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A battery manufacturing system typically includes a serial production line with multiple inspection stations and repair processes. In such systems, productivity and quality are tightly coupled. Variations in battery quality may add up along the line so that the upstream quality may impact the downstream operations. The repair process after each inspection can also affect downstream quality behavior and may further impose an effect on the throughput of conforming batteries. In this article, an analytical model of such an integrated productivity and quality system is introduced. Analytical methods based on an overlapping decomposition approach are developed to estimate the production rate of conforming batteries. The convergence of the method is analytically proved and the accuracy of the estimation is numerically justified. In addition, bottleneck identification methods based on the probabilities of blockage, starvation, and quality statistics are investigated. Indicators are proposed to identify the downt...

This article considers the optimal design of unpaced assembly lines. Two key decisions in designing an unpaced assembly line are the allocation of work to the stations and the allocation of buffer storage space between the stations. To the best of the author's knowledge, this is the first article to jointly optimize both the allocation of workload and the allocation of buffer spaces simultaneously when the objective is to maximize the revenue from throughput minus the cost of work-in-process inventory. Exact solutions are provided for small lines (three or four stations) with a fixed kind of processing time distribution (exponential or Erlang). Ten observations are made about the characteristics of the allocation of workload and buffer spaces. Heuristics are suggested for designing lines with more stations or different processing time distributions. A simulation study is done to test the observations and heuristics for longer lines and different processing time distributions (lognormal). Significant savin...

This article investigates methods for reallocation of service capacities in open Jackson networks in order to minimize either a system's mean total work-in-process or its response time. The focus is mainly on a method called node generation, by which capacity can be transferred from a node in echelon j to a newly generated node in echelon j + 1. The proposed procedure is compared with the more conventional capacity redistribution method, by which capacity can be transferred from any node in echelon j to existing successor nodes in echelon j + 1. Formulation of each method as a mathematical programming problem reveals the structure of the optimal solution for both problems. The motivation for considering these approaches stems from real-life settings, in particular, from a production line or supply chains where the two types of capacity reallocation are applied. Heuristic methods are developed to solve relatively large networks in tractable time. Numerical results and analyses are presented.

This article presents an approximative analysis of production lines with fluid flow, consisting of a number of machines or servers in series and a finite buffer between each pair of servers. Each server suffers from operationally dependent breakdowns, characterized by exponentially distributed up- and downtimes. An iterative method is constructed that efficiently and accurately estimates performance characteristics such as throughput and mean total buffer content. The method is based on decomposition of the production line into single-buffer subsystems. Novel features of the method are (i) modeling of the aggregate servers in each subsystem; (ii) equations to iteratively determine the processing behavior of these servers; and (iii) use of matrix-analytic techniques to analyze each subsystem. The proposed method performs well on a large test set, including long and imbalanced production lines. For production lines with imbalance in mean downtimes, it is shown that a more refined modeling of the servers in each subsystem leads to significantly better performance. Keywords: Production line, finite buffer, fluid flow, approximation, decomposition

This article considers two unreliable batch machines with a finite buffer in between. Batch machines process a set of parts simultaneously; the maximum number in the set is the size of the machine. The purpose of this article is threefold: (i) to present a model of these systems and its exact analysis; (ii) to present new qualitative insights and interpretations of system behavior; and (iii) to present the comparison between full-batch and partial-batch policies. We demonstrate new generalized conservation of flow and flow-rate–idle-time relationships. Various performance measures of interest such as production rate, mean size of batches served in each machine, machine efficiencies, probabilities of blocking and starvation, and expected in-process inventory are presented. A reversibility property is demonstrated and deadlock behavior is described. The effect of the size of machines on performance measures is examined, new phenomena and insights are observed, and possible interpretations are presented. [Su...

