In the first part of this thesis, we focus on the operations of global container liner networks. The costly operations of empty container repositioning are necessitated by the imbalance of cargo flows across regions. Up to 40 and 60 % of containers shipped from Europe and North America to Asia are empty, respectively. Repositioning costs are sizable, often amounting up to 5 to 6% of a shipping lines revenue. Therefore, identifying an optimal repositioning schedule to rebalance empty containers with minimal cost is one of the most critical planning problems in liner shipping. This is often complicated by the stochastic nature of demand and long transportation lead times. In this chapter, we formulate a multiple-stage stochastic programming problem for the optimal repositioning...[ Read more ]

In the first part of this thesis, we focus on the operations of global container liner networks. The costly operations of empty container repositioning are necessitated by the imbalance of cargo flows across regions. Up to 40 and 60 % of containers shipped from Europe and North America to Asia are empty, respectively. Repositioning costs are sizable, often amounting up to 5 to 6% of a shipping lines revenue. Therefore, identifying an optimal repositioning schedule to rebalance empty containers with minimal cost is one of the most critical planning problems in liner shipping. This is often complicated by the stochastic nature of demand and long transportation lead times. In this chapter, we formulate a multiple-stage stochastic programming problem for the optimal repositioning of containers for a liner shipping network. As the problem is highly complex, the stochastic programming formulation is not computationally tractable. Therefore, we utilize emerging techniques in robust optimization to provide a tight approximation (bound) on the stochastic version of the problem. The resulting formulation is a second-order cone program (SOCP) and is computationally tractable. With this approximation, we perform computational experiments to evaluate the effectiveness of different repositioning policies.

In the second part, the discussion is centered on sustainable transportation scheduling. Clean and efficient public transit is a critical link to a greener environment. Numerous cities over the world are adopting solutions that involve the use of electric bus (EB) technologies. However, for pure plug-in EBs without internal combustion engines, the travel range on a full charge is typically limited to around 100 miles, less than the typical distance travelled by an urban bus in one day. Because recharging is time-consuming, special attention must be paid in scheduling EB operations to ensure that they can be sufficiently recharged during the day to be able to complete all assigned trips. In this chapter, we formulate EB scheduling problems for a single route as mixed-integer linear programs (MILP). Our model incorporates considerations of the charging or battery swapping operations. Furthermore, we utilize techniques from robust optimization to account for uncertainties in battery power consumption during trips.