Optimal Size and Location of Warehouses under Risk of Failure

Nowadays, every company faces challenges that seem to be loaded with a contradiction: how to reduce operations and transportation costs while increasing customer satisfaction levels. Designing a supply chain network is an effective solution to such an issue. Supply chain network design involves making decisions about the number, sizes, and locations of the facilities in a supply chain. The focus of this study is how to choose appropriate warehouse locations and sizes in supply chain network design. The study is divided into two parts. In the second part, the risk of warehouse failure is considered while in the first part, it is not. Three sets of mathematical optimization models for warehouse location and branch assignment were developed. The first set of mathematical optimization models covered the case of warehouse location without risk. Two sets of decision variables were introduced to determine the locations for new warehouses and assign warehouses to branches. The second set of mathematical optimization models covered the warehouse location problem under the risk of warehouse failure. Again, two sets of decision variables were introduced. The first set of decision variables helped in determining the locations for new warehouses, and the second set helped in assigning a primary and a backup warehouse to each branch. The backup warehouse to be used in case of failure of the primary warehouse. The third set of mathematical optimization models covered the case in which some warehouses can be fortified to become totally risk-free. Each branch was either assigned to a primary fortified warehouse only or to a primary warehouse that was not fortified and a secondary fortified warehouse. Fortification model required an additional variables indicating which warehouses to be fortified. Warehouses with multiple capacity levels and multiple part category types were considered, which is a contribution to the topic of warehouse disruption risk. Specialized warehouses were also considered in this dissertation, which is another contribution of this dissertation. Some linearization and relaxation methods were used to help in solving the three models. Further, a solution methodology was presented based on the solution to scenario subproblems that are more easily, i.e., more quickly, solved. This requires an algorithm to determine the scenarios. Each scenario represents the number and sizes of warehouses needed to be built. The scenarios are novel in that they do not specify a subset of warehouses to be opened, but rather they specify the number of warehouses of each size to be opened. The results showed the effectiveness of the proposed solution methodology by application to an example based on a case study of a Canadian company; and a created example based on European cities