Optimisation of a Crossdocking Distribution Centre Simulation Model

TL;DR

This research enhances the efficiency of simulating and optimizing a Crossdocking distribution centre by applying Common Random Numbers, leading to more precise performance measures and fewer simulation runs for optimal results.

Contribution

The paper demonstrates how using Common Random Numbers improves simulation output precision and reduces the number of runs needed for optimization in a Crossdocking model.

Findings

Common Random Numbers improve output measure precision

Optimization with CRN requires fewer simulation runs

Enhanced simulation efficiency achieved

Abstract

This paper reports on continuing research into the modelling of an order picking process within a Crossdocking distribution centre using Simulation Optimisation. The aim of this project is to optimise a discrete event simulation model and to understand factors that affect finding its optimal performance. Our initial investigation revealed that the precision of the selected simulation output performance measure and the number of replications required for the evaluation of the optimisation objective function through simulation influences the ability of the optimisation technique. We experimented with Common Random Numbers, in order to improve the precision of our simulation output performance measure, and intended to use the number of replications utilised for this purpose as the initial number of replications for the optimisation of our Crossdocking distribution centre simulation model. Our results demonstrate that we can improve the precision of our selected simulation output performance measure value using Common Random Numbers at various levels of replications. Furthermore, after optimising our Crossdocking distribution centre simulation model, we are able to achieve optimal performance using fewer simulations runs for the simulation model which uses Common Random Numbers as compared to the simulation model which does not use Common Random Numbers.