The availability of digital infrastructures and the fast-paced development of accompanying revolutionary technologies have triggered an unprecedented reliance on Artificial intelligence (AI) techniques both in theory and practice. Within the AI domain, Machine Learning (ML) techniques stand out as essential facilitator largely enabling machines to possess human-like cognitive and decision making capabilities. This paper provides a focused review of the literature addressing applications of emerging ML tools to solve various Project Scheduling Problems (PSPs). In particular, it employs bibliometric and network analysis tools along with a systematic literature review to analyze a pool of 104 papers published between 1985 and August 2021. The conducted analysis unveiled the top contributing authors, the most influential papers as well as the existing research tendencies and thematic research topics within this field of study. A noticeable growth in the number of relevant studies is seen recently with a steady increase as of the year 2018. Most of the studies adopted Artificial Neural Networks, Bayesian Network and Reinforcement Learning techniques to tackle PSPs under a stochastic environment, where these techniques are frequently hybridized with classical metaheuristics. The majority of works (57%) addressed basic Resource Constrained PSPs and only 15% are devoted to the project portfolio management problem. Furthermore, this study clearly indicates that the application of AI techniques to efficiently handle PSPs is still in its infancy stage bringing out the need for further research in this area. This work also identifies current research gaps and highlights a multitude of promising avenues for future research.

The Multi-skilled Resource Constrained Project Scheduling Problem (MS-RCPSP) is a complex and multi-faceted problem that involves scheduling activities whilst considering various resource constraints. These constraints include limited availability of workers, equipment, and materials, with each activity requiring a minimal set of skills to be executed. Furthermore, for a better resemblance to reality, workers/machines are assumed to be multi-skilled/multi-purpose posing another dimension of complexity to the problem. The objective is to minimize project duration, cost, or other relevant criteria while accounting for the inherent resources flexibility. This paper provides a systematic review of the literature pertaining to MS-RCPSP, and an in-depth analysis of 171 papers published between 2000 and 2021 inclusive. The conducted bibliometric analysis identifies the top contributing authors, most influential papers, existing research tendencies, and thematic research topics within the field. In addition, this review highlights different aspects of the MS-RCPSP, spanning the significance of performance measures, solution approaches, application areas, and the incorporation of time constraints. While project completion time, cost, and tardiness are common performance indicators, other measures such as multi-skilled staff assignment and schedule robustness are also deemed important. Although various methods have been employed to solve the MS-RCPSP including exact and approximate approaches, the selection of the most-suited approach depends on the problem’s scale, complexity, and constraints, necessitating careful consideration of each method’s strengths and weaknesses. Interestingly, several studies have jointly addressed resource and time constraints in the context of MS-RCPSP, often considering tardiness, and have proposed different algorithms, models, and metaheuristics to tackle these challenges. This paper clearly highlights research gaps and promising avenues for future research. This work provides valuable insights for project managers to effectively schedule tasks in the presence of multiple flexible resources.

The failure of one solder joint out of the hundreds of joints in a system compromises the reliability of the electronics assembly. Thermal cycling is a result of both creep–fatigue mechanisms working together. To better understand the failure process in thermal cycling, it is crucial to analyze both the effects of creep and fatigue mechanisms in a methodical manner. In this work, individual solder junctions are subjected to accelerated shear fatigue testing to investigate the effects of creep and fatigue on joint dependability at room temperature. A modified fixture is used to conduct fatigue tests on an Instron 5948 micromechanical tester. SAC305 joints with an OSP surface finish were cycled under stress control at first, and then the strain was maintained for a set amount of time. In this investigation, three stress amplitudes of 16, 20, and 24 MPa are used, together with varying residence periods of 0, 10, 60, and 180 s. The fatigue life of solder junctions is described for each testing condition using the two-parameter Weibull distribution. Additionally, as a function of stress amplitude and residence time, a dependability model is created. For each testing scenario, the progression of the stress–strain loops was studied. By quantifying relevant damage metrics, such as plastic work per cycle and plastic strain at various testing circumstances, the damage due to fatigue is distinguished from creep. To investigate the relationships between plastic work and plastic strain with fatigue life, the Coffin–Manson and Morrow Energy model is used. The results indicate that using greater stress magnitudes or longer dwell periods significantly shortens fatigue life and dramatically increases plastic work and plastic strain. The housing impact is significant; in some circumstances, testing with a longer dwelling period and lower stress amplitude resulted in more damage than testing with a shorter dwelling period and higher stress levels. When illustrating the fatigue behavior of solder junctions under various stress amplitudes or dwellings, the Coffin–Manson and Morrow Energy model were both useful. In the end, general reliability models are developed as functions of plastic work and plastic strain.

This paper investigates the views of healthcare researchers and professionals on the implementation of the Quality Management System (QMS) approach using a 5-point Likert scale survey. Researchers and healthcare professionals who observed or participated in QMS implementation were surveyed. Multiple channels, including occupational societies, social networking, i.e., LinkedIn, hospital’s directories, award recipients, academic researchers, and professional connections, made it possible to reach this particular sample. Participants were surveyed using a series of questions with a total of 56 questions. The survey was administrated through the web portal of Qualtrics and then analyzed both on Qualtrics and SPSS software packages. Descriptive Statistics, Exploratory Factor Analysis (EFA), and Linear Regression were employed to draw conclusions. The final sample group consisted of 71 participants representing a range of healthcare settings. EFA was conducted, producing a model of 10 emergent factors and an outcome for total improvement. Regression modeling revealed the Critical Success Factors (CSFs) and the interaction between emergent factors. The results indicated that QMS Implementation Culture, Structure, and Managerial Training are critical to the QMS implementation success. This research helps quality professionals enhance their ability to prioritize elements affecting the successful implementation of the QMS.

Multi-response optimization problems investigation is a crucial element in initiatives designed to enhance quality and overall productivity for manufacturing processes. Since no particular algorithm can be employed for all multi-response problems, defining the method that is utilized as a problem-solving technique is a vital step in the process factors optimization. Identifying a formal procedure of implementing the improvement approach in a multi-criteria decision-making problem is a critical need to ensure the consistency and sustainability of the enhancement methods. In this study, a Plan–Do–Check–Act (PDCA) framework is implemented for a case study in the food industry under which a multi-response optimization problem is investigated. The design of experiment (DOE) is used to examine the effect of process parameters on the quality responses by using the Taguchi method to find the optimal setting for each parameter. An orthogonal array (OA) and signal-to-noise (SNR) ratio is employed to investigate the performance characteristics. Each performance characteristic is then converted into a signal-to-noise ratio, and all the ratios are then fed into a fuzzy model to produce a single comprehensive output measure (COM). The average COM values for various factor levels are calculated, and the level that maximizes the COM value for each factor is identified as the optimal level. Results indicated the effectiveness of the applied method to find the optimal factor levels for the multi-response optimization problem under study. The global optimal factor levels that are driven from the fuzzy logic for the studied parameters are 1250, 40, 7.5, and 1:2, for the speed, frying time, cooking time, and the coating ratio, respectively. Means of all the studied quality characteristics were closer to the target values when compared with the initial factors’ settings.