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ABSTRACT EXPLORING THE RELATIONSHIP BETWEEN SUPPLY NETWORK CONFIGURATION, INTER-ORGANIZATIONAL INFORMATION SHARING AND PERFORMANCE By MARCIA DALEY August 2008 Committee Chair: Dr. Subhashish Samaddar Major Department: Decision Science Critical to the success of a firm is the ability of managers to coordinate the complex network of business relationships that can exist between business partners in the supply network. However many managers are unsure on how best to leverage their resources to capitalize on the information sharing opportunities that are available in such networks. Although there is significant research on information sharing, the area of inter-organizational information sharing (IIS) is still evolving and there is limited research on IIS in relation to systemic factors within supply networks. To help fill this gap in the literature, a primary focus of this dissertation is on the relationship between the design of the supply network and IIS. The design of the supply network is characterized by the supply network configuration which is comprised of (1) the network pattern, (2) the number of stages in the supply network, and (3) where the firm is located in that supply network. Four different types of IIS are investigated, herein. These types of IIS are a function of the frequency with which information is shared and the scope of information shared. Type 1 (Type 2) IIS is the low (high) frequency state where only operational information is shared. Similarly, Type 3 (Type 4) is the low (high) frequency state where strategic information is shared. The argument is that the type of IIS varies depending on the configuration of the supply network and that this relationship is influenced by the coordination structure established between firms in the network. The second focus of this dissertation deals with the relationship between IIS and performance. Research findings on the benefits to be gained from IIS have been ambiguous, with some researchers claiming reduced cost in the supply network with IIS, and others finding minimal or no benefits. To add clarity to these findings, the role that uncertainty plays in the relationship between IIS and performance is examined. The thesis presented is that the positive relationship between IIS types and the performance of the supply network is impacted by process uncertainty (i.e. the variability in process outcomes and production times), and partner uncertainty. Social network theory and transaction cost economics provide the theoretical lens for this dissertation. A model is developed and will be empirically validated in a cross-sectional setting, utilizing a sampling frame randomly selected and comprised of supply management executives from various industries within the United States.

The motion planning problem can be formulated as a Markov decision process (MDP), if the uncertainties in the robot motion and environments can be modeled probabilistically. The complexity of solving these MDPs grow exponentially as the dimension of the problem increases and hence, it is nearly impossible to solve the problem even without constraints. Using hierarchical methods, these MDPs can be transformed into a semi-Markov decision process (SMDP) which only needs to be solved at certain landmark states. In the deterministic robotics motion planning community, sampling based algorithms like probabilistic roadmaps (PRM) and rapidly exploring random trees (RRTs) have been successful in solving very high dimensional deterministic problem. However they are not robust to system with uncertainties in the system dynamics and hence, one of the primary objective of this work is to generalize PRM/RRT to solve motion planning with uncertainty. We first present generalizations of randomized sampling based algorithms PRM and RRT, to incorporate the process uncertainty, and obstacle location uncertainty, termed as "generalized PRM" (GPRM) and "generalized RRT" (GRRT). The controllers used at the lower level of these planners are feedback controllers which ensure convergence of trajectories while mitigating the effects of process uncertainty. The results indicate that the algorithms solve the motion planning problem for a single agent in continuous state/control spaces in the presence of process uncertainty, and constraints such as obstacles and other state/input constraints. Secondly, a novel adaptive sampling technique, termed as "adaptive GPRM" (AGPRM), is proposed for these generalized planners to increase the efficiency and overall success probability of these planners. It was implemented on high-dimensional robot n-link manipulators, with up to 8 links, i.e. in a 16-dimensional state-space. The results demonstrate the ability of the proposed algorithm to handle the motion planning problem for highly non-linear systems in very high-dimensional state space. Finally, a solution methodology, termed the "multi-agent AGPRM" (MAGPRM), is proposed to solve the multi-agent motion planning problem under uncertainty. The technique uses a existing solution technique to the multiple traveling salesman problem (MTSP) in conjunction with GPRM. For real-time implementation, an ?inter-agent collision detection and avoidance? module was designed which ensures that no two agents collide at any time-step. Algorithm was tested on teams of homogeneous and heterogeneous agents in cluttered obstacle space and the algorithm demonstrate the ability to handle such problems in continuous state/control spaces in presence of process uncertainty.