Problems of inventory control and customer admission control are considered for a manufacturing system that produces one product to meet random demand. Four admission policies are investigated: lost sales, complete backordering, randomized admission, and partial backordering. These policies are combined with an integral inventory control policy, which releases raw items only when an incoming order is accepted and keeps the inventory position (total inventory minus outstanding orders) constant. The objective is to determine the inventory level and the maximum number of backorders, as well as the admission probability that maximize the mean profit rate of the system. The system is modeled as a closed queueing network and its performance is computed analytically. The optimal parameters for each policy are found using exhaustive search and convex analysis. Numerical results show that managing inventory levels and sales jointly through partial backordering achieves higher profit than other control policies.

This paper proposes a framework towards a universal definition of recyclability of materials using fuzzy logic. Recyclability is defined from a global perspective by aggregating the technology and economics of the recycling processes, legislative support for recycling, quality of recycled materials, and environmental impact of waste. These principal factors have indicators of their own and are computed via fuzzy logic. Recyclability is assessed qualitatively using linguistic "if-then" rules and quantitatively on a 0 to 1 scale for a number of materials of high economic and industrial importance. A sensitivity analysis with respect to indicators reveals the most important factors that affect recycling of a given material. Resource economists could set priorities for critical indicators to increase the recyclability of materials according to the sensitivity analysis

This paper presents a fuzzy inference framework for the assessment of recyclability of materials. Recyclability is defined from a global perspective by aggregating basic indicators which influence the technology and economics of the recycling processes, policies related to recycling, quality of recycled materials, and environmental impact of waste. The resulting fuzzy system is a network of inference stages in which the output of an inference stage is passed to other stages, and so forth. Conditions on the parameters of the fuzzy system are given under which the overall recyclability is an increasing function of the recyclability indicators. A sensitivity analysis with respect to indicators is used to identify the major factors influencing the recycling rate. The model is applied to a number of materials of high economic and industrial importance.

This paper outlines a methodology for the definition and assessment of sustainability and recyclability using fuzzy logic.

Two of the objectives of Internet are to increase network capacity and offer high quality of differentiated services for traffic with real-time and nonreal-time requirements. Differentiated services (Diff-Serv) were established to fulfill such objectives. Up until now, several Diff-Serv schemes have been proposed which, among others, handle drop and delay priorities. These two priorities raise important optimization issues for the Internet but their relationship remains an open problem. This paper presents a fuzzy control algorithm to select packets in a fair and efficient manner. Simulation shows that the fuzzy controller is better than a crisp one when the fairness issue is raised.

Two outstanding problems of admission control and scheduling in networks with three and two workstations, respectively, are solved using fuzzy logic. Neither problem has been tackled up until now analytically, whereas the fuzzy approach provides computational solutions. In the first case, we have one workstation with two parallel ones. A reward is earned whenever the first stage accepts a customer and a holding cost is incurred by a customer in queue in the second stage. The class of customer to be next served by the first stage is dynamically selected so as to maximize an average benefit over an infinite horizon. In the second case, there are two parallel servers and three arrival processes generated by independent Poisson streams. Each server has its own queue and receives customers from its own arrival stream. A third arrival stream consists of customers with resource demand on both servers. Each customer pays a holding cost per unit time in the system. Again, the scheduling policy is specified which minimizes the average cost. The fuzzy models are new in this context and tackle computationally problems for which we have not analytical solutions.

Screening (100% inspection) is a common practice in quality engineering. Traditional screening procedures seek upper and lower specification limits for a quality characteristic associated with some product, such that the total quality cost per item is minimized. This practice ignores the effect of parts rejection on product lead times, inventory carrying costs, shortage costs, throughput, and, eventually, total profit rate of the system. In this paper, a single-stage production system is considered and two different control policies are jointly determined with the inspection plans. Numerical experiments indicate that such coordinated policies achieve a better performance than independently determined quality and production control policies.

Two outstanding problems of admission control and scheduling in networks with three and two workstations respectively are solved using fuzzy logic. In the first case we have one workstation with two parallel ones. A reward is earned whenever the first stage accepts a customer and a holding cost is incurred by a customer in the queue in the second stage. The class of customer to be served next by the first stage is dynamically selected so as to maximize an average benefit over an infinite horizon. In the second case there are two parallel machines and three arrival processes generated by independent Poisson streams. Each machine has its own queue and receives parts from its own arrival stream. The third arrival stream consists of parts with resource demand on both machines. Each customer pays a holding cost per unit time in the system. Again, the scheduling policy is specified which minimizes the average cost. The fuzzy models are new in this context and tackle problems that cannot be solved by analysis.

