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.

We address the problem of minimizing makespan on identical parallel machines. We propose new lower bounding strategies and heuristics for this fundamental scheduling problem. The lower bounds are based on the so‐called lifting procedure. In addition, two optimization‐based heuristics are proposed. These heuristics require iteratively solving a subset‐sum problem. We present the results of computational experiments that provide strong evidence that the new proposed lower and upper bounds consistently outperform the best bounds from the literature.

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.

This paper focused on the resolution of the project's assignment problem. Several heuristics have been developed and proposed in this paper to serve as lower bounds to our studied problem. In a developing country, it is interesting to make an equitable distribution of projects in different cities in order to guarantee equality and regional development. Each project is characterized by its budget. The problem is to find an appropriate schedule to assign all projects to all cities. This appropriate schedule seeking the maximization of the budget in the city that having the minimum budget. In this paper, six heuristics were proposed to carry out the objective of resolving the studied problem. The experimental results show that the algorithm given by the heuristic P r 6 outperforms all other heuristics cited in this paper.done regarding the performance of other heuristics and the exact solution of the studied problem.

The aim of this research work is to find algorithms solving an NP-hard problem by elaborating several heuristics. This problem is to find an appropriate schedule to assign different projects, which will be expected to generate fixed revenues, to several cities. For this work, we assume that all cities have the same socioeconomic and strategic characteristics. The problem is as follow. Given a set of projects which represented by its expected revenues. The objective is to distribute on several cities all projects with a minimum expected revenues gap between cities. Thus, our objective is to minimize the expected revenue gap. The suitable assignment is searching equity between cities. In this paper, we formulate mathematically the studied problem to find an approximate solutions and apply some methods to search resolution of the studied problem.