Two approaches for solving the buffer allocation problem in unreliable production lines

This paper presents an integrated approach to solve the buffer allocation problem in unreliable production lines so as to maximize the throughput rate of the line with minimum total buffer size. The proposed integrated approach has two control loops; the inner loop and the outer loop. While the inner loop control includes an adaptive tabu search algorithm proposed by Demir et al. [8], binary search and tabu search are proposed for the outer loop. These nested loops aim at minimizing the total buffer size to achieve the desired throughput level. To improve the efficiency of the proposed tabu search, alternative neighborhood generation mechanisms are developed. The performances of the proposed algorithms are evaluated by extensive computational experimentation, and the results are reported.     

An adaptive tabu search approach for buffer allocation problem in unreliable non-homogenous production lines

The buffer allocation problem, i.e. how much buffer storage to allow and where to place it within the line, is an important research issue in designing production lines. In this study, a novel adaptive tabu search approach is proposed for solving buffer allocation problem in unreliable and non-homogeneous production lines. The objective is to maximize the throughput of the line, which is constrained by the capacity of each buffer space and also the total buffer capacity to allocate to these spaces. Besides proposing a new strategy to tune the parameters of tabu search adaptively during the search, an experimental study is carried out to select an intelligent initial solution scheme among three alternatives so as to decrease the search effort to obtain the best solutions. The performance of the proposed approach is evaluated by computational tests and very promising results are obtained.     

Hybrid approach for buffer allocation in open serial production lines

The buffer allocation problem is an NP-hard combinatorial optimization problem involving the determination of the number of buffers in buffer locations required to increase the efficiency of a production line. Researchers in this field have proposed various optimization techniques to solve the problem for different types of production system configurations. In this study, a hybrid approach-based simulation optimization is proposed to determine the buffer sizes required in open serial production lines to maximize the average production rate of the system. This approach involves the use of a search tool and an evaluative tool. A hybrid approach using a genetic algorithm and simulated annealing is used as a search tool to create candidate buffer sizes. As an evaluative tool, discrete event simulation modeling is used to obtain the average production rate of the line. The performance of the proposed approach and the power of the hybridization are investigated for various serial line configurations. Promising results demonstrate the efficacy of the proposed hybrid approach for the buffer allocation problem in open serial lines.     

On the workload and ‘phaseload’ allocation problems of short reliable production lines with finite buffers

Serial flow or production lines are modeled as tandem queueing networks and formulated as continuous-time Markov chains to investigate how to maximize throughput or minimize the average work-in-process (WIP) when the total service time and the total number of service phases among the stations are fixed (these are the workload and ‘phaseload’ allocation problems, respectively). This paper examines both the effect of the kind of service time distribution on the optimal workload allocation in order to maximize throughput or minimize the average WIP of perfectly reliable production lines.

The new approach of this work is the differentiation of the number of service phases of the service time distribution which is assumed to be of phase type at all stations of the flow line and the placement of storage space (buffers) between any two successive stations in order to examine the effect of these factors to the form of the optimal workload vector t and the optimal service phase vector ph.     

Buffer allocation in unreliable production lines using a knowledge based system

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 throughout. 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.     

基于改进降顶算法的非可靠生产线最优缓存分配

缓存分配是生产系统设计中一个重要的优化问题.基于非可靠连续生产线递推分解方法及其统计特性,提出在给定缓冲配制条件下的蒙特卡洛生产线吞吐量仿真估算方法,相比于传统的吞吐量估算模型该方法能更准确地描述各种生产场景.通过构造具有记忆性的禁忌集改进了传统降顶算法,并将其应用于实际缓存最优分配方案搜索中.仿真结果表明,对于各种规模的平衡生产线和非平衡生产线,改进降顶算法都可以快速有效地搜索到最优解.     

