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This book gathers selected contributions by top Portuguese and international researchers in the field of Operations Research, presented at the 19th Congress of APDIO (Portuguese Association of Operational Research). The papers address a broad range of complex real-world problems, which are approached using recent theoretical techniques. Of particular interest are the applications of e.g. linear, nonlinear and mixed-integer programming, multiobjective optimization, metaheuristics and hybrid heuristics, multicriteria decision analysis, data envelopment analysis, clustering techniques and decision support systems, in such varied contexts as: supply chain management, scheduling problems, production management, logistics, energy, finance and healthcare.

This conference, organized by APDIO and held in Aveiro, Portugal in September 2018, offered an ideal opportunity to discuss the latest developments in this field and to build new bridges between academic researchers and practitioners. Summarizing the outcomes, this book offers a valuable tool for all researchers, students and practitioners who wish to learn about the latest trends in this field.

Inhaltsverzeichnis

Frontmatter

Towards an Integrated Framework for Aerospace Supply Chain Sustainability

Abstract
Supply chains have become one of the most important strategic themes in the aerospace industry in recent years as globalization and deep technological changes have altered the industry at many levels, creating new dynamics and strategies. In this setting, sustainability at the supply chain level is an emerging research topic, whose contributions aim to support businesses into the future. To do so the development of new products and the response to new industry requirements, while incorporating new materials appears as a path to follow, which require more resilient and agile supply chains, while guaranteeing their sustainability. Such supply chains will be better prepared for the future complex challenges and risks faced by the aerospace companies. Such challenges are addressed in this work, where an integrated framework is proposed to contribute to the resilience and sustainability of aerospace supply chains. Using different analysis methods, the framework addresses four important challenges in the context of aerospace supply chain sustainability: evolution and new trends, performance assessment, supplier selection, and supply chain design and planning.
Cátia Barbosa, Nuno Falcão e Cunha, Carlos Malarranha, Telmo Pinto, Ana Carvalho, Pedro Amorim, M. Sameiro Carvalho, Américo Azevedo, Susana Relvas, Tânia Pinto-Varela, Ana Cristina Barros, Filipe Alvelos, Cláudio Alves, Jorge Pinho de Sousa, Bernardo Almada-Lobo, José Valério de Carvalho, Ana Barbosa-Póvoa

Critical Node Detection with Connectivity Based on Bounded Path Lengths

Abstract
For a given graph representing a transparent optical network, a given weight associated to each node pair and a given positive integer c, the Critical Node Detection problem variant addressed here is the determination of the set of c nodes that, if removed from the graph, minimizes the total weight of the node pairs that remain connected. In the context of transparent optical networks, a node pair is considered connected only if the surviving network provides it with a shortest path not higher than a given positive value T representing the optical transparent reach of the network. Moreover, the length of a path depends both on the length of its links and on its number of intermediate nodes. A path-based Integer Linear Programming model is presented together with a row generation approach to solve it. We present computational results for a real-world network topology with 50 nodes and 88 links and for \(c=2\) up to 6. The optimal results are compared with node centrality based heuristics showing that such approaches provide solutions which are far from optimal.
Fábio Barbosa, Agostinho Agra, Amaro de Sousa

A Benders Decomposition Algorithm for the Berth Allocation Problem

Abstract
In this paper we present a Benders decomposition approach for the Berth Allocation Problem (BAP). Benders decomposition is a cutting plane method that has been widely used for solving large-scale mixed integer linear optimization problems. On the other hand, the Berth Allocation Problem is a NP-hard and large-scale problem that has been gaining relevance both from the practical and scientific points of view. In this work we address the discrete and dynamic version of the problem, and develop a new decomposition approach and apply it to a reformulation of the BAP based on the Heterogeneous Vehicle Routing Problem with Time Windows (HVRPTW) model. In a discrete and dynamic BAP each berth can moor one vessel at a time, and the vessels are not all available to moor at the beginning of the planning horizon (there is an availability time window). Computational tests are run to compare the proposed Benders Decomposition with a state-of-the-art commercial solver.
Flávia Barbosa, José Fernando Oliveira, Maria Antónia Carravilla, Eduardo Ferian Curcio

