A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem

Chetty, Sivashan; Adediran, Adewumi

doi:10.1155/2015/350308

Cited by 6 publications

(3 citation statements)

References 22 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem was solved by NSGA-II, an obtained Pareto solution set was ranked by the TOPSIS algorithm, and then the best possible solution was chosen. Also, in [7], [8] the allocation of a large number of jobs required to be scheduled on multiple and identical machines which run in parallel was proposed. They compared Tabu Search (TS) and Simulated Annealing (SA) performance of the eBPA.…”

Section: Omid Sojoodi Sheijani and Ali Izadimentioning

confidence: 99%

Time Optimization during Software Implementation for Timely Delivery Using Meta-Heuristic Algorithms

Sheijani¹,

Izadi²

2019

IJMLC

View full text Add to dashboard Cite

Scheduling of software development and implementation projects is one of the most important and challenging issues facing project managers in the highly competitive software industry. In the market, the final output of software is its price. The price of software is the foundation of the quality and application of that software. As a matter of course, innovation in the idea of software and, consequently, the time spent on designing it can determine its value and price. Therefore, proper scheduling, which is ultimately absolutely crucial for the release of a software application, plays a significant role in the success of that software. There are usually software tools for project management that, using algorithms and collecting data from the program and the conditions of its creation, can identify the best time of release for the software release. In this paper, we try to predict the time needed to make software using meta-heuristic algorithms and through rating tasks, schedule them in a way that the software can be operational in the least time. This paper is a graft between software engineering and meta-heuristic algorithms. The results of various researches have confirmed the superiority of the PSO algorithm to solve this problem compared to other algorithms. This algorithm results in answers with 9 repetitions and within a time span of 27.19 seconds.

show abstract

Section: Omid Sojoodi Sheijani and Ali Izadimentioning

confidence: 99%

Time Optimization during Software Implementation for Timely Delivery Using Meta-Heuristic Algorithms

Sheijani¹,

Izadi²

2019

IJMLC

View full text Add to dashboard Cite

show abstract

“…The ACP problem is reformulated in considering two fundamental market economic factors, namely economy of scale and the demand and supply relations. Furthermore, this study seizes the opportunity to investigate the newly introduced enhanced Best Performance Algorithm (eBPA) [ 17 ]. The solutions determined by the eBPA will be compared against the well-known Tabu Search (TS) and Simulated Annealing (SA) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors

Adewumi

Chetty

2017

PLoS ONE

Self Cite

View full text Add to dashboard Cite

The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA’s results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems.

show abstract

“…This approach is commonly used in solving energy-conscious scheduling [10][11][12][13]. However, because scheduling is known as an NP-hard problem, the use of metaheuristics is redundant [14][15][16]. The idea of making machines idle when the electricity price is high and operating when the electricity price is low has been applied in a single machine scheduling [17].…”

mentioning

confidence: 99%

Solving Just-in-Time Single Machine Scheduling with Variable Discrete Speed Machine using Hybrid NSGA-II

Kurniawan

Irman

Febianti

et al. 2021

JTI

View full text Add to dashboard Cite

Due to industrialization and population growth, increasing energy demand can lead to energy scarcity because non-renewable resources are primarily used as energy sources. In addition, carbon dioxide gas, the waste of industrialization, can harm the environment. Therefore, environmentally friendly methods are encouraged in the industrial environment as energy preservation and climate change mitigation. This research discusses just-in-time single machine scheduling that takes into account energy consumption. In this research, energy consumption depends on the machine’s speed. The objectives are minimizing the just-in-time (JIT) penalty (the sum of weighted earliness/tardiness) and energy consumption. This research proposed a hybrid NSGA-II with a local search to solve the multi-objective scheduling problem. Thus, solving the JIT single-machine scheduling problem considers energy consumption to conserve energy and increase production efficiency. Numerical experiments demonstrated that the hybrid NSGA-II with local search is more effective than the standard NSGA-II in solving the problem. Therefore, decision-makers can use the scheduling model to select alternative solutions that consider energy and the environment without sacrificing efficiency.

show abstract

A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem

Cited by 6 publications

References 22 publications

Time Optimization during Software Implementation for Timely Delivery Using Meta-Heuristic Algorithms

Time Optimization during Software Implementation for Timely Delivery Using Meta-Heuristic Algorithms

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors

Solving Just-in-Time Single Machine Scheduling with Variable Discrete Speed Machine using Hybrid NSGA-II

Contact Info

Product

Resources

About