HBBA: hybrid algorithm for buffer allocation in tandem production lines

In this paper, we consider the problem of buffer space allocation for a tandem production line with unreliable machines. This problem has various formulations all aiming to answer the question: how much buffer storage to allocate between the processing stations? Many authors use the knapsack-type formulation of this problem. We investigate the problem with a broader statement. The criterion depends on the average steady-state production rate of the line and the buffer equipment acquisition cost. We evaluate black-box complexity of this problem and propose a hybrid optimization algorithm (HBBA), combining the genetic and branch-and-bound approaches. HBBA is excellent in computational time. HBBA uses a Markov model aggregation technique for goal function evaluation. Nevertheless, HBBA is more general and can be used with other production rate evaluation techniques.     

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

One of the major design problems in the context of manufacturing systems is the well-known Buffer Allocation Problem (BAP). This problem arises from the cost involved in terms of space requirements on the production floor and the need to keep in mind the decoupling impact of buffers in increasing the throughput of the line. Production line designers often need to solve the Buffer Allocation Problem (BAP), but this can be difficult, especially for large production lines, because the task is currently highly time consuming. Designers would be interested in a tool that would rapidly provide the solution to the BAP, even if only a near optimal solution is found, especially when they have to make their decisions at an operational level (e.g. hours). For decisions at a strategic level (e.g. years), such a tool would provide preliminary results that would be useful, before attempting to find the optimal solution with a specific search algorithm.     

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.     

Genetic algorithm for buffer size and work station capacity in serial-parallel production lines

Recently, many production lines that have complicated structures such as parallel, reworks, feed-forward, etc., have become widely used in high-volume industries. Among them, the serial-parallel production line (S-PPL) is one of the more common production styles in many modern industries. One of the methods used for studying the S-PPL design is through a genetic algorithm (GA). One of the important jobs in using a GA is how to express a chromosome. In this study, we attempt to find the nearest optimal design of a S-PPL that will maximize production efficiency by optimizing the following three decision variables: buffer size between each pair of work stations, machine numbers in each of the work stations, and machine types. In order to do this we present a new GA-simulation-based method to find the nearest optimal design for our proposed S-PPL. For efficient use of a GA, our GA methodology is based on a technique that is called the gene family arrangement method (GFAM), which arranges the genes inside individuals. An application example shows that after a number of operations based on the proposed simulator, the nearest optimal design of a S-PPL can be found. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

MOHHA求解不可靠生产线多目标缓冲区分配问题

研究含有可失效机器的不可靠串行生产线上缓冲区容量分配问题,以最大化生产线生产率和最小化生产线缓冲区总量为多目标,提出一种多目标混合启发式算法（MOHHA）。采用了离散事件仿真方法,建立生产率与缓冲区容量分配的仿真模型作为算法的评价工具。算法第一步求得生产线最大生产率,并作为目标生产率进一步求解最小化缓冲区总量。通过三组测试算例,与以往研究结果作对比验证了算法的有效性和优越性。     

Mixed-model U-shaped assembly lines: Balancing and comparing with straight lines with buffers and parallel workstations

In the paper a genetic algorithm approach is proposed to balance asynchronous mixed-model U-shaped lines with stochastic task times. U-shaped lines have become popular in recent years for their ability to outperform straight assembly lines in terms of line efficiency. The great majority of studies in the literature deal with paced synchronous U-shaped lines. Asynchronous lines can be more efficient than synchronous lines, but are more difficult to study, due to blocking and starvation phenomena caused by the variability of completion times: this makes it difficult to calculate the effective throughput. This variability, that in straight lines comes from the stochastic nature of task times and from the changing of models entering the line, is even higher in U-shaped lines, where an operator can work at two different models in the same cycle at the two sides of the line. For this reason, the genetic algorithm proposed is coupled to a parametric simulator for the evaluation of the objective function, which contains the simulated throughput. Two alternative chromosomal representations are tested on an ample set of instances from the literature. The best solutions are also compared with the best solutions known in the literature, on the same instances, for straight lines with buffers and parallel workstations. From the comparison it turns out that U-shaped lines are generally more efficient with respect to straight lines with buffers. This is because crossover work centers naturally act similarly to unitary buffers, providing two places in which two loads can be placed simultaneously. The superiority of U-shaped lines holds true as long as it is possible to take full advantage of the employment of crossover work centers. For particular types of instances, depending on the distribution of task times, this possibility decreases, so that straight lines with parallel workstations and buffers are preferable.     

