Responsive Contingency Planning for Supply Chain Disruption Risk Mitigation

Contingent sourcing from a backup resource is an effective risk mitigation strategy under major disruptions. The production volumes and speeds of the backup resource are important protection design considerations, as they affect recovery. The objective of this dissertation is to show that cost-effective protection of existing supply networks from major disruptions result from planning appropriate volume and response speeds of a backup production facility prior to the disruptive event by considering operational aspects such as congestion that may occur at facilities. Contingency strategy are more responsive and disruption recovery periods can be shortened through such prior planning. The dissertation focuses on disruption risk arising from intelligent or pre-meditated attacks on supply facilities. An intelligent attacker has the capability to create worst case loss depending on the protection strategy of a given network. Since the attacker seeks the maximum loss and the designer tries to identify the protection scheme which minimizes this maximum loss, there exists an interdependence between attack and protection decisions. Ignoring this characteristic leads to suboptimal mitigation solutions under such disruptions. We therefore develop a mathematical model which utilizes a game theoretic framework of attack and defense involving nested optimization problems. The model is used to decide optimal selection of backup production volume and the response speeds, the facilities to build such capability within the available budget. The reallocation of demands from a disrupted facility to an undisrupted facility in a contingency strategy leads to congestion of the undisrupted facility, which may result in longer lead times and reduced throughput during disruption periods, thereby limiting the effectiveness of a contingency strategy. In the second part of the dissertation, we therefore analyze congestion effects in responsive contingency planning. The congestion cost function is modeled and integrated into the mathematical model of responsive contingency planning developed in the first part of the dissertation. The main contribution of this dissertation is that a decision tool has been developed to plan protection of an existing supply networks considering backup sourcing through gradual capacity acquisition. The solution methodology involving recursive search tree has been implemented which allows exploring protection solutions under a given budget of protection and multiple combinations of response speeds and production capacities of a backup facility. The results and analysis demonstrate the value of planning for responsive contingency in supply chains subject to risks of major disruptions and provide insights to aid managerial decision making

Scheduling to Optimize Due Date Performance under Uncertainty of Processing Times

Bibliography: p. 119-122This thesis contributes to the theoretical and practical aspects of scheduling research. It is dedicated to the analysis of scheduling a set of jobs on a single machine when the jobs have uncertain processing times and conformance to the due date is the performance objective. The findings reveal that scheduling based on the point estimates of the processing times, when times are actually uncertain, may not lead to an optimal job sequence. As well, the decisions as to when each job should start on the machine may not be optimal. These decisions are important when costs are associated with both early and tardy completion of jobs. A stochastic scheduling methodology, based on sampling using simulation and optimization using evolutionary search, has been introduced. Results and behaviour have been evaluated and compared with single-machine deterministic scheduling, based on optimization using point estimates. Furthermore, the methodology has also been extended to the two-machine flow shop problem. Results confirm performance improvement using stochastic scheduling