We consider Max-min Share (MmS) fair allocations of indivisible chores (items with negative utilities). We show that allocation of chores and classical allocation of goods (items with positive utilities) have some fundamental connections but also differences which prevent a straightforward application of algorithms for goods in the chores setting and viceversa. We prove that an MmS allocation does not need to exist for chores and computing an MmS allocation - if it exists - is strongly NP-hard. In view of these non-existence and complexity results, we present a polynomial-time 2-approximation algorithm for MmS fairness for chores. We then introduce a new fairness concept called optimal MmS that represents the best possible allocation in terms of MmS that is guaranteed to exist. We use connections to parallel machine scheduling to give (1) a polynomial-time approximation scheme for computing an optimal MmS allocation when the number of agents is fixed and (2) an effective and efficient heuristic with an ex-post worst-case analysis.

Algorithms for Max-Min Share Fair Allocation of Indivisible Chores

This volume presents meta-heuristics approaches for scheduling problems arising in industrial and manufacturing applications. Nowadays, metaheuristics have become a de facto approach to tackle in practice with the complexity of scheduling problems. Early work applied evolutionary computing methods to scheduling problems. The present volume is novel in many respects. First, the proposed approaches comprise a variety of meta-heuristics (Genetic Algorithms, Memetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Tabu Search, Scatter Search, Variable Neighborhood Search). Second, in most cases, hybridization is approached as the most effective way to achieve state-of-the art results. Third, and most importantly, the scheduling problems arising in real life applications and real world data instances are solved using these meta-heuristics; these applications comprise reconfigurable manufacturing systems, lot sizing and scheduling in industry, railway scheduling and process, supply chain scheduling and scheduling problem arising in a real-world multi-commodity Oil-derivatives Pipeline. Finally, scheduling problems and meta-heuristics are presented in a comprehensive way making this volume and interesting contribution to the research on scheduling in industrial and manufacturing applications.

Metaheuristics for Scheduling in Industrial and Manufacturing Applications

Purpose: The purpose of the research is to develop a conceptual model of intellectual production and consumption.

Intellectual Production and Consumption: A New Reality of the 21 st Century

The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

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Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications.

The static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. This problem is treated by the Bee Colony Optimization (BCO) meta-heuristic. The BCO algorithm belongs to the class of stochastic swarm optimization methods inspired by the foraging habits of bees in nature. To investigate the performance of the proposed method extensive numerical experiments are performed. Our BCO algorithm is able to obtain the optimal value of the objective function in the majority of test examples known from literature. The deviation of non-optimal solutions from the optimal ones in our test examples is at most 2%. The CPU times required to find the best solutions by BCO are significantly smaller than the corresponding times required by the CPLEX optimization solver. Moreover, our BCO is competitive with state-of-the-art methods for similar problems, with respect to both solution quality and running time. The stability of BCO is examined through multiple executions and it is shown that solution deviation is less than 1%.

Bee colony optimization for scheduling independent tasks to identical processors

Tight bounds for the identical parallel machine scheduling problem

In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression-based predictive model for the classification of diabetes. The classifier has three predictors age, b/a, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.

/pdf/classification-of-diabetes-using-photoplethysmogram-ppg-d160ofcg9d.pdf

Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1475-3995.2007.00605.x

Tight bounds for the identical parallel machine‐scheduling problem: Part II

We propose an exact branch-and-bound algorithm for the problem of maximizing the minimum machine completion time on identical parallel machines. The proposed algorithm is based on tight lower and upper bounds as well as an effective symmetry-breaking branching strategy. Computational results performed on a large set of randomly generated instances attest to the efficacy of the proposed algorithm.

/pdf/maximizing-the-minimum-completion-time-on-parallel-machines-5yxk6sjzi9.pdf

Maximizing the minimum completion time on parallel machines

Achieving community immunity against the coronavirus disease 2019 (COVID-19) depends on vaccinating the largest number of people within a specific period while taking all precautionary measures. To address this problem, this paper presents a smart parking system that will help the health crisis management committee to vaccinate the largest number of people with the minimum period of time while ensuring that all precautionary measures are followed, through a set of algorithms. These algorithms seek to ensure a uniform distribution of persons in parking. This paper proposes a novel complex system for smart parking and nine algorithms to address the NP-hard problem. The experimental results demonstrate the performance of the proposed algorithms in terms of gap and time. Applying these algorithms to smart cities to ensure precautionary measures against COVID-19 can help fight against this pandemic.

/pdf/intelligent-algorithms-and-complex-system-for-a-smart-w7juoh82in.pdf

Mahdi Jemmali

Papers

Tight bounds for the identical parallel machine scheduling problem

Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling.

Tight bounds for the identical parallel machine‐scheduling problem: Part II

Maximizing the minimum completion time on parallel machines

Intelligent algorithms and complex system for a smart parking for vaccine delivery center of COVID-19