Near optimal buffer allocation in remanufacturing systems with N-policy

We introduce a near optimal buffer allocation plan (NOBAP) specifically developed for a cellular remanufacturing system with finite buffers where the servers follow N-policy. The term N-policy is used for the situation where the server leaves primary work to tend to an external workload assigned to him (such as processing additional tasks or performing preventive maintenance of equipments) every time the server becomes idle and does not return back to his primary work until the queue size in front of the primary work reaches a threshold value of N (?1). The remanufacturing system considered here consists of three modules, viz., the disassembly module for returned products, the testing module and the remanufacturing module. In order to analyze the system we propose an algorithm that uses an open queueing network, decomposition principle and expansion methodology. The buffer allocation algorithm distributes a given number of available buffer slots among the remanufacturing system stations to optimize the system’s performance. The algorithm has been rigorously tested using a variety of experimental conditions. From the results, it is clear that the algorithm’s performance is robust, consistent and produces excellent results.     

一种规模化混杂生产线缓冲区容量优化分配技术

针对传统技术难以解决规模化混杂生产线缓冲区容量分配问题(Buffer allocation problem, BAP), 提出了一种规模化生产线递阶分解建模并行寻优技术(Hierarchical decomposition modeling parallel optimizing technique of large-scale production lines, HDMPOT). 该技术结合混杂生产线系统综合方法与分解方法的技术思想, 兼顾生产线平衡性与系统规模, 将原系统递阶分解为包含虚拟生产线在内的n + 1个子生产线系统, 通过求解子系统的最优解构造原系统的渐近最优解, 并在系统递阶建模阶段, 提出了一种设备模糊聚类的辅助方式; 同时, 基于混杂生产线系统综合方法, 提出了一种系统渐次综合的初解改进确定方法; 并提出了一种通过构造动态步长来设计领域结构的改进型禁忌搜索算法(Simple tabu search, STS), 对子系统进行并行寻优. 最后, 对技术算法的收敛性进行了证明. 提出的生产线递阶分解建模并行寻优技术具有一般性, 对受设备随机故障等随机事件影响的生产线, 尤其是规模化生产线系统其他优化、控制问题也具有借鉴和参考价值.     

Markovian analysis of production lines with Coxian-2 service times

This paper is concerned with the analysis of reliable production lines. The service times at each station of the line are assumed to follow the Coxian-2 distribution. Raw material arrives at the first station of the line which is assumed that is never empty. Buffers of non-identical capacities are allowed between successive stations. The structure of the transition matrices of these specific type of production lines is examined and a recursive algorithm is developed for generating them, for any number of stations K . This method allows one to obtain the exact solution of a sparse linear system by the use of the Gauss–Seidel method. From the solution of these systems the throughput rate of the production lines is calculated. However, this algorithm is not computationally efficient as it is restricted by the size of the problem. The main contribution of this paper is the study of the transition matrices of production lines with Coxian service times.     

An inspection policy for shredder equipment used in steel production lines considering buffer level and operating time

On the basis of scrap metal recycling, the shredding process transforms a large variety of objects composed predominantly of steel in crunched scrap. The main equipment used in this process is called shredder: a device consisting of several hammers attached in a horizontal rotor. Due to high efforts involved in the transformation process, the maintenance is an essential aspect to ensure the continuity of the shredder operation. A non-efficient maintenance policy can have negative consequences for the shredder performance, whose failure can stop all downstream processes for which it provides the basic input (crunched scrap). In order to improve the performance of the steel production line, we are introducing an opportunistic inspection policy that makes use of the crushed scrap buffer level. The policy guides the execution of an inspection after a time T of continuous operation, when the system fails, when the buffer is full or empty, or when the opportunity criterion (operation time ≥ t AND buffer level ≥ h) is verified, whichever occurs first. At an inspection, the shredder is stopped, the hammers are inspected, and the failed and defective ones are replaced by new units. A discrete-event simulation model was developed and applied to the context of a real steel production line of a company. The optimal opportunistic inspection policy was obtained, and its performance was compared with that of the policy currently adopted by the company. The results showed the possibility of obtaining significant economic savings. A sensitivity analysis showed a cost reduction of around 95 %.     

