Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Capacity Optimization Planning System (Caps)
Interfaces
Impact of forecasting error on the performance of capacitated multi-item production systems
Computers and Industrial Engineering - Special issue: Selected papers from the 27th international conference on computers & industrial engineering
Real Optimization with SAP APO
Real Optimization with SAP APO
Production Planning by Mixed Integer Programming (Springer Series in Operations Research and Financial Engineering)
Introduction to Computational Optimization Models for Production Planning in a Supply Chain
Introduction to Computational Optimization Models for Production Planning in a Supply Chain
A heuristic algorithm for master planning that satisfies multiple objectives
Computers and Operations Research
Modeling and analysis of semiconductor manufacturing in a shrinking world: challenges and successes
Proceedings of the 40th Conference on Winter Simulation
Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies
Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies
Information modelling of the complex system of the parallel manufacturing process
ACMIN'12 Proceedings of the 14th international conference on Automatic Control, Modelling & Simulation, and Proceedings of the 11th international conference on Microelectronics, Nanoelectronics, Optoelectronics
Using iterative simulation to incorporate load-dependent lead times in master planning heuristics
Proceedings of the Winter Simulation Conference
| Hi-index | 0.01 |
In this paper, we propose heuristic approaches for solving master planning problems that arise in semiconductor manufacturing networks. The considered problem consists of determining appropriate wafer quantities for several products, facilities, and time periods by taking demand fulfillment (i.e., confirmed orders and forecasts) and capacity constraints into account. In addition, fixed costs are used to reduce production partitioning. A mixed-integer programming (MIP) formulation is presented and the problem is shown to be NP-hard. As a consequence, two heuristic procedures are proposed: a product based decomposition scheme and a genetic algorithm. The performance of both heuristics is assessed using randomly generated test instances. It turns out that the decomposition scheme is able to produce high-quality solutions, while the genetic algorithm achieves results with reasonable quality in a short amount of time.