Optimization model for transportation planning with demand uncertainties

The purpose of this paper is to investigate the total cost function of an inventory system with a reorder point/order quantity policy where the lead time is controllable based on the cost paid by the buyer for the service.

Cost functions are presented to investigate how the changes in lead time affect different components of inventory cost in the present of random demand. Two methods including an iteration technique and Simulated Annealing (SA) algorithm are presented to deal with the cost optimization issue. The application of proposed model is illustrated using numerical case scenarios.

The cost functions show that besides ordering cost, change in stochastic demand during lead time is the major factor that affects the other cost components such as holding and penalty costs. This finding is validated by numerical study. Results also show that performance of SA algorithm is highly similar to iteration methodology, while the former one is easier in application.

This paper develops less complex, more pragmatic methods, easily adoptable by logistics managers for cost minimization. This paper also analyzes and highlights the unique characteristics and features of these two approaches that can help practitioners in making the right choice when faced with the identified logistics issue.

This research explicitly investigate impacts of changing lead time on inventory cost components which enables informed decision making and inventory system planning for cost optimization by logistics practitioners. Two methodologies that can be easily used by practitioners without deep mathematical analysis and is cost effective are introduced to solve the optimization problem. Detailed roadmaps of how to implement proposed approaches have been illustrated by different case scenarios.