Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Management

Program Name/Specialization

Operations and Supply Chain Management

Faculty/School

Lazaridis School of Business and Economics

First Advisor

Dr. Michael Haughton

Advisor Role

Thesis Supervisor

Abstract

This dissertation includes three related papers to investigate different methods that can help transport providers improve their operational efficiency. The first paper models and measures the profit improvement trucking companies can achieve by collaborating with their clients to obtain advance load information (ALI). The core research method is to formulate a comprehensive and flexible mixed integer mathematical model and implement it in a dynamic rolling horizon context. The findings illustrate that access to the second day ALI can improve the profit by an average of 22%. We also found that the impact of ALI depends on radius of service and trip length but statistically independent of load density and fleet size.

The second paper investigates the following question of relevance to truckload dispatchers striving for profitable decisions in the context of dynamic pick-up and delivery problems: "since not all future pick-up/delivery requests are known with certainty, how effective are alternative methods for guiding those decisions?" We propose an intuitive policy and integrate it into a new two-index mixed integer programming formulation, which we implement using the rolling horizon approach. On average, in one of the practical transportation network settings, the proposed policy can, with just second-day ALI, yield an optimality ratio equal to almost 90% of profits in the static optimal solution. We enhance the proposed policy by adopting the idea of a multiple scenario approach. In comparison to other dispatching methods, our proposed policies were found to be very competitive in terms of solution quality and computational efficiency.

Finally, inspired by a real-life third party logistic provider, the third paper addresses a dynamic pickup and delivery problem with full truckload (DPDFL) for local operators. The main purpose of this work is to investigate the impact of potential factors on the carriers’ operational efficiency. These factors, which are usually under managerial influence, are vehicle diversion capability, the DPDFL decision interval, and how far in advance the carrier knows of clients’ shipment requirements; i.e., ALI. Through comprehensive numerical experiments and statistical analysis, we found that the ALI and re-optimization interval significantly influence the total cost, but that diversion capability does not. A major contribution of this work is that we develop an efficient benchmark solution for the DPDFL’s static version by discretization of time windows. We observed that three-day ALI and an appropriate decision interval can reduce deviation from the benchmark solution to less than 8%.

Convocation Year

2015

Convocation Season

Fall

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