A new method for the placement of buffers in serial production lines

The placement of buffers in a production line is an old and well-studied problem in industrial engineering/operations research that is still relevant today. Decisions regarding the amount and placement of buffers in a production line occur repeatedly in system design. In this paper we document a new buffer placement method for serial production lines that operate under a variety of assumptions. The method uses information generated in a production line simulation, whose conceptual representation of job flow and workstation interaction can be described with a network, to place buffers in order to maximise throughput. This buffer placement method is very efficient and can be implemented in a spreadsheet. We demonstrate the effectiveness of the method by comparing our results against those produced by a genetic algorithm for buffer placement. Experiments are conducted on a variety of test cases. This new buffer placement optimisation method will permit designers to quickly and effectively evaluate many design alternatives and thus improve final production system performance.     

A two-phase heuristic algorithm for determining buffer sizes of production lines

In-process inventories are often used in many manufacturing systems to smooth and balance work flow. The buffer design problem of determining the best size of storage space between operations (or workstations) is one that has been approached in past and current research issues. Although past research attempted to apply traditional optimization methods to the buffer design problems, the applications revealed that some fundamental drawbacks were occurring due to the unique characteristics of the buffer design in the production lines. This paper addresses the unique characteristics involved in the buffer design, discusses drawbacks of traditional optimization methods applied, and finally presents an efficient two-phase heuristic method using a dimension reduction strategy and a buffer utilization-based beam search method, fn addition, numerical results show sensitivity analyses for the performance of the heuristic method with respect to parameters involved in the second phase of the method.     

Genetic algorithm based optimal placement of PIR sensors for human motion localization

This paper studies the optimal placement of pyroelectric infrared (PIR) sensors in developing the infrared motion sensing system for human motion localization. In particular, we explore the use of genetic algorithm (GA) in optimizing both the deployment and the modulated field of view (FOV) of the PIR sensors for improving the localization performance. Two criteria, the average and maximum localization errors, are used to evaluate the localization performance. In addition, the numerical analysis is presented to offer a guidance on the searching spaces of the design parameters in implementing GA optimization. The proposed GA-based design approach is validated by means of both simulation and experimental studies in the context of human-following mobile robots.     

An evolutionary algorithm for the optimal design of inductionmotors

This paper describes the application of an evolutionary algorithm to the design of induction motors. It is shown that the use of an evolutionary algorithm offers advantages over other approaches. These include a high rate of global convergence and the ability to handle discrete variables     

An optimal algorithm for multiplication in F256/F4

Applying an interpolation algorithm invented by D. V. Chudnovsky and G. V. Chudnovsky to the Fermat-curvex ³ + y³ = 1 overF ₄, we present a bilinear algorithm for the multiplication in theF ₄-algebraF ₂₅₆ which requires 8 essential multiplications.     

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.     

基于蒙特卡洛方法的触发器聚类布局算法

针对触发器聚类的布局问题,提出了一种基于蒙特卡洛方法的触发器聚类布局算法。该方法的核心思想为设计合理的评分函数,并通过蒙特卡洛方法模拟触发器聚类在版图上的随机游走过程,获取样本,再通过评分函数评价样本质量,逐步地在可行域中搜索最优解。实验结果表明,相较于现有的方法,该方法在总线长与时序性能两方面,分别得到平均约9%与17%的改善与提升。此外,该方法具有较强的健壮性,适用于复杂度较高或者版图利用率较高的设计,并能够快速收敛,以较小的时间代价完成优化。     

Intelligent dynamic control policies for serial production lines

Heuristic production control policies such as CONWIP, kanban, and other hybrid policies have been in use for years as better alternatives to MRP-based push control policies. It is a fact that these policies, although efficient, are far from optimal. Our goal is to develop a methodology that, for a given system, finds a dynamic control policy via intelligent agents. Such a policy while achieving the productivity (i.e., demand service rate) goal of the system will optimize a cost/reward function based on the WIP inventory. To achieve this goal we applied a simulation-based optimization technique called Reinforcement Learning (RL) on a four-station serial line. The control policy attained by the application of a RL algorithm was compared with the other existing policies on the basis of total average WIP and average cost of WIP. We also develop a heuristic control policy in light of our experience gained from a close examination of the policies obtained by the RL algorithm. This heuristic policy named Behavior-Based Control (BBC), although placed second to the RL policy, proved to be a more efficient and leaner control policy than most of the existing policies in the literature. The performance of the BBC policy was found to be comparable to the Extended Kanban Control System (EKCS), which as per our experimentation, turned out to be the best of the existing policies. The numerical results used for comparison purposes were obtained from a four-station serial line with two different (constant and Poisson) demand arrival processes.     

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.     

