Buffer Allocation Problem Research Articles

Analysis and design of split and merge unpaced assembly systems by metamodelling and stochastic search

The study presents a new approach in optimal interstage buffer allocation problem of split-and-merge unpaced open assembly systems, which are increasingly being used in modern manufacturing systems, particularly in automotive industries. Allocations of interstage buffers to accommodate the work-in-process inventories are optimized in an attempt to maximize the overall system production rate. A simulation model developed is used in conjunction with genetic algorithms (GA) to find optimal interstage buffer configurations yielding a maximum production rate. However, simulation is extremely time-consuming due to lengthy computational requirements, especially when used in a stochastic search algorithm. In an attempt to overcome this problem, an alternative approach in simulation metamodelling based on artificial neural networks (ANN) is developed. The optimization problem previously conducted through simulation and GA is reconsidered by integrating the metamodelling approach into the GA, replacing the simulation model. The new ANN-GA approach not only gives solutions with no statistically significant difference in comparison with the original simulation GA approach, but also demands significantly less computational time. The proposed methodology intends to help practising system design engineers to take quicker decisions regarding the assembly system design parameters. The potential of metamodelling to solve manufacturing systems problems are also discussed.

Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach

This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems (AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network (ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach.

A new approach for buffer allocation in unreliable production lines

In this article, we describe a new local search approach for solving the buffer allocation problem in unreliable production lines. In this problem, we need to determine near optimal buffer allocation plans in large production lines and the objective is to maximize the average throughput. An analytical decomposition-type approximation is used to estimate the production line throughput. The proposed approach allows the allocation plan to be calculated subject to a given amount of total buffers slots in a computationally efficient way. Within an acceptable amount of time, our algorithm produced better results than simulated annealing for existing benchmark problems.

Discrete stochastic optimization using variants of the stochastic ruler method

AbstractWe present two random search methods for solving discrete stochastic optimization problems. Both of these methods are variants of the stochastic ruler algorithm. They differ from our earlier modification of the stochastic ruler algorithm in that they use different approaches for estimating the optimal solution. Our new methods are guaranteed to converge almost surely to the set of global optimal solutions under mild conditions. We discuss under what conditions these new methods are expected to converge faster than the modified stochastic ruler algorithm. We also discuss how these methods can be used for solving discrete optimization problems when the values of the objective function are estimated using either transient or steady‐state simulation. Finally, we present numerical results that compare the performance of our new methods with that of the modified stochastic ruler algorithm when applied to solve buffer allocation problems. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.

The buffer allocation problem for general finite buffer queueing networks

The Buffer Allocation Problem (BAP) is a difficult stochastic, integer, nonlinear programming problem. In general, the objective function and constraints of the problem are not available in a closed form. An approximation formula for predicting the optimal buffer allocation is developed based upon a two-moment approximation formula involving the expressions for M/ M/1/ K systems. The closed-form expressions of the M/ M/1/ K and M/ G/1/ K systems are utilized for the BAP in series, merge, and splitting topologies of finite buffer queueing networks. Extensive computational results demonstrate the efficacy of the approach.

Application of the Cross-Entropy Method to the Buffer Allocation Problem in a Simulation-Based Environment

The buffer allocation problem (BAP) is a well-known difficult problem in the design of production lines. We present a stochastic algorithm for solving the BAP, based on the cross-entropy method, a new paradigm for stochastic optimization. The algorithm involves the following iterative steps: (a) the generation of buffer allocations according to a certain random mechanism, followed by (b) the modification of this mechanism on the basis of cross-entropy minimization. Through various numerical experiments we demonstrate the efficiency of the proposed algorithm and show that the method can quickly generate (near-)optimal buffer allocations for fairly large production lines.

Combining Routing and Buffer Allocation Problems in Series-Parallel Queueing Networks

Given a series-parallel queueing network topology with exponential servers of finite capacity, a systematic design methodology is presented that approximately solves the optimal routing and buffer space allocation problems within the network. The multi-objective stochastic nonlinear programming problem in integer variables is described and a two-stage iterative optimization procedure is presented which interconnects the routing and buffer space allocation problems. The algorithmic procedure couples the Expansion method, a decomposition method for computing performance measures in queueing networks with finite capacity, along with Powell's unconstrained optimization procedure which allocates the buffers and a multi-variable search procedure for determining the routing probabilities. The effectiveness and efficiency of the resulting two-stage design methodology is tested and evaluated in a series of experimental designs along with simulations of the network topologies.

