A Hybrid Genetic Algorithm for the Single Machine Scheduling Problem
Journal of Heuristics
A genetic algorithm to minimize maximum lateness on a batch processing machine
Computers and Operations Research
Single machine scheduling with a variable common due date and resource-dependent processing times
Computers and Operations Research
A Multiple-Criterion Model for Machine Scheduling
Journal of Scheduling
A hybrid genetic algorithm for the job shop scheduling problems
Computers and Industrial Engineering
Flow-shop scheduling for three serial stations with the last two duplicate
Computers and Operations Research
A decision support system for luggage typesetting
Expert Systems with Applications: An International Journal
Cooperative Multisite Production Re-scheduling
CDVE '08 Proceedings of the 5th international conference on Cooperative Design, Visualization, and Engineering
Expert Systems with Applications: An International Journal
A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop
Expert Systems with Applications: An International Journal
Assembly line balancing in garment industry
Expert Systems with Applications: An International Journal
A multi-objective approach to the application of real-world production scheduling
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Production scheduling is one of the major issues in production planning and control of individual production units which lies on the heart of the performance of manufacturing organizations. Traditionally, production planning decision, especially scheduling, was resolved through intuition, experience, and judgment. Machine loading is one of the process planning and scheduling problems that involves a set of part types and a set of tools needed for processing the parts on a set of machines. It provides solution on assigning parts and allocating tools to optimize some predefined measures of productivity. In this study, Ion Plating industry requires similar approaches on allocating customer's order, i.e. grouping production jobs into batches and arrangement of machine loading sequencing for (i) producing products with better quality products; and (ii) enabling to meet due date to satisfy customers. The aim of this research is to develop a Machine Loading Sequencing Genetic Algorithm (MLSGA) model to improve the production efficiency by integrating a bin packing genetic algorithm model in an Ion Plating Cell (IPC), such that the entire system performance can be improved significantly. The proposed production scheduling system will take into account the quality of product and service, inventory holding cost, and machine utilization in Ion Plating. Genetic Algorithm is being chosen since it is one of the best heuristics algorithms on solving optimization problems. In the case studies, industrial data of a precious metal finishing company has been used to simulate the proposed models, and the computational results have been compared with the industrial data. The results of developed models demonstrated that less resource could be required by applying the proposed models in solving production scheduling problem in the IPC.