Capacitated Vehicle Routing Problem with Heterogeneous Fixed Proprietary Fleet and Outsourcing Delivery—A Clustering-Based Approach

Abstract
This paper describes a solution method that was created with the objective of obtaining a more efficient finished goods distribution process for a food industry company. The finished goods distribution process involves the use of the companys own fleet to serve a specific group of customers, and the use of outsourcing transportation services that can make direct and transshipment customer deliveries. The complexity of the problem is due to the need to decide which customers should be served by each of the outsourcing transportation services, direct or transshipment, and to find cost efficient solutions for the multiple vehicle routing problems created. First, an original clustering method consisting of a logical division of the customer orders using a delivery ratio based on the transportation unit cost, distance and order weight, is used to define customer clusters by service type. Then, an exact method based on a mixed integer programming model, is used to obtain optimal vehicle routing solutions, for each cluster created. The solution method for the company real instances, proved able to reach the initial proposed objectives and obtain promising results that suggest an average reduction of 34% for the operational costs, when compared to the current distribution model of the company.
Ricardo Bertoluci, António G. Ramos, Manuel Lopes, João Bastos

Modeling Supply Chain Network: A Need to Incorporate Financial Considerations

Abstract
In the past few years, important supply chain decisions have captured managerial interest. One of these decisions is the design of the supply chain network incorporating financial considerations, based on the idea that establishment and operating costs have a direct effect on the company’s financial performance. However, works on supply chain network design (SCND) incorporating financial decisions are scarce. In this work, we address a SCND problem in which operational and investment decisions are made in order to maximize the company value, measured by the Economic Value Added, while respecting the usual operational constraints, as well as financial ratios and constraints. This work extends current research by considering debt repayments and new capital entries as decision variables, improving on the calculation of some financial values, as well as introducing infrastructure dynamics; which together lead to greater value creation.
Alexandra Borges, Dalila B. M. M. Fontes, José Fernando Gonçalves

Performance Evaluation of European Power Systems

Abstract
Electric power systems are facing significant challenges regarding their organization and structure. Energy infrastructures are crucial to ensure a transition to low-carbon societies, contributing to sustainable development. This paper uses Data Envelopment Analysis to compare the performance of the power systems in 16 European countries using data available to the public. Three perspectives were considered, focusing on technical aspects affecting quality of service, network costs and environmental impact. It is proposed a new formulation of the DEA model that estimates a composite indicator (CI) aggregating individual indicators which should be minimized. The benchmarking results can give insights to electric operators, regulators and decision-makers on the strengths and weakness of national power systems and disclose the potential for performance improvements. Based on the outcomes from the CI model, Austria, Croatia, Denmark, Germany, Greece, Ireland, Italy and Netherlands are identified as the benchmarks for the power systems in the Europe. The discussion of the results is intended to raise public awareness on the performance of the European power systems and contribute to the definition of public policies for the promotion of continuous improvement.
Mário Couto, Ana Camanho

The Demand for Healthcare Services and Resources: Patterns, Trends and Challenges in Healthcare Delivery

Abstract
Together with the significant improvement in health and longevity came a number of health and economic concerns related to the demand for healthcare services and resources: changes in the patterns of health and illness, increasing amount and complexity of healthcare services demanded, rising health expenditures and uncertainty about whether there will be enough human, physical and financial resources to deliver the healthcare services needed. This paper aims to draw attention to the importance of planning the demand for healthcare in the aforementioned context, to create awareness of the need for a comprehensive study on the demand for healthcare services and resources and to propose an integrated approach for planning them, to inform managers and policy-makers on what can be the main challenges on assuring healthcare delivery in the future.
Sofia Cruz-Gomes, Mário Amorim-Lopes, Bernardo Almada-Lobo

Planning the Delivery of Home-Based Long-Term Care: A Mathematical Programming-Based Tool to Support Routes’ Planning

