Design of production-inventory control systems incorporating fast-sampling control policies with implementation lead time

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.

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Comparison between continuous review inventory control systems for perishables with deterministic lifetime and lead time

2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO) ◽

10.1109/icmsao.2013.6552713 ◽

2013 ◽

Author(s):

Chaaben Kouki ◽

Zied Jemai ◽

Evren Sahin ◽

Yves Dallery

Keyword(s):

Control Systems ◽

Inventory Control ◽

Lead Time ◽

Continuous Review

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Functionalities of production-inventory control systems

Production Planning & Control ◽

10.1080/09537289108919350 ◽

1991 ◽

Vol 2 (3) ◽

pp. 219-227 ◽

Cited By ~ 8

Author(s):

J. MAES ◽

L. N. VAN WASSENHOVE

Keyword(s):

Control Systems ◽

Inventory Control ◽

Production Inventory

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A Refinement of the Classical Order Point Model

International Journal of Information Systems and Supply Chain Management ◽

10.4018/jisscm.2012070103 ◽

2012 ◽

Vol 5 (3) ◽

pp. 43-57 ◽

Cited By ~ 1

Author(s):

Farhad Moeeni ◽

Stephen Replogle ◽

Zariff Chaudhury ◽

Ahmad Syamil

Keyword(s):

Control Systems ◽

Inventory Control ◽

Lead Time ◽

Random Variables ◽

Potential Effect ◽

Inventory Models ◽

Inventory Policies ◽

Sampling Variability ◽

Sampling Variation ◽

Production And Inventory Control

Factors such as demand volume and replenishment lead time that influence production and inventory control systems are random variables. Existing inventory models incorporate the parameters (e.g., mean and standard deviation) of these statistical quantities to formulate inventory policies. In practice, only sample estimates of these parameters are available. The estimates are subject to sampling variation and hence are random variables. Whereas the effect of sampling variability on estimates of parameters are in general well known in statistics literature, literature on inventory control policies has largely ignored the potential effect of sampling variation on the validity of the inventory models. This paper investigates the theoretical effect of sampling variability and develops theoretically sound inventory models that can be effectively used in different inventory policies.

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