deserved wider dissemination in more detailed form. This book is the outcome. It equips practitioners with solutions to real-life decision problems, offers researchers examples of the practical application of operations research methods, and provides Master’s and PhD students with suggestions for research development in various fields.

Inhaltsverzeichnis

Frontmatter

Logistics

Frontmatter

A Two-Phase Approach for an Integrated Order Batching and Picker Routing Problem

Abstract
This article addresses an integrated warehouse order picking problem. The company HappyChic is specialized in men’s ready-to-wear. A central warehouse is dedicated to supplying, every day, the shops of one brand. We focus on the picking area of this warehouse which relies on human picking system. For each picking wave (period of a working day), a set of customer orders has to be prepared. An order is a set of product references, with quantities, i.e., the numbers of items required. The problem consists in jointly deciding: (1) the division of orders into several boxes, respecting weight and size constraints; (2) the batching of boxes into trolleys, that implicitly defines the routing into the picking area. The objective function aims to minimize the total distance. To deal with the large size instances of HappyChic in short computation times, we design a heuristic method based on the split and dynamic programming paradigms. The results are very convincing: the total covered distance decreases by more than 20%. Moreover, we propose an adaptation of the algorithm to prepare homogeneous boxes with respect to classes of products. The logistic department of HappyChic is convinced by results obtained in this research work, and the warehouse management system is currently being updated in order to integrate the proposed algorithm.
Martin Bué, Diego Cattaruzza, Maxime Ogier, Frédéric Semet

Creation of Optimal Service Zones for the Delivery of Express Packages

Abstract
Service zones design is a crucial step in optimizing express delivery services which simplify the planning of everyday distribution routes while favoring the driver familiarity with the delivery zone. We describe a Decision Support System (DSS) for the definition of service zones based on an advanced multi-attribute clustering algorithm. The DSS has been used successfully by a major provider in The Netherlands to support the redesign of its distribution system for express parcel delivery.
Tiziano Parriani, Matteo Pozzi, Daniele Vigo, Frans Cruijssen

Solving a Three-Dimensional Bin-Packing Problem Arising in the Groupage Process: Application to the Port of Gioia Tauro

Abstract
In this paper we consider the consolidation operations in the groupage process. Containers, arriving from different origins carry objects with the same destination. We address the problem of inserting objects with the same destination into a new container. It is a three-dimensional bin-packing problem where several operational constraints are taken into account. We develop a simple but effective heuristic to solve the problem. This is based on the well-known next fit procedure where the concept of multi-layer is introduced. The defined heuristic allows to save empty space in the container dealing with a high impact on the real application. We present the main features of a decision support system that can help the logistic operators to implement a consolidation/deconsolidation hub.
Luigi Di Puglia Pugliese, Francesca Guerriero, Roberto Calbi

A New Software System for Optimizing the Operations at a Container Terminal

Abstract
This paper describes the procedures implemented in the software system developed for the CONTRAST project to optimize the logistic operations at a container terminal. In particular, we consider the problems of minimizing the number of reshuffle operations and designing the routes of the vehicles inside the yard. Minimizing the number of reshuffle operations required to empty a container yard is addressed in the literature as the Block Relocation Problem and it is known to be NP-hard. Here we implemented two heuristic procedures that provide feasible solutions to the problem when new containers enter the yard or when some container must be reallocated for any reason.
Tiziano Bacci, Stefano Conte, Domenico Matera, Sara Mattia, Paolo Ventura

Product Design

Frontmatter

Design Optimization of Synchronous Reluctance Motor for Low Torque Ripple

Abstract
The aim of this paper is to optimize the design of multiple flux barriers Synchronous Reluctance Motor in order to smooth the torque profile without rotor skewing. A new strategy is proposed by modelling the particular optimal design problem as mixed integer constrained minimization of a suitable objective function. The procedure has allowed to optimize the rotor shape for minimum torque ripple starting from an existing stator core.
Andrea Credo, Andrea Cristofari, Stefano Lucidi, Francesco Rinaldi, Francesco Romito, Marco Santececca, Marco Villani

A Variant of the Generalized Assignment Problem for Reliable Allocation of Sensor Measurements in a Diagnostic System

Abstract
Tokamaks are experimental reactors that currently represent the most promising approach to produce electricity by means of the nuclear fusion reactions. In a tokamak a fully ionized gas, called plasma, in which the nuclear reaction occurs, is confined by means of strong magnetic fields. Its performance, physics knowledge and operation safety are significantly affected by several plasma parameters that are controlled by the so called magnetic control system. However, these parameters cannot be directly measured, but they are estimated by the so called magnetic diagnostic, by combining the information from several sensors. More precisely, different combinations of measurements, which may imply also a different quality of the estimation, can be used to infer the same set of plasma parameters. Each acquisition unit should acquire a set of measurements that allows to reconstruct all the plasma parameters that are needed to operate the machine. In this context, it is fundamental to determine an effective assignment of the sensor measurements to the acquisition units, satisfying capacity constraints and, at the same time, maximizing the overall reliability of the magnetic diagnostic system against possible failures of its components. This problem has been tackled in literature as an original variant of the generalized assignment problem. In this work, we provide a description of the problem itself, as well as a presentation of the related formulations. These formulations are then experienced on real-world test cases in order to show their applicability. Such test cases have been derived from the requirements defined for the JET tokamak, which is the world’s largest tokamak currently in operation. We conclude with a discussion on future research perspectives.
Gianmaria De Tommasi, André C. Neto, Antonio Sforza, Claudio Sterle

