Abstract
Many companies face capacity limitations that impair them to satisfy potential demand. In this context, sales/marketing teams have to decide which demand segments the company should prioritize. In business -to-business contexts, it is common that this selection includes customers with and without a contract. On the operations side, the production teams are interested in finding the most efficient usage for the available capacity. However, decision-making approaches to face such a challenge are scarce. In this paper, we propose a scenario-based robust optimization model to support the sales and marketing teams to define the most profitable sales plan in a setting of limited capacity, to serve multiple customers that can be either non -contractual or operate under quantity-flexibility contracts. The proposed model integrates contract design, portfolio selection, and tactical production planning decisions. By employing our model, we are able to quantify how a product's inclusion in a contract relates not only to its own profitability but also to the profitability of the remaining products that might be offered to the customer using the same resources. Regarding the optimal flexibility level to offer to a customer, it is explained by the expected sales volume, the discount rate depending on the flexibility level, and the demand variability expectation. We expect this approach supports industrial companies in defining the mid-term sales plan and deciding on the conditions to offer to contract customers.

Abstract
Although remote work was already possible and used in some contexts, the COVID-19 pandemic made it normal and, in some situations, even mandatory. This was the case in Portugal and in particular in its software industry. Given this abrupt change in how we work, it became pressing to investigate the impacts of this profound change to remote work, so that we can cope with the potential negative consequences (professional, personal, etc.). Thus, the goal of this work is to study the impact of the referred change to remote work, due to the COVID-19 pandemic, on software professionals in Portugal. To achieve this goal, a survey was prepared and distributed via email, LinkedIn, and Instagram. In total, 176 valid answers were collected from software professionals working in Portugal from 38 different companies. After the performed statistical analysis on the targeted population and focusing on the 10 elaborated research questions, two major findings can be concluded with certainty: (i) having worked in a remote regime before the pandemic period has a strong relationship with a higher frequency of use of teleconference tools after this period, and (ii) participants who do not feel safe about coming back to a fully on-site regime are more likely to prefer a fully remote regime than the ones who feel safe, while the latter group is more likely to prefer a hybrid regime.

Abstract
This article addresses the Production Routing Problem (PRP), which consists of determining, in an integrated way, production and inventory planning, and vehicle routing to minimize the costs involved. In the problem, a plant is responsible for producing several types of products to meet the known demand of a set of customers using a homogeneous fleet of vehicles over the planning horizon. In the literature, evolutionary approaches have not been explored in depth for the PRP, specifically for the problem with multiple products. Thus, this work mitigates this gap, presenting a novel Memetic Algorithm and testing its effectiveness on randomly generated sets of instances, comparing the results obtained with a commercial optimization solver. In our solution approach, several classic operators from the literature were implemented. Furthermore, we propose four novel genetic operators. In addition, we evaluated the proposed method's performance in classical instances of literature considering a single item. The computational experiments were carried out to assess the impact of the numerous parameter combinations involving the metaheuristic, and, from statistical analyses, we evidence the proposed technique's robustness. Computational experiments showed that our proposed method outperforms the commercial solver Gurobi in determining feasibly high-quality solutions, mainly on large instances for the PRP with multiple items.

Abstract
In a world facing unprecedented challenges, such as climate changes and growing social problems, the pharmaceutical industry must ensure that its supply chains are environmentally sustainable and resilient, guaranteeing access to key medications even when faced with unanticipated disruptions or crises. The core goal of this work is to develop an innovative simulation-based approach to support more informed and effective decision making, while establishing reasonable trade-offs between supply chain robustness and resiliency, operational efficiency, and environmental and social concerns. Such a decision-support system will contribute to the development of more resilient and sustainable pharmaceutical supply chains, which are, in general, critical for maintaining access to essential medicines, especially during times of crises or relevant disruptions. The system will help companies to better manage and design their supply chains, providing a valuable tool to achieve higher levels of resilience and sustainability. The study we conducted has two primary contributions that are noteworthy. Firstly, we present a new advanced approach that integrates multiple simulation techniques, allowing for the modeling of highly complex environments. Secondly, we introduce a new conceptual framework that helps to comprehend the interplay between resiliency and sustainability in decision-making processes. These two contributions provide valuable insights into understanding complex systems and can aid in designing more resilient and sustainable systems.

Abstract
In this paper, we consider the two-dimensional Variable-Sized Cutting Stock Problem (2D-VSCSP) with guillotine constraint, applied to the home textile industry. This is a challenging class of real-world prob-lems where, given a set of predefined widths of fabric rolls and a set of piece types, the goal is to de-cide the widths and lengths of the fabric rolls to be produced, and to generate the cutting patterns to cut all demanded pieces. Each piece type considered has a rectangular shape with a specific width and length and a fixed demand to be respected. The main objective function is to minimize the total amount of the textile materials produced/cut to satisfy the demand. According to Wascher, Hau ss ner, & Schu-mann (2007), the addressed problem is a Cutting Stock Problem (CSP), as the demand for each item is greater than one. However, in the real-world application at stake, the demand for each item type is not very high (below ten for all item types). Therefore, addressing the problem as a Bin-Packing Problem (BPP), in which all items are considered to be different and have a unitary demand, was a possibility. For this reason, two approaches to solve the problems were devised, implemented, and tested: (1) a CSP model, based on the well-known Lodi and Monaci (2003) model (3 variants), and (2) an original BPP-based model. Our research shows that, for this level of demand, the new BPP model is more competitive than CSP models. We analyzed these different models and described their characteristics, namely the size and the quality of the linear programming relaxation bound for solving the basic mono-objective variant of the problem. We also propose an epsilon-constraint approach to deal with a bi-objective extension of the problem, in which the number of cutting patterns used must also be minimized. The quality of the models was evaluated through computational experiments on randomly generated instances, yielding promising results.(c) 2022 Published by Elsevier B.V.

Abstract
Thin films of Ag/Fe were deposited on the core of multimode optical fibers. The deposited film shows sensitivity to both refractive index and MF changes. Simulation work based on TMM formalism confirms experimental response. © Optica Publishing Group 2023, © 2023 The Author(s)