Formulation of integer programming model for balancing and scheduling of production line having shared resources

In a mass production environment where resources such machines are shared, a balanced and scheduled production flow line can play a pivotal role through the enhancement of line efficiency by minimizing production time. Balancing of production flow line is a classical OR problem which has been addres...

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Bibliographic Details
Main Authors: Emrul Kays, H. M., Karim, A. N. Mustafizul, Abdesselam, M., Al Hazza, Muataz Hazza Faizi, Sarker, M. Ruhul Amin
Format: Conference or Workshop Item
Language:English
Published: 2014
Subjects:
Online Access:http://irep.iium.edu.my/36174/
http://irep.iium.edu.my/36174/
http://irep.iium.edu.my/36174/1/437.pdf
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Summary:In a mass production environment where resources such machines are shared, a balanced and scheduled production flow line can play a pivotal role through the enhancement of line efficiency by minimizing production time. Balancing of production flow line is a classical OR problem which has been addressed extensively during the last few decades. But only few researches focused on the resource constrained line balancing problem (RCLBP) where the elementary tasks can be performed by a single and/or multiple resources. In reality utilization of the limited capacities of such special machines, equipment, or resources, due to the prohibitively high investment cost, is a very common shop-floor phenomenon. For such cases, an integrated model capable of balancing, scheduling and allocating the resources sequentially to the appropriate task(s) is necessary. In this paper instead of treating the RCLBP in a traditional manner, we introduced a new dimension to it by formulating a relevant mathematical model through application of integer programming method for production environment where several tasks are performed using a particular resource through incorporation of the positive zoning and the shared resource constraints. The model allows the paralleling of workstations to cope with the tasks having processing times exceeding the local work-station cycle time.