It has been recognized that selecting the right product and service supplier can reduce the costs and improve the corporate competitiveness. The same is valid for selecting the right transportation mode and carrier selection process. This thesis examines the transportation mode selection decision in quantitative supply chain planning models.
The contribution of this work is twofold:
In the first part of the thesis an extensive literature review provides a systematic classification of the supply chain planning problems that integrate transportation mode selection decisions. The literature review starts with the empirical studies that identify the main criteria used by logistics managers’ when choosing a transportation mode. This is then followed by a brief review of multi-criteria decision methods and models where transportation mode is selected in isolation from other supply chain planning decisions. Further in the review the strategic network design, tactical inventory planning and operational supply chain planning problems that integrate transportation mode choice are classified and analysed, suggesting directions for future research. The results of the review show that transportation mode selection decision needs to be integrated with supply chain problems, as the transportation costs in practice depend on the shipment size. These costs need to be modelled accurately, reflecting the realistic discount schedules and capacity limits, to be able to find true optimal solutions. In the majority of the models simplification of transportation cost modelling exists, and it is often assumed that only a single transportation mode is available. However, in the real practice the shippers may choose among different transportation alternatives and switch from one to another as needed. Some studies recognize that cost savings may be obtained by considering availability of multiple modes and by allowing use of several modes for shipping the order quantity simultaneously. Order split, when each product supplier can deliver a fraction of the total demand to reduce the costs, has been widely studied in supplier selection literature. However, the review results show that the number of studies that consider availability of multiple transportation modes and possibility for their combination is rather limited, leading to proposition of novel models in the second part of this thesis.
In the second part of the thesis a novel dynamic lot-sizing model, motivated by a realistic business case, which allows combination of multiple modes with various cost functions, is proposed and tested. A computational study is conducted to test the efficiency of several mathematical programming formulations of the model, as well as to examine the model parameters that impact computational complexity. A study that investigates the economical benefits of transportation mode combinations and the model parameters contributing to cost savings is also performed.
The proposed model is further extended by considering multiple stocking locations that order from the same supplier and by introduction of lateral transshipments. This study is motivated by a real-life decision problem faced by a logistics company responsible for distribution of beverages in Scandinavian countries. Novel ways for potential cost reduction have been identified when introducing lateral transhipments, namely through usage of transportation cost discounts and availability of multiple modes. The model has been tested on several examples, considering various transshipment policies, which define the locations that can send and receive transshipments, as well as different transportation strategies, which specify number of modes that can be used in each period. The test results show that the size of the potential savings depends on the problem parameters, and the transshipment policies and transportation strategies applied.
This thesis has identified a number of areas for future research that incorporates realistic decision problems, especially when logistics and in particular transportation services are outsourced.