Authors:
Jean-Baptiste Ringard
1
;
Bhushan S. Purohit
2
and
Bhupesh Kumar Lad
2
Affiliations:
1
French Institute for Advanced Mechanics, France
;
2
Indian Institute of Technology Indore, India
Keyword(s):
Integrated Planning, Supplier Planning, Scheduling, Preventive Maintenance, Simulation, Optimization.
Related
Ontology
Subjects/Areas/Topics:
Application Domains
;
Artificial Intelligence
;
Automotive Industry
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Laboratory Simulation Software
;
Mobile Software and Services
;
Optimization Issues
;
Planning and Scheduling
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Symbolic Systems
;
Telecommunications
;
Wireless Information Networks and Systems
Abstract:
Operations management decisions related to production, maintenance, inventory and supplier selection has attracted researchers since long. Traditionally each of these areas was planned and optimized individually. Soon interdependencies between these elements of value chain were realized, which prompted researchers towards integrated planning of these functions. Superiority of integrated approach over conventional operations management approaches has already been demonstrated in past. Therefore, models integrating shop floor functions like production planning, maintenance planning and inventory planning are abundant in recent literature. However, there exist functions which significantly contribute towards operations planning, but have still not been considered for integration. One such important area is procurement planning (supplier order allocation).Current work aims to integrate procurement decisions with maintenance and production plan so as to minimize Total Cost of Operations (
TCO). It considers a stochastic environment where production and maintenance processes are imperfect and where there is significant dubiety related to demand and supply of material. Further, present model considers uncertainties in parameters like supplier quality, machine yield etc., by using appropriate probability distributions for these parameters. Therefore a simulation based Genetic Algorithm (GA) approach is used to solve this optimization problem. The final results are illustrated in the form of an integrated operations plan. It explicitly communicates (i) Order quantity for individual suppliers (ii) Job production sequence (iii) Production lot size (iv) Preventive maintenance schedule for individual machine components. Current work aims to contribute towards development of a paradigm where multiple disjoint functions are integrated at planning level itself.
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