Open AccessDEVELOPING AGENT BASED HEURISTIC OPTIMISATION SYSTEM FOR COMPLEX FLOW SHOPS WITH CUSTOMER-IMPOSED PRODUCTION DISRUPTIONS
Author(s) -
Tunde Victor Adediran,
Ammar AlBazi
Publication year - 2018
Publication title -
journal of ict
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.217
H-Index - 10
eISSN - 2180-3862
pISSN - 1675-414X
DOI - 10.32890/jict2018.17.2.8255
Subject(s) - original equipment manufacturer , production (economics) , automotive industry , computer science , heuristic , lean manufacturing , process (computing) , order (exchange) , build to order , industrial engineering , operations research , manufacturing engineering , operations management , business , engineering , artificial intelligence , finance , economics , macroeconomics , aerospace engineering , operating system
The study of complex manufacturing flow-shops has seen a number of approaches and frameworks proposed to tackle various production-associated problems. However, unpredictable disruptions, such as change in sequence of order, order cancellation and change in production delivery due time, imposed by customers on flow-shops that impact production processes and inventory control call for a more adaptive approach capable of responding to these changes. In this research work, a new adaptive framework and agent-based heuristic optimization system was developed to investigate the disruption consequences and recovery strategy. A case study using an Original Equipment Manufacturer (OEM) production process of automotive parts and components was adopted to justify the proposed system. The results of the experiment revealed significant improvement in terms of total number of late orders, order delivery time, number of setups and resources utilization, which provide useful information for manufacturer’s decision-making policies.