بحوث سكوبس — رقية احمد مطر الجادري

This research applies the Cuckoo Search Algorithm, specifically the Original Cuckoo Search(CS), Improved Cuckoo Search(ICS), and Global Feedback cuckoo search(GFCS) with different values of parameters instead of using a fixed value of probability a banda (Pa) which equal to 0.25 by another researcher to solve the problem of Job Shop Scheduling. The goal is to modify the method to improve its effectiveness and total completion time (Makespan) using benchmark datasets for basic scheduling problems, and suggest using the Cauchy distribution, with its ability to generate random numbers from distant points, and the stronger perturbation ability of Cauchy variation compared to Gaussian variation, along with Levy flight, effectively prevent the cuckoo algorithm from falling into local optima. Notably, when the step size is 0.1 and Pa is 0.1, with a population of 10 and 100 iterations, GFCS obtains the ideal Makespan of 140 when applied to the (20 × 20) set. Also, the Cauchy distribution for the CS produces the best results compared levy distribution that increases the variety of the nests, enabling escape from regional extreme values and fostering global search. © 2024 University of AlKafeel.

This study introduces an iterative scheduling method that combines two approaches for managing repetitive construction projects: the Critical Path Method (CPM) and the Repetitive Scheduling Method (RSM). The primary objective of this study is to demonstrate how optimization techniques can be applied to minimize the cost of construction projects within a defined range, spanning from the shortest to the longest possible project durations. In the shortest project duration (as determined by CPM), all activities are allocated idle times based on precedence constraints, while in the longest duration (as determined by RSM), there is no idle time allocated. To calculate the optimal schedule, a computerized iterative method specifically designed for this purpose considers all possible combinations of activities with and without idle time. The optimum schedule is the one that minimizes the total project cost. The study reveals that by using an Excel spreadsheet, it is feasible to deterministically optimize the cost of repetitive construction projects, achieving the minimum cost. This minimization process can also be implemented as a Python application. Notably, this proposed system provides multiple optimal solutions, enabling managers to select the most suitable one. This advantage distin-guishes it from conventional methods, such as genetic algorithms and other optimization techniques. However, there are some limitations when applying this application, one of which is the maximum capacity available to run the application. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).