We study a single-stage production system that produces one product type. The system employs a base stock policy to maintain an inventory of finished items and cope with random demand. During stockout periods, the system incurs three types of potential customer loss: (a) balking, i.e., arriving customers may be unwilling to place orders and leave immediately; (b) rejection, i.e., the system rejects new customer orders if its backlog has reached a certain limit, called the base backlog; (c) reneging, i.e., outstanding customers waiting in queue may become impatient and withdraw their orders. The objective is to determine the base stock and base backlog that maximize the mean profit rate of the system. This quantity is estimated analytically using a finite capacity M/M/1 queueing model, in which the arrival rate is a decreasing but otherwise arbitrary function of the backlog and customer reneging times have an arbitrary but known distribution. Certain properties are established which ensure that the optimal control parameters can be determined in finite time by exhaustive search. The model is then extended to take into account a fixed order quantity policy for replenishing raw material. Numerical results show that managing inventories and backlog jointly achieves higher profit than other control policies.

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.

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.

We consider finite-buffered production networks, in which each machine is capable of performing a single assembly and/or disassembly operation and it is subject of operation-dependent failures. We address the problem of allocating processing and repair rates to each machine so as to maximize the throughput of the system. This problem is shown to be equivalent to a convex minimization problem. The optimal allocation is found using a steepest-descent algorithm that employs discrete-event simulation for performance evaluation and gradient estimation.

We consider an assembly/disassembly production network with finite buffers, unreliable machines, and a number of skilled operators and repairmen. Each machine is capable of performing a single, assembly and/or disassembly operation. We assume that the processing and repair times at each machine are exponentially distributed and their rates depend on the number of operators and repairmen assigned to that machine. The problem is to allocate work force to each machine so as to maximize the throughput of the system. This problem is shown to be equivalent to a convex minimization problem. The optimal allocation is found using a steepest-descent algorithm and discrete-event simulation for performance evaluation and gradient estimation.

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.>