Epidemics of infectious disease can be traced back to the early days of mankind. Only in the last two centuries vaccination has become a viable strategy to prevent such epidemics. In addition to the clinical efficacy of this strategy, the behavior and public attitudes affect the success of vaccines. This paper describes modeling the efficacy of vaccination considering the cost and benefit of vaccination to individual players. The model is based on the public goods game and is presented as a spatial game on a lattice. Using this model, individuals can contribute to the public health by paying the cost of vaccination or choose to be protected by the public who is vaccinated rather than pay the cost and share the risk of vaccination. Thus, in this model individuals can choose to stay susceptible, can become infected, or choose to vaccinate once in each episode. This paper presents the behavioral changes of the population and the cost to the society as a function of the cost of vaccines, cost of being infected, and the "fear factor" created by the public media.

Group consensus is an important method for making business decisions. In this paper, the consensus process is defined as a dynamic and interactive group decision process, which is coordinated by a moderator who helps the experts to gradually move their opinions closer to each other. This paper describes the importance of group consensus and the need to minimize the cost of this process. Furthermore, this paper describes the costs associated with decision making using group consensus and then describes three methods of reaching consensus assuming quadratic costs for a single-criterion decision problem. The first method finds the group opinion (consensus) that yields the minimum cost of reaching throughout the group. The second method finds the opinion with the minimum cost of the consensus provided that all experts must be within a given distance of the group opinion. The last method finds the maximum number of experts that can fit within the consensus, given a specified budget constraint.

Group decision making is a common and important activity in everyday life. In many cases, due to inherent uncertainty, experts cannot express their score or preference using exact numbers. The use of linguistic labels makes expert judgment more reliable and informative for decision-making. One of the problems of group decision making in fuzzy domains is aggregating experts' opinions, expressed using linguistic labels, into a group opinion. This aggregation allows the group to select the most "preferred" alternative from a finite set of candidates. The aggregation of individual judgments into a group opinion requires a measured level of consensus. In this paper, by introducing a new linguistic-labels aggregation operation, we present a procedure for handling an autocratic group decision-making process under linguistic assessments. The methodology presented results in two consequent outcomes: a group-based recommendation, and a score for each expert, reflecting the expert's contribution towards the group recommendation. By changing the weights of the experts based on their contributions, we increase the consensus and reinforce the common decision, without forcing the experts to modify their opinions. This methodology allows an autocratic decision maker to use a diversified group of consultants for a succession of decisions reaching a high level of consensus

Shop floor control has been a key area for research for several decades. Most of the control methods developed are centralized and static. This paper presents a real-time distributed shop floor control system which dynamically routes and schedules the jobs among several nonidentical candidate machines using real-time information. The approach used is based on game-theory where the operations to be performed on the parts act as players with the alternate machines used as their strategies. Each machine exchanges information about its status with the other machines, and each machine displays some characteristic feature that affects its behavior. The objective of the players is to maximize their payoff. The payoff to an operation using a particular strategy considers the present system status and the resulting future status, thus avoiding myopic decisions. The results of the games-theoretic approach are compared to four dispatching rules under varying arrival rates and failures rates of the machines. A simulation study shows that each dispatching rule performs well within a small range of system parameters. The game theoretic approach, however, adapted itself well to the various system conditions, and performs well over the entire range of arrival rates and failure rates.

The classification model based on a decision tree representation offers a solution to the real-time scheduling, sequencing, and routing of jobs on various machines at a manufacturing shop floor. In the paper, a decision tree classifier is induced from data generated from simulation runs of a real-time distributed shop floor control system. The simulation evaluates the various possible states of the shop floor, using information about the existing machines, the expected waiting time in the queues, the number of jobs waiting in each queue, the throughput rate, cycle time, and the average earliness and tardiness. The decision tree classifier develops a description or model for each type of machine behavior, which can be used to select the best machine behavior with respect to the machine's goal.

A novel approach to the modeling and analysis of an FMS (flexible manufacturing system) is presented, focusing on shop-floor control processes. This approach is based on concurrent logic programming, which uses the theoretical foundation of communicating sequential processes and guarded commands with nondeterminacy. The modeling approach is implemented using Flat Concurrent Prolog (FCP) on a Sun workstation or on a VAX machine. The properties of concurrent logic programming, as demonstrated by using FCP, include: (1) hierarchical description; (2) support for timing specifications; (3) modeling of concurrency and synchronization; (4) support for generic modeling; (5) facilitation of data transfer and complex communication; (6) capability of performance evaluation; (7) capability of static and dynamic analysis; (8) debugging capabilities; and (9) simulation ability.>

Intelligent real-time control of a flexible manufacturing system (FMS) is a complicated task. The scheduling of machines and routing of jobs are nonpolynomial problems that have to be solved fast and accurately to obtain the advantages of an FMS. Such control is done hierarchically: part routing is considered in the cell level and the equipment control strategy is reviewed in the workstation level. Two methods of controlling an FMS are described, one for each level. The one that controls the workstation equipment utilizes a decision tables in which the system states are correlated to the decision variables. The method that routes parts between the machines in the cell uses a knowledge base that stores facts about the facility and recognizes evidence to generate a decision. Both methods are presented, along with results from simulation experiments.>