A mathematical model on EPQ for stochastic demand in an imperfect production system

In the paper, we develop an EPQ (economic production quantity) inventory model to determine the optimal buffer inventory for stochastic demand in the market during preventive maintenance or repair of a manufacturing facility with an EPQ (economic production quantity) model in an imperfect production system. Preventive maintenance, an essential element of the just-in-time structure, may cause shortage which is reduced by buffer inventory. The products are sold with the free minimal repair warranty (FRW) policy. The production system may undergo “out-of-control” state from “in-control” state, after a certain time that follows a probability density function. The defective (non-conforming) items in “in-control” or “out-of-control” state are reworked at a cost just after the regular production time. Finally, an expected cost function regarding the inventory cost, unit production cost, preventive maintenance cost and shortage cost is minimized analytically. We develop another case where the buffer inventory as well as the production rate are decision variables and the expected unit cost considering the above cost functions is optimized also. The numerical examples are provided to illustrate the behaviour and application of the model. Sensitivity analysis of the model with respect to key parameters of the system is carried out.     

Optimal lot sizing in an unreliable two-stage serial production–inventory system

This paper deals with a two‐stage lot sizing problem in an unreliable production environment in which the machine at the first stage (stage 1) is failure‐prone while the machine at the final stage (stage 2) is failure‐free. The process goods are obtained in batches by manufacturing and are transferred continuously from stage 1 to stage 2 where the finished goods are produced and then shipped out to customers. If the machine at stage 1 breaks down then the production of the interrupted lot is not resumed. Instead, a new production cycle is initiated after machine repair. The model is formulated assuming that the production rate of the machine at stage 1 is greater than that at stage 2 and the time to machine failure and repair time are arbitrarily distributed. Specific formulation of the model under exponential failure and exponential repair time distributions is derived and a procedure for finding the optimal production policy is presented. The dependence of the optimal production policy on the model parameters is also examined with numerical examples.     

A parallel tabu search for solving the primal buffer allocation problem in serial production systems

This paper presents a novel parallel tabu search (PTS) algorithm equipped with a proper adaptive neighborhood generation mechanism to solve the primal buffer allocation problem, which consists of minimizing the total buffer capacity of a serial production system under a minimum throughput rate constraint. An evaluative method based on a specific algorithm has been implemented to simulate the system behavior. In order to validate the effectiveness of the proposed PTS a mixed integer linear programming-based simulation/optimization approach and several metaheuristics from the relevant literature have been implemented. Since most metaheuristics are sensitive to the parameter setting, a proper calibration analysis based on a non-parametric test has been performed. Then, a comprehensive comparison analysis, concerning with both quality of solutions and computational efficiency, has been carried out. Finally, through the numerical results obtained from PTS, a multi-factorial experimental analysis has been developed to analyze the influencing factors of the problem under investigation.     

Optimizing class-constrained wafer-to-order allocation in semiconductor back-end production

This paper studies the problem of allocating semiconductor wafers to customer orders with the objective of minimizing the overallocation prior to assembly. It is an important problem for back-end semiconductor manufacturing as overallocation may have severe impact on operational performance due to excess inventory and unnecessarily occupied manufacturing equipment. In practice, a wafer can contain dies from several different die classes, making the wafer-allocation problem more challenging. As a novel contribution of this work, we explicitly consider the existence of multiple die classes on a wafer in the wafer-allocation problem. An integer linear programming formulation of the class-constrained wafer allocation problem is provided. The formulation is further extended to be more flexible by allowing the dies from different classes on the same wafer to be allocated to distinct customer orders. A real-world case study from the back-end assembly and test facility of a semiconductor manufacturer is presented. Experiments with real-world data show that the proposed method significantly reduces the overallocation performance in current practice and allows planners to quantify the value of flexibility in wafer allocation.