A dynamic programming algorithm for the bufferallocation problem in homogeneous asymptotically reliable serial production lines

In this study, the buffer allocation problem (BAP) in homogeneous, asymptotically reliable serial production lines is considered. A known aggregation method, given by Lim, Meerkov, and Top (1990), for the performance evaluation (i.e., estimation of throughput) of this type of production lines when the buffer allocation is known, is used as an evaluative method in conjunction with a newly developed dynamic programming (DP) algorithm for the BAP. The proposed algorithm is applied to production lines where the number of machines is varying from four up to a hundred machines. The proposed algorithm is fast because it reduces the volume of computations by rejecting allocations that do not lead to maximization of the line′s throughput. Numerical results are also given for large production lines.

Optimal buffer allocation in production lines

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.

A Genetic Algorithm for the Allocation of Buffer Storage Capacities in a Production Line with Unreliable Machines

In this paper, we consider a flow-line manufacturing system organized as a series of workstations separated by finite buffers. The failure and repair times of machines are supposed to be exponentially distributed. The production rate of each machine is deterministic, and different machines may have different production rates. The buffer allocation problem consists in determining the buffer capacities with respect to a given optimality criterion, which depends on the average production rate of the line, the buffer acquisition and installation cost, and the inventory cost. For this problem we propose a genetic algorithm where the tentative solutions are evaluated with an approximate method based on the Markov-model aggregation approach.

A heuristic algorithm for the buffer allocation in unreliable unbalanced production lines

In this work we investigate the buffer allocation problem (BAP) in short unbalanced production lines consisting of up to six machines that are subject to breakdowns. Times to failure are assumed exponential whereas service and repair times are allowed to follow any Erlang- k distribution (with k≥1). An algorithm that is based on the sectioning (segmentation) approach was developed which solves the BAP. This, in conjunction with a method not previously reported that determines a “good” initial solution for the buffer allocation constitutes the main contribution of the present work. The accuracy of the proposed heuristic algorithm is remarkably good and its convergence is fast making it a promising tool that can be implemented in conjunction with a fast decomposition method to solve the BAP in large production lines.

Buffer allocation in flow-shop-type production systems with general arrival and service patterns

This study investigates the buffer allocation strategy of a flow-shop-type production system that possesses a given total amount of buffers and finite buffer capacity for each workstation as well as general interarrival and service times in order to optimize such system performances as minimizing work-in-process, cycle time and blocking probability, maximizing throughput, or their combinations. In theory, the buffer allocation problem is in itself a difficult NP-hard combinatorial optimization problem, it is made even more difficult by the fact that the objective function is not obtainable in closed form for interrelating the integer decision variables (i.e., buffer sizes) and the performance measures of the system. Therefore, the purpose of this paper is to present an effective design methodology for buffer allocation in the production system. Our design methodology uses a dynamic programming process along with the embedded approximate analytic procedure for computing system performance measures under a certain allocation strategy. Numerical experiments show that our design methodology can quickly and quite precisely seek out the optimal or sub-optimal allocation strategy for most production system patterns. Scope and purpose Buffer allocation is an important, yet intriguingly difficult issue in physical layout and location planning for production systems with finite floor space. Adequate allocation and placement of available buffers among workstations could help to reduce work-in-process, alleviate production system's congestion and even blocking, and smooth products manufacturing flow. In view of the problem complexity, we focus on flow-shop-type production systems with general arrival and service patterns as well as finite buffer capacity. The flow-shop-type lines, which usually involve with product-based layout, play an important role in mass production type of manufacturing process organization such as transfer line, batch flow line, etc. The purpose of this paper is to present a design methodology with heuristic search and imbedded analytic algorithm of system performances for obtaining the optimal or sub-optimal buffer allocation strategy. Successful use of this design methodology would improve the production efficiency and effectiveness of flow-shop-type production systems.

Minimizing WIP inventory in reliable production lines

This work deals with the well-known buffer allocation problem in short reliable production lines. The objective is to find the optimal buffer allocation (OBA) that minimizes the average work-in-process (WIP) inventory, subject to a minimum required throughput. The study leads to some insights concerning the evolution of the throughput and WIP as a function of the ordered buffer allocations. In the case of balanced lines, it is observed that the so-called self-similarity phenomenon prevails. It is also pointed out that the choice of the throughput level is critical for finding the OBA that minimizes the average WIP. Also a heuristic algorithm is proposed to find the OBA, which reduces the search space by over 50% compared to enumeration.

