Abstract
A recent relevant innovation in last-mile delivery is to consider the possibility of goods being delivered by couriers appointed through crowdsourcing. In this paper we focus on the setting of in-store customers delivering goods, ordered by online customers, on their way home. We assume that not all the proposed delivery tasks will necessarily be accepted, and use logistic regression to model the crowd agents' willingness to undertake a delivery. This model is then used to build a novel compensation scheme that determines reward values, based on the current plan for the professional fleet's routes and on the couriers' probabilities of acceptance, by employing a direct search algorithm that seeks to minimise the expected cost.

Abstract
We study a setting in which a company not only has a fleet of capacitated vehicles and drivers available to make deliveries but may also use the services of occasional drivers (ODs) willing to make deliveries using their own vehicles in return for a small fee. Under such a business model, a.k.a crowdshipping, the company seeks to make all the deliveries at the minimum total cost, i.e., the cost associated with their vehicles plus the compensation paid to the ODs.We consider a stochastic and dynamic last-mile delivery environment in which customer delivery orders, as well as ODs available for deliveries, arrive randomly throughout the day, within fixed time windows.We present a novel deep reinforcement learning (DRL) approach to the problem that can deal with large problem instances. We formulate the action selection problem as a mixed-integer optimization program.The DRL approach is compared against other optimization under uncertainty approaches, namely, sample -average approximation (SAA) and distributionally robust optimization (DRO). The results show the effective-ness of the DRL approach by examining out-of-sample performance.

Abstract
Kidney exchange programs (KEPs) represent an additional possibility of transplant for patients suffering from end-stage kidney disease. If a patient has a willing living donor with whom the patient is not compatible, the pair recipient-donor can join a pool of incompatible pairs and, if compatibility between recipient and donor in two or more pairs exists, organs can be exchanged between them. The problem can be modelled as an integer program that in general aims at finding the pairs that should be selected for transplant such that maximum number of transplants is performed. In this paper, we consider that for each recipient there may exist a preference order over the organs that he/she can receive, since a recipient may be compatible with several donors but the level of compatibility with the recipient might vary for different donors. Under this setting, the aim is to find the maximum cardinality stable exchange, a solution where no blocking cycle exists, i.e., there is no cycle such that all recipients prefer the donor in that cycle rather than that in the exchange. For this purpose we propose four novel integer programming models based on the well-known edge and cycle formulations, and also on the position-indexed formulation. These formulations are adjusted for both finding stable and strongly stable exchanges under strict preferences and for the case when ties in preferences may exist. Further-more, we study a situation when the stability requirement can be relaxed by addressing the trade-off between maximum cardinality versus number of blocking cycles allowed in a solution. The effectiveness of the proposed models is assessed through extensive computational experiments on a wide set of in-stances. Results show that the cycle-edge and position-indexed formulations outperform the other two formulations. Another important practical outcome is that targeting strongly stable solutions has a much higher negative impact on the number of transplants (with an average reduction of up to 20% for the bigger instances), when compared to stable solutions.

Abstract
Power transformers are one of the main elements of a power grid, and their downtime impacts the entire network. Repairing their failures can be very costly, so sophisticated maintenance techniques are necessary. To attempt to solve this problem, we developed a mixed-integer nonlinear optimization model that, focusing on a single power transformer, both schedules this maintenance and also decides how much of the hourly demand it will satisfy. A high level of load on a power transformer increases its temperature, which increases its degradation, and so these two decisions have to be carefully balanced. We also consider that power transformers have several components that degrade differently. Our model becomes very difficult to solve even in reasonably sized instances, so we also present an iterative refinement heuristic.

Abstract
Interactive Voice Assistants (IVAs) are intelligent conversational agents capable of communicating with users using natural language. Although IVAs are more frequent in our lives, customer experience research with these agents is still in its infancy. This article aims to identify the factors that form the customer experience (CX) with Alexa and assesses its impact on traditional marketing outcomes: satisfaction and recommendation. This research presents a conceptual model of CX with IVAs and an empirical validation of the model using Structural Equation Modelling based on a sample of 580 IVA users. The results confirm that CX is a multidimensional higher-order construct composed of six factors (usefulness, ease of use, trust, privacy concerns, communication skills, and enjoyment). We also highlight the positive impact of experience on satisfaction and recommendation. Finally, we test the enthusiasm moderating role, showing its negative influence on the investigated relationships. Theoretical and practical implications are discussed.

Abstract
Background. Traditional burning is a practice with social and ecological value used worldwide. However, given the often improper and negligent use of fire, this practice is often associated with rural fire ignitions.Aims. Systematise experts' understanding of traditional burning and identify its challenges in the Portuguese context.Methods. Twenty-eight Portuguese experts from industry, academia, NGOs and public entities with in-depth involvement in fire and forest management were interviewed to create a mental model of traditional burning in Portugal.Key results. Eight dimensions were identified: motivations behind traditional burning, alternative solutions, risks before a traditional burn, risks during a traditional burn, underlying causes of risk, exogenous elements and factors, potential impacts, and activities leading to a successful traditional burn.Conclusions. This study provides a comprehensive understanding of traditional burn practice in the Portuguese context and offers a baseline to support stakeholders and policymakers in managing traditional burning's social and environmental impacts in the future.Implications. This research offers several implications across the eight dimensions identified, including the need to improve regulations on the use of fire and fuel reduction policies, promote fire use education and feasible and affordable alternatives to traditional burning, and increase communities' commitment to mitigation actions.