We consider the problem of optimal control of queueing systems with heterogeneous servers in parallel. The system objective is to assign customers dynamically to idle servers based on the state of the system so as to minimize the average cost of holding customers. Three cases, either known in the literature or new, are studied in detail: queueing systems with server heterogeneity in-service rates, in-service functions, and both in-service rates and in-service functions. An approach is presented using fuzzy control to solve these problems. Simulation shows that this approach is efficient and promising, especially in cases where analytical solutions do not exist.

We consider two queues in tandem, each of which has its own input of arriving customers, which, in turn, may either be accepted or rejected. Suppose that the system receives a fixed reward for each accepted customer and pays a holding cost per customer per unit time in the system. The objective is to dynamically determine the optimal admission policies based on the state of the system so as to maximize the average profit (reward minus cost) over an infinite horizon. This model finds applications in flow control in communication systems, industrial job shops, and traffic-flow systems. In this paper, a novel approach is presented using fuzzy control. This approach explicitly finds the optimal policy, which up until now has defied analytical solutions.

We consider queueing systems in which the service rate is the controlled variable. The cost depends on the queue length and selected rate. The objective is to choose the service rate dynamically, based on the state of the system so as to minimize the average cost over an infinite horizon. Six classes, either known in the literature or new, are studied in detail: queueing systems with vacations, single-server queueing systems with and without switching costs, and tandem queueing systems with and without service costs. A novel approach is presented here using fuzzy control to solve these problems. Simulation shows that the approach is efficient and promising, especially in cases where analytical solutions do not exist.

This paper considers the problem of optimal control to tandem two-station production networks with two types of parts. Part routing, service rate selection and flow control, are simultaneously considered. We use fuzzy control to solve the problem. Simulation shows that the approach is efficient and promising.

We consider queueing systems in which the number of servers is the controlled variable. The cost structure includes service cost, switching cost and holding cost. The objective is to adjust the number of servers dynamically based on the state of the system so as to minimize the average cost over an infinite horizon. In this paper, a novel approach is presented using fuzzy control to solve this classical problem. Two cases, M/M/1 and M/M/m queueing systems with vacations, are studied in detail. Simulation shows that this new approach is efficient and promising, especially in cases where analytical solutions do not exist.

Flexibility is recognized as an important feature in manufacturing. This paper suggests a knowledge-based methodology for the measurement of manufacturing flexibility. We claim that flexibility is an inherently vague notion and an essential requirement in its measurement is the involvement of human perception and belief. Nine different flexibility types are measured, while the overall flexibility is given as the combined effect of these types. Knowledge is represented via IF (fuzzy antecedents) THEN (fuzzy consequent) rules, which are used to model the functional dependencies between operational characteristics, such as setup time and cost, versatility, part variety, transfer speed, etc. The proposed scheme is illustrated through an example.

The problem of optimal control of queueing systems with heterogeneous servers is solved using fuzzy logic. Two known and one new case are tackled. Simulation shows that this approach is efficient and promising.

Production networks with finite buffers, random rates, and breakdowns are analyzed using an analytic/simulation model. Random rates are approximated by piecewise deterministic variables and segments of the network are collapsed into single servers between events. Occurrence of events represents the slow nonlinear system dynamics handled by simulation. The fast piecewise linear dynamics between events are handled algebraically. An additional approximation is used for discrete-part traffic which is represented by a continuous flow. Extensive experimentation with a multitude of networks and comparison with a conventional simulator show that the model is quite fast and accurate, thus becoming a promising candidate for design purposes.

We consider a serial production system with unreliable machines maintained by a limited number of repairmen, and finite storage between machines. Processing times may be random variables with exponential or gamma distributions, or deterministic. We develop a continuous-flow model for such a system utilizing simulation and analysis. Random processing times are approximated by sums of deterministic variables using a simple probabilistic technique. The model observes a limited number of events which are sufficient to determine system performance and mean buffer levels. By appropriately reducing the rates of starved and blocked machines and using analysis to compute the times of next event at each machine and buffer, discrete part computations are avoided. It is demonstrated that this approximate model is highly accurate and faster by a factor of 3 or more when compared to conventional simulators. The paper addresses also optimal repair allocation to maximize the expected throughput of the system. Two different approaches are proposed: perturbation analysis and experimental evaluation of various nonpreemptive rules for assigning a repairman to failed machines.>