An exact approach for the reliable fixed-charge location problem with capacity constraints

Introducing capacities in the reliable fixed charge location problem is a complex task since successive failures might yield in high facility overloads. Ideally, the goal consists in minimizing the total cost while keeping the expected facility overloads under a given threshold. Several heuristic approaches have been proposed in the literature for dealing with this goal. In this paper, we present the first exact approach for this problem, which is based on a cutting planes algorithm. Computational results illustrate its good performancePostprint (published version

Application of Condition-based Maintenance in Control of a Supply Chain Network under Stochastic Disruption

The thesis develops novel and proactive optimal control policies for a partially observable facility, which is subject to stochastic disruptions. Unlike traditional Supply Chain Networks (SCN), where established facilities are considered to be continuously available, a more practical scenario is developed. More specifically, in the proposed frameworks, the aforementioned assumption is relaxed such that the facilities are subject to stochastic disruptions potentially leading to costly failures. In such practical scenarios, it is critical and of paramount importance for the established facilities to operate with the highest achievable reliability in the presence of disruptions and degradation. In this regard, this thesis provides a conceptual framework to obtain an optimal control policy for an already established facility subject to stochastic disruptions/degradation such that the disruptions have a direct effect on the connection links within the SCN. The level of degradation of a facility is modeled as a N state continuous time hidden-Markov process with N −1 operational and unobservable states together with one observable failure state. The facility is monitored periodically to observe the level of degradation. If the degradation level exceeds a critical state, a preventive action, namely partial fortification, will be performed. On the other hand, when the degradation level exceeds the failure state, a corrective action, namely full fortification, will be performed which brings the facility to the healthy state. The model is extended to the scenario where an integrated model of Statistical Process Control (SPC) and maintenance planning is considered and the optimal control limit policy is achieved based on a novel Bayesian control chart. The control problems under consideration are formulated in a Partially Observable Markov Decision Process (POMDP) framework to find the optimal preventive level in order to minimize the long-run expected average cost. A comprehensive sensitivity analysis is performed to evaluate the performance of proposed models

Risk Assessment and Collaborative Information Awareness for Plan Execution

Joint organizational planning and plan execution in risk-prone environment, has seen renewed research interest given its potential for agility and cost reduction. The participants are often asked to quickly plan and execute tasks in partially known or hostile environments. This requires advanced decision support systems for situational response whereby state-of-the-art technologies can be used to handle issues such as plan risk assessment, appropriate information exchange, asset localization and adaptive planning with risk mitigation. Toward this end, this thesis contributes innovative approaches to address these issues, focusing on logistic support over risk-prone transport network as many organizational plans have key logistic components. Plan risk assessment involves property evaluation for vehicle risk exposure, cost bounds and contingency options assessment. Appropriate information exchange involves participant specific shared information awareness under unreliable communication. Asset localization mandates efficient sensor network management. Adaptive planning with risk mitigation entails limited risk exposure replanning, factoring potential vehicle and cargo loss. In this pursuit, this thesis first investigates risk assessment for asset movement and contingency valuation using probabilistic model-checking and decision trees, followed by elaborating a gossip based protocol for hierarchy-aware shared information awareness, also assessed via probabilistic model-checking. Then, the thesis proposes an evolutionary learning heuristic for efficiently managing sensor networks constrained in terms of sensor range, capacity and energy use. Finally, the thesis presents a learning based heuristic for cost effective adaptive logistic planning with risk mitigation. Instructive case studies are also provided for each contribution along with benchmark results evaluating the performance of the proposed heuristic techniques