Cash-flow-oriented buffer allocation in stochastic flow lines

This paper defines the problem of buffer space allocation in flow lines as an investment problem. A model is developed and solution techniques are described that can be used to determine buffer allocations that maximize the expected net present value of the investment, including machines, buffers and inventory. Production rates and inventory levels are explicitly linked to projected cash flow to assess the economic consequences of the buffer allocation. Several examples of linear flow lines as well as assembly/disassembly systems and flow lines with rework loops are analysed numerically. These examples demonstrate the importance of the buffer allocation problem and provide intuition with respect to the structure of optimized solutions. A result is that as product quality in a system with a rework loop improves, an optimally designed system can receive more buffer spaces and may use more inventory. This possibly counterintuitive result is due to the economic perspective on the buffer allocation problem.

A Simulated Annealing Approach for Buffer Allocation in Reliable Production Lines

We describe a simulated annealing approach for solving the buffer allocation problem in reliable production lines. The problem entails the determination of near optimal buffer allocation plans in large production lines with the objective of maximizing their average throughput. The latter is calculated utilizing a decomposition method. The allocation plan is calculated subject to a given amount of total buffer slots in a computationally efficient way.

Stochastic algorithms for buffer allocation in reliable production lines

The allocation of buffers between workstations is a major optimization problem faced by manufacturing systems designers. It entails the determination of optimal buffer allocation plans in production lines with the objective of maximizing their throughput. We present and compare two stochastic approaches for solving the buffer allocation problem in large reliable production lines. The allocation plan is calculated subject to a given amount of total buffer slots using simulated annealing and genetic algorithms. The throughput is calculated utilizing a decomposition method.

Buffer allocation in unreliable production lines using a knowledge based system

One of the most important but difficult optimization problems concerns the optimal allocation of buffers in a production system (line) with stochastic inputs and outputs. The buffer allocation problem is a non-linear problem with integer variables and there exists no closed-form solution for the objective function. The optimization of production lines performance is a problem of great complexity and, therefore, of significant research interest. The problem may involve the optimization of many conflicting objectives, such as increasing throughput and reducing work-in-process time. The majority of existing studies have used various heuristics and search methods based on operations research. These methods have been proved to be computationally inefficient, especially for large production lines. This paper presents ASBA2, a knowledge based system that determines near optimal buffer allocation plans, with the objective of maximising production lines throughput. The allocation plan is calculated subject to a given amount of total buffer slots, in a computationally efficient way. ASBA2 operates in close cooperation with a simulation method, which provides ASBA2 with performance measures concerning production line behaviour. Moreover, to evaluate results provided by ASBA2, we have utilized an exact numerical algorithm for calculating the throughput of unreliable production lines.

OPTIMAL BUFFER ALLOCATION IN ASYNCHRONOUS CYCLIC MIXED‐MODEL ASSEMBLY LINES

This paper considers a problem of optimal buffer allocation in cyclic asynchronous mixedmodel assembly lines with deterministic processing times. An analytical model is used to provide new insights into properties of optimal buffer allocation, that is, a buffer configuration that guarantees the highest possible throughput rate on the assembly line with a minimum number of buffers. Optimal buffer configuration is characterized, and an efficient algorithm to find such a configuration is developed. The approach proposed in this paper also provides insights on how to allocate a given number of buffers to workstations on the assembly line to maximize the throughput rate.

Optimal buffer storage allocation in balanced reliable production lines

This work deals with the buffer allocation problem in balanced production lines. The workstations of the lines consist of single machines which are assumed to be perfectly reliable, i.e., they don't break down. The processing times follow the exponential or Erlang- k ( k=2,3,4) distribution. The paper presents two basic design rules that were extracted for the optimal buffer allocation in these types of lines using enumeration. We have developed a search technique that gives the optimal buffer allocation very fast. This is a modified Hooke–Jeeves search algorithm that exploits the two design rules to give an initial buffer allocation close to the optimal one. The rules are exploited via the use of the proposed Allocation Routine. The latter was found to give the optimal solution in almost all cases examined in this study.

Price-based buffer sizing in assembly systems

Internal transfer pricing mechanism will be applied to the problem of buffer allocation in the context of interfacing an assembly line with multiple parallel production lines. Economic effect of buffer sizing iin assembly will be analyzed by means of internal transfer pricing. New internal transfer pricing scheme will be designed to increase the efficiency of assembly system globally not sub-optimally. This new approach will enhance the economic benefit of production channel between an assembly line and multiple production lines. This study will give the basic idea for the application of internal transfer pricing mechanism in various kinds of manufacturing system to increase economic efficiency globally.