Consider a sequence of items produced on a high-speed mass production line which is subject to a random failure. When an item in the sequence is inspected it is possible to obtain directional information about the exact timing of a process failure—before or after producing the inspected item. Using this directional information this paper proposes Bayesian inspection procedures that deal with three related problems: (i) how often to inspect items on the production line; (ii) how to conduct the search for more defective items; and (iii) when to stop the search process and salvage the remaining items. Based on various cost factors, the problem of optimal inspection interval, optimal search process and an optimal stopping rule is formulated as a profit-maximization model via a dynamic programming approach. For the production process with an unknown failure rate, Bayesian methods of estimating the process failure rate are proposed. The proposed Bayesian inspection procedures can be applied to a wide variety of...

An approximation for the performance analysis of single-server tandem queues with small buffers and generally distributed service times is presented. The approximation is based on the decomposition of the tandem queue into subsystems, the parameters of which are determined by an iterative algorithm. By employing a detailed description of the service process of each subsystem, it proved possible to obtain an accurate approximation of performance characteristics such as throughput and mean sojourn time. The proposed technique significantly outperforms existing methods.

This paper presents approximation methods for the performance analysis of long manufacturing lines, i.e. lines with more than two machines and one buffer, that have both quality and operational failures. We describe three different versions of long flow lines that differ in the locations of the inspection stations and in the sets of machines that each inspection station monitors. We explain a transformation method that approximates long manufacturing lines that have quality and operational failures with long lines that only have operational failures. Such lines can be evaluated by decomposition methods. We introduce other approximations to quantify the effects of the separation of inspections from operations. Comparison with simulation shows that the solution methods provide reliable performance estimates.

The steady-state performance of production systems with unreliable machines has been analyzed extensively during the last 50 years. In contrast, the transient behavior of these systems remains practically unexplored. Transient characteristics, however, may have significant manufacturing implications. Indeed, if, for example, transients are sluggish and the steady state is reached only after a relatively long settling time, the production system may lose some of its throughput, thus leading to a lower efficiency. This paper is devoted to analytical and numerical investigation of the transient behavior of serial production lines with machines having the Bernoulli reliability model. The transients of the states (i.e., the probabilities of buffer occupancy) are described by the Second Largest Eigenvalue (SLE) of the transition matrix of the associated Markov chain. The transients of the outputs (i.e., production rate, PR, and work-in-process, WIP) are characterized by both the SLE and Pre-Exponential Factors ...

The introduction of multiple, independent production lines has helped many firms to increase their production flexibility, provide for redundancy when equipment breaks down, reduce idle time and labor costs, and achieve many other benefits. This paper introduces and formalizes the multiple U-line balancing problem. Optimal solution methodologies are provided for Type I (minimize the number of stations for a given cycle time), Type II (minimize the cycle time for a given number of stations), and cost-minimization line-balancing problems. A branch-and-bound algorithm is also developed for the situation in which equipment requirements are dependent on the line configuration and the task assignment to stations. Computational results indicate that the greatest benefit of exploiting multiple lines occurs for smaller cycle-time problems that require higher output.

This paper studies serial flow lines, in which each station consists of multiple identical reliable parallel machines. The parallel machines of different work stations are not necessarily identical, viz., station processing times are assumed to be exponentially distributed with non-identical mean service rates. Initially, a model consisting of two stations with multiple parallel machines and an intermediate buffer is solved analytically, by developing a recursive algorithm that generates the transition matrix for any value of the intermediate buffer capacity. This model is used as a decomposition block for solving larger lines. More specifically, the decomposition block is solved via exact Markovian analysis and then the decomposition equations and an algorithm that simultaneously solves them are derived in order to evaluate the performance measures of large production systems with multiple parallel-machine stations. Numerical results are provided for large production lines with up to 1000 workstations. T...

In this paper, analytical models are developed to study the performance of a transfer production line featuring Andon. In addition to providing analytical expressions to evaluate effective production rates, we investigate conditions under which Andon should be introduced and implemented. Some practical rules are derived to guide operations management on the factory floor. We show that when average repair times are short, introducing Andon to stop the line and repair all defects on the line is an effective way to achieve a high throughput of non-defective jobs.