Production Planning and Scheduling

Frontmatter

Forecasting Methods and Optimization Models for the Inventory Management of Perishable Products: The Case of “La Centrale del Latte di Vicenza SpA”

Abstract
This paper is a report on a joint project by the Department of Management, Economics and Quantitative Methods of the University of Bergamo (in collaboration with the Department of Economics and Management of the University of Brescia) and the Italian company “La Centrale del Latte di Vicenza SpA” producing different types of milk, yogurt, vegetable drinks, cream, butter, cheese, eggs and vegetables. The aim of the project was to provide forecasting methods and optimization models, to improve the demand forecasts of perishable products and to better manage inventory levels in a Material Requirements Planning (MRP) setting.
Luca Bertazzi, Francesca Maggioni

Production Scheduling and Distribution Planning at FATER S.p.A.

Abstract
We present the relevant outcomes of the research co-operation between Università della Calabria and FATER S.p.A., an Italian nationwide company holding several leading brands in the area of disposable diapers. The aim of such co-operation has been to tackle some issues regarding production scheduling and distribution management at FATER. In particular, two problems have been addressed: (i) the optimal lot-size management of the FIX2 production line located in the Pescara plant, and the distribution of goods from the production plant to the distribution depots covering the entire national territory. As for the former, the classic production-rotation model has been adapted to the FATER framework for taking into account different possible scenarios in terms of manpower weekly shifts. As for the latter, an inventory–routing model adopting a simplified column-generation approach has been proposed. We describe the two optimization problems and the adopted solution approaches, giving also some details on the related software packages implementing such methods, along with the relevant results.
Giovanni Giallombardo, Giovanna Miglionico, Lucia Nitoglia, Sergio Pelle

Social Applications

Frontmatter

IoT Flows: A Network Flow Model Application to Building Evacuation

Abstract
This chapter presents a real-time emergency evacuation handling system based on internet of things (IoT) technologies. The IoT infrastructure has a core computational component that is in charge of minimizing the time necessary to evacuate people from a building. The space and time dimension are discretized according to metrics and models in literature, as well as original methods. The component formulates and solves a linearized, time-indexed flow problem on a network that represents feasible movements of people at a suitable frequency. Accurate parameter setting makes the computational time to solve the model compliant with real-time use. An application of the proposed IoT system and its core algorithm to handle safe evacuation test in Palazzo Camponeschi—a building in L’Aquila (Italy) now and then used for exhibitions—is described, and diverse uses of the methodology are presented.
Claudio Arbib, Mahyar T. Moghaddam, Henry Muccini

From Pallets to Puppies: Using Insights from Logistics to Save Animals

Abstract
The Kentucky Humane Society (KHS) is a private, nonprofit organization dedicated to saving every healthy, behaviorally sound animal that they take in. They are also the largest no-kill animal shelter in Kentucky, USA. Due to limited resources, the KHS has a great need for an optimized methodology for allocating available animals to their adoption facilities. Workers at the KHS believe that a capacity constraint is their most limiting factor. The purpose of this work is to develop an allocation model that will improve the throughput rate of the animals to be adopted, thereby freeing up extra capacity more quickly than current practices. The allocation model presented here assigns animals to adoption locations based on their expected length of stay at all available locations. The resulting allocation minimizes the expected length of stay for these animals. In order to make the model easy to use, we have implemented a software with a user-friendly interface.
M. Gentili, E. Gerber, K. Gue

Improving Social Assistance Services for Minors and Disabled People by Using Multiobjective Programming

Abstract
Relying on multiobjective programming techniques, we have developed an optimization software to improve the services provided by the social co-operative OMNIA. OMNIA’s mission is to supply home-care assistance to children and people with disabilities. Our method is intended to optimize the social workers’ shift planning aiming at, on the one hand, maximizing the overall quality of the social care services, on the other hand, minimizing costs associated with OMNIA’s activity. In particular, our software provides Pareto optima of the resulting (difficult) bi-objective model by resorting to both a standard a priori weighted-sum approach and a new MINLP no-preference (hypervolume maximization-type) method. The product of this research is successfully employed by OMNIA.
Lorenzo Lampariello, Andrea Manno, Simone Sagratella

