Sustainable Phosphorus Fertilizer Supply Chain Management to Improve Crop Yield and P Use Efficiency Using an Ensemble Heuristic–Metaheuristic Optimization Algorithm

Shokouhifar, Mohammad; Sohrabi, Mahnaz; Rabbani, Motahareh; Molana, Seyyed Mohammad Hadji; Werner, Frank

doi:10.3390/agronomy13020565

Cited by 26 publications

(9 citation statements)

References 43 publications

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“…In order to develop this research further, it is suggested to consider the uncertainty in the important parameters of the mathematical model and implement robust optimization [33] to deal with such uncertainties. Moreover, due to the high complexity of the proposed mathematical model, it is suggested to apply efficient meta-heuristic algorithms such as a firefly algorithm (FFA) [34], whale optimization algorithm (WOA) [35], or pareto-based metaheuristics [36], to handle the complexities of the mathematical model.…”

Section: Discussionmentioning

confidence: 99%

Mathematical Modeling and Exact Optimizing of University Course Scheduling COnsidering Preferences of Professors

Chen¹,

Werner²,

Shokouhifar³

2023

Preprint

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University course scheduling (UCS) is one of the most important and time-consuming issues that all educational institutions face yearly. Most of the existing techniques to model and solve UCS problems have applied approximate methods, which are different in terms of efficiency, performance, and optimization speed. Accordingly, this research aims to apply an exact optimization method to provide an optimal solution to the course scheduling problem. In other words, in this research, an integer programming model is presented to solve the USC problem. In this model, hard and soft constraints include the facilities of classrooms, courses of different levels and compression of students' curriculum, courses outside the faculty and planning for them, and the limited time allocated to the professors. The objective is to maximize the weighted sum of allocating available times to professors based on their preferences in all periods. To evaluate the presented model's feasibility, it is implemented using the GAMS software. Finally, the presented model is solved in a larger dimension using a real data set from a college in China and compared with the current program in the same college. The obtained results show that considering the mathematical model's constraints and objective function, the faculty courses' timetable is reduced from 4 days a week to 3 working days. Moreover, master courses are planned in two days, and the courses in the educational groups do not interfere with each other. Furthermore, by implementing the proposed model for the real case study, the maximum teaching hours of the professors are significantly reduced. The results demonstrate the efficiency of the proposed model and solution method in terms of optimization speed and solution accuracy.

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Section: Discussionmentioning

confidence: 99%

Mathematical Modeling and Exact Optimizing of University Course Scheduling COnsidering Preferences of Professors

Chen¹,

Werner²,

Shokouhifar³

2023

Preprint

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“…In the field of agriculture, researchers are committed to developing or introducing new technologies to enhance the security and reliability of agricultural product supply, including Quick Response (QR) Codes [ 36 ], P-fertilizer supply chain management (PFSCM) [ 37 ], Radio Frequency Identification (RFID) [ 38 ], the Internet of Things (IoT) [ 39 ], and blockchain technology [ 40 ].…”

Section: Related Workmentioning

confidence: 99%

A Redactable Blockchain-Based Data Management Scheme for Agricultural Product Traceability

Yang,

Li,

Chen

et al. 2024

Sensors

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With the development of agricultural information technology, the Internet of Things and blockchain have become important in the traceability of agricultural products. Sensors collect real-time data in agricultural production and a blockchain provides a secure and transparent storage medium for these data, which improves the transparency and credibility of agricultural product traceability. However, existing agricultural product traceability solutions are limited by the immutability of the blockchain, making it difficult to delete erroneous data and modify the scope of data sharing. This damages the credibility of traceability data and is not conducive to the exchange and sharing of information among enterprises. In this article, we propose an agricultural product traceability data management scheme based on a redactable blockchain. This scheme allows agricultural enterprises to encrypt data to protect privacy. In order to facilitate the maintenance and sharing of data, we introduce a chameleon hash function to provide data modification capabilities. Enterprises can fix erroneous data and update the access permissions of the data. To improve the efficiency of block editing, our scheme adopts a distributed block editing method. This method supports threshold editing operations, avoiding single-point-of-failure issues. We save records of data modifications on the blockchain and establish accountability mechanisms to identify malicious entities. Finally, in this paper we provide a security analysis of our proposed solution and verify its effectiveness through experiments. Compared with the existing scheme, the block generating speed is improved by 42% and the block editing speed is improved by 29.3% at 125 nodes.

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“…Feature selection poses a challenging problem, acknowledged as non-deterministic polynomial hard (NP-hard) [34], making exact (exhaustive) search methods impractical due to their computational complexity and time requirements. Therefore, heuristic and metaheuristic algorithms and their hybridizations become essential in this context [35]. When crafting a metaheuristic algorithm for an NP-hard problem, a delicate balance between exploration and exploitation must be carefully maintained to optimize search algorithms [36].…”

Section: Feature Selection Using Pcc-gwomentioning

confidence: 99%

Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data

Shokouhifar,

Hasanvand,

Moharamkhani

et al. 2024

Algorithms

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Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in preventing adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an ensemble heuristic–metaheuristic feature fusion learning (EHMFFL) algorithm for heart disease diagnosis using tabular data. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost techniques. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic–metaheuristic approach that integrates the heuristic knowledge of the Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets, yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.

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Sustainable Phosphorus Fertilizer Supply Chain Management to Improve Crop Yield and P Use Efficiency Using an Ensemble Heuristic–Metaheuristic Optimization Algorithm

Cited by 26 publications

References 43 publications

Mathematical Modeling and Exact Optimizing of University Course Scheduling COnsidering Preferences of Professors

Mathematical Modeling and Exact Optimizing of University Course Scheduling COnsidering Preferences of Professors

A Redactable Blockchain-Based Data Management Scheme for Agricultural Product Traceability

Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data

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