Manufacturing systems typically contain processing and assembly stages whose output quality is significantly affected by the output quality of preceding stages. The deficiencies of using standard statistical process-monitoring procedures in such systems have been highlighted in the literature. This article proposes a procedure to monitor process and product quality in multistage systems. By accounting for the quality of the input to each stage, the procedure not only detects the presence of out-of-control conditions but also helps to identify the stages responsible for such departures. We extend previous research to the common case where the process parameters are unknown. An extensive performance study shows that the procedure is effective in detecting out-of-control conditions and that it convincingly outperforms existing methods. We illustrate the use of the procedure using production line data from a major electronics manufacturer. †Deceased

Much of the previous research on unpaced production lines with variable processing times has addressed either the issue of the allocation of work to the stations or the allocation of storage space to the buffers between stations. Our focus is instead on the simultaneous optimization of the workload allocation and the buffer allocation. We use a basic cost-based model that includes both revenue per unit of throughput and cost per unit of buffer space. Using both exponential and Erlang processing times, exact numerical results are obtained for the underlying Markov chain for cases where the number of states range up to over 2000 000. We investigate how the bowl phenomenon for workload allocation and the storage bowl phenomenon for buffer allocation interact when performing both allocations simultaneously. We also find counterexamples to a conjecture previously published in the literature that a balanced buffer allocation is optimal when the total number of buffer spaces is an integer multiple of the number ...

This paper provides a quantitative characterization of the smallest, i.e., lean, buffer capacity necessary and sufficient to attain a desired throughput in serial production lines with identical exponential machines. The development is carried out in terms of normalized buffer capacity and production line efficiency. The smallest normalized buffer capacity required to ensure the desired line efficiency is referred to as the Lean Level of Buffering (LLB). Exact formulas for the LLB in two- and three-machine lines are presented and an approximate expression for the LLB in lines with more than three machines is derived. Along with these analytical results, several qualitative insights into the nature of lean buffering in serial production lines are presented.

This paper presents an overlapping decomposition method to approximate the throughput of production systems with rework loops. The idea is to decompose the system into overlapped serial production lines, with the overlapping machines modified to accommodate the interactions with machines and buffers in other lines. The convergence of the iterative procedure and the uniqueness of the solution are proved analytically. The accuracy of the estimate is justified numerically and illustrated by a case study at an automotive assembly plant.

This paper presents a decomposition method for evaluating the performance of a production line with finite buffers controlled by the Control Point Policy (CPP), a policy with two parameters per machine. (One parameter is the buffer size; the other is a local hedging point.) Policies with two parameters per machine show very good performance while remaining simple to use. However, decomposition methods have not yet been developed for their analysis. We consider a production line, with exponentially distributed processing, failure, and repair times, controlled by a CPP. We decompose this line into two-machine CPP-controlled lines, which considerably simplifies the decomposition equations. Furthermore, the information loops are then included in the building blocks, and can be solved numerically. A numerical study shows that the method is accurate.

The optimal allocation of buffers in production lines is an important research issue in the design of a manufacturing system. We present a new hybrid algorithm for this complex design problem: the hybrid Nested Partitions (NP) and Tabu Search (TS) method. The Nested Partitions method is globally convergent and can utilize many of the existing heuristic methods to speed up its convergence. In this paper, we incorporate the Tabu Search heuristic into the NP framework and demonstrate through numerical examples that using the hybrid method results in superior solutions. Our numerical results illustrate that the new algorithm is very efficient for buffer allocation problems in large production lines.

In this paper, a model of a cyclic production line is introduced and analyzed. Using Taylor series expansion, we present an approximation method for the analysis of the average steady-state throughput of cyclic production lines with unreliable machines, finite buffers and a fixed number of pallets circulating in the line. Using the approximation method, we suggest an analytic method to calculate the optimal number of pallets that maximizes the line throughput. Also, we show an absolute optimal number of pallets exists which is independent of the machine parameters.

