Dementia is a neuropsychiatric brain disorder that usually occurs when one or more brain cells stop working partially or at all. Diagnosis of this disorder in the early phases of the disease is a vital task to rescue patients’ lives from bad consequences and provide them with better healthcare. Machine learning methods have been proven to be accurate in predicting dementia in the early phases of the disease. The prediction of dementia depends heavily on the type of collected data which usually are gathered from Normalized Whole Brain Volume (nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected from Magnetic Resonance Imaging (MRIs). Other biological features such as age and gender can also help in the diagnosis of dementia. Although many studies use machine learning for predicting dementia, we could not reach a conclusion on the stability of these methods for which one is more accurate under different experimental conditions. Therefore, this paper investigates the conclusion stability regarding the performance of machine learning algorithms for dementia prediction. To accomplish this, a large number of experiments were run using 7 machine learning algorithms and two feature reduction algorithms namely, Information Gain (IG) and Principal Component Analysis (PCA). To examine the stability of these algorithms, thresholds of feature selection were changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has resulted in 7×9 + 7×7= 112 experiments. In each experiment, various classification evaluation data were recorded. The obtained results show that among seven algorithms the support vector machine and Naïve Bayes are the most stable algorithms while changing the selection threshold. Also, it was found that using IG would seem more efficient than using PCA for predicting Dementia. These promising results open the door to a new era of early prognosis of Alzheimer’s Disease and Related Dementias (ADRD).

Operating in today’s highly competitive global markets, transnational enterprises always seek to optimize internal vendor-buyer coordinated systems to ensure timeliness and quality deliveries, given the reality of unreliable machines and limited capacity. To facilitate accurate decision making to help organizations gain competitive advantages in such situations, this study explores an intra-supply-chain problem featuring a partial outsourcing batch fabrication plan, random scrap, Poisson-distributed breakdown rate, and multiple shipments of end-product. First, we build a model to characterize the problem clearly. Then, we carry out formulations, analyses, and derivations of the model to attain the problem’s cost function. We then use differential calculus and propose a specific algorithm to confirm the convexity of the obtained cost function and derive the optimal runtime. Finally, we offer a numerical illustration to demonstrate the result’s applicability for other business circumstances. Additional elements of the problem are then discussed, including the individual and combined influence of variations in scrap, outsourcing, breakdown, and shipping frequency. The features of an optimal operating policy and cost relevant parameters are now revealed to assist management with strategic planning and decision making in real-world intra-supply-chain environments.

After the outbreak of COVID-19, Taiwan has implemented rigorous border control and taken specific measures such as virus detection, contact tracing, and quarantine since 2020. Its epidemic prevention performance has been quite outstanding. Even in May 2021, when the epidemic situation worsens, the people in Taiwan fully cooperate with the government’s control measures so as to successfully alleviate and control the epidemic in less than three months. Among them, the detection policy has played a pivotal role. We analyze and discuss the false positive and false negative problems from rapid antigen and PCR detection in the screening policy as well as the timing of using these two instruments. This paper provides theoretical verification of the appropriateness of screening policy in Taiwan, offering a few feasible suggestions for related policies in other countries or regions at different stages of this and other potential epidemics.

With increasing concerns and challenges to climate change in recent years, green operations management (GOM) has gained significant attention from society for achieving sustainable growth. GOM is a set of practices that can be applied in production processes to produce goods with improved productivity and significantly reduced threats of carbon emission to the environment and Mother Nature. GOM mainly includes green manufacturing, green design, green logistics, and green purchases. In the paper, fuzzy best-worst method (FBWM) is used to determine the best and worst criteria affected by GOM practices. Thus, the paper attempts to explicitly analyze and highlight the significance of GOM in preserving the environment and manage the triple bottom line for achieving overall sustainable business operations.

Multi-Objective (MO) optimization is a well-known research field with respect to the complexity of production planning and scheduling. In recent years, many different Evolutionary Computation (EC) methods have been applied successfully to MO production planning and scheduling. This paper is focused on making a review of MO production scheduling methods, starting from production scheduling presentation, notation and classification. The research field of EC methods is presented, then EC algorithms` classification is introduced for the purpose of production scheduling optimization. As a main goal, MO optimization is focused on hybrid EC methods, and presenting their advantages and limitations. Finally, a survey of five scientific databases is presented, with the analysis of the scientific publications the terminology development of the scientific field is presented. Using the citation analysis of the scientific publications, the application for the MO optimization in manufacturing scheduling is discussed.

