Among the different tasks in production logistics, job scheduling is one of the most important at the operational decision-making level to enable organizations to achieve competiveness. Scheduling consists in the allocation of limited resources to activities over time in order to achieve one or more optimization objectives. Flow-shop (FS) scheduling problems encompass the sequencing processes in environments in which the activities or operations are performed in a serial flow. This type of configuration includes assembly lines and the chemical, electronic, food, and metallurgical industries, among others. Scheduling has been mostly investigated for the deterministic cases, in which all parameters are known in advance and do not vary over time. Nevertheless, in real-world situations, events are frequently subject to uncertainties that can affect the decision-making process. Thus, it is important to study scheduling and sequencing activities under uncertainties since they can cause infeasibilities and disturbances. The purpose of this paper is to provide a general overview of the FS scheduling problem under uncertainties and its role in production logistics and to draw up opportunities for further research. To this end, 100 papers about FS and flexible flow-shop scheduling problems published from 2001 to October 2016 were analyzed and classified. Trends in the reviewed literature are presented and finally some research opportunities in the field are proposed

Despite being a preventable disease, cervical cancer continues to be a public health concern, affecting mainly lower and middle-income countries. Therefore, in Bogotá a home-visit based program was instituted to increase screening uptake. However, around 40% of the visited women fail to attend their Pap smear test appointments. Using this program as a case study, this paper presents a methodology that combines machine learning methods, using routinely collected administrative data, with Champion’s Health Belief Model to assess women’s beliefs about cervical cancer screening. The aim is to improve the cost-effectiveness of behavioural interventions aiming to increase attendance for screening. The results presented here relate specifically to the case study, but the methodology is generic and can be applied in all low-income settings. This is a cross-sectional study using two different datasets from the same population and a sequential modelling approach. To assess beliefs, we used a 37-item questionnaire to measure the constructs of the CHBM towards cervical cancer screening. Data were collected through a face-to-face survey (N = 1699). We examined instrument reliability using Cronbach’s coefficient and performed a principal component analysis to assess construct validity. Then, Kruskal–Wallis and Dunn tests were conducted to analyse differences on the HBM scores, among patients with different poverty levels. Next, we used data retrieved from administrative health records (N = 23,370) to fit a LASSO regression model to predict individual no-show probabilities. Finally, we used the results of the CHBM in the LASSO model to improve its accuracy. Nine components were identified accounting for 57.7% of the variability of our data. Lower income patients were found to have a lower Health motivation score (p-value < 0.001), a higher Severity score (p-value < 0.001) and a higher Barriers score (p-value < 0.001). Additionally, patients between 25 and 30 years old and with higher poverty levels are less likely to attend their appointments (O.R 0.93 (CI: 0.83–0.98) and 0.74 (CI: 0.66–0.85), respectively). We also found a relationship between the CHBM scores and the patient attendance probability. Average AUROC score for our prediction model is 0.9. In the case of Bogotá, our results highlight the need to develop education campaigns to address misconceptions about the disease mortality and treatment (aiming at decreasing perceived severity), particularly among younger patients living in extreme poverty. Additionally, it is important to conduct an economic evaluation of screening options to strengthen the cervical cancer screening program (to reduce perceived barriers). More widely, our prediction approach has the potential to improve the cost-effectiveness of behavioural interventions to increase attendance for screening in developing countries where funding is limited.

While, at its inception in 1993, the health care system in Colombia was publicized as a paradigm to be copied across the developing world, numerous problems in its implementation have led to, what is now, an inefficient and crisis-ridden health system. Furthermore, as a result of inappropriate tools to measure the quality of the health service providers, several corruption scandals have arisen in the country. This study attempts to tackle this situation by proposing a strategy for the quality assessment of the health service providers (Entidades Promotoras de Salud, EPS) in the Colombian health system. In particular, as a case study, the quality of the treatment of stomach cancer is analyzed. The study uses two complementary techniques to address the problem. These techniques are applied based on data of the treatment of gastric cancer collected on a nation-wide scale by the Colombian Ministry of Health and Welfare. First, Data Envelopment Analysis (DEA) and the Malmquist Index (MI) are used to establish the most efficient EPS’s within the system, according to indicators such as opportunity indicators. Second, sequential clustering algorithm, related to process mining a field of data mining, is used to determine the medical history of all patients and to construct typical care pathways of the patients belonging to efficient and inefficient EPS’s. Lastly, efforts are made to identify traits and differences between efficient and inefficient EPS’s. Efficient and inefficient EPS were identified for the years 2010 and 2011. Additionally, a Malmquist Index was used to calculate the relative changes in the efficiency of the health providers. Using these efficiency rates, the typical treatment path of patients with gastric cancer was found for two EPSs: one efficient and another inefficient. Finally, the typical traits of the care pathways were established. Combining DEA and process mining proved to be a powerful approach understanding the problem and gaining valuable insight into the inner workings of the Colombian Health System, especially in terms of the treatment process performed by health care providers in critical illnesses such as cancer. However, no sufficiently compelling results were found to establish the contribution of such a combination to evaluate the quality in the delivery of health services.