P
Pupong Pongcharoen
Researcher at Naresuan University
Publications - 59
Citations - 1039
Pupong Pongcharoen is an academic researcher from Naresuan University. The author has contributed to research in topics: Scheduling (production processes) & Genetic algorithm. The author has an hindex of 16, co-authored 56 publications receiving 914 citations. Previous affiliations of Pupong Pongcharoen include Newcastle University & University of Newcastle.
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Determining optimum Genetic Algorithm parameters for scheduling the manufacturing and assembly of complex products
TL;DR: The overall objective is to use the most efficient Genetic Algorithm parameters that achieve minimum total costs and minimum spread, to solve a very large scheduling problem that is computationally expensive.
Application of Firefly Algorithm and Its Parameter Setting for Job Shop Scheduling
Aphirak Khadwilard,Sirikarn Chansombat,Thatchai Thepphakorn,Warattapop Chainate,Pupong Pongcharoen +4 more
TL;DR: The application of a recent developed metaheuristic called Firefly Algorithm for solving JSSP is presented, the parameter setting of the proposed algorithm is investigated, and the performance of the FA performance is compared using various parameter settings.
Journal ArticleDOI
Stochastic Optimisation Timetabling Tool for university course scheduling
TL;DR: The Stochastic Optimisation Timetabling Tool (SOTT) is described, which has been developed for university course timetabling and includes a repair process, which ensures that all infeasible timetables are rectified.
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The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure
TL;DR: The development of a genetic algorithms based scheduling tool that takes into account multiple resource constraints and multiple levels of product structure is described and it is demonstrated that the schedules produced perform significantly better than those produced by the company using a conventional planning method.
Journal ArticleDOI
A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm
TL;DR: Novel modified Backtracking Search Algorithms (mBSAs) that solved the stochastic DFLP with heterogeneous sized resources and the combination of material flow and redesign costs were minimised.