The Study of Buffer Allocation Problem in Complex Production Line

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

Over the past few decades, buffer allocation problems have been investigated extensively. It is widely known that buffers play important roles in the manufacturing system, including preparing disruption and improving efficiency. However, due to a physical limit for buffer space and the related cost burden, optimal buffer allocation has become an important issue. In this paper, a solution for optimal buffer allocation in a flexible manufacturing system (FMS) with finite buffers is presented. Unlike most previous studies, which mainly focused on input buffers only and operation dispatching in simple production lines, this paper considers both input and output buffers together in complex production lines where multiple workstations, multiple automatic guided vehicles, and complicated paths exist. Also, we address a practical buffer allocation problem by increasing total number of buffers and changing a buffer profile (capacities of multiple input/output buffers). A simulation and a genetic algorithm are used to solve our problem. While the simulation models the process of the complex FMS, the genetic algorithm determines the optimal buffer allocation. The proposed solution performs better than the conventional heuristic used in practice.

The buffer allocation problem is an NP-hard combinatorial optimization problem and it is an important research issue in designing manufacturing systems. The problem deals with finding optimal buffer sizes to be allocated into buffer areas in a production system to achieve a specific objective. This paper presents a comprehensive survey on buffer allocation problem in production systems. To provide a systematic review of current relevant research, first studies are grouped in two categories: 1. Reliable production lines, 2. Unreliable production lines. Next, the studies in each group are reviewed based on topology of the production line, the solution methodologies suggested and the objective function employed. The aim of this review is twofold. First, it provides an overview of recent advances in the field in order to highlight the new trends in solution methodology. Second, it presents ideas for future research by identifying gaps in the current literature.

Buffer allocation in unreliable production lines using a knowledge based system

A production line is an important class of manufacturing system when large quantities of identical or similar products are to be produced. The performance of a production line is highly influenced by machine failures. When a machine fails, it is then be unavailable during a certain amount of time required to repair it. Analysis of production lines divides into three types: analytical, approximation and simulation models. The analytical and approximation models have assumptions, which make these models unrealistic such as reliable workstations, certain processing distribution, the first workstation cannot be starved and the last workstation cannot be blocked. The main problems in production lines treatment are the calculation of throughput and average levels of buffers because of the great size of state space. An analytical model is reviewed to clarify the limitations to use such treatment in real production lines. Simulation modeling of production lines is considered very important for designers interested in: Workload Allocation Problem (WAP), Server Allocation Problem (SAP), and Buffer Allocation Problem (BAP). This paper studies and analyzes the performance keys, which effect on production lines. A simulation model is developed by using ARENA software and used to analyze and test several bottlenecks that are causing severe congestions in different areas on the production line and could resolve all of these bottlenecks. In this paper, an actual cement production line is used as a case study. After a simulation time of 13 days, a simulation results show the line bottlenecks, workstations utilization, buffer capacities and the line production rate. The outputs clarify redesign of allocation of buffers, which verify an optimum size could be made; it might be taken into consideration when designers implement such lines. Finally modified better workstations utilization, buffer capacities and the line production rate with an increase about 15% of the production rate and economizing of 37 % from buffer capacities

The performance of unpaced production lines with unbalanced mean operation times and unreliability patterns

Productivity Improvement Through Time Study Approach: A Case Study from an Apparel Manufacturing Industry of Pakistan

In manufacturing, machine selection and buffer allocation are extremely important and widely studied problems, as both can significantly affect the system performance. Solving either problem alone in a deterministic setting is already challenging. In this study, however, we simultaneously solve a high dimensional machine selection and buffer allocation problem in a highly stochastic and complex production line environment. The objective is to simultaneously select the optimal combination of machine type and buffer size allocation to maximize production throughput under limited cost and total buffer size constraints. We develop an efficient, interactive two-stage optimization method, called Machine Selection and Buffer Allocation algorithm (MSBA), that consists of a rapid machine selection (RMS) procedure and an adaptive global particle and local hyperbox search (AGPLHS) algorithm to solve the problem via simulation optimization. MSBA achieves seamless integration of RMS and AGPLHS so as to enable the simultaneous machine selection and buffer allocation to be solved efficiently. Furthermore, we compare our overall framework with three common existing methods (Genetic Algorithm, Nelder-Mead Algorithm and Adaptive Tabu Search). It is shown that our interactive two-stage framework outperforms the competing algorithms both in terms of effectiveness and efficiency. Note to Practitioners—Two important problems to solve in many production processes such as semiconductor manufacturing are machine selection and buffer allocation. Various methodologies have been utilized to solve each of the two problems individually. This work proposes an interactive two-stage simulation optimization algorithm which simultaneously solves both problems in complex, high dimensional and profoundly stochastic environments. While the most basic problem setting is a serial multi-workstation environment, complexities such as re-work stations, merging, splitting, parallel machine, and multi-product processor can be incorporated into the simulation model and solved using our proposed methodology. The methodology is user-friendly and intuitive yet adaptable enough to handle a wide range of production line environments or solve other stochastic optimization problems which require the simultaneous optimization of a mixture of binary and integer-based solutions. The numerical study, consisting of two different job-shop environments and including three benchmark algorithms, provides evidence for the efficiency of the proposed algorithm. Sensitivity analysis on user-defined parameters is also provided for the benefit of practitioners.

