Comparison stochastic optimisation approaches for the multi-mode resource-constrained multi-project scheduling problem

Although lettuce is one of the most important vegetable crops cultivated in Brazil, producers conduct seedling production empirically, as there are no published reports on the optimal start time and management strategy for seedling fertigation. The present aimed to assess the influence of fertigation management on the growth, physiological aspects and nutritional status of lettuce seedlings and to determine the optimal fertigation start time and frequency. Two experiments were conducted, each with a randomized block design and six repetitions. The first consisted of six treatments, namely six fertigation start times at 0, 3, 6, 9, 12, and 15 d after emergence (DAE), and the second consisted of five treatments, representing different application frequencies at 3, 4, 5, 6, and 7 d intervals. The assessment of nutrient accumulation levels and biometric and physiological characteristics of the seedlings were performed after transplanting. Fertigation start times significantly affected 14 of the 18 variables assessed, particularly the number of leaves, shoot dry weight, leaf area, initial chlorophyll fluorescence, and P, K, Ca, Mg, and S accumulation. The best results for ten variables were obtained when fertigation began at emergence, with values 17.77 - 35.63% higher than those at fertigation onset at 15 DAE. Application frequency only influenced chlorophyll content and N, P, K, and S accumulation, with optimal results obtained at 3 - 6 d intervals. Beginning fertigation at plant emergence favors dry weight production, nutrition and photosynthesis and shortens the production time of lettuce seedlings. The optimal start time for lettuce seedling fertigation is at emergence, with application performed every 6 d.

Framework for multi-project scheduling in product development based on available data

A variable neighborhood search approach for the resource-constrained multi-project collaborative scheduling problem

Multi-project scheduling with multi-skilled workforce assignment considering uncertainty and learning effect for large-scale equipment manufacturer

Many organizations struggle with too many projects in the “pipeline” based on limited resource capacity. I have heard numerous stories by attendees of my Project Portfolio Management class at the University of Wisconsin‐Madison of the “pain points” caused by this situation, such as personnel and resources being pulled off one project to work on another and then pulled off that project to work on another one, or personnel working on so many concurrent projects at once that these personnel are overwhelmed with trying to split their time so fine. Then they don't know which project to focus on first.

Integrating a multi-objective optimization framework into a structural design software

Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resource-constrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge.

Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resource-constrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge.

This paper presents a new way to solve the general resource-constrained multi-project scheduling problem (RCMPSP) which changes the condition that multiple projects cannot carry out simultaneously due to the fixed limit to the resource usage. We define a lower bound and an upper bound to limit the resource usage of each activity in multi-project. Then, the priority rule based heuristic is introduced and one of the most suitable priority rule based heuristic is chosen to deal with the resource-constrained multi-project scheduling problem. Finally, an example is given to demonstrate the improvement of the resource-constrained multi-project's model. Result of the example shows that the multi-project schedule can be more efficient with the bounds of resource usage and priority rule-Maximum Total Work Content (MAXTWK) based heuristic.

This paper aims to present a comprehensive review of advanced techniques and models with a specific focus on deep neural network (DNN) for resource-constrained environments (RCE). The paper contributes by highlighting the RCE devices, analyzing challenges, reviewing a broad range of optimization techniques and DNN models, and offering a comparative assessment. The findings provide potential optimization techniques and recommend a baseline model for future development. It encompasses a broad range of DNN optimization techniques, including network pruning, weight quantization, knowledge distillation, depthwise separable convolution, residual connections, factorization, dense connections, and compound scaling. Moreover, the review analyzes the established optimization models which utilizes the above optimization techniques. A comprehensive analysis is conducted for each technique and model, considering its specific attributes, usability, strengths, and limitations in the context of effective deployment in RCEs. The review also presents a comparative assessment of advanced DNN models’ deployment for image classification, employing key evaluation metrics such as accuracy and efficiency factors like memory and inference time. The article concludes with the finding that combining depthwise separable convolution, weight quantization, and pruning represents potential optimization techniques, while also recommending EfficientNetB1 as a baseline model for the future development of optimization models in RCE image classification.<p> </p>

In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. The multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem is extended to incorporate indicators on agents’ payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.

