Abstract
The classical multi-level lot-sizing and scheduling problem formulations for process industries rarely address perishability issues, such as limited shelf lives of intermediate products. In some industries, ignoring this specificity may result in severe losses. In this paper, we start by extending a classical multi-level lot-sizing and scheduling problem formulation (MLGLSP) to incorporate perishability issues. We further demonstrate that with the objective of minimising the total costs (purchasing, inventory and setup), the production plans generated by classical models are often infeasible under a setting with perishable products. The model distinguishes different perishability characteristics of raw materials, intermediates and end products according to various industries. Finally, we provide quantitative insights on the importance of considering perishability for different production settings when solving integrated production planning and scheduling problems.

Abstract
Physician emigration can either function as an escape valve to help the health labour market clear from a supply surplus, or aggravate the problem further in case of a shortage. Either way, policy-makers should be particularly aware and devise policies to minimize the occurrence of an imbalance in the physician workforce, which may require physician retention policies if barriers to entry and other market rigidities can not be removed. To this purpose we have developed an agent-based computational economics model to analyse physician emigration, and have used it to study the impact of potential short- and long-term retention policies. As a real case study we have calibrated it with data from Portugal, which features a very particular health system with many rigidities. Results show that all policies are capable of increasing the workforce size, but not all reduce emigration. Also, the effect of return migration is non-negligible, and may substantially offset the impact on the workforce size. Furthermore, the welfare impact of the policies varies considerably. Whether policies to retain physicians should be enacted or whether policy makers should let physicians go will depend on the type of imbalance present in the health system.

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.

Abstract
Procurement plays an essential role in the supply of materials for the production of goods or products. The success of procurement management to fulfill demand with high service levels and on-time delivery relies on the suppliers' performance. Suppliers should be appropriately selected to source materials with the right quality, in the right quantity, at the right time, and for the right price. The scope of this problem, as well as other aspects such as the sourcing strategy, will depend on the type of items. Critical items, which represent high-profit impacts and high supply risks, should be approached comprehensively by considering all the main activities of the procurement process. This study focuses on a supplier selection problem integrated with inventory management under a multi-sourcing strategy, by taking into account stochastic demand and supply disruptions. This problem is approached by a simulation-optimization method, composed of discrete-event simulation and a genetic algorithm (GA). Finally, a numerical example is provided to illustrate the solution procedure.

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. © 2019, Springer Nature Switzerland AG.

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. © 2019, Springer Nature Switzerland AG.