Abstract
The adequate planning of home-based long-term care (HBLTC) is essential in the current European setting where long-term care (LTC) demand is increasing rapidly, and where home-based care represents a potential cost-saving alternative from traditional inpatient care. Particularly, this planning should involve proper route planning to ensure visits of health professionals to patients’ homes. Nevertheless, literature in the specific area of HBLTC planning is still scarce. Accordingly, this paper proposes a tool based on a mathematical programming model—the \(LTC^{routes}\)—for supporting the daily planning of routes to visit LTC patients’ homes in National Health Service-based countries. The model allows exploring the impact of considering different objectives relevant in this sector, including the minimization of costs and the maximization of service level. Patients’ preferences, traffic conditions and budget constraints are also considered in the proposed model. To illustrate the applicability of the model, a case study based on the National Network of LTC in Portugal is analyzed.
Daniel Espadinha, Teresa Cardoso-Grilo

Selection of a Strategic Plan Using an Integrated AHP-Goal Programming Approach

Abstract
This work proposes a multi-criteria decision making model to assist in the choice of a strategic plan for a world-class company. The Balanced Scorecard (BSC) is a support tool of Beyond Budgeting that translates a company’s vision and strategy into a coherent set of performance measures. However, it does not provide help in choosing a strategic plan. The selection of a strategic plan involves multiple goals and objectives that are often conflicting and incommensurable. This paper proposes an integrated Analytic Hierarchy Process-Goal Programming (AHP-GP) approach to select such a plan. This approach comprises two stages. In the first stage, the AHP is used to evaluate the relative importance of the initiatives with respect to financial indicators/KPIs; while in the second stage a GP model incorporating the AHP priority scores is developed. The GP model selects a set of initiatives that maximizes the earnings before interest and taxes (EBIT) and minimizes the Capital Employed (CE). The proposed method was evaluated through a case study.
Dalila B. M. M. Fontes, Teresa Pereira, Márcia Oliveira

Improving Inventory Management in an Automotive Supply Chain: A Multi-objective Optimization Approach Using a Genetic Algorithm

Abstract
Inventory management represents a cornerstone inherent to any supply chain, regardless of industry type. Nevertheless, uncertainty phenomena related to demand and supply can induce overstock or even inventory stock-outs occurrences which, in turn, jeopardize one of the major principles of supply chain management: deliver the right product at the right place, at the right time and to the right cost. This situation may also be aggravated in automotive supply chains, due to their complexity in terms of entities involved. This research paper explores a multi-objective optimization model and applies it to a real industrial company, to address an inventory management problem. Moreover, a genetic algorithm is used to determine solutions corresponding to the order size and to a safety factor system. The obtained results are compared to the current strategy adopted by the company. At this point, the advantages and the drawbacks of the model implementation are assessed. Based on a set of logistic performance indicators, it is showed that the adoption of a smaller order size is potentially beneficial to the overall levels of inventory and to the value of inventory on–hand, without compromising the service level. Assertively, the proposed model reveals to be an useful tool to practitioners involved in automotive electronic supply chains.
João N. C. Gonçalves, M. Sameiro Carvalho, Lino Costa

Consistent Consolidation Strategies in Grocery Retail Distribution

Abstract
In the food retail sector, maintaining the food quality across the supply chain is of vital importance. The quality of the products is dependent on its storage and transportation conditions and this peculiarity increases the supply chain complexity relatively to other types of retailers. Actually, in this industry there are three types of food supply chains: frozen, chilled and ambient. Moreover, food retailers run different store formats, of different sizes, assortments and sales volume. In this study we research the trade-off between consolidating a range of products in order to perform direct deliveries to the stores versus performing separate delivery routes for products with different transportation requirements. A new consistency dimension is proposed regarding the periodicity that a consolidation strategy is implemented. The aim of this paper is to define a consolidation strategy for the delivery mode planning that allows to smooth the complexity of grocery retail operations. A three-step approach is proposed to tackle a real size problem in a case-study with a major Portuguese grocery retailer. By changing the consolidation strategy with a complete consistent plan the company could reach annual savings of around 4%.
Sara Martins, Pedro Amorim, Bernardo Almada-Lobo