We consider a production system consisting of >N processing stages. The actual leadtimes at the stages are stochastic. The objective is to determine the planned leadtimes at each stage so as to minimize the expected total inventory costs, tardiness penalties, and a backlog penalty for not meeting demand due date at the last stage. Recursive relationships are used for automatic generation and efficient computation of the objective function. The efficiency of the proposed algorithms allows us to obtain new insights regarding operating policies, leadtime delivery reliability, and production line design. The problem is formulated as a convex non-linear programming problem. The latter is then solved using classical convex optimization algorithms. For the special case of exponentially distributed leadtimes, the objective function is derived in a closed form.

This paper presents an analytical method for evaluating the performance of production lines with a finite buffer and two unreliable machines. Unlike in earlier papers, each machine can fail in more than one way. For each failure mode, geometrically distributed times to failure and times to repair are specified. The method evaluates the steady-state probabilities of the states of the system with a computational effort that depends only on the number of failure modes considered and not on the capacity of the buffer. A comparison of performance of the method with those obtained with existing techniques that consider only one failure mode is reported.

We consider the problem of choosing the number and type of machines for each station in a new production line where the sequence of processes (i.e., manufacturing recipe) has already been established. We formulate a model to minimize cost (investment plus operating) subject to constraints on throughput and cycle time. Using queueing network approximations within a dynamic programming framework, we develop a line design algorithm that works in station-wise fashion. For computational tractability, we must discretize a continuous state space. However, we are able to compute bounds on the error in the cost function as a guide to the appropriate choice of grid size. We conclude by applying our algorithm to an industrial problem that motivated this work.

One-piece flow job shop production environments in high labor turnover environments are oftentimes characterized by late shipments and incomplete orders due to random and variable yield. The problem addressed in this paper is that of determining an order release policy for a one-piece flow prototype production line in order to meet demand in an environment subject to random and variable yield induced by high labor turnover. This paper is motivated by a problem encountered in a facility that produces prototype parts. The problem at hand can be defined as that of determining a lot size to release into the line to minimize deviations from the demand (i.e., target batch quota) for single-piece flow production in an environment characterized by random and variable yield. The objective of this research was to investigate the utility of recursive linear control as a mechanism for releasing parts into a single-piece flow production cell, and to develop a control algorithm using a Kalman filter. The methodology is evaluated via simulation using actual demand and yield data simulated from a distribution fit from historical data. Order release strategy and yield were found to be the dominant variables for complete and on-time orders without excess and shortages, and linear recursive control was found to be effective for tracking, monitoring and adjusting the release of parts into a single-piece flow job shop in an environment characterized by random and variable yield.

Now-a-days shorter product life cycles and increased demands for customization make it difficult to produce some products on traditional production lines. Often the best that can be done is to produce them in batch flow systems that have been improved through the incorporation of line flow principles. This is one-piece flow manufacturing. Traditional cells with irregular material flows are replaced by U-shaped production lines within which flow is regular and paced by a cycle time and between which flow is controlled by pull signals. This tutorial examines the research literature on one-piece flow manufacturing. It begins with the decisions rules that determine when one-piece flow is appropriate. Next the unique elements of one-piece flow (takt time, standard work, flow manufacturing on U-shaped lines, pull production, and jidoka) are reviewed. Then the mathematical models that are used to design one-piece flow systems are examined. Finally areas where more research is needed are discussed.

