Inventory Routing Problem under Dynamic, Uncertain and Green Considerations

PhD student: 
Starting date: 
October 2013
Defense date: 
Wednesday 14 June 2017
Host institution: 

The inventory management and transportation are two main activities of supply chain management. The joint optimization of these two activities is known as Inventory Routing Problem (IRP).

The main objective of IRP is to determine the set of retailers to be delivered to in each period, the delivery sequence for each vehicle, and the quantities of goods delivered to each retailer for each period of a planning horizon. The traditional IRPs are faced different problems, caused mainly by lack of complete and/or timely information such as shifts in demand, traffic caused by a sudden vehicles accident, etc. sharing of updated and reliable logistics information can meaningful improve the efficiency of IRP. Moreover, because of the specificity of IRP in urban logistic, it is important to tack into account other criteria as social, environmental criteria and service level that could be in conflict. The main objective of this thesis is to (i) choose appropriate social, environmental and service level criteria, (ii) integrate them in mathematical models, and (iii) study the impact of these criteria on dynamic optimization of IRPs for perishable products under uncertain parameters. For this purpose, three mathematical models are proposed.

The first model is multi-objective mathematical model in order to make a trade-off between service level, environmental criteria and economic. To decrease quantity of expired products, a nonlinear step function as holding cost function is integrated in the model. Moreover, to solve the problem a fuzzy possibilistic approach is applied to handle uncertain parameters. In the second model, a bi-objective mathematical model is proposed to study impact of social issues on the IRPs. In the proposed model, first objective function concerns economic criteria while the second one social issues. A scenario-based stochastic approach is developed to cope with uncertainty in the model. Finally, the third model concerns impact of using real-time information in efficiency of IRPs. It is noteworthy that, according significant role of perishable products in the both financially and ecology sides of IRPs, perishable products are considered in all three proposed model while even proposed models are appropriate to nonperishable ones as well. The results show that a dynamic management is more efficient than the static one.

KEYWORDS : Inventory routing problem; Sustainable IRP; Dynamic IRP; Customer satisfaction level; Perishable Products; Stochastic mathematical modeling; Multi-objective optimization.