In the world of digitization, e-commerce practices has become more popular and attracts manufacturers to combine their traditional retail channel with an e-channel. To add some salient features in the existing study, this study develops an optimal pricing and profit decision model for manufacturer-led dual-channel supply chain configurations; namely Vertically Integrated Dual-Supply Chain (VID-SC), Decentralized Dual-channel Supply Chain (DD-SC), Partially Integrate Dual-Supply Chain (PID-SC) and Horizontally Integrated Dual-Supply Chain (HID-SC). The aim of this study is to examine the effect of selected decision parameters namely cooperative advertisement, delivery lead time and free-riding on price and profit of manufacturer-led dual supply chain configurations. A linear programming for profit maximization is developed and backward induction method is used to find the optimum values of price and profit. A numerical analysis is performed to evaluate the effect of selected decision parameters on price and profit. To check the robustness of the outcomes an interaction plot is made to indicate the relationship between the selected decision parameters on optimum price. The best fit values of these decision parameters lead to the optimum price and the profit. The study helps to find the best fit value of the selected decision parameters for their specified dual-channel configuration. As a result, the model contributes as a guideline moreover it is proficient to guide manufacturers and channel members as a decision making practices without actual implementation of any strategy or policy.

The Additive Manufacturing (AM) scheduling problem is becoming a very felt issue not only by the scholars but also by the practitioners who are looking to this new technology as a new integrated part of their traditional production systems. They need new scheduling models to adapt the traditional scheduling rules to the changed ones of the additive manufacturing. This paper deals with the enhancement of a scheduling problem for additive manufacturing just present in literature and the presentation of a new meta-heursitic (adapted to the new requirements of the additive manufacturing technology) based on the tabu-search algorithms.

Jaya algorithm has recently been proposed, which is simple and efficient meta-heuristic optimization technique and has received a great attention in the world of optimization. It has been successfully applied to some thermal, design and manufacturing associated optimization problems. This paper aims to analyze the performance of Jaya algorithm for permutation flow-shop scheduling problem which is a well-known NP-hard optimization problem. The objective is to minimize the makespan. First, to make Jaya algorithm adaptive to the problem, a random priority is allocated to each job in a permutation sequence. Second, a job priority vector is converted into job permutation vector by means of Largest Order Value (LOV) rule. An exhaustive comparative study along with statistical analysis is performed by comparing the results with public benchmarks and other competitive heuristics. The key feature of Jaya algorithm of simultaneous movement towards the best solution and going away from the worst solution enables it to avoid being trapped in the local optima. Furthermore, the uniqueness of Jaya algorithm compared with any other evolutionary based optimization technique is that it is totally independent of specific parameters. This substantially reduces the computation effort and numerical complexity. Computational results reveal that Jaya algorithm is efficient in most cases and has considerable potential for permutation flow-shop scheduling problems.

Workload control theory seeks to align capacity and demand to improve delivery performance. However, workload control researchers mainly focused on input control, which regulates the input of work to the production system, thereby neglecting output control, which uses capacity adjustments to regulate the outflow of the work. Moreover, few existing studies on output control investigate a temporarily increase in capacity. This paper introduces a new search direction for output control which does not require an increase in capacity – labour flexibility. Idle operators can move from their workstation to another, thus temporarily increasing the output of that workstation without extra capacity. Using simulation of a five workstations flow shop line, we highlight the positive performance effect of labour flexibility. However, this comes at the cost of high labour movement. Introducing a load-based constraint on when workers are allowed to significantly reduces labour movement, while realizing most of the performance improvement observed for unconstrained labour movement. This has important implications for future research and practice.

The Medical System of Transportation deals with two online real-time decisions: ambulance dispatching and relocation. Dispatching consists of selecting which ambulance to send to an emergency call; relocation consists of determining how to modify the location of available ambulances in response to changes in the system’s state. Although the literature regarding this problem is extensive, only a limited number of online real-time approaches for ambulance management have been proposed, much less one taking into consideration different types of emergencies and vehicles. This paper proposes an online real-time matheuristic algorithm that combines: i) a new preparedness index defined as the availability probability of a multi-server queue model which is used as an optimization objective and as a control variable for relocation strategies, ii) two mathematical models to solve the relocation problem, one oriented to the maximization of coverage and other to the minimization of the maximum relocation time, and iii) two heuristic algorithms oriented to the maximization of the preparedness level, one to solve the dispatch problem and other to solve the location problem of one ambulance. The computational experiments, based on discrete event simulation and historical data of Bogotá, Colombia, have shown their capability to adequately respond to the necessities of real-time operation.