Home>Operations Research>Theses list

*Mixed-model assembly line sequencing problem.
*Just-in-time U-shaped assembly line balancing.
*A Benders decomposition-based procedure to solve a finite horizon, multi-produ
*Resource-constrained scheduling and production planning: Linear programming-b
*A new job shop scheduling heuristic.
*Logic and layout optimizaton for sequential circuits.
*Scheduling multiproduct flows in pipelines.
*Steady-state simulation and fuel cost minimization of gas pipeline networks.
*Environmental policy modeling and computation: A variational inequality approa
*Efficient algorithms for various supply chain problems.
*Information systems for coordination under demand uncertainty.
*Emerging problems in supply chain management.
*Enterprise resource planning systems in operations management: A model, an in
*Organizational level E-business resource allocations: An analytical approach t
*Pricing decisions to provide quality-of-service differentiated telecommunicati
*Shortest paths and multicommodity network flows.
*Applying optimization techniques to improve E-business performance.
*Vigilance taxonomy in safety-critical monitoring systems (SCMS).
*Adaptive online optimization of Markov reward processes with application to pr
*Determination of mass excavation haul distance and direction by linear optimiz
*Affectation statique et dynamique des usagers dans un reseau de transport avec
*Parallelisation d'un algorithme d'optimisation par colonies de fourmis pour la
*Topologie et routage dans les reseaux de communications (French text).
*Optimal haul truck allocation in the syncrude mine.
*Naval battle simulation system optimization.
*A computational model for naturalistic decision-making in the context of milit
*Using decision models to automate and individualize interactive decision suppo
*Optimization models and algorithms for batch process scheduling under uncertai
*Optimizing Web server load distribution with evolutionary computation.
*A parametric solution for local and global optimization.
*The effect of adding domain knowledge on the learning of rule-based models for
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