A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem

Zheng, Jianguo; Wang, Yilin

doi:10.3390/app112110102

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…The third approach considered is genetic algorithm (E3) which is basically a conventional search-based optimization algorithm which arrives to final result from a set of population on the basis of fitness function [59][60] [61]. The fourth approach for comparison is the most frequently used bat algorithm (E4) which is basically a metaheuristic method capable of fulfilling demands of global optimization for large software global project [62][63] [64]. The fifth and sixth approach considered in analysis is another most frequently used Dolphin algorithm (E5) and hybrid model of Dolphin-Bat algorithm (E6) [41].…”

Section: Results Accomplishedmentioning

confidence: 99%

OLCE: Optimized Learning-based Cost Estimation for Global Software Projects

Rao

Bolla

Mummana³

et al. 2022

Preprint

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Software Cost Estimation (SCE) is an integral part of pre-development stage of software project with a target to accomplish a better visibility towards possible risk while gaining more information towards reaching success rate to meet the deadline of delivery. Irrespective of multiple research contribution model towards SCE, the problem and challenges towards accurate cost estimation in presence of dynamicity and uncertainty is yet not reported to be accomplished. Apart from this, learning-based models are slowly gaining pace in almost every field and yet it is still in nascent stage of progress in software engineering. Therefore, the proposed manuscript introduces Optimized Learning-based Cost Estimation (OLCE) which is a novel learning-based model capable of accurate prediction considering global and large scale software project. The proposed system harnesses the learning potential from artificial neural network integrated with novel search-based approach for optimizing the learning method considering the benchmarked COCOMO NASA 2 dataset. The study outcome shows OLCE offers 50% faster response time with approximately 73% of accuracy compared to existing models that are reportedly found to be adopted for SCE. Hence, OLCE is found to offer a balance between accuracy and computational efficiency during SCE.

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Section: Results Accomplishedmentioning

confidence: 99%

OLCE: Optimized Learning-based Cost Estimation for Global Software Projects

Rao

Bolla

Mummana³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Compared to state-of-the-art ABC, a Whale Optimization Algorithm, and ABC with random neighbourhood selection, the hybrid approach demonstrated superior performance across all benchmark instances, with a notable advantage in large-scale ones. Zheng & Wang [74] proposed a hybrid Bat Algorithm and VNS to solve a variant of the PFSP. The algorithm ensures population diversity by classifying it into two types: search-type population and captive population.…”

Section: Related Workmentioning

confidence: 99%

An Improved Saturation Degree-Based Constructive Heuristic for Master Surgery Scheduling Problem: Case Study

Razali,

Rahman,

Ayob

et al. 2024

IEEE Access

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The Master Surgery Scheduling Problem (MSSP) can be described as a timetabling problem involving assigning surgery groups to operating theatre (OT) time slots. Previous MSSP optimization models considered throughput, waiting measures, resource utilization, costs, and schedule assignment objectives, neglecting consecutive days assignment preferences and surgical equipment-sharing limitations. Furthermore, previous works utilize greedy constructive heuristics to produce solutions, which increases quality but decreases feasibility. Our prior study demonstrated that the saturation degree heuristic enhances feasibility by considering assignment difficulty during event selection. However, its impact on solution quality remained unexplored. Therefore, this study proposes an improved saturation degree-based constructive heuristic that integrates objective function value for event selection to increase both quality and feasibility. The algorithm sorts surgery groups based on unit scores, prioritizing lower assignment difficulty and higher objective value. The highest-scoring group is assigned to its feasible slot with the highest slot score, following similar goals. In case of no feasible slots, the repair mechanism vacates the highest swap score slot, assessing the impact on quality and feasibility. A new mathematical model is also formulated, incorporating novel objectives regarding consecutive days assignment preference and surgical equipmentsharing limitations. Validated using real-world data from Hospital Canselor Tuanku Muhriz, the proposed algorithm is evaluated considering repair mechanism usage for feasibility and objective function value for quality. The algorithm is benchmarked against greedy, random, regret-based, and saturation degree-based constructive heuristics. Our algorithm achieved a 14.63% improvement in feasibility compared to the original variant. Its objective function value is over two times better than the closest competitor and 2.6 times superior to the original variant. Comparison with the hospital's actual plan demonstrates competitive objective function value and a more balanced waiting time distribution among surgical groups. Our study showcases that a saturation degree-based constructive heuristic considering objective function value has increased solution quality while maintaining feasibility.

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“…The originality of the following paper written by Zheng J. and Wang Y. [11] lies in an application of the hybrid bat optimization algorithm, which is based on the variable neighborhood structure, and two learning strategies to solve a three-stage distributed assembly permutation flow shop scheduling problem (DAPFSP) with the aim to minimize the makespan. The proposed algorithm is firstly designed to increase the population diversity by classifying the populations which solves the difficult trade-off between convergence and diversity of the bat algorithm.…”

Section: Description Of the Papersmentioning

confidence: 99%