Abstract
Vertical pulp and paper production is challenging from a process point of view. Managers must deal with floating bottlenecks, intermediate storage levels, and by-product production to control the whole process while reducing unexpected downtimes. Thus, this paper aims to address the integrated lot sizing and scheduling problem considering continuous digester production, multiple paper machines, and a chemical recovery line to treat by-products. The aim is to minimize the total production cost to meet customer demands, considering all productive resources and encouraging steam production (which can be used in power generation). Production planning should define the sizes of production lots, the sequence of paper types produced in each machine, and the digester working speed throughout the planning horizon. Furthermore, it should indicate the rate of byproduct treatment at each stage of the recovery line and ensure the minimum and maximum storage limits. Due to the difficulty of exactly solving the mixed integer programming model representing this problem for realworld instances, mainly with planning horizons of over two weeks, constructive and improvement heuristics are proposed in this work. Different heuristic combinations are tested on hundreds of instances generated from data collected from the industry. Comparisons are made with a commercial Mixed-Integer and Linear Programming solver and a hybrid metaheuristic. The results show that combining the greedy constructive heuristic with the new variation of a fix-and-optimize improvement method delivers the best performance in both solution quality and computational time and effectively solves realistic size problems in practice. The proposed method achieved 69.41% of the best solutions for the generated set and 55.40% and 64.00% for the literature set for 1 and 2 machines, respectively, compared with the best solution method from the literature and a commercial solver.

Abstract
Supplier selection for strategic items requires a comprehensive framework dealing with qualitative and quantitative aspects of a company's competitive priorities and supply risk, decision scope, and uncertainty. In order to address these aspects, this study aims to tackle supplier selection for strategic items with a multi-sourcing, taking into account multi-criteria, incorporating uncertainty of decision-makers judgment and supplier-buyer parameters, and integrating with inventory management which the past studies have not addressed well. We develop a novel two-phase solution approach based on integrated multi-criteria decision -making (MCDM) and multi-objective simulation-optimization (S-O). First, MCDM methods, including fuzzy AHP and interval TOPSIS, are applied to calculate suppliers' scores, incorporating uncertain decision makers' judgment. S-O then combines the (quantitative) cost-related criteria and considers supply disruptions and uncertain supplier-buyer parameters. By running this approach on data generated based on previous studies, we evaluate the impact of the decision maker's and the objective's weight, which are considered important in supplier selection.

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
Background and aims: currently, most endoscopy software only provides limited statistics of past procedures, while none allows patterns to be extrapolated. To overcome this need, the authors applied business analytic models to pre-dict future demand and the need for endoscopists in a ter-tiary hospital Endoscopy Unit. Methods: a query to the endoscopy database was per-formed to retrieve demand from 2015 to 2021. The graphi-cal inspection allowed inferring of trends and seasonality, perceiving the impact of the COVID-19 pandemic, and se-lecting the best forecasting models. Considering COVID-19's impact in the second quarter of 2020, data for esoph-agogastroduodenoscopy (EGD) and colonoscopy was estimated using linear regression of historical data. The actual demand in the first two quarters of 2022 was used to validate the models. Results: during the study period, 53,886 procedures were requested. The best forecasting models were: a) simple sea-sonal exponential smoothing for EGD, colonoscopy and percutaneous endoscopic gastrostomy (PEG); b) double ex-ponential smoothing for capsule endoscopy and deep en-teroscopy; and c) simple exponential smoothing for endo-scopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS). The mean average percent-age error ranged from 6.1 % (EGD) to 33.5 % (deep en - teroscopy). Overall, 8,788 procedures were predicted for 2022. The actual demand in the first two quarters of 2022 was within the predicted range. Considering the usual time allocation for each technique, 3.2 full-time equivalent en-doscopists (40 hours-dedication to endoscopy) will be re-quired to perform all procedures in 2022. Conclusions: the incorporation of business analytics into the endoscopy software and clinical practice may enhance resource allocation, improving patient-focused deci-sion-making and healthcare quality.

Abstract
Original Equipment Manufacturers (OEMs) have sought new supply chain paradigms that allowed them to focus on core activities, i.e. overall product design and commercialisation. This pursuit led to partnerships with a new generation of tier-1 strategic suppliers acting as integrators. Integrators are not only responsible for system supply, but also for system design. However, critical integrators were not able to live up to their new roles, which led to costly delays in development and production. These failures highlight the ineptitude of current risk management practices employed by OEMs. To support OEMs in implementing a more differentiated and suitable approach to the use of integrators, this paper proposes a mathematical programming model for Supply Chain Design (SCD). Instead of looking at the introduction of integrators as a dichotomous decision, the model suggests the optimal number of integrators, i.e. systems, and individual part suppliers. We propose new measures for integration risk, which build upon current risk assessment practices. Robust optimisation is used to study the effect of uncertainty over baseline risk values. All approaches were tested using both randomly generated instances and real data from a large European OEM in the aerospace industry.