Abstract
Nowadays there are several countries running independent kidney exchange programmes (KEPs). These programmes allow a patient with kidney failure, having a willing healthy but incompatible donor, to receive a transplant from a similar pair where the donor is compatible with him. Since in general larger patient-donor pools allow for more patients to be matched, this prompts independent programmes (agents) to merge their pools and collaborate in order to increase the overall number of transplants. Such collaboration does however raise a problem: how to assign transplants to agents so that there is a balance between the contribution each agent brings to the merged pool and the benefit it gets from the collaboration. In this paper we propose a new Integer Programming model for multi-agent kidney exchange programmes (mKEPs). It considers the possible existence of multiple optimal solutions in each matching period of a KEP and, in consecutive matching periods, selects the optimal solution among the set of alternative ones in such a way that in the long-term the benefit each agent gets from participating in the mKEP is balanced accordingly to a given criterion. This is done by use of a memory mechanism. Extensive computational tests show the benefit of mKEPs, when compared to independent KEPs, in terms of potential increase in the number of transplants. Furthermore, they show that, under different policies, the number of additional transplants each agent receives can vary significantly. More importantly, results show that the proposed methodology consistently obtains more stable results than methodologies that do not use memory.

Abstract
The complex multi-criteria optimisation problems arising in Kidney Exchange Programmes have received considerable attention both in practice and in the scientific literature. Whereas theoretical advancements are well reviewed and synthesised, this is not the case for practice. We present a synthesis of models and methods applied in present European Kidney Exchange Programmes, which is based on detailed descriptions we created for this purpose. Most descriptions address national programmes, yet we also present findings on emerging cross-national programmes. The synthesis provides a systematic and detailed description of the models and methods the programmes use, revealing important commonalities as well as considerable variation among them. Rather than distilling a single best practice from these results, we find that the variation in models and methods arises because of variation in country characteristics, policies, and ethics. The synthesised state of the art may benefit future national and cross-national initiatives and direct future theoretical contributions within and across the boundaries of the Operations Research discipline. (c) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
We study last-mile delivery with the option of crowd shipping, where a company makes use of occasional drivers to complement its vehicle's fleet in the activity of delivering products to its customers. We model it as a data-driven distributionally robust optimization approach to the capacitated vehicle routing problem. We assume the marginals of the defined uncertainty vector are known, but the joint distribution is difficult to estimate. The presence of customers and available occasional drivers can be random. We adopt a strategic planning perspective, where an optimal a priori solution is calculated before the uncertainty is revealed. Therefore, without the need for online resolution performance, we can experiment with exact solutions. Solving the problem defined above is challenging: not only the first-stage problem is already NP-Hard, but also the uncertainty and potentially the second-stage decisions are binary of high dimension, leading to non-convex optimization formulations that are complex to solve. We propose a branch-price-and-cut algorithm taking into consideration measures that exploit the intrinsic characteristics of our problem and reduce the complexity to solve it.

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
Kidney exchange programs (KEP) allow an incompatible patient-donor pair, whose donor cannot provide a kidney to the respective patient, to have a transplant exchange with another in a similar situation if there is compatibility. Exchanges can be performed via cycles or chains initiated by non-directed donors (NDD), i.e., donors that do not have an associated patient. The objective for optimization in KEP is generally to maximize the number of possible transplants. Following the course of recent approaches that consider a dynamic matching (exchanges are decided every time a pair or a NDD joins the pool), in this paper we explore two matching policies to find feasible exchanges: periodic, where the algorithm runs within some period (e.g each 3 month); and greedy, in which a matching run is done as soon as the pool is updated with a new pair or NDD. For each policy, we propose a matching algorithm that addresses the waiting times of pairs in a pool. We conduct computational experiments with the proposed algorithms and compare the results with those obtained when periodic and greedy matching aim at maximizing the number of transplants.

Abstract
In this paper we present simulations for international kidney exchange programmes (KEPs). KEPs are organised in more than ten countries in Europe to facilitate the exchanges of immunologically incompatible donors. The matching runs are typically conducted in every three months for finding optimal exchanges using hierarchical optimisation with integer programming techniques. In recent years several European countries started to organise international exchanges using different collaboration policies. In this paper we conduct simulations for estimating the benefits of such collaborations with a simulator developed by the team of the ENCKEP COST Action. We conduct our simulations on generated datasets mimicking the practice of the three largest KEPs in Europe, the UK, Spanish and the Dutch programmes. Our main performance measure is the number of transplants compared to the number of registrations to the KEP pools over a 5-year period, however, as a novelty we also analyse how the optimisation criteria play a role in the lexicographic and weighted optimisation policies for these countries. Besides analysing the performances on a single instance, we also conduct large number of simulations to obtain robust findings on the performance of specific national programmes and on the possible benefits of international collaborations.