State-dependent Control of a Single Stage Hybrid System with Poisson Arrivals

We consider a single-stage hybrid manufacturing system where jobs arrive according to a Poisson process. These jobs undergo a deterministic process which is controllable. We define a stochastic hybrid optimal control problem and decompose it hierarchically to a lower-level and a higher-level problem. The lower-level problem is a deterministic optimal control problem solved by means of calculus of variations. We concentrate on the stochastic discrete-event control problem at the higher level, where the objective is to determine the service times of jobs. Employing a cost structure composed of process costs that are decreasing and strictly convex in service times, and system-time costs that are linear in system times, we show that receding horizon controllers are state-dependent controllers, where state is defined as the system size. In order to improve upon receding horizon controllers, we search for better state-dependent control policies and present two methods to obtain them. These stochastic-approximation-type methods utilize gradient estimators based on Infinitesimal Perturbation Analysis or Imbedded Markov Chain techniques. A numerical example demonstrates the performance improvements due to the proposed methods.     

Adaptive call admission control in circuit-switched networks

We consider threshold-based admission control policies for traffic in fixed-route circuit-switched networks, and develop a scheme for adjusting the threshold parameters online so that, as operating conditions in the network change, the thresholds "adapt" with the objective of minimizing a weighted sum of call blocking probabilities. An algorithm for estimating online the sensitivity of the call blocking metric with respect to thresholds is presented. The formal optimization problem over the set of discrete threshold parameters is solved by means of a conversion to an optimization problem over a set of auxiliary real-valued parameters. Such threshold-based policies, though conservative at low traffic rates, have the advantage of being simple to implement, distributed in nature, adaptive, and not requiring explicit distributional modeling assumptions. Numerical results included in the paper indicate that at higher traffic rates these simple policies yield the same performance as more complex and less flexible call admission schemes     

Joint gateway selection,transmission slot assignment,routing and power control for wireless mesh networks

Wireless mesh networks (WMNs) provide cost effective solutions for setting up a communications network over a certain geographic area. In this paper, we study strategic problems of WMNs such as selecting the gateway nodes along with several operational problems such as routing, power control, and transmission slot assignment. Under the assumptions of the physical interference model and the tree-based routing restriction for traffic flow, a mixed integer linear programming (MILP) formulation is presented, in which the objective is to maximize the minimum service level provided at the nodes. A set of valid inequalities is derived and added to the model in an attempt to improve the solution quality. Since the MILP formulation becomes computationally infeasible for larger instances, we propose a heuristic method that is aimed at solving the problem in two stages. In the first stage, we devise a simple MILP problem that is concerned only with the selection of gateway nodes. In the second stage, the MILP problem in the original formulation is solved by fixing the gateway nodes from the first stage. Computational experiments are provided to evaluate the proposed models and the heuristic method.     

Constrained Optimal Hybrid Control of a Flow Shop System

We consider an optimal control problem for the hybrid model of a deterministic flow shop system, in which the jobs are processed in the order they arrive at the system. The problem is decomposed into a higher-level discrete-event system control problem of determining the optimal service times, and a set of lower-level classical control problems of determining the optimal control inputs for given service times. We focus on the higher-level problem which is nonconvex and nondifferentiable. The arrival times are known and the decision variables are the service times that are controllable within constraints. We present an equivalent convex optimization problem with linear constraints. Under some cost assumptions, we show that no waiting is observed on the optimal sample path. This property allows us to simplify the convex optimization problem by eliminating variables and constraints. We also prove, under an additional strict convexity assumption, the uniqueness of the optimal solution and propose two algorithms to decompose the simplified convex optimization problem into a set of smaller convex optimization problems. The effects of the simplification and the decomposition on the solution times are shown on an example problem.     

Receding Horizon Control of Mixed Line Flow Shop Systems

We consider reliable mixed line flow shop systems that are composed of controllable and uncontrollable machines. These systems are assumed to receive arrivals at random instants and process jobs deterministically in the order of arrival so as to depart them before their deadlines that are revealed at the time of arrival. We model these flow shops as serial networks of queues operating under a non-preemptive first-come-first-served policy. Defining completion-time costs for jobs and process costs at controllable machines, a stochastic convex optimization problem is formulated where the control variables are the constrained service times of jobs at the controllable machines. As an on-line solution method to determine these service times, we propose a receding horizon controller, which solves a deterministic problem at each decision instant. We quantify the available future information by the look-ahead window size. Numerical examples demonstrate the value of information and that the no-waiting property of the full-information case is not observed in the partial-information case.     

Service Time Optimization of Mixed-Line Flow Shop Systems

We consider deterministic mixed-line flow shop systems that are composed of controllable and uncontrollable machines. Arrival times and completion deadlines of jobs are assumed to be known, and they are processed in the order they arrive at the machines. We model these flow shops as serial networks of queues operating under a non-preemptive first-come-first-served policy, and employ max-plus algebra to characterize the system dynamics. Defining completion-time costs for jobs and service costs at controllable machines, a non-convex optimization problem is formulated where the control variables are the constrained service times at the controllable machines. In order to simplify this optimization problem, under some cost assumptions, we show that no waiting is observed on the optimal sample path at the downstream of the first controllable machine. We also present a method to decompose the optimization problem into convex subproblems. A solution algorithm utilizing these findings is proposed, and a numerical study is presented to evaluate the performance improvement due to this algorithm.      

A Subgradient Descent Algorithm for Optimization of Initially Controllable Flow Shop Systems

We consider an optimization problem for deterministic flow shop systems processing identical jobs. The service times are initially controllable; they can only be set before processing the first job, and cannot be altered between processes. We derive some waiting and completion time characteristics for fixed service time flow shop systems, independent of the cost formulation. Exploiting these characteristics, an equivalent convex optimization problem, which is non-differentiable, is derived along with its subgradient descent solution algorithm. This algorithm not only eliminates the need for convex programming solvers but also allows for the solution of larger systems due to its smaller memory requirements. Significant improvements in solution times are also observed in the numerical examples.