Abstract
In this paper, we explore the value of considering simultaneous pickups and deliveries inmulti-product inventory-routing problems both with deterministic and uncertain demand. Wepropose a multi-commodity, develop an exact branch-and-cut algorithm with patching heuristicsto efficiently tackle this problem, and provide insightful analyses based on optimal plans. Thesimplicity of the proposed approach is an important aspect, as it facilitates its usage in practice,opposed to complicated stochastic or probabilistic methods. The computational experimentssuggest that in the deterministic demand setting, pickups are mainly used to balance initialinventories, achieving an average total cost reduction of 1.1%, while transshipping 2.4% oftotal demand. Under uncertain demand, pickups are used extensively, achieving cost savings of up to 6.5% in specific settings. Overall, our sensitivity analysis shows that high inventory costsand high degrees of demand uncertainty drive the usage of pickups, which, counter-intuitively, are not desirable in every case

Abstract
Often, real-life problems require modelling several response variables together. This work analyses a multivariate linear regression model when the data are censored. Censoring distorts the correlation structure of the underlying variables and increases the bias of the usual estimators. Thus, we propose three methods to deal with multivariate data under left censoring, namely Expectation Maximization (EM), DataAugmentation (DA) and Gibbs Sampler with Data Augmentation (GDA). Results from a simulation study showthat both DA and GDA estimates are consistent for low and moderate correlation. Under high correlation scenarios, EM estimates present a lower bias.

Abstract
Wastewater treatment plants constitute an essential part of the sewage system. They have the role of removing pollutants from wastewater to enable the safe disposal of the treated effluent in the natural environment. This research seeks to evaluate plants' efficiency and effectiveness, which involves minimizing energy consumption while obtaining a quality level of the treated water aligned with legislation requirements. We explore two policy scenarios regarding the measurement of effluent quality. The first assumes that pollutants' emission quotas (EQs) are fixed at each plant. The second assumes that quotas are set for the receiving waters (e.g., river or watercourse in the natural environment) so that trade-offs in EQs among plants sharing the same discharge site are possible. This latter scenario requires a system-wide analysis to identify optimal targets for pollutants removal at each plant that allow fulfilling the expected average quality levels of the effluent discharged. This paper develops a methodology to fully realize the potential for energy savings based on an innovative mixed-integer linear programming model. This model follows the data envelopment analysis axioms to estimate the frontier of the production possibility set. The approach proposed is tested in a real-world context using the plants of a Portuguese water company. The results show that the two scenarios combining efficiency and effectiveness perspectives have advantages in terms of energy savings compared to the conventional situation focused only on efficiency gains. The saving potential is slightly higher in the scenario allowing reallocation of EQs among plants.

Abstract
This paper describes the development of a mechanism to deal with time windows constraints. To the best of our knowledge, the time windows constraints are difficult to be fulfilled even for state-of-the-art methods. Therefore, the main contribution of this paper is to propose a new computational technique to deal with such constraints to ensure the algorithm convergence. We test such technique in two metaheuristics to solve the discrete and dynamic Berth Allocation Problem. A data set generator was created, resulting in a diversity of problems in terms of time windows constraints. A detailed computational analysis was carried out to compare the performance for each metaheuristic.

Abstract
Recently there has been an increasing demand for technologies (automated and intelligent machines) that brings benefits to organizations and society. Similar to the widespread use of personal computers in the past, today's needs are towards facilitating human-machine technology appropriation, especially in highly risky and regulated industries like robotics, manufacturing, automation, military, finance, or healthcare. In this context, trust can be used as a critical element to instruct how human-machine interaction should occur. Considering the context-dependency and multidimensional trust, this study seeks to find a way to measure the effects of perceived trust in a collaborative robot (cobot), regardless of its literal credibility as a real person. This article aims at translating, adapting, and validating a Human-Computer Trust Scale (HCTM) in human-robot interaction (HRI) context and its application to cobots. The Human-Robot Interaction Trust Scale (HRITS) involved 239 participants and included eleven items. The 2nd order CFA with a general factor called trust have proven to be empirically robust (CFI=.94; TLI=.93; SRMR=.04; and RMSEA=.05) [CR=.84; AVE=.58, and MaxRH=.92]; results indicated a good measurement of the general factor trust, and the model satisfied the criteria for measure trust. An analysis of the differences in perceptions of trust by gender was conducted using a t-test. This analysis showed that statistical differences by gender exist (p=.04). This study's results allowed for a better understanding of trust in HRI, specifically regarding cobots. The validation of a Portuguese scale for trust assessment in HRI can give a valuable contribution to designing collaborative environments between humans and robots.

Abstract

The role of the digital economy is undoubtful these days, with new digital services and technologies appearing regularly, creating new products, and digitalizing others. With the technology exploring new limits, the growth tendency of this economy is clear, and more incumbents will face serious competition, which can’t be ignored. Several of these startups have untapped markets that are not being addressed, exploring that disruption leads to more innovative solutions that will likely lead to better services for people and organizations. Those startups might, though, move up the ladder and take away the market from those incumbents; with this work, we aim to analyze the role of digital economies in market disruption through the lenses of a case study and understand to what extent that case is looking at disrupt an existing market