Clinicians often use validated risk models to guide treatment decisions for cardiovascular risk reduction. The most common risk models for predicting cardiovascular risk are the UKPDS, Framingham, and Archimedes models. In this article, the authors propose a model to optimize the selection of patients for statin therapy of hypercholesterolemia, for patients with type 2 diabetes, using each of the risk models. For each model,they evaluate the role of age, gender, and metabolic state on the optimal start time for statins. Using clinical data from the Mayo Clinic electronic medical record, the authors construct a Markov decision process model with health states composed of cardiovascular events and metabolic factors such as total cholesterol and high-density lipoproteins. They use it to evaluate the optimal start time of statin treatment for different combinations of cardiovascular risk models and patient attributes. The authors find that treatment decisions depend on the cardiovascular risk model used and the age, gender, and metabolic state of the patient. Using the UKPDS risk model to estimate the probability of coronary heart disease and stroke events, they find that all white male patients should eventually start statin therapy; however, using Framingham and Archimedes models in place of UKPDS, they find that for male patients at lower risk, it is never optimal to initiate statins. For white female patients, the authors also find some patients for whom it is never optimal to initiate statins. Assuming that age 40 is the earliest possible start time, the authors find that the earliest optimal start times for UKPDS, Framingham, and Archimedes are 50, 46, and 40, respectively, for women. For men, the earliest optimal start times are 40, 40, and 40, respectively. In addition to age, gender, and metabolic state, the choice of cardiovascular risk model influences the apparent optimal time for starting statins in patients with diabetes.

In view of the fact that the critical chain method (CCM) is mainly used in single project scheduling, this article improves the method and applies it to multi-project scheduling. Taking into consideration the flow of the drum resource within and among sub-projects, this study proposes methods that can be used to identify the critical chain and buffer settings, based on the concepts of the task chain, sub-project drum resource flow, and multi-project drum resource flow. The improvement of the CCM includes two aspects. First, by analysing the impact of the drum buffer and capacity constrained buffer on the project buffer and feeding buffer, the calculation methods of these buffers are optimised. Second, a risk contribution index is designed for the modification of these buffers, in order to further improve their anti-interference ability. Then, combined with the improved CCM and the different hierarchical scheduling objectives, a critical chain resource-constrained multi-project scheduling model with a hierarchical strategy is proposed as a way of solving multi-project scheduling plans. Eight theoretical test cases with different scales, and a practical example under different risk levels of uncertainty are used to test this model. The results show that the stability of the scheduling plans clearly improved, and the project duration and tardiness costs were significantly reduced, thus proving the effectiveness of the model.

Mobility-Aware Adaptive Transmission in the Mobile Ad-Hoc Network

PurposeThis research aims to introduce an algorithm, the Multi-objective Giant Pacific Octopus Optimizer (MOGPOO) and demonstrate its use in balancing three parameters: time, cost, quality in the Multi-Mode Resource-constrained Multi-Project Scheduling Problem (MRCMPSP).Design/methodology/approachThe aim of the project is to combine the Multi-objective Optimization with a Giant Pacific Octopus Optimizer (GPOO) – swarm intelligence approach. The study compared the derivatives of the MOGPOO optimization method to those of other algorithms, such as the Multi-objective Slime Mold Algorithm (MOSMA) and the Multi-objective Grey Wolf Optimization (MOGWO), and testing the model’s performance and assessing the construction problem, a total of nine projects in the essential MRCMPSP problem are taken into consideration from the resources that are accessible.FindingsCompared to the other approaches, MOGPOO performed better on the majority of the assessment criteria. This study and its results can be useful for researchers working on multi-project scheduling models. In addition, the superiority of MOGPOO becomes increasingly evident as the complexity of the problem increases.Originality/valueA hybrid swarm intelligence model and strategy that allows Multi-Mode Resource-constrained Multi-Project Scheduling Problem in construction management is presented in the study in order to optimize the ideal solution in the search space. Based on the findings of this study’s testing procedure, a strong model that outperformed the examined models was constructed.