Resource planning for a complex remanufacturing system is in general extremely difficult in terms of, e.g., problem size and uncertainties. In many cases, simulation is the only way to select a good plan among a great number of candidates. When there exist complicated constraints, direct selection could be very inefficient since many candidates may not be feasible but cannot be excluded beforehand. To meet the challenge, a machine learning method is introduced in this paper to perform feasibility analysis. The rough set theory is first applied to establish the relationship between a plan and its feasibility and an iterative reinforcement process is applied to enhance confidence. The numerical testing results show that this method is promising and scalable for the large-scale problems. The research lays a basis for developing an efficient simulation-based optimization method with complicated constraints. Note to Practitioners-This paper was motivated by the resource planning problem for a complex remanufacturing system, which is very important but in general extremely difficult to deal with in terms of, e.g., problem size and uncertainties. Simulation is probably the only way available to select a good plan among a number of candidates. When there exist complicated constraints, simulation becomes even more difficult and selection through simulation could be very inefficient since many candidates may not be feasible but cannot be excluded before simulation. Determining feasibility beforehand is extremely difficult by analytical or numerical methods. This paper suggests a new method using a machine learning-based approach to predict the plan feasibility required in practical applications and can be considered as the first step for optimization-based planning. By applying the rough set theory, the prediction rules are obtained or learned from a training dataset generated by simulation. Then, an iterative reinforcement process is applied to enhance the confidence of learning and to perform iterative retraining on new datasets by the rough set method to generate new rules to add to the knowledge base until the preset threshold is satisfied. The numerical testing results show that the above method is capable of determining the feasible plans for a remanufacturing system with good accuracy. The method is efficient and scalable for the large-scale problems. The method developed in the paper is being incorporated in the framework of ordinal optimization, and a new constrained ordinal optimization method has been developed for remanufacturing planning.

Motivated by the problem of wireless data link layer security, this paper proposes SPRiNG, a simple protocol for secure point-to-point communication. SPRiNG uses synchronized pseudo random number generation to generate authenticator variables and fresh encryption keys on a per frame basis. A major design goal for SPRiNG was simplicity and compatibility with the existing 802.11b WEP protocol. Though motivated by wireless security, SPRiNG is generic and can be applied anywhere secure point-to-point communication is required. Unlike many proposed security schemes, SPRiNG demonstrates that security does not always necessitate complexity.

Efficient techniques for estimating performance measures in communication networks in a steady-state or transient setting are developed. These techniques may be used in a simulation environment or in connection with real-time observations. For Markov chain models, the recently proposed standard clock approach is extended, and a class of real-time algorithms for simultaneously generating multiple sample paths under different parameter sets is presented. Attention is focused on the link crash time estimation problem, where a 'crash' is defined as the first time a buffer overflows, given some initial conditions. An algorithm for estimating crash times under various traffic shocks is derived, where all estimates are obtained in parallel to an actual network's normal operation. An algorithm for crash time estimation of a G/D/1 link model is also derived using a different (perturbation-analysis-based) approach. Finally, extensive simulation results are provided to validate the proposed algorithms and compare them to brute-force simulation.>

An overview of discrete-event dynamic system (DEDSs) is given, explaining what they are and discussing their modeling. The types of models are outlined, and factors to be taken into account in modeling are examined. They are: the discontinuous nature of discrete events; the continuous nature of most performance measures; the importance of probabilistic formulation; the need for hierarchical analysis; the presence of dynamics; and the feasibility of the computational burden. It is stressed that DEDS research must include experiment as well as theory.>

A new approach for estimating the throughput sensitivity with respect to routing probabilities in queueing networks and flexible manufacturing systems is proposed. The approach converts the problem to a problem of calculating the sensitivity of throughput with respect to service distributions using perturbation analysis.

This paper studies the problem of query optimization in a distributed database. Assuming a linear additive cost function (in volume of data moved), we present a fast algorithm for finding the optimal program that answers a class of common queries, called chain queries. The key to the problem formulation and to the derivation of an efficient algorithm is an elegant parameterization of the database state against which the query is to be answered. This parameterization then enables us to characterize the set of potentially optimal programs, which in turn leads to a fast dynamic programming algorithm. Since in practice the needed parameters may not be available to the database system, we also discuss how to deal with partial parameterizations of the database state.

In this paper we outline a general methodology illustrated by specific examples for the optimal interpretation of a class of semi-join queries in distributed database systems.

An exponentially convergent and finite algorithm is presented for the determination of the solution α of the linear inequalities Aα>0 for a given matrix A, or for determining the non-existence of solution for Aα>0. This result is useful in threshold-switching theory and in pattern classification problems. Experiments indicate extremely rapid convergence of the method.