Research Article
Multi-objective Linear Programming: A Survey
Paschal Bisong Nyiam*
,
Abdellah Salhi
Issue:
Volume 11, Issue 5, October 2026
Pages:
78-105
Received:
30 March 2026
Accepted:
30 March 2026
Published:
21 September 2026
Abstract: This paper presents a state-of-the-art survey of major algorithms suggested to solve Multiple Objective Linear Programming (MOLP). We have comprehensively reviewed MOLP papers that have appeared since 1964. The algorithms are considered in two broad categories: Non-Interactive algorithms and interactive ones. Interactivity in our view is an essential feature of usable tools. It enhances applicability and robustness of methods on one hand, but hinders them on the other by the mere fact that intervention is required. Note that the Non-Interactive algorithms include the Simplex, Interior Point, Objective Space based algorithms and relevant Nature-inspired population-based stochastic algorithms which are becoming more and more prominent. Note also that in the objective space methods, the simplex algorithm or the dual simplex algorithm are being invoked during the search process. This suggest that they should be put in the simplex based class. However, for more clarity and given that there is a strong trend to refer to them as objective space methods, we prefer to put them on their own since their underlying philosophy is different from that of the simplex based methods. While the Interactive ones only consist of the Simplex and Interior Point algorithms. An illustration of representative algorithms of each category and a tabulated summary of all algorithms are included.
Abstract: This paper presents a state-of-the-art survey of major algorithms suggested to solve Multiple Objective Linear Programming (MOLP). We have comprehensively reviewed MOLP papers that have appeared since 1964. The algorithms are considered in two broad categories: Non-Interactive algorithms and interactive ones. Interactivity in our view is an essenti...
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Research Article
Confidence-Weighted Multiscale Point-to-Plane Change Detection for Point-Cloud-Based Object Monitoring
Issue:
Volume 11, Issue 5, October 2026
Pages:
106-117
Received:
7 September 2026
Accepted:
17 September 2026
Published:
30 September 2026
Abstract: Repeated three-dimensional point clouds provide a practical basis for monitoring local geometric deformation, but conventional pointwise or single-scale comparison is sensitive to residual registration error, nonuniform sampling, measurement noise, missing observations, and isolated outliers. To address these limitations, this study develops an interpretable confidence-weighted multiscale point-to-plane detector for spatially coherent change in repeated point clouds. Local normals are estimated from covariance matrices at several neighbourhood scales; point-to-plane residuals are fused across scales, modulated by a planarity-density confidence term, thresholded with the median absolute deviation (MAD), and filtered by neighbourhood persistence. The MATLAB implementation was evaluated on a controlled non-planar synthetic surface with Gaussian deformation, rigid displacement, heterogeneous noise, nonuniform sampling, dropout, and outliers. Across 30 independent Monte Carlo realisations, the complete method achieved precision 0.836 ± 0.030, recall 0.967 ± 0.012, F1 0.897 ± 0.018, IoU 0.813 ± 0.029, and FPR 0.0071 ± 0.0018. The single-scale point-to-plane baseline reached F1 0.625 ± 0.021, while confidence-weighted multiscale scoring without persistence reached 0.640 ± 0.019. Trimmed ICP reduced robust nearest-neighbour RMSE from 7.262 ± 0.032 mm to 0.771 ± 0.009 mm. Across tested deformation amplitudes from 3 to 10 mm, F1 increased from 0.749 to 0.930. The results show that spatial persistence provides the largest improvement in false-alarm suppression, with confidence weighting adding a complementary gain. All results are synthetic; sensor-specific detection limits and field accuracy are not claimed.
Abstract: Repeated three-dimensional point clouds provide a practical basis for monitoring local geometric deformation, but conventional pointwise or single-scale comparison is sensitive to residual registration error, nonuniform sampling, measurement noise, missing observations, and isolated outliers. To address these limitations, this study develops an int...
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