Abstract
In this paper, we investigate a new variant of the multi-commodity network flow problem, taking into consideration multiple transport lines and time windows. This variant arises in a city logistics environment, more specifically in a long-haul passenger transport system that is also used to transport urban freight. We propose two mixed integer programming models for two objective functions: minimization of network operational costs and minimization of travel times. Since the problems under study are NP-hard, we propose three size reduction heuristics. In order to assess the performance of the proposed algorithms, we carried out computational experiments on a set of synthetic problem instances. We use the relative percentage deviation as performance criterion. For the cost objective function, a LP-and-Fix algorithm outperforms other methods in most tested instances, but for the travel time, a hybrid method (size reduction with LP-and-Fix algorithm) is, in general, better than other approaches.

Abstract
In this research, we present several extensions for the resource-constrained project scheduling problem with alternative subgraphs (RCPSP-AS). First of all, we investigate more complex variants of the alternative project structure. More precisely, we consider nested alterative subgraphs, linked alternative branches, multiple selection, caused and closed choices, and split choices. Secondly, we introduce non-renewable resources in the RCPSP-AS in order to implicitly avoid certain combinations of alternatives given a limited availability of this resource over the complete project horizon. We formulate both the basic RCPSP-AS and its extensions as an ILP model and solve it using Gurobi. The computational experiments are conducted on a large set of artificial project instances as well as three case studies. The results show the impact of the different extensions on the project makespan and the computational complexity. We observe that combinations of the proposed extensions might imply complex alternative project structures, resulting in an increasing computational complexity or even infeasible solutions. The analysis of the three case studies shows that it is hard to find feasible solutions with a small time limit or optimal solutions with a larger time limit for projects with a realistic size in terms of the number of activities or alternatives.

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
Robotic process automation (RPA) is a recent technology that has recently become increasingly adopted by companies as a solution for employees to focus on higher complexity and more valuable tasks while delegating routine, monotonous and rule-based tasks to their digital colleagues. The increased interest, reflected in the increasing number of articles regarding approaches and test cases, has triggered the necessity for a summary that could extract the more generalisable ideas and concepts about these software robots. This paper used a Systematic Literature Review (SLR) approach to find and synthesise information from articles obtained on this subject. This research identified the most general implementation approaches of successful RPA adoption cases, observed benefits, challenges commonly faced by organisations, characteristics that make processes more suitable for RPA, and research gaps in the current literature. The findings presented in this paper have two purposes. The first is to provide a way for companies and organisations to become more familiar with good practices regarding the adoption of robotic process automation. The second is to foster further research on the subject by complementing the current knowledge and proposing new paths for research. © 2022 D. A. da Silva Costa et al.

Abstract

Abstract
Problem definition: Online retailers are on a consistent drive to increase on-time delivery and reduce customer lead time. However, in reality, an increasing share of consumers places orders early. Academic/practical relevance: Such advance demand information can be deployed strategically to reduce costs and improve the customer service experience. This requires inventory and allocation policies that make optimal use of this information and that induce consumers to place their orders early. An increasing number of online retailers not only offer customers a choice of lead time but also, actively back-order missing items from a consumer basket. Methodology: We develop new allocation policies that commit to a customer order upon arrival of the order rather than at the moment the order is due. We provide analytical results for the performance of these allocation policies and evaluate their behavior with real data from a large food retailer. Results: Our policy leads to a higher fill rate at the expense of a slight increase in average delay. The analysis based on real-life data suggests a sizeable impact that should impact current best practices in online retail. Managerial implications: With the changing landscape in online retail, customers increasingly place baskets of orders that they would like to receive at a planned and confirmed moment in time. Especially in grocery, this has grown fast. This fundamentally changes the strategic management of inventory. We demonstrate that online retailers should commit early to customer orders to enhance the customer service experience and eventually, to also create opportunities for reducing the cost of operations. Superscript/Subscript Available