This paper proposes a methodology for the evaluation of the performance of multistage multiproduct production lines, based on discrete-event simulation models, and developed in an Arena simulation environment. Its purpose is that of analysing blockage and starvation phenomena on a complex production line so as to assist in the decision-making process, since it allows for a greater understanding of the functioning of the production line. This is undertaken through the variation of different parameters, thus contributing to a better specification, characterization and definition of the most efficient planning and control system to be implemented in multistage multiproduct manufacturing processes. The methodology was validated by performing different simulation experiments and will be applied to a multistage multiproduct production line, based on a real case, with a closed-loop network configuration of machines and intermediate buffers consisting of conveyors, which is very common in the automobile sector. There are, in fact, very few studies undertaken which use simulation as a support tool in the decision-making process in the context of networks of closed-loop production lines.

This paper proposes a new algorithm based on a non-linear programming approach to deal with the buffer allocation problem in the case of unreliable production lines. Processing, failure and repair times are assumed to be random variables exponentially distributed. The proposed approach can be used to solve the different versions of the buffer allocation problem: primal, dual and generalized.This method is based on the modeling and the analysis of the serial production line using an equivalent machines method. The idea is to model the different possible states of each buffer using dedicated birth-death Markov processes to calculate the blockage and starvation probabilities of each machine. Then, each original machine is replaced by an equivalent one taking into account these probabilities.A comparative study based on different test instances issued from the literature is presented and discussed. The obtained results show the effectiveness and the accuracy of the proposed approach.

This paper investigates the role of buffer storage capacity in the unbalanced production line where cycle times vary from stage to stage, by analyzing the effects of degree of unbalance and buffer storage capacity on the production rate and the mean number of units in the storage area. It is shown that if buffer installation, is not possible, the line should be designed to have the same cycle times over all stages. When buffer installation is possible, the line should be designed in such a way that the stage production rates are the same by being unbalanced deliberately if the characteristics of breakdown and repair of the stages are not identical. The improvement of production rate by the provision of a buffer can be expected in this case. Due to technical considerations, if the line cannot be designed such that the stage production rates are almost the same, then it is unlikely that the installation of a buffer will be justified.

On Local Optima Distribution in Buffer Allocation Problem for Production Line with Unreliable Machines

In this paper, we refer to a real case study of an industrial partner recently committed to its project on the design of a multi-unit and multi-product manufacturing system. Although the considered problem refers to an actual complex manufacturing system, it can be theoretically classified as a union of two key problems that need to be solved during the transfer line design stage: the transfer line balancing problem (TLBP) and the buffer allocation problem (BAP). As two closely related problems, TLBP and BAP usually have similar optimizing directions and share the same purpose: finding a balance between the performance of the transfer line system as well as minimizing investment costs. These problems are usually solved sequentially, but this leads to solutions close to a local optimum in the solution space and not to the global optimum of the overall problem. This paper presents a multi-objective optimization for concurrently solving transfer line balancing and buffer allocation problems. The new approach is based on a combination of evolutionary and heuristic-based algorithms and takes into account the uncertainty of market demand. To validate the proposed approach, an industrial case study in a multi-unit manufacturing system producing multiple products (four engine blocks) is discussed.

Variability management aims at taming variability in large and complex software product lines. To efficiently manage variability, it has to be modeled using formal representations, such as feature or decision models. Such models are efficient in many domains, where variability is about switching on and off features, or using parameters to customize products of the product line. However, variability can be represented in the form of a topology in domains where variability is about connecting components in a certain order, in specific interconnected hierarchies, or in different quantities.In this experience report, we explore topological variability within a case study of large-scale fire alarm systems. We identify core characteristics of the variability, derive modeling requirements, model the variability using UML2 class diagrams, and discuss the applicability of further variability modeling languages. We show that, although challenging, class diagrams can suffice to represent topological variability in order to generate a configurator tool. In contrast, modeling parallel and recursive structures, cycles, informal constraints, and orthogonal hierarchies were among the main experienced challenges that require further research.

In this paper, we propose an artificial neural network (ANN) based buffer allocation scheme for ATM networks. The proposed scheme assumes limited buffer capacity and nonuniform incoming traffic patterns. The effective utilization of limited buffer capacity under nonuniform traffic conditions is a major concern in maintaining acceptable level of quality of service in these high speed communication networks. The proposed ANN based approach takes advantage of the favorable control characteristics of neural networks such as high adaptability and high speed collective computing power for effective buffer utilization. The proposed model uses complete sharing buffer allocation strategy and enhances its performance for high traffic loads and highly asymmetric traffic patterns by regulating the buffer allocation process dynamically via a neural network based controller. In this study, we considered the buffer allocation problem in the context of routing optimization in ATM networks.