Transportation

Frontmatter

An Algorithm for the Optimal Waste Collection in Urban Areas

Abstract
In waste collection, one of the most important decision regards the routing and the scheduling of the service. In this context, during the Optimization for Networked Data in Environmental Urban Waste Collection (ONDE-UWC) project, an innovative optimization method for tackling those decisions has been developed in collaboration with the company Cidiu S.p.A (www.​cidiu.​to.​it). The importance of the method is three-folds. First, it is innovative because it does not impose periodic routes. Second, it uses information coming from IoT sensors in order to build statistical models for the waste collection evolution. Third, the developed method has shown great usability and performance in the real field.
Edoardo Fadda, Guido Perboli, Roberto Tadei

A Decision Support System for Earthwork Activities in Construction Logistics

Abstract
Making decisions in a complex system such as the construction of a highway is a hard task that involves a combinatorial set of possibilities, concerning thousands of interrelated activities and resources over several years. In this paper we describe a decision support system (DSS) developed to assist project managers in decision making for the construction of the Autostrada Pedemontana Lombarda highway, in Italy. The considered problem evaluates the earthwork activities in detail and defines the minimum cost earthwork plan satisfying all constraints. The proposed DSS involves the use of linear programming to solve the earthwork problem in a two-phase approach: in the first phase, an aggregate model determines the feasibility of the overall project, whereas in the second phase, disaggregate models determine the actual flows of each material. The DSS gathers the needed information directly from the master plan commonly used by the company and provides as output a set of visual solutions. The solution are yielded in short times and can be run many times with different data sets supporting a fast evaluation of different decisions. The provided solutions are also optimized and could substitute the previous manual results. The DSS has been proved to be very effective for assisting the project managers of the above highway construction and is currently in use in other projects.
Mauro Dell’Amico, Guenther Fuellerer, Gerhard Hoefinger, Stefano Novellani

A Comparison of Optimization Models to Evaluate the Impact of Fuel Costs When Designing New Cruise Itineraries

Abstract
The itinerary design is a problem that belongs to the class of cruise supply. The development of cruises’ itineraries to offer on the market is a long process. Given a ship located in a specific world’s basin, given a duration and a homeport, an itinerary has to be planned choosing a sequence of ports to visit among a set of available ones. The itinerary must be characterized by a schedule, which comprehends the arrival time and the departure time of the ship for each port. The objectives to pursue in the decisions are the maximization of both the customer satisfaction and the revenue and the minimization of the costs. In this paper the focus is on costs, that depend on fuel consumption and ports’ services. The fuel cost is function of the speed of the ship. Starting from a model recently proposed in the literature for solving the cruise itinerary designing problem (CIDP) (Ambrosino et al. in Proceedings of 15th IFAC Symposium on Control in Transportation Systems, Savona, Italy, June 2018, [3]) in which fuel costs were based on classes of speed, a different model has been developed for including a more precise fuel cost computation. The fuel costs represent a great part of the company costs, influencing the operative margin. Thus, the fuel consumption is here computed in terms of metric tons for each single speed. The model has been tested solving real cases of Costa Crociere in the Mediterraneo basin. The obtained solutions have been compared, both in terms of cruise route and objectives values with those obtained by the previous model used by the company to define optimal itineraries.
Daniela Ambrosino, Veronica Asta, Federico Bartoli

An Integrated Algorithm for Shift Scheduling Problems for Local Public Transport Companies

Abstract
This paper presents an integrated approach to solve two shift scheduling problems for local public bus companies: the first one aims at finding a schedule for vehicles, given a set of rides to do; the second one aims at assigning drivers to vehicle schedules. The first subproblem to be faced is the Multiple Depot Vehicle Scheduling Problem, while the second problem to deal with is the Crew Scheduling Problem where each trip is assigned to a driver. These problems are known to be NP-Hard. The main difference with respect to the literature is that the mathematical model, and the related algorithm, are designed based on real world-requirements. Computational results have been carried out on large real-word instances.
Claudio Ciancio, Demetrio Laganá, Roberto Musmanno, Francesco Santoro

A Tool for Practical Integrated Time-Table Design and Vehicle Scheduling in Public Transport Systems

Abstract
Planning of services and resources for public transport systems is a complex process. In practice the planning is usually decomposed into several stages, where service levels are decided first, while vehicles and drivers are only optimized later on. We describe the new TTD.XT tool, developed by M.A.I.O.R. S.r.l. in collaboration with the University of Pisa, that improves the efficient utilization of expensive resources by simultaneously optimizing timetabling and vehicle scheduling. We quickly present the underlying mathematical model and (math-heuristic) algorithm, and compare the solutions constructed by the tool for real-world bus transport planning instances for three major Italian cities with those produced by the standard sequential decision process; the former are significantly better than the latter both in terms of objective function value and when judged by experts of the field.
Samuela Carosi, Antonio Frangioni, Laura Galli, Leopoldo Girardi, Giuliano Vallese
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