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.     

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.     

Optimal buffer allocation in short μ-balanced unreliable production lines

In this work, we investigate the optimal buffer allocation in short μ-balanced production lines consisting of machines that are subject to breakdown. Repair times and times to failure are assumed exponential, whereas service times are allowed to follow the Erlang-k distribution (with k=1, 2, 4 and 8). By an improved enumeration procedure and applying the evaluative algorithm of Heavey et al. (European Journal of Operational Research 1993;68:69–89) for the calculation of throughput, we have examined in a systematic way several systems with 3, 4, 5, 6 and 7 stations and with a different total number of buffer slots. We have been able to give answers to some critical questions. These include the effect of the distribution of the service and repair times, the availability of the stations and the repair rates on the optimal buffer allocation and the throughput of the lines.     

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.     

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.     

An accurate and practical buffer allocation model for the buffer cache based on marginal gains

A new dynamic buffer allocation strategy based on the notion of marginal gains is presented for the buffer cache that is used by the operating system to store frequently accessed disk blocks in main memory, and the performance of the proposed strategy is compared with those of previous allocation strategies. In the proposed strategy, marginal gain values are predicted by exploiting functions that approximate the expected number of buffer hits per unit time. Experimental results from both trace-driven simulation and an actual implementation in the FreeBSD operating system show that the proposed strategy accurately predicts the marginal gain values for various workloads resulting in significantly improved buffer hit ratios.     

Simultaneously solving the transfer line balancing and buffer allocation problems with a multi-objective approach

Two of the most researched problems on transfer line, transfer line balancing problem (TLBP) and buffer allocation problem (BAP), are usually solved separately, although they are closely interrelated. When machine tools have different reliability, the traditional balancing approaches lead to a deviation of the production rate from the actual throughput, which is used as the objective of the following optimization on BAP. This may not only reduce the solution space of BAP, but also bring about a biased overall result.In this paper, the simultaneous solution of these two problems is presented, which includes transfer line balancing problem, BAP, and selection of line configuration, machine tools and fixtures. Production rate computed through simulation software and total cost considering machine tools and buffer capacities are used as two objective functions. The problem is solved applying a multi-objective optimization approach. Two well-known evolutionary algorithms are considered: Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). A real case study related to automotive sector is used to demonstrate the validity of the proposed approach.     

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.     

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.     

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.     

Optimal buffer allocation in finite closed networks with multiple servers

In this study, we present an optimal buffer allocation procedure for closed queueing networks with finite buffers. The performance measures are evaluated using the expanded mean value analysis, and the solution procedure is incorporated into a nonlinear optimization scheme to arrive at the sub-optimal buffer space vector. The effectiveness of the method is demonstrated through several numerical experiments. Discussions on convergence and computational complexity are also included.     

Multi-objective imperfect maintenance optimization for production system with an intermediate buffer

In several production systems, buffer stocks are built between consecutive machines to ensure the continuity of supply during interruptions of service caused by breakdowns or planned maintenance actions. However, in previous research, maintenance planning is performed individually without considering buffer stocks. In order to balance the trade-offs between them, in this study, an integrated model of buffer stocks and imperfective preventive maintenance for a production system is proposed. This paper considers a repairable machine subject to random failure for a production system by considering buffer stocks. First, the random failure rate of a machine becomes larger with the increase of the number of random failures. Thus, the renewal process is used to describe the number of random failures. Then, by considering the imperfect maintenance action reduced the age of the machine partially, a mathematical model is developed in order to determine the optimal values of the two decision variables which characterize the proposed maintenance strategy and which are: the size of the buffer stock and the maintenance interval. The optimal values are those which minimize the average total cost per time unit including maintenance cost, inventory holding cost and shortage cost, and satisfy the availability constraint. Finally, a heuristic procedure is used to solve the proposed model, and one experiment is used to evaluate the performance of the proposed methods for joint optimization between buffer stocks and maintenance policy. The results show that the proposed methods have a better performance for the joint optimization problem and can be able to obtain a relatively good solution in a short computation time.     

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     

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.     

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.     

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.     

A rescheduling and cost allocation mechanism for delayed arrivals

We propose a solution to the problem of rescheduling a sequence of arrivals that are subject to a delay event at a common destination. Such situations include jobs arriving at a single production facility, aircraft whose landings are postponed, and ships that are inbound to a dock or lightering facility. Each arrival faces a nonlinear cost due to the delay, but the delay costs can be mitigated by allowing the arrivals to be reordered. We optimize the reordering process by designing a Vickrey–Clarke–Groves (VCG) mechanism to construct a payoff matrix describing the amounts necessary to move the currently assigned arrival slots either earlier or later. Using this payoff matrix, we compute the optimal reordering of the arrivals by utilizing the well-known solution to the assignment problem, which maximizes the benefit in a computationally efficient fashion. The VCG mechanism is strategyproof, that is, no arrival has an incentive to misreport the value of moving up or down in the sequence. We also show that participating in the centralized process is to no arrival׳s disadvantage. Because VCG procedures in general are subject to budget deficits, we provide alternative mechanisms to overcome this difficulty. Finally, we carry out computational experiments demonstrating that the VCG mechanism can be implemented for realistically-sized problem sets and that the cost savings are significant.     

基于格局变换策略的不等圆Packing问题求解算法*

采用基于格局变换策略的算法ACP-Solver求解不等圆Packing问题。ACP-Solver由连续优化方法、格局变换算子和接收准则组成。连续优化方法可从任一初始格局收敛至对应的局部最优格局。格局变换算子将当前格局变换为新格局。接收准则决定是否接收变换所得格局。基于24个国际公开算例的计算实验表明,ACP-Solver能在可接受的计算时间内改进或持平绝大多数算例的当前最优记录。实验结果表明了ACP-Solver的高效性能。     

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.