Abstract
Besides fuel and waste distribution, one core application of multi-compartment vehicles (MCVs) is the distribution of groceries, as they enable retailers to jointly transport products with different temperature requirements, thus reducing the number of visits to a store. Grocery stores usually define preferable time windows that depend on the temperature of products (for example, fresh products in the morning) to indicate when deliveries should occur to better plan their in-store operations. Distribution planning therefore needs to take these preferences into consideration to obtain consistent delivery times. This work extends the research on multi-compartment vehicle routing problems (MCVRPs) by tackling a multi-period setting with a product-oriented time window assignment. In this problem, a fleet of MCVs is used for distribution and a unique time window for the delivery of each product segment to each store is defined consistently throughout the planning horizon. An ALNS is proposed to solve the product-oriented time window assignment for MCVRP. Daily and weekly operators are developed respectively focusing on the improvement of routing aspects of the problem on each day and aligning the time window assignment consistently throughout the planning horizon. The approach is tested on benchmark instances from the literature to demonstrate its effectiveness. We also use direct information from retail practice and enhance this with simulated data to further generalize our findings. The numerical experiments demonstrate that planning consistent MCV distribution leads to better overall solutions than the ex-post time window assignment of daily plans, facilitating more on-time deliveries.

Abstract
An holistic approach to decision support systems for intelligent public lighting control, must address both energy efficiency and maintenance. Currently, it is possible to remotely control and adjust luminaries behaviour, which poses new challenges at the maintenance level. The luminary efficiency depends on several efficiency factors, either related to the luminaries or the surrounding conditions. Those factors are hard to measure without understanding the luminary operating boundaries in a real context. For this early stage on preventive maintenance design, we propose an approach based on the combination of two models of the network, wherein each is representing a different but complementary perspective on the classifying of the operating conditions of the luminary as normal or abnormal. The results show that, despite the expected and normal differences, both models have a high degree of concordance in their predictions. © 2020, Springer Nature Switzerland AG.

Abstract
In this chapter, we outline the issue of education in the field of Operations Research (OR) and discuss various educational resources that are currently available, with a main focus on the most important international resources, but also with an emphasis on what is currently done in our home country, Portugal. The identification of shortcomings of education in OR and opportunities for its development will follow from the analysis of these resources. By choosing the word “education” over “teaching”, the aim is to stress the fact that (formal) teaching is nothing but one of the multiple aspects of education, whatever the field may be. Finally, we conclude that the dissemination and promotion of the field of OR intimately relates to issues related to education in this field. It is shown that these activities create a direct impact on the ability to attract publics into educational activities in this area, such as students enrolment on courses and programs with a high OR content. © 2018, Springer International Publishing AG.

Abstract
This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences.

Abstract
Production scheduling in the presence of real-time events is of great importance for the successful implementation of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various stochastic real-time events which continuously forces reconsideration and revision of pre-established schedules. In an uncertain environment, efficient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This reality motivated us to develop a tool that attempts to start filling the gap between scheduling theory and practice. The developed prototype is connected to the MRP software and uses meta heuristics to generate a predictive schedule. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the tool starts rescheduling through a dynamic-event module that combines dispatching rules that best fit the performance measures pre-classified by Kano’s model. The proposed tool was tested in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model. © 2020, Springer Nature Switzerland AG.

Abstract
This article is about seeking a good feasible solution in a reasonable amount of computation time to the three-dimensional Multiple Bin Size Bin Packing Problem (MBSBPP). The MBSBPP studied considers additional constraints encountered in real world air transportation situations, such as cargo stability and the particular shape of containers. This MBSBPP has already been formulated as a Mixed Integer linear Programming problem, but as yet only poor results have been achieved for even fairly small problem sizes. The goal of the work this paper describes is to develop heuristics that are able to quickly provide good initial feasible solutions for the MBSBPP. Three methodologies are considered, which are based on the decomposition of the original problem into easier subproblems: the matheuristics Relax-and-Fix, Insert-and-Fix and Fractional Relax-and-Fix. They have been parametrised on real data sets and then compared to each other. In particular, two of these techniques show promising results in reasonable computational times.