Abstract
Project management is a critical success factor for any kind of project. When projects involve complex multi-organisational structures, requiring demanding collaborative processes, project management is a complex system in itself. Although this topic has been studied for decades, the ever-changing morphology of projects induced by the increasingly intricate financing schemes, call for a frequent and updated understanding of how the projects initiate, run, and close. Large-scale integrated collaborative projects are a recent example of complex collaborative projects that were not studied yet but can provide important insights for the project and innovation management fields. These projects are carried out typically by a large consortium including research organisations, sectorial technological centres, technology providers and end-user companies, having a significant impact on the technological innovation produced for a specific sector. This chapter reports a multiple case study of four large-scale integrated projects, in Portugal, following an inductive research design. The results showed that collaboration creation and collaboration management are crucial processes for such projects, with challenges intensified by the differences in goals and expectations of researchers and practitioners. The chapter will hopefully contribute to the future development of new models, tools and techniques that will improve the efficiency and effectiveness of this type of projects, by providing a systematisation of the challenges faced and how they were overcome during the cases analysed. © 2021, Springer Nature Switzerland AG.

Abstract
The development of the Internet created new technologies that are changing the way of doing business, particularly Industry 4.0 in current days. One challenge of the implementation of new technologies is the change required from companies' business models. However, the literature concerning Industry 4.0 is mainly focused on technological innovations and less in their impact on business models. This paper aims to understand the impacts of the digitalization process inherent to Industry 4.0 on business models. To achieve this purpose, an exploratory multiple-case study based on semi-structured interviews was conducted in two Portuguese medium companies from two different sectors. Findings show that besides companies being able to change to a customer-oriented approach, individualized mass production may not be the only purpose of this transformation. Networking is particularly appealing for small and medium enterprises, once they usually have fewer resources to dedicate to innovation projects. Additionally, in the era of globalization, logistic costs are still an obstacle for serving international markets. Finally, social media are seen as an internal tool of communication for business-to-business companies. Being one of the first empirical studies of the Portuguese context, it aims to diminish the lack of literature concerning this particular topic and enable future researches on the use of business models as a management tool. Secondly, it intends to help managers develop or redesign business models adjusted to a more dynamic and competitive environment. © 2021 IEEE.

Abstract
Collaboration between companies in transportation problems seeks to reduce empty running of vehicles and to increase the use of vehicles' capacity. Motivated by a case study in the food supply chain, this paper examines a lateral collaboration between a leading retailer (LR), a third party logistics provider (3 PL) and different producers. Three collaborative strategies may be implemented simultaneously, namely pickup-delivery, collection and cross-docking. The collaborative pickup-delivery allows an entity to serve customers of another in the backhaul trips of the vehicles. The collaborative collection allows loads to be picked up at the producers in the backhauling routes of the LR and the 3 PL, instead of the traditional outsourcing. The collaborative cross-docking allows the producers to cross-dock their cargo at the depot of another entity, which is then consolidated and shipped with other loads, either in linehaul or backhaul routes. The collaborative problem is formulated with three different objective functions: minimizing total operational costs, minimizing total fuel consumption and minimizing operational and CO2 emissions costs. The synergy value of collaborative solutions is assessed in terms of costs and environmental impact. Three proportional allocation methods from the literature are used to distribute the collaborative gains among the entities, and their limitations and capabilities to attend fairness criteria are analyzed. Collaboration is able to reduce the global fuel consumption in 26% and the global operational costs in 28%, independently of the objective function used to model the problem. The collaborative pickup-delivery strategy outperforms the other two in the majority of instances under different objectives and parameter settings. The collaborative collection is favoured when the ordering loads from producers increase. The collaborative cross-docking tends to be implemented when the producers are located close to the depot of the 3 PL.

Abstract
Distribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor-intensive warehouses, partially due to time-consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short-sighted. This work presents a three-step methodology that uses probabilistic simulation, optimization, and event-based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete-event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.

Abstract
This paper proposes a multi-objective mixed-integer linear programming to model a cold chain with complementary operations on a hierarchical hub network. Central hubs are linked to each other in the first level of the network and to the star network of the lower-level hubs. As for a case study, different hub levels provide various refreshing or freezing operations to keep the perishable goods fresh along the network. Disruption is formulated by the consideration of stochastic demand and multi-level freshness time windows. Regarding the solution, a genetic algorithm is also developed and compared for competing the large-sized networks.

Abstract
In the current global market, managing supply is not a straightforward process and it becomes even more complex as uncertainty and disruptions occur. In order to mitigate their impact, the selection of suppliers of strategic items should have a more holistic view of the operations in the supply chain. We propose an integrated model for supplier selection, considering inventory management and inbound transportation. We approach this problem, incorporating stochastic demand and suppliers' imperfect quality. Imperfect quality triggers additional costs, including external failure and holding costs. Supply disruptions also affect the suppliers' lead time, resulting in delivery delays. We develop a methodology to address this challenge with simulation-optimisation. A genetic algorithm determines supplier selection decisions, while inventory decisions are computed analytically. Discrete-event simulation is used to evaluate the overall performance, as well as to update the lead time dynamically, according to the disruptions. Finally, sensitivity analysis providing managerial insights reveals that criteria in supplier selection should be given a different priority depending on the characteristics of the items, and the effectiveness of disruption mitigation strategies depends on the disruption characteristics.