Operations Research and Engineered Systems Lab (ORES)

Optimization, and the design and analysis of engineered systems, are particularly challenging and important examples of making decisions from data. Examples of such systems include:

• Manufacturing
  • Optimizing Factory Scheduling
  • Nanoscale Film Growth
  • Laser Stabilization
  • Chemical Process Control Design
• Transportation/Robotics
  • Traffic System Optimization
  • Robotics maneuvering walking/rolling/flying

The ORES lab investigates computational requirements and algorithm development related to the design, control, and optimization of such systems. Working with the CEFS Lab, supply chains, inventory control, logistics, and distribution systems, etc. are also relevant to the ORES Lab’s focus. Working with the CBES Lab, drug, molecular, and genetic design are also applicable, while working with the PSHS Lab, health services, urban planning, emergency response, and human security systems are fitting targets for our analysis.

Engineered systems are increasingly complex, with distributed sensing, computation, and actuation nodes linked by communication networks. This project considers how the structure and dynamics of the system relate to its vulnerability to attack, where an attacker’s objectives may include destabilization, state hijacking, or system inference attacks.

Flexible production systems use the same sequence of machines to produce different products. The recipe for making each product, however, may tie up each machine differently, skip some machines, or require reprocessing on certain machines. Moreover, the factory may contain multiple copies of a given machine that operate in parallel (to speed up that stage of production), different machines may process different batch sizes of different products, and there may or may not be temporary storage facilities between machines. Scheduling the optimal production sequence to manufacture a specified quota for each type of product is computationally difficult; we explore efficient approximations for this and related problems. This includes how to best invest resources for additional machines, or how to determine how operational constraints on the duration of the workday and workweek affect the optimal schedule and the associated return on investment of the capital equipment in the factory.

Papers

• Model Approximation for Batch Flow Shop Scheduling with Fixed Batch Sizes
• A Dynamic Workflow Framework for Mass Customization Using Web Service and Autonomous Agent
• An Approximation Method for Sequencing of a Batch Manufacturing System
• Model Reduction for a Class of Input-Quantized Systems in the Max-Plus Algebra
• Monotonically Improving Error Bounds for a Sequence of Approximations for Makespan Minimization of Batch Manufacturing Systems
• A Decision-Friendly Approximation Technique for Scheduling Multipurpose Batch Manufacturing Systems

Industrial-scale gang-run printing demands that orders for different products be combined in efficient ways to minimize waste and the associated production costs. Bad choices about which products to print now can lead to the need for wasteful runs in the future. This project approaches this issue as an open-loop control problem, with solutions drawn from integer linear programming.

Sending video over the internet typically requires protocols that chop the video content into segments and adjust the quality level for each segment. Higher quality segments demand more bandwidth, so variations in available bandwidth can result in poor playback performance. This project considers control policies for client quality selection schemes that determine whether a client should request more packets for a given segment, increasing the quality of that segment, or request packets for future segments, planning ahead for smooth playback.

Wireless mesh networks provide a cost-efficient alternative to wired network infrastructures in several applications. However, because wireless signals broadcast can interfere with each other, current medium access control protocols result in serious deficiencies of fairness and under-utilization of the network. Much recent work in the literature has explored designing rate controllers for wireless networks based on mathematical optimization.

This project explores ways to improve the modeling of resource constraints on wireless networks in order to achieve more accurate optimization problems from which to base the rate controllers. It also seeks methods by which proposed controllers can be compared in a precise manner, and determining upper bounds on the performance of any controller.