Abstract
This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application.

Abstract
Three-Dimensional Packing Problems (3D-PPs) can be applied to effectively reduce logistics costs in various areas, such as airline cargo management and warehouse management. In general, 3D-PP studies can be divided into two different streams: those tackling the off-line problem, where full knowledge about items is available beforehand; and those tackling the on-line (real-time) problem, where items arrive one by one and should be packed immediately without having full prior knowledge about them. During the past decades, off-line and online 3D-PPs have been studied in the literature with various constraints and solution approaches. However, and despite the numerous practical applications of on-line problems in real-world situations, most of the literature to date has focused on off-line problems and is quite sparse when it comes to on-line solution methods. In this regard, and despite the different nature of on-line and off-line problems, some approaches can be applied in both environments. Hence, we conducted an in-depth and updated literature review to identify and structure various constraints and solution methods employed by researchers in off-line and on-line 3D-PPs. Building on this, by bringing together the two separate streams of the literature, we identified several off-line approaches that can be adopted in on-line environments. Additionally, we addressed relevant research gaps and ways to bridge them in the future, which can help to develop this research field.

Abstract
First-mile logistics tackles the movement of products from retailers to a warehouse or distri-bution centre. This first step towards the end customer has been pushed by large e-commerce platforms forming extensive networks of partners and is critical for fast deliveries. First-mile pickup requires efficient methods different from those developed for last-mile delivery, among other reasons due to the complexity of cargo features and volume - increasing the relevance of advanced packing methods. More importantly, the problem is essentially dynamic and the pickup process, in which the vehicle is initially empty, is much more flexible to react to disruptions arising when the vehicles are en route. We model the static first-mile pickup problem as a vehicle routing problem for a hetero-geneous fleet, with time windows and three-dimensional packing constraints. Moreover, we propose an approach to tackle the dynamic problem, in which the routes can be modified to accommodate disruptions - new customers' demands and modified requests of known customers that are arriving while the initially established routes are being covered. We propose three reactive strategies for addressing the disruptions depending on the number of vehicles available, and study their results on a newly generated benchmark for dynamic problems. The results allow quantifying the impact of disruptions depending on the strategy used and can help the logistics companies to define their own strategy, considering the characteristics of their customers and products and the available fleet.

Abstract
Different variations of the classic cutting stock problem (CSP) have emerged and presented increasingly complex challenges for scientists and researchers. One of these variations, which is the central subject of this work, is the two-dimensional cutting stock problem with usable leftovers (2D-CSPUL). In these problems, leftovers can be generated to reduce waste. This technique has great practical importance for many companies, with a strong economic and environmental impact. In this paper, a non-linear mathematical model and its linearization are proposed to represent the 2D-CSPUL. Due to the complexity of the model, a heuristic procedure was also proposed. Computational tests were performed with instances from the literature and randomly generated instances. The results demonstrate that the proposed model and the heuristic procedure satisfactorily solve the problem, proving to be adequate and beneficial tools when applied to real situations.

Abstract
We study a variant of the Probabilistic Travelling Salesman Problem arising when retailers crowdsource last-mile deliveries to their own customers, who can refuse or accept in exchange for a reward. A planner must identify which deliveries to offer, knowing that all deliveries need fulfilment, either via crowdsourcing or using the retailer's own vehicle. We formalise the problem and position it in both the literature about crowdsourcing and among routing problems in which not all customers need a visit. We show that to evaluate the objective function of this stochastic problem for even one solution, one needs to solve an exponential number of Travelling Salesman Problems. To address this complexity, we propose Machine Learning and Monte Carlo simulation methods to approximate the objective function, and both a branch-and-bound algorithm and heuristics to reduce the number of evaluations. We show that these approaches work well on small size instances and derive managerial insights on the economic and environmental benefits of crowdsourcing to customers.

Abstract
We propose a new approach for the lastmile delivery problem where, besides serving as collecting points of orders for customers, parcel lockers are also used as transshipment nodes in a 2-echelon delivery system. Moreover, we consider that a customer (occasional courier) visiting a locker may accept a compensation to make a delivery to another customer on their regular traveling path. The proposed shared use of the locker facilities - by customers that prefer to self-pick up their orders, and also as a transfer deposit for customers that prefer home delivery - will contribute to better usage of an already available storage capacity. Furthermore, the use of occasional couriers (OCs) brings an extra layer of flexibility to the delivery process and may positively contribute to achieving some environmental goals: although non-consolidation of deliveries may, at first sight, seem negative, by only considering OCs that would go to the locker independently of making or not a delivery on their way home, and their selection being constrained by a maximum detour, the carbon footprint can be potentially reduced when compared to that of dedicated vehicles. We present a mixed-integer linear programming formulation for the problem that integrates three delivery options - depot to locker, depot to locker followed by final delivery by a professional fleet, and depot to locker followed by final delivery by an OC. Furthermore, to assess the impact of OCs' no show on the delivery process, we extend the formulation to re-schedule the delivery of previous undelivered parcels, and analyze the impact of different no-show rates. Thorough computational experiments show that the use of OCs has a positive impact both on the delivery cost and on the total distance traveled by the dedicated fleets. Experiments also show that the negative impact of no-shows may be reduced by using lockers with higher capacities.