| dc.description.abstract | This study investigates the optimal order allocation strategy for Arevo Inc., considering
the risks associated with supplier delay, production disruption, and stochastic demand.
The research methodology employed Particle Swarm Optimization (PSO) and Exact
method to optimize the order allocation decisions. The study design involved analyzing
historical data, simulating different scenarios, and evaluating the performance of the
proposed strategies. The procedures included collecting data on historical supplier
delays, production disruptions, and demand patterns. A mathematical model was
developed to integrate these factors and optimize the order allocation decisions. The
PSO and Exact method were implemented to find the globally optimal solution. The
results of the analysis demonstrated the effectiveness of the proposed approach. By
considering the risks of supplier delay, production disruption, and stochastic demand,
the optimized order allocation strategy improved Arevo Inc.'s overall performance. The
simulations showed that the proposed strategy minimized total ordering costs, reduced
the supply risk, and also improved customer satisfaction. In conclusion, the findings of
this study have several potential implications. Firstly, the optimal order allocation
strategy can help Arevo Inc. mitigate the risks associated with supplier delay, production
disruption, and stochastic demand. Secondly, the proposed approach can be generalized
to other companies operating in similar industries facing similar risks. Based on the
results, some recommendations can be made for Arevo Inc. and other companies.
Firstly, it is essential to establish strong supplier relationships and monitor supplier
performance regularly to minimize the risks of supplier delay. Secondly, implementing
efficient production planning and scheduling systems can help reduce the chances of
production disruptions. Lastly, adopting robust demand forecasting methods and using
safety stock buffers can help manage the risks associated with stochastic demand. | en_US |
