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Background and Objectives: Malaria is a disease that can result in a variety of complications. Diagnosis is carried out by an optical microscope and depends on operator experience. The use of artificial intelligence to identify morphological patterns in erythrocytes would improve our diagnostic capability. The object of this study was therefore to establish computer viewing models able to classify blood cells infected with Plasmodium spp. to support malaria diagnosis by optical microscope. Materials and Methods: A total of 27,558 images of human blood sample extensions were obtained from a public data bank for analysis; half were of parasite-infected red cells (n = 13,779), and the other half were of uninfected erythrocytes (n = 13,779). Six models (five machine learning algorithms and one pre-trained for a convolutional neural network) were assessed, and the performance of each was measured using metrics like accuracy (A), precision (P), recall, F1 score, and area under the curve (AUC). Results: The model with the best performance was VGG-19, with an AUC of 98%, accuracy of 93%, precision of 92%, recall of 94%, and F1 score of 93%. Conclusions: Based on the results, we propose a convolutional neural network model (VGG-19) for malaria diagnosis that can be applied in low-complexity laboratories thanks to its ease of implementation and high predictive performance.

Purpose This study aims to reduce carbon emissions and costs in an automobile production plant by improving the operational management efficiency of a serial assembly line assisted by a feeding electric tow vehicle (ETV). Design/methodology/approach A multi-objective function is formulated to minimize the energy consumption of the ETV from which emissions and costs are measured. First, a mixed-integer linear programming model is used to solve the feeding problem for different sizes of the assembly line. Second, a bi-objective optimization (HBOO) model is used to simultaneously minimize the most eco-efficient objectives: the number of completed runs (tours) by the ETV along the assembly line, and the number of visits (stops) made by the ETV to deliver kits of components to workstations. Findings The most eco-efficient strategy is always the bi-objective optimal solution regardless of the size of the assembly line, whereas, for single objectives, the optimization strategy differs depending on the size of the assembly line. Research limitations/implications Instances of the problem are randomly generated to reproduce real conditions of a particular automotive factory according to a previous case study. The optimization procedure allows managers to assess real scenarios improving the assembly line eco-efficiency. These results promote the implementation of automated control of feeding processes in green manufacturing. Originality/value The HBOO-model assesses the assembly line performance with a view to reducing the environmental impact effectively and contributes to reducing the existent gap in the literature. The optimization results define key strategies for manufacturing industries eager to integrate battery-operated motors or to address inefficient traffic of automated transport to curb the carbon footprint.

Purpose – The purpose of this study is to firstly investigate the efficiency of the most commonly used performance measures for minimizing the Number of Workstations (NWs) in approaches addressing Simple Assembly Line Balancing Problem (SALBP) for both straight and U-shaped line. Secondly, this study aims to provide a comparative evaluation of 20 constructive heuristics to find solutions to the SALBP-1. Design/methodology/approach – 200 problems are solved by 20 different constructive heuristics for both straight and U-shaped assembly line. Moreover, several comparisons have been made to evaluate the performance of constructive heuristics. Findings – Minimizing the Smoothness Index (SI) is not necessarily equivalent to minimizing the NWs, therefore, it should not be used as the fitness function in approaches addressing the SALBP-1. Line efficiency (LE) and the idle time (IT) are indeed reliable performance measures for minimizing the NWs. The most promising heuristics for straight and U-shaped line configurations for SALBP-1 are also ranked and introduced. Practical implications – Results are expected to help scholars and industrial practitioners to better design effective solution methods for having a most balance assembly line. This study will further help with choosing the most proper heuristic with regard to the problem specifications and line configuration. Originality/value – There is limited research assessing the efficiency of the common objectives for SALBP-1. This study is among the first to prove that minimizing the workload smoothness is not equivalent to minimizing the NWs in SALBP-1 studies. This work is also one of the first attempts for evaluating the constructive heuristics for both straight and U-shaped line configurations.

Purpose The purpose of this study is to first investigate the efficiency of the most commonly used performance measures for minimizing the number of workstations (NWs) in approaches addressing simple assembly line balancing problem (SALBP) for both straight and U-shaped line, and second to provide a comparative evaluation of 20 constructive heuristics to find solutions to the SALBP-1. Design/methodology/approach A total of 200 problems are solved by 20 different constructive heuristics for both straight and U-shaped assembly line. Moreover, several comparisons have been made to evaluate the performance of constructive heuristics. Findings Minimizing the smoothness index is not necessarily equivalent to minimizing the NWs; therefore, it should not be used as the fitness function in approaches addressing the SALBP-1. Line efficiency and the idle time are indeed reliable performance measures for minimizing the NWs. The most promising heuristics for straight and U-shaped line configurations for SALBP-1 are also ranked and introduced. Practical implications Results are expected to help scholars and industrial practitioners to better design effective solution methods for having the most balanced assembly line. This study will further help with choosing the most proper heuristic with regard to the problem specifications and line configuration. Originality/value There is limited research assessing the efficiency of the common objectives for SALBP-1. This study is among the first to prove that minimizing the workload smoothness is not equivalent to minimizing the NWs in SALBP-1 studies. This work is also one of the first attempts for evaluating the constructive heuristics for both straight and U-shaped line configurations.

Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process.

This research addresses the fluctuating demand and high variability in healthcare systems. These system’s variations need to be considered whilst at the same time making efficient use of the systems’ resources. Patient dependency fluctuation, which makes determining the level of adequate staffing highly complex, is among the variations addressed. Dealing with variability is found to be a key feature in the design, planning and evaluation of healthcare systems. Healthcare providers are facing increasing challenges resulting from an aging population, higher patient expectancies, a shortage of healthcare professionals, as well as increasing costs and reduced funding. Despite the accentuated need for effective healthcare systems and efficient use of resources, many healthcare organisations are inadequately designed and, moreover, poorly managed. Hospital systems consist of complex interrelations between relatively small units, each of which is sensitive to stochastic variations in demand. In addition to this aspect of the system view, a critical resource for the patients’ wellbeing and survival is the staffing level of nurses. This puts the planning and scheduling of human resources as one of the system’s foremost aims. Current tools for staffing and personnel planning in healthcare organisations do not take into consideration the workload variations that result from the variable nature of patient dependency levels. The work presents the empirical findings of a number of case studies conducted at a regional hospital in Sweden. Principles and practical suggestions for the robust system design of inpatient wards using Discrete Event Simulation (DES) have been identified. Although DES techniques have, in principle, all the features for modelling the variation and stochastic nature of systems, DES has not been previously used for workload studies of inpatient wards. The main contribution of this work is therefore how a combination of DES and the data of Patient Classification Systems (PCSs) can be used to model workload variations and, subsequently, plan the nurse staffing requirements in systems with high variability. The work presented gives step by step guidance in how the analysis and subsequent modelling of an inpatient ward should be carried out. It defines a novel modelling methodology for patient dependency variations and length of stay modelling of a patient’s dependency progression, including an adaptation to the ward’s discharge figures. The modelling approach opens a novel way of analysing and evaluating the system design of inpatient wards.

Facility layout design (FLD) is becoming more challenging than ever. In particular, modern day manufacturing industry requires advancing from a traditional approach of mass production to one of mass customization including increased flexibility and adaptability. There are several software tools that can support facility layout design among which simulation and optimization are the most powerful – especially when the two techniques are combined into simulation-based optimization (SBO). The aim of this study is to identify the challenges of SBO in FLD of production systems. In doing so, this paper uncovers the challenges of SBO and FLD, which are so far addressed in separate streams of literature. The results of this study present two novel contributions based on two case studies in the Swedish manufacturing industry. First, that challenges of SBO in FLD, previously identified in literature, do not hold equal importance in industrial environments. Our results suggest that challenges in complexity, data noise, and standardization take precedence over challenges of SBO in FLD previously reported in literature. Second, that the origin of challenges of SBO in FLD are not technological in nature, but stem from the increased complexity of factories required in modern day manufacturing companies.

Facility layout design (FLD) is becoming more challenging than ever as manufacturing moves from a traditional emphasis on mass production to an emphasis on mass customization, which requires increased flexibility and adaptability. Of the software tools that support FLD, simulation and optimization are the most powerful - especially when combined in simulation-based optimization (SBO). The aim of this study is to identify the challenges of using SBO in FLD of production systems. To date, the challenges of SBO and FLD have been addressed in separate streams of literature. This paper also presents two novel contributions based on two case studies involving Swedish manufacturers. First, it shows that the challenges of using SBO in FLD identified in the literature are not the most important in industrial environments, where precedence must be given to the challenges of complexity, data noise, and standardization. Second, it shows that the challenges of SBO in FLD are not technological in nature but stem from the increased complexity of the factories required by modern manufacturing companies.

This paper focuses on the problem of supplying the workstations of assembly lines with components during the production process. For that specific problem, this paper presents a Mixed Integer Linear Program (MILP) that aims at minimizing the energy consumption of the supplying strategy. More specifically, in contrast of the usual formulations that only consider component flows, this MILP handles the mass flow that are routed from one workstation to the other.