Jorge Haddock

Multiple and bicriteria scheduling: A literature survey

Abstract Real life scheduling problems require the decision maker to consider a number of criteria before arriving at any decision. A solution which is optimal with respect to a given criterion might be a poor candidate for some other. The trade-offs involved in considering several different criteria provide useful insights to the decision maker. Thus considering problems with more than one criterion is more releBANt in the context of real life scheduling problems. Surprisingly, research in this important field has been scarce when compared to research in single criterion scheduling. In this paper, we provide a detailed literature survey of multiple and bicriteria problems in scheduling. We also provide a broad classification scheme for scheduling problems.

Simulation optimization using simulated annealing

Abstract The purpose of this study is to investigate the feasibility of using a simulated annealing algorithm in conjunction with a simulation model to find the optimal parameter levels at which to operate a system. In particular, we discuss an effort to use simulated annealing to find a combination of input parameter values for a model which optimizes a nonconvex, nonconcave objective function of the input parameters. In the absence on an optimal annealing schedule, we demonstrate that multiple runs of the simulated annealing algorithm can result in an optimal or near-optimal solution to the problem.

A combined branch-and-bound and genetic algorithm based approach for a flowshop scheduling problem

In this paper, we study the application of a meta-heuristic to a two-machine flowshop scheduling problem. The meta-heuristic uses a branch-and-bound procedure to generate some information, which in turn is used to guide a genetic algorithms search for optimal and near-optimal solutions. The criteria considered are makespan and average job flowtime. The problem has applications in flowshop environments where management is interested in reducing turn-around and job idle times simultaneously. We develop the combined branch-and-bound and genetic algorithm based procedure and two modified versions of it. Their performance is compared with that of three algorithms: pure branch-and-bound, pure genetic algorithm, and a heuristic. The results indicate that the combined approach and its modified versions are better than either of the pure strategies as well as the heuristic algorithm.

An expert system framework based on a simulation generator

Expert Systems (ES) implementations automatically perform tasks for which specially trained or talented people have been re quired. Fifth generation simulation systems integrate the tools developed in the fourth generation and capture the knowledge of the expert programmer as well as that of the simulation model ing expert. Haddock has programmed a user- oriented simula tion generator for the design and control of flexible manufac turing systems (FMS). The development of an ES based on the generator is described in this paper. The system to be described solely requires knowledge of the system to be simulated from the user. FORTRAN written sub routines, incorporated within the software structure of SIMAN, interpret the results of experimental runs and make statistical inferences about the performance measure. Simulation generators can assist simulationists in model develop ment and update, as well as in the analysis of alternative sce narios. A very desirable feature of Intelligent Front Ends (IFEs) is to have the capabilities of analyzing their output. These capa bilities not only reduce the total time required to perform the simulation, but also prevent its misuse.

A Simulation Generator for Flexible Manufacturing Systems Design and Control

Abstract Recent developments in simulation languages have enabled the modeling of complex and large systems. Systems modeling using simulation has many advantages. Even though the creation of realistic models using a simulation language is becoming easier, it is not trivial. Simulation languages are not designed to be problem-specific. Furthermore, the art of systems modeling can be quite time consuming. This paper describes a user-oriented simulation generator for the design and control of flexible manufacturing systems (FMS). The simulation generator serves as a preprocessor to the SIM AN simulation language. The generator converts data into a simulation model and automatically runs the simulation program. Standard output is provided by the simulation program, as well as plots and histograms of performance measures of interest to the analyst. Model development is significantly simplified by the simulation generator described in this paper. Simulation generators can be developed in a relatively short per...

Data-driven generic simulators for flexible manufacturing systems

Despite the apparent move toward using data-driven simulators in manufacturing modelling, as opposed to simulation languages and packages that require programming, there have been few rational efforts to evaluate the development and use of these tools. As the number of tools continues to grow, such evaluation is necessary if simulation users are going to make sensible informed choices. This paper presents two specialized data-driven simulators developed to model Flexible Manufacturing Systems called RENSAM (Rensselaer Simulator for Automated Manufacturing) and RENVIS (Rensselaer Visual Interactive Simulator). Experience with these packages leads to consideration of the benefits of using such tools. Advantages include the ease with which models can be developed and the rapid pace of that development, and the enforcement of proper statistics collection; disadvantages include misplaced perception of how easy the tool is to use, weaknesses in implementation and the limitations of the simulator. It is shown th...

A generative simulation-optimization system

Abstract The system described in this paper combines a simulation generator and an optimization subroutine. The simulation generator translates a development-oriented description of the system into a simulation program using the SIMAN simulation language. A set of search procedures is combined with the simulation program to assist in the optimization process. The optimization technique and the procedures required to invoke the module within a SIMAN simulation program are explained in the paper. The simulation generator program interactivity assists in the design of experimental models for inventory control systems. It provides an automatically improved experimental design and analysis of the system with the possible direct involvement of a decision maker. This paper shows how simulation generator programs facilitate the creation and execution of simulation models. The described system assists the decision maker in the analysis of the system by providing optimization procedures. The combination of a simulation generator program with optimization procedures not only makes simulation experimentation available to more practitioners, but shortens the process time at each step. Therefore, the analyst will not only be more willing to use simulation as a tool, but also has more time to analyze and design the system.

The binary bootstrap: inference with autocorrelated binary data

We introduce the binary bootstrap for inference with autoconelated binary data. Weempirically evaluate the standard eirors and confidence intervals created with the binary bootstrap using four stochastic process with known results: Bernoulli trials, first-order Markov processes, and long customer delays in M/M/l and D/M/10 queues. The binary bootstrap has certain advantagesover the conventional batch means method for creating confidence intervals on the probability oflong delay using only one run of a discrete-event simulation

ADAPTIVE CONTROLLERS TO INTEGRATE SPC AND EPC

Integrating the use of statistical process control (SPC) with engineering process control (EPC) enhances the results of industrial processes. Often it is assumed that disturbances in an industrial process may be immediately found and removed. It is believed that these disturbances can be removed more quickly when one observes the triggered out-of-control signal of SPC schemes. But this detection, removal, and correction of the problems in the process is seldom quick or easy. Integrating industrial standard schemes of SPC and EPC, this study aims to detect and identify a disturbance and begin an adaptive control mode to compensate for the disturbances. Superior process control can be achieved through the application of the proposed integrated SPC and EPC schemes discussed in this paper.

The threshold bootstrap: a new approach to simulation output analysis

The threshold bootstrap (TB) is a promising new method of inference for a single autocorrelated data series, such as the output of a discrete event simulation. The method works by resampling runs of data created when the series crosses a threshold level, such as the series mean. We performed a Monte Carlo evaluation of the TB using three types of data: white noise, first-order autoregressive, and delays in an M/M/1 queue. The results show that the TB produces accurate and tight estimates of the standard deviation of the sample mean and valid confidence intervals.