A genetic algorithm to optimize multiproduct multiconstraint inventory control systems with stochastic replenishment intervals and discount

2010 ◽  
Vol 51 (1-4) ◽  
pp. 311-323 ◽  
Author(s):  
Ata Allah Taleizadeh ◽  
Seyed Taghi Akhavan Niaki ◽  
Mir-Bahador Aryanezhad ◽  
Alireza Fallah Tafti
Keyword(s):  
Genetic Algorithm ◽  
Control Systems ◽  
Inventory Control

Multi-objective particle swarm optimisation approach for production-inventory control systems

2018 ◽  
Vol 13 (4) ◽  
pp. 1037-1056 ◽  
Author(s):  
Huthaifa AL-Khazraji ◽  
Colin Cole ◽  
William Guo
Keyword(s):  
Control System ◽  
Control Systems ◽  
Inventory Control ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Inventory Level ◽  
Content Type ◽  
Multi Objective ◽  
Production Inventory

Purpose This paper aims to optimise the dynamic performance of production–inventory control systems in terms of minimisation variance ratio between the order rate and the consumption, and minimisation the integral of absolute error between the actual and the target level of inventory by incorporating the Pareto optimality into particle swarm optimisation (PSO). Design/method/approach The production–inventory control system is modelled and optimised via control theory and simulations. The dynamics of a production–inventory control system are modelled through continuous time differential equations and Laplace transformations. The simulation design is conducted by using the state–space model of the system. The results of multi-objective particle swarm optimisation (MOPSO) are compared with published results obtained from weighted genetic algorithm (WGA) optimisation. Findings The results obtained from the MOPSO optimisation process ensure that the performance is systematically better than the WGA in terms of reducing the order variability (bullwhip effect) and improving the inventory responsiveness (customer service level) under the same operational conditions. Research limitations/implications This research is limited to optimising the dynamics of a single product, single-retailer single-manufacturer process with zero desired inventory level. Originality/value PSO is widely used and popular in many industrial applications. This research shows a unique application of PSO in optimising the dynamic performance of production–inventory control systems.

Application of Genetic Algorithms in Inventory Control

2016 ◽  
pp. 1087-1098
Author(s):  
Vinod Kumar Mishra
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Inventory Control ◽  
Future Trend ◽  
Production Engineering ◽  
Mathematical Sciences ◽  
Adaptive Heuristic ◽  
Definition Of ◽  
Key Terms

The genetic algorithm (GA) is an adaptive heuristic search procedures based on the mechanics of natural selection and natural genetics. Inventory control is widely used in the area of mathematical sciences, management sciences; system science, industrial engineering, production engineering etc. but they have wide differences in mathematical and computation maturity. This chapter enables the reader to understand the basic theory of genetic algorithm and how to apply the genetic algorithms for optimizing the parameters in inventory control The current and future trend of the research with the definition of key terms of genetic algorithm has also incorporated in this chapter.

Sign in / Sign up

Export Citation Format

Share Document