Analysis of Metaheuristic Solutions to the Response Time Variability Problem

The problem of variation in the response time is known as response time variability problem (RTVP). It is combinatorial NP-hard problem which has a broad range of real-life applications. The RTVP arises whenever events, jobs, clients or products need to be sequenced so as to minimize the variability of the time they wait for their next turn in obtaining the resources they need to advance. In RTVP the concern is to find out near optimal sequence of jobs with objective of minimizing the response time variability. The metaheuristic approaches to solve the RTVP are: Multi-start (MS), Greedy Randomized Adaptive Search Procedure (GRASP) and Particle Swarm Optimization (PSO). In this paper, the computational result of MS and GRASP will be analyzed.

Institutions

Publication Details

Journal
Api Journal of Science
Published
2024-12-31
DOI
https://doi.org/10.3126/ajs.v1i1.75493
Primary Topic
Advanced Multi-Objective Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Metaheuristic Solutions to the Response Time Variability Problem

Api Journal of Science
Advanced Multi-Objective Optimization Algorithms
article

Analysis of Metaheuristic Solutions to the Response Time Variability Problem

article en

Abstract

The problem of variation in the response time is known as response time variability problem (RTVP). It is combinatorial NP-hard problem which has a broad range of real-life applications. The RTVP arises whenever events, jobs, clients or products need to be sequenced so as to minimize the variability of the time they wait for their next turn in obtaining the resources they need to advance. In RTVP the concern is to find out near optimal sequence of jobs with objective of minimizing the response time variability. The metaheuristic approaches to solve the RTVP are: Multi-start (MS), Greedy Randomized Adaptive Search Procedure (GRASP) and Particle Swarm Optimization (PSO). In this paper, the computational result of MS and GRASP will be analyzed.

Api Journal of ScienceVol. 1
Tribhuvan University (NP)
Openalex Percentile: Top 69%
Advanced Multi-Objective Optimization Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.