Abstract
This work addresses the lot-sizing and scheduling problem under multistage demand uncertainty. A flexible production system is considered, with the possibility to adjust the size and the schedule of lots in every time period based on a rolling-horizon planning scheme. Computationally intractable multistage stochastic programming models are often employed on this problem. An adaptation strategy to the multistage setting for two-stage programming and robust optimization models is proposed. We also present an approximate heuristic strategy to address the problem more efficiently, relying on multistage stochastic programming and adjustable robust optimization. In order to evaluate each strategy and model proposed, a Monte Carlo simulation experiment under a rolling-horizon scheme is performed. Results show that the strategies are promising in solving large-scale problems: the approximate strategy based on adjustable robust optimization has, on average, 6.72% better performance and is 7.9 times faster than the deterministic model.

Abstract
In the retail industry, there are multiple products flowing from different distribution centers to brick-and-mortar stores with distinct characteristics. This industry has been suffering radical changes along the years and new market dynamics are making distribution more and more challenging. Consequently, there is a pressure to reduce shipment sizes and increase the delivery frequency. In such a context, defining the most efficient way to supply each store is a critical task. However, the supply chain planning decision that tackles this type of problem, delivery mode planning, is not well defined in the literature. This paper proposes a definition for delivery mode planning and analyzes multiple ways retailers can efficiently supply their brick-and-mortar stores from their distribution centers. The literature addressing this planning problem is reviewed and the main interdependencies with other supply chain planning decisions are discussed.

Abstract
The present paper proposes two hybrid genetic algorithms as decision-making techniques for operational level decisions in the Glass Container Industry (GCI). The proposed methods address a production scenario where one new furnace and the related machines must be added to the current industrial plant. The configurations for each machine connected in a furnace is a decision to be taken, which depends on demand forecasts for glass containers within a time horizon. It is a tactical and operational level decisions that must be efficiently made. A mathematical formulation is first presented to describe precisely the objective and constraints for such problem. The formulation will also allow solving the problem instances by applying an exact method. Next, a hybrid approach combining genetic algorithms with mathematical programming techniques, and a greedy filter heuristic is proposed to solve the same problem instances. The set of instances is generated with data provided by a GCI located in Portugal and Brazil. The results reported indicate that the hybrid genetic algorithms return solutions able to support the operational and tactical decisions.

Abstract
Background: Ensuring healthcare delivery is dependent both on the prediction of the future demand for healthcare services and on the estimation and planning for the Health Human Resources needed to properly deliver these services. Although the Health Human Resources planning is a fascinating and widely researched topic, and despite the number of methodologies that have been used, no consensus on the best way of planning the future workforce requirements has been reported in the literature. This paper aims to contribute to the extension and diversity of the range of available methods to forecast the demand for Health Human Resources and assist in tackling the challenge of translating healthcare services to workforce requirements. Methods: A method to empirically quantify the relation between healthcare services and Health Human Resources requirements is proposed. For each one of the three groups of specialties identified-Surgical specialties, Medical specialties and Diagnostic specialties (e.g., pathologists)-a Labor Requirements Function relating the number of physicians with a set of specialty-specific workload and capital variables is developed. This approach, which assumes that health managers and decision-makers control the labor levels more easily than they control the amount of healthcare services demanded, is then applied to a panel dataset comprising information on 142 public hospitals, during a 12-year period. Results: This method provides interesting insights on healthcare services delivery: the number of physicians required to meet expected variations in the demand for healthcare, the effect of the technological progress on healthcare services delivery, the time spent on each type of care, the impact of Human Resources concentration on productivity, and the possible resource allocations given the opportunity cost of the physicians' labor. Conclusions: The empirical method proposed is simple and flexible and produces statistically strong models to estimate Health Human Resources requirements. Moreover, it can enable a more informed allocation of the available resources and help to achieve a more efficient delivery of healthcare services.

Abstract
BackgroundUnderstanding the perceived importance of Patient-Centered Care (PCC) among Palestinian doctors and how the provider and other clinical characteristics may impact their views on PCC is essential to determine the extent to which PCC can be implemented. This study investigates the provision of PCC among hospital doctors in a developing and unstable country, namely, Palestine.MethodsThis descriptive, cross-sectional research employed self-report survey among 369 Palestinian doctors working in hospitals in 2016. Respondents completed the Provider-Patient Relationship Questionnaire (PPRQ) and were asked to rate the importance of 16 PCC subjects in a context-free manner. Then they scored the existence of eight contextual attributes in their workplace.ResultsAlthough 71.4% of the participants got training in communication, only 45% of the participants knew about PCC. 48.8% of doctors considered the exchange of information with patients most important PCC component. Clustering identified three groups of doctors: 32.4% of doctors reported good perceptions of PCC, 47.5% moderate; and 20.1% poor. Older, married, and specialist doctors and those familiar with PCC are more likely classified in the good cluster. Results revealed a significant difference between doctors' views based on their gender, experience, marital status, previous knowledge about PCC, and type of hospital in favor of males, experienced, married, familiar with PCC, and doctors in private hospital respectively. The level of job interest, nurses' cooperation, the tendency of patients to hide information, and doctor's friendly style were positively related with more perceived importance of PCC.ConclusionWe identified benchmark doctors who perceive the high relative importance of PCC. Our results highlighted knowledge gaps and training weaknesses among doctors in public and private hospitals in respect to their views on PCC. Decision makers may invest in the determined contextual predictors to enhance attitudes towards PCC. This work doesn't address patients' views on PCC.

Abstract
BackgroundThe structure, function, and capacity of the health care system in the Occupied Palestinian Territories (OPT) had been largely shaped by the complex political history of the country. Since the establishment of the Palestinian Authority in 1994, the reform efforts were subsidized much by the international aids to rebuild the country's institutional capacity. No previous studies have provided a realistic evaluation of Palestinian achievements in the conduct of public healthcare, we examine the relative productive efficiency of public hospitals (their managers' success in the usage of resources) during 2010-2015 within West Bank and Jordan. Then, we estimate the efficiency of policies within which managers operate (the program efficiency) across the two countries.MethodsWe employ the Data Envelopment Analysis (DEA) models to distinguish between within-country managerial efficiencies and public policy program efficiencies across the two countries. The study follows two key steps, the first step evaluates managerial efficiencies and explores trends in performance within each country. Then, we examine the program efficiencies across the two countries.ResultsPublic hospitals improved their year-specific overall efficiency from 75 to 80% in the West Bank and from 78 to 86% in Jordan in 2010 and 2015 respectively. Changes in efficiency are driven by scale effects in West Bank and by managerial enhancements in Jordan. Program efficiency in West Bank outperformed Jordan during 2010-2012, there was no significant difference in mean program efficiencies between the two countries during 2013-2015.ConclusionsThis work addresses a gap in the DEA literature by empirically investigating the efficiency of public hospitals as distinct from program efficiency in a developing country, namely, Palestine. Findings stimulate hospital managers to enhance potential improvements, policymakers to allocate resources, and international donors to focus on the right adoption of new technology to get better benefits from their considerable investments in public hospitals.