In this paper, an information theoretic approach is applied to analyze the performance of a decentralized control system. The control system plays the role of a correcting device which decreases the uncertainties associated with state variables of a production line by applying an appropriate ‘‘correcting signal’’ for each deviation from the target. In particular, a distributed feedback control policy is considered to govern a transfer production line, which consists of machines and buAers and processes a single part type in response to a stochastic demand. It is shown how the uncertainty of the demand propagates dynamically into the production system, causing uncertainties associated with buAer levels and machine production rates. The paper proposes upper estimates for these uncertainties as functions of the demand variance, parameters of the distributed controllers and some physical properties of the production line. The bounds are based on dynamic entropy measures of the system state and the control variables. Some practical implications into the area of decentralized controller design are proposed, an information-economical analysis is presented and a numerical study is performed.

We consider the optimal allocation of buffer storage spaces in unpaced production lines with variable processing times. It is known that for a fixed total number of buffer spaces, the production rate can often be improved by deliberately unbalancing the buffer allocations in an inverted bowl pattern (the “storage bowl phenomenon”), with more storage space allocated to the internal stations than to the end stations. It has been hypothesized that if the total number of buffer spaces is also a decision variable, then for a reasonable cost model the optimal allocation would have one additional storage space at each of the internal stations. This paper uses a cost model to show that the optimal allocation is more complicated than this. For balanced lines, the inverted bowl pattern is typically optimal, but the bowl shape becomes more and more pronounced (in an absolute sense) with larger numbers of buffer spaces. However, the relative shape of the bowl stays fairly constant. However, if the workload is unbalanced, the buffer space pattern deviates from the bowl pattern by reducing the number of buffer spaces in buffers that are not adjacent to the bottleneck machine.

In this paper we address the scheduling problem in unpaced synchronous mixed-model production lines operated under a cyclic scheduling policy. We first discuss operations of a production line with the synchronous transfer of parts. We then present an integer programming formulation of the problem. The problem, however, is NP-hard, and for its exact solution we propose an implicit enumeration scheme. We discuss a property of the scheduling problem which allows us to effectively solve large size instances of the problem. We also present an approximate solution procedure with very good average performance. Useful managerial insights are obtained as we search for ways to improve the performance of synchronous lines. The relaxation of one of our original assumptions in the scheduling problem formulation results in an easy problem whose solution generates the absolute best in throughput performance configuration of the production line. Implementation of this solution, however, requires increasing the number of buffers in the line. We suggest other performance improvement ways to better balance the tradeoff between throughput and average Work-In-Progress (WIP) inventory in the line.

The optimal allocation of buffer capacity in unbalanced production lines with reliable but variable workstations is a complex and little-researched topic. Analytic formulas for the throughput of these lines do not exist, so simulation is the only practical alternative for estimating throughput. Exhaustive search over all possible buffer allocations quickly becomes impractical beyond short lines and few buffers. Thus an algorithm is needed to efficiently find optimal or near-optimal allocations. We develop a simple search algorithm for determining the optimal allocation of a fixed amount of buffer capacity in an n-station serial line. The algorithm, which is an adaptation of the Spendley-Hext and Nelder-Mead simplex search algorithms, uses simulation to estimate throughput for every allocation considered. An important feature of the algorithm is that the simulation run length is adjusted during the running of the algorithm to save simulation run time when high precision in throughput estimates is not needed, and to ensure adequate precision when it is needed. We describe the algorithm and show that it can reliably find the known optimal allocation in balanced lines. Then we test the ability of the algorithm to find optimal allocations in unbalanced lines, first for cases in which the optimal allocation is known, and subsequently for cases in which the optimal allocation is not known. We focus particularly on lines with multiple imbalances in means and variances. In general, our algorithm proves highly efficient in finding a near-optimal allocation with short simulation run times. It also usually finds the true optimal allocation, but it is in the nature of this problem that many buffer allocations differ in throughput by small amounts that are difficult to resolve even with long simulation runs.