Dynamic Approaches to Solve the Smart Waste Collection Routing Problem

Abstract
A Dynamic Inventory Routing Problem model embedded into a rolling horizon solution approach is developed, along this paper, to solve the Smart Waste Collection Routing Problem. This allows the definition of dynamic waste collection routes that explore the use of real-time information on the bins fill-level, over a medium-term horizon. Opposite to a published short-term approach, based on the solution of the Vehicle Routing Problem with Profits that maximize daily profits, the present approach leads to better results translated into higher operational profits. This evidence is shown through the comparison of the solution of both the short-term and the medium-term approaches in a set of small instances where different active rolling horizon intervals are tested. A large instance obtained from a real waste collection system case study is also studied, and the results confirm the conclusions obtained when solving smaller instances.
Carolina Soares de Morais, Tânia Rodrigues Pereira Ramos, Ana Paula Barbosa-Póvoa

Supply Chain Resilience: An Optimisation Model to Identify the Relative Importance of SC Disturbances

Abstract
Supply Chains (SC) have been facing a vast set of events that can endanger their operations and produce permanent damage, which are unknown until its occurrence. This led to an increased awareness of SC Resilience to deal with such SC uncertainty. This work uses a Mixed Integer Linear Programming model to study the relative importance of the different types of events that threaten SC operations exploring the use of two indicators Expected NPV (ENPV) and Expected Service Level (ECSL). From this work, it can be said that upstream disturbances are of greater importance and because of that should be more efficiently managed, generating value and ultimately a competitive advantage to companies that deploy resilience concerns to their operations.
João Pires Ribeiro, Ana Barbosa-Póvoa

Supply Chain Purchasing Domain Optimization in a Portuguese Retail Company

Abstract
In this paper we address a case study of the purchasing management section in a Portuguese company in the Retail sector. The purchasing management costs in this company were found to depend largely on the storage mode of the products. Therefore we developed a mathematical model for optimizing the purchasing management costs. In the model we address how to order the products and which storage mode to choose, in order to minimize costs and fulfil the demand. Real instances regarding the monthly demand for one year are tested and the results show that the model can reduce the ratio between operational costs and merchandise costs in almost every instance.
Ana Teixeira, Eliana Costa e Silva, Cristina Lopes, João Ferreira Santos

Environmental Performance Assessment of European Countries

Abstract
The European Union (EU) has been promoting an integrated approach to climate protection and energy policy, through a set of key objectives for 2020, 2030 and 2050, linking Europe’s green agenda with its need for energy security and competitiveness. This paper aims to evaluate the environmental efficiency of European Countries from 2010 to 2015 towards 2020 targets, through a Data Envelopment Analysis (DEA) model. The DEA model assesses the ability of each country in minimizing current resources while maximizing the gross domestic product (GDP) and minimizing undesirable outputs, such as GhG emissions. The DEA model is based on Directional Distance Function (DDF), imposing weak disposability for the undesirable output (UO). Results obtained show that globally, in the period under analysis, the EU has increased its environmental efficiency which is consistent with the analysis of the indicators of the 2020 climate and energy package.
Clara B. Vaz, Ângela P. Ferreira

A Column Generation-Based Diving Heuristic for Staff Scheduling at an Emergency Medical Service

Abstract
Staff scheduling involves assigning people to tasks organized in working shifts. It is a complex and time-consuming activity common to several real-world companies while still typically a hand-made task. These problems are usually conditioned by legal and working rules, and by personal preferences. Thus, the challenge is to find schedules that most accurately fit the functionality of the services and equity issues. For this purpose, a column generation-based diving heuristic is proposed to solve a staff scheduling problem at an Emergency Medical Service. The approach is generic and possibly adjusted to several realities and companies. In this context, the heuristic is applied to a real-life problem at Instituto Nacional de Emergência Médica (INEM), obtaining good quality solutions in relatively short running times. The best-found solution is compared with an implemented schedule at INEM, strengthening the practical value of this approach. The ultimate goal is to develop automated tools to support INEM in their staff scheduling activities.
Hendrik Vermuyten, Joana Namorado Rosa, Inês Marques, Jeroen Beliën, Ana Barbosa-Póvoa
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