An Improvement of Instance Generator Featuring Assembly Operations with Parallel Machines for Multi-Level and Multi-Operation Scheduling Problems

Songserm, Watchara; Latthawanichphan, Jakkrit; Wuttipornpun, Teeradej; Sukkerd, Watcharapan

doi:10.1109/ri2c51727.2021.9559812

Cited by 2 publications

(2 citation statements)

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“…These data are generated by a new instance generator called i-IGSP that is developed by King Mongkut's University of Technology North Bangkok (KMUTNB) and Rajamangala University of Technology Phra Nakhon (RMUTP). It is the instance generator capable of generating hypothetical data for various production shop characteristics (Latthawanichphan et al, 2019;Songserm et al, 2021). Note that all the time parameters are uniformly distributed similar to other researches in the literature (Chang et al, 2009;Pan et al, 2017;Vallada & Ruiz, 2009).…”

Section: Details Of Case Studies and Experimentsmentioning

confidence: 99%

“…Note that the 5-job and 10-job problems are considered as small-scale problems, the 20-job and 50-job problems as medium-scale problems, and the 100-job and 200-job problems as large-scale problems. In addition, to represent different characteristics of a given job size in terms of processing time and due time data, a set of five instances for each job size problem is further generated by different seed numbers (Latthawanichphan et al, 2019;Songserm et al, 2021) resulting in 30 different problem instances. These instances are used to prove that the proposed hybrid algorithms work well for various problem characteristics.…”

Section: Details Of Case Studies and Experimentsmentioning

confidence: 99%

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Novel Hybrid Algorithms for a Single Machine Scheduling Problem With an Overtime Constraint

Latthawanichphan

Sukkerd

Songserm

et al. 2022

International Journal of Knowledge and Systems Science

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In this paper, a single machine scheduling problem with an overtime constraint is studied. The objective is to minimise the total penalty cost defined as the sum of tardy, early, and overtime costs. Three novel hybrid algorithms that hybridise a new heuristic with genetic algorithm, tabu search, and simulated annealing, referred to as GAH, TSH, and SAH, are proposed to solve the problem. In each iteration of the proposed hybrid algorithms, a given metaheuristic is used to determine a sequence of jobs, whereas a new heuristic is used to minimise the total penalty cost of the sequence using a backward-forward scheduling technique and a penalty cost trade-off process. Exhaustive experiments are conducted to evaluate the effectiveness of the proposed hybrid algorithms. For medium-scale and large-scale problems, TSH with its best common parameter setting referred to as TSH2, clearly outperforms the exact algorithm, whereas both algorithms can obtain the optimal solution for small-scale problems. In addition, the computational time of TSH2 is in an acceptable range for the planner.

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