This research focuses on CONWIP, a closed production control system where all containers traverse a circuit incorporating the entire production line. We develop estimates, for an important level of work in process inventory, for four important performance measures: the means and variances of time between departures and flowtime. We develop our estimates through the concept of a “conceptual bottleneck machine”. This concept enables us to develop an analogy between deterministic and stochastic systems. This concept also allows us to handle migrating bottlenecks, an issue generally neglected. The model is widely applicable, assuming only finite means and variances of the processing time distributions. We test our model computationally, both against existing models and on a wide range of randomly generated problems. Finally we detail insights, obtained from our analytical model, into how CONWIP production systems operate. These insights enable us to explain the sources of the values of our performance measures, thus aiding system design and modification.

In this study, we are concerned with finding the minimum-total-buffer allocation for a desired throughput in production lines with phase-type processing times. We have implemented a dynamic programming algorithm that uses a decomposition method to approximate the system throughput at every stage. We provide numerical examples to show the buffer allocation and compare the corresponding simulated throughput and its bounds with the desired throughput.

Several researchers have previously studied the problem of allocating buffer storage to maximize the throughput of a production line for a given total amount of buffer space. In this paper we study the optimal buffer allocation problem of minimizing the average work-in-process subject to a minimum required throughput and a constraint on the total buffer space. Although these two buffer allocation problems are closely related, our results show, surprisingly, that their optimal buffer allocations have very different patterns. Specifically, we show that the optimal buffer allocations for the problem considered here generally exhibit a monotonically increasing property where an increasing amount of buffer space is assigned toward the end of the line. This monotonically increasing property generally holds for both the balanced and unbalanced lines. On the basis of our empirical results, we develop a good heuristic for selecting the optimal buffer allocations.

The purpose of this paper is to propose an analytical method for the performance evaluation of closed-loop production lines with unreliable machines and finite buffers. Such a system consists of a set of K machines separated by buffers of finite capacity. There is a fixed number of pallets circulating in the closed loop. We assume that machines have deterministic processing times but are subject to failures. Failures and repair times are exponentially distributed. We approximate the behavior of this system by a continuous flow model. The continuous flow model is then analyzed with a decomposition technique, which is similar to that used for (open) production lines. Numerical experiments are reported that show that the results provided by this method are in general fairly accurate.

This paper considers a class of assembly systems with long cycle times (low volume of output) and highly expensive components or subassemblies. Systems such as these are typical for companies in the aerospace industry assembling missiles and airplanes. As each unit of the product moves along the assembly line, its value increases owing to additional parts or components installed and the additional work performed. We show that sequencing activities according to ascending values of the ratios of the 'value added' to activity duration minimizes inventory holding cost within a given workstation. A branch-and-bound procedure is then used to allocate activities optimally to a given number of workstations. The objective function used in this paper is to maximize the net profit of a production line, which comprises net revenues minus inventory holding costs and fixed costs of workstations. The design of the assembly line is affected by two decision variables: number of workstations and cycle time. Finally, it is shown that a 'balanced' line is not necessarily an optimal one and 'pushing' activities to the right (the end of the assembly line) may reduce total holding costs and improve the profitability of the line.

The optimal placement of a predetermined amount of buffer capacity in balanced serial production lines is a well-understood problem: in lines with moderate variability, the optimal allocation involves equal numbers of buffers at each site; in lines with severe variability, the equal allocation is modified slightly to place more buffers toward the center of the line. Buffering unbalanced lines is a much less well-understood problem. We study the problem of buffering serial lines with moderate variability and a single bottleneck; i.e., a single station with a larger mean processing time than all other stations. Our analysis shows that a bottleneck station draws buffers toward it, but the optimal allocation depends on the location and severity of the bottleneck, as well as the number of buffers available. Furthermore, relatively large imbalances in mean processing times are required to shift the optimal buffer allocation away from an equal allocation. Finally, line length appears to have a relatively small e...

