Abstract
This paper addresses a distribution problem involving a set of different products that need to be distributed among a set of geographically disperse retailers and transported from the single warehouse to the aforementioned retailers. The disfribution and transportation are made in order to satisfy retailers' demand while satisfying storage limits at both the warehouse and the retailers, transportation limits between the warehouse and the retailers, and other operational constraints. This problem is combinatorial in nature as it involves the assignment of a discrete finite set of objects, while satisfying a given set of conditions. Hence, we propose a genetic algorithm that is capable of finding good quality solutions. The genetic algorithm proposed is used to a real case study involving the disfribution of eight products among 108 retailers from a single warehouse. The results obtained improve on those of company's current practice by achieving a cost reduction of about 13%.

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
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%. © 2019, Springer Nature Switzerland AG.

Abstract
In this paper, a mathematical model is developed to tackle a Consistent Vehicle Routing Problem, which considers customers with multiple daily deliveries and different service level agreements such as time windows, and release dates. In order to solve this problem, an instance size reduction algorithm and a mathematical programming based decomposition approach are developed. This solution approach is benchmarked against a commercial solver. Results indicate that the method solves instances of large size, enabling its application to real-life scenarios. A case study in a pharmaceutical distribution company is analyzed. Consistent routes are planned for several warehouses, comprising hundreds of orders. A simulation model evaluates the performance of the generated route plans. Significant improvements in terms of the total distance traveled and the total travel times are obtained when compared to the company's current planning process.

Abstract
This paper introduces a new research focus for the problem of flow control. Most of the research until this point in this topic comes in the form of heuristics and flow control protocols, from which we can highlight Kanban and CONWIP. These protocols have as common ground the fact that both impact flow by limiting the amount of WIP (work in process) that circulates through a production route. These limits are not static in a sense that one limit defined for a given period will not suffice for all possible conditions the future may entail. Therefore, we need strategies to find which values for the WIP caps are best (according to an optimization target), given a production system state and a customer demand level. We propose the use of a Reinforcement learning (RL) agent and introduce the problem within the framework of a reinforcement learning problem, showing that for a simulated system it is possible to reduce WIP levels up to 43% without losses in throughput (TH). As an introduction to the flow control problem comparisons between push and pull systems are made resorting to the use of discrete event simulations. We simulated a CONWIP and a push protocol and comparisons are made in terms of cycle-time, throughput and customer lead-time. The work points-out that within the field of industrial management research terms such as cycle-time, customer lead-time, and lead-time are sometimes used interchangeably, which may lead to unnecessary confusion and hindered understanding of the subject matter. Specifically, we show that cycle-time reduction does not lead directly to customer lead-time reduction in a make to order environment.

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.