Article ID Journal Published Year Pages File Type
5470355 Procedia CIRP 2017 6 Pages PDF
Abstract
Manufacturing companies are confronted with increasing market volatility, which is characterized by the increasing of product variants and decreasing of production volume of each variant to meet customers' demands. To stay competitive and to fulfill quickly changing market needs, manufacturing companies have to cope with rapidly changing customer demands concerning product types, quantities and delivery dates and have the ability to enable a fast modification and system-change of future manufacturing systems. Production planning and control must compensate the resulting fluctuations in capacity demand. This paper deals with a problem of joint optimization of production planning and capacity adjustment based on products specifications, delivery time constraints and reconfigurable machines capabilities for assembly systems. At the production planning level, the production planning problem consists in a multi-product capacitated lot-sizing problem. At the capacity adjustment level, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality. In this context, the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. The main objective of this paper is to determine simultaneously the economic production quantity of each product variants and the optimal capacity of assembly systems to ensure the adherence to delivery dates. A stochastic mathematical model is developed and solved using a simulation optimization approach based on the response surface methodology. The obtained results show clearly strong interactions between production quantity, delivery time constraints and capacity of assembly line which confirm the necessity of jointly considering production planning and capacity adjustment in an integrated model.
Related Topics
Physical Sciences and Engineering Engineering Industrial and Manufacturing Engineering
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