In this paper we study lot-sizing and changeover decisions in production schedules that are implemented on a rolling-horizon basis. The study is carried out by comparing production schedules for the packaging operations of a pharmaceutical company that produces various products on several capacitated production lines. The schedules are obtained by solving a mixed-integer programming formulation by using a heuristic procedure. We examine the effects of the number of periods in the planning horizon, the starting inventory and the demand fluctuation on the schedules. We show empirically that the saving in annual production cost diminishes rapidly as more demand periods are added to the planning horizon. Computational results are reported.

A real-time algorithm is developed for scheduling single-part-type production lines with work-in-process inventory buffers. We consider three classes of activities: operations, failures and repairs, and starvation and blockage. The scheduling objectives are to keep the actual production close to the demand, the work-in-process (WIP) inventory level low, and the cycle time short. A three-level hierardhical controller is constructed to regulate the production. At the top level, we determine the desirable buffer sizes and the target production level for each operation. At the middle level is a production flow rate controller that recalculates the production rates whenever a machine fails or is starved or blocked. The loading times for individual parts are determined at the bottom level of the hierarchy. The production scheduling algorithm is evaluated by using computer simulations for a variety of cases. Compared with a transfer line policy, a significant improvement in system performance is observed.

The design of inventory control policies for serial systems is a topic currently being explored by a number of researchers. Our goal -in two papers - is to synthesize and extend some of these efforts. We consider, simultaneously, four sources of variability in production lines - processing time variability, machine breakdowns, rework and yield loss - and show some similarities and differences in their effects on the performance of the line. In this paper we introduce an enhanced model that accommodates raw material and demand uncertainity and demonstrate that many of the sample path results obtained previously can be extended. The main objectives of this paper are: (1) to demonstrate that Constant-Work-in-Process (CONWIP) and the traditional kanban control are just two extremes in a finite family of implementable pull controls; (2) to show that while machine breakdowns, rework and random processing times have a similar effect in terms of the optimal decisions, yield losses in the line may have to be manag...

We analyze the interface between a supplier and an assembly facility, where direct shipments are made from one to the other. The final manufacturing step at the supplier involves multiple components produced on a single machine or production line. The assembly facility uses these components at a constant rate. The supplier incurs a sequence-independent setup cost and/or setup time each time the production line is changed over from one component to another. On the other hand, setup costs and times for the assembly facility are negligible. We consider two types of delivery cost: a fixed charge for each delivery, and a fixed-charge-per-truck cost. We develop a heuristic procedure to find a ‘just-in-time’ schedule in which one production run of each product and a subsequent delivery of these products to the assembly facility occur in each cycle. The objective is to find the cycle duration that minimizes the average cost per unit time of transportation, inventory at both the supplier and the assembly facility,...

We consider the problem of determining a maximum throughput cyclic schedule for the operations of a material handling hoist in an automated electroplating line. The proposed algorithm applies a set of simple algebraic inequalities to derive candidate schedules and uses a branch-and-bound-based search process to identify the optimal one. Computational results with both benchmark and random test problems are presented.

An FIS is defined as an Inspection System where the inspection routines are determined just prior to performing the inspection. The goal is to determine the inspection routine so as to optimize global objectives (such as cost minimization) subject to global and local constraints (such as total inspection time available). The concept of an FIS provides the basis for the development of intelligent automated inspection systems. In this paper, the concept of a Flexible Inspection System (FIS) for Serial Multistage production lines is introduced, the modelling implications discussed, and numerical examples are presented.

A hybrid method that combines human intelligence, an optimization technique (semi-Markov decision model) and an artificial neural network to solve real-time scheduling problems is proposed. The proposed method consists of three phases: data collection, optimization, and generalization. The testbed of this approach is the robot scheduling problem in a circuit board production line where one overhead robot is used to transport jobs through a line of sequential chemical process tanks. Because chemical processes are involved in this production system, any mistiming or misplacing will result in defective jobs. The proposed hybrid system performs better than the human scheduler from whom the models were formulated, both in terms of productivity and quality.

We develop structural results and an approximation for the throughput of an assembly system fed by multi-station fabrication lines where releases are governed by the CONWIP protocol and all machines have deterministic processing times but are subject to random outages. This formulation is motivated by a printed circuit board manufacturing process. We demonstrate that while throughput of such systems is nondecreasing in machine speed, there are cases where throughput declines when mean time between failures (MTBF) increases or mean time to repair (MTTR) decreases. Using the concept of "deterministic steady state," which describes the behavior of the system in die absence of failures, we derive a simple, closed-form approximation for throughput. Comparisons with simulation show that this approximation is robust over a wide range of conditions. Finally, we observe that throughput tends to be higher when the bottleneck is located in fabrication rather than assembly.

This paper develops an efficient analytical method for the analysis of an n-machine production line with unreliable machines and random processing times. The behavior of the n-machine line is approximated by the behaviors of the (n — l) two-machine lines based on the decomposition. Simulation and numerical experiment show that the analytical method works well and is very efficient.

This paper studies the effect of the coefficient of variation of operation times on the optimal allocation of storage space in production line systems. The operation times at each station are modelled by a two-stage Coxian distribution. This work extends the results of our previous study of the storage allocation problem with exponentially distributed operation times. Interpreting Stage 1 of the two-stage Coxian distribution as the normal service for an item at a station and Stage 2 as down time at the station, our model can also be used to study the effect of breakdowns on the allocation of storage space in production line systems. The results show that the “bowl effect” whereby the center stations should be given preferential treatment becomes more pronounced with higher variability in the operation times. Another general conclusion is that the overall optimal storage allocation commonly follows a “storage bowl phenomenon” whereby the allocation of buffer storage space fits an inverted bowl pat...

A flow-shop-type production line where the stations are subject to breakdown is modeled as a series of queues. The objective is to find the allocation of interstage buffer capacities that maximizes total profit. The stations, which are modeled as single-server queueing systems, have completion-time distributions of two-stage Coxian type. After a standard transformation to a phase-type state representation, the new system gives rise to a Markov chain. The balance equations for this chain are solved by successive approximations to find the steady-state probability distribution of the number of items at each station, once the buffer capacities are given. A search procedure has been employed to find the optimal buffer capacities.

This paper develops a model which simultaneously considers the minimization of cycle time and the number of workstations in an assembly line balancing problem. The model is developed from such known zero-one formulations as the Patterson and Albract [13] and the Thangavelu and Shetty [17] models. The flexibility provided by the model should prove especially valuable in the planning stages of a line balancing operation.

This paper is concerned with a production planning problem of allocating production items to production shifts on production lines in a multiple objective decision environment. The problem is modeled and solutions are generated using integer goal programming techniques. Objectives are formulated not only in terms of minimizing the sum of deviations from goal target levels, but also in terms of minimizing the maximum deviation. Test results indicate that the model structure and solution process utilized can provide decision makers with a good set of representative solutions in a rather complex multiple objective decision environment.

An assembly system consists of m production lines, each of which produces a certain type of component. The components are then fed into an assembly stage, where each set of m components, one unit from each different type, are assembled. Due to uncertainties involved in the production lines, the component yields are random. The optimization problem is to find the run quantities of the components, in order to minimize the total setup cost while guarantee at a certain level the probability of achieving a given target yield from the assembly stage. Solution procedures are developed that efficiently solve the problem under convex setup costs and increasing failure rate (IFR) component yield distributions. Also studied are the computation of the expected yield and the effect of variability.

Analysis of the part selection policy in the case of a flexible manufacturing cell producing different parts for several emanating lines is presented. The discrete response control problem is formulated as an undiscounted semi-Markovian decision problem whose decision epochs occur when the cell completes a part and must decide what type of part to fabricate next. Design objective is to minimize the expected shortage penalty per unit time incurred by the production lines when they run out of inputs. Optimal production policies are characterized, and a discussion of related industrial implementation issues is given.