An iterative bidirectional heuristic placement algorithm for solving the two-dimensional knapsack packing problem

This article presents an efficient heuristic placement algorithm, namely, a bidirectional heuristic placement, for solving the two-dimensional rectangular knapsack packing problem. The heuristic demonstrates ways to maximize space utilization by fitting the appropriate rectangle from both sides of the wall of the current residual space layer by layer. The iterative local search along with a shift strategy is developed and applied to the heuristic to balance the exploitation and exploration tasks in the solution space without the tuning of any parameters. The experimental results on many scales of packing problems show that this approach can produce high-quality solutions for most of the benchmark datasets, especially for large-scale problems, within a reasonable duration of computational time.     

An adaptive heuristic cross-entropy algorithm for optimal design of water distribution systems

The optimal design problem of a water distribution system is to find the water distribution system component characteristics (e.g. pipe diameters, pump heads and maximum power, reservoir storage volumes, etc.) which minimize the system's capital and operational costs such that the system hydraulic laws are maintained (i.e. Kirchhoff's first and second laws), and constraints on quantities and pressures at the consumer nodes are fulfilled. In this study, an adaptive stochastic algorithm for water distribution systems optimal design based on the heuristic cross-entropy method for combinatorial optimization is presented. The algorithm is demonstrated using two well-known benchmark examples from the water distribution systems research literature for single loading gravitational systems, and an example of multiple loadings, pumping, and storage. The results show the cross-entropy dominance over previously published methods.     

An improved Intelligent Water Drops algorithm for achieving optimal job-shop scheduling solutions

Job-shop scheduling is a typical NP-hard problem which has drawn continuous attention from researchers. In this paper, the Intelligent Water Drops (IWD) algorithm, which is a new meta-heuristics, is customised for solving job-shop scheduling problems. Five schemes are proposed to improve the original IWD algorithm, and the improved algorithm is named the Enhanced IWD algorithm (EIWD) algorithm. The optimisation objective is the makespan of the schedule. Experimental results show that the EIWD algorithm is able to find better solutions for the standard benchmark instances than the existing algorithms. This paper has made a contribution in two aspects. First, to the best of the authors’ knowledge, this research is the first to apply the IWD algorithm to the job-shop scheduling problem. This work can inspire further studies of applying IWD algorithm to other scheduling problems, such as open-shop scheduling and flow-shop scheduling. Second, this research further improves the original IWD algorithm by employing five schemes to increase the diversity of the solution space as well as the solution quality.     

Scheduling duplicate serial stations in transfer lines

To support the production rate, some transfer line stations with long cycle times must be duplicated. A job is processed in only one of the duplicate stations. If laid out in parallel, this duplication essentially doubles the capacity, but, for practical reasons, these duplicate stations are often laid out in a series. The serial layout has a capacity less than the parallel's, and also raises some operational questions. We enunciated and analysed this actual problem of operating duplicate stations in series. We simulated an existing closed-loop transfer-line with duplicate stations in series using the actual failure and repair characteristics of the automated stations, and compared a new heuristic with three simple policies.     

Real-time optimal spatiotemporal sensor placement for monitoring air pollutants

Clean Technologies and Environmental Policy - Air pollution exposure assessment involves monitoring of pollutant species concentrations in the atmosphere along with their health impact assessment...     

Predictive models for performance evaluation of serial production lines

An efficient predictive model is proposed for analysing the throughput rate of balanced serial production lines where all stations are identical and buffers between stations have the same capacity. The predictive model is based on the simulation data. The experimental design and analysis technique is used to conduct the simulation experiments and the regression analysis method is used to build the predictive model. The model can be applied to a wide range of values in terms of the coefficient of variation of processing times at stations, the buffer size between stations and the number of stations in a system. Comparisons are made with simulation results and previous works. The model can also be extended to account for situations where different stations have different coefficients of variation of processing times.     

Optimal buffer allocation strategy for minimizing work-in-process inventory in unpaced production lines

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.     

A stochastic programming model for resequencing buffer content optimisation in mixed-model assembly lines

In mixed-model assembly lines, smooth operation of the assembly line depends on adherence to the scheduled sequence. However, during production process, this sequence is altered both intentionally and uninstentionally. A major source of unintentional sequence alteration in automobile plants is the paint defects. A post-paint resequencing buffer, located before the final assembly is used to restore the altered sequence. Restoring the altered sequence back to the scheduled sequence requires three distinct operations in this buffer: Changing the positions (i.e. resequencing) of vehicles, inserting spare vehicles in between difficult models and replacing spare vehicles with paint defective vehicles. We develop a two-stage stochastic model to determine the optimal number of spare vehicles from each model-colour type to be placed into the Automated Storage and Retrieval System resequencing buffer that maximises the scheduled sequence achievement ratio (SSAR). The model contributes to the literature by explicitly considering above three distinct operations and random nature of paint defect occurrences. We use sample average approximation algorithm to solve the model. We provide managerial insights on how paint entrance sequence, defect rate and buffer size affect the SSAR. A value of stochastic solution shows that the model significantly outperforms its deterministic counterpart.     

Grammatical evolution in developing optimal inventory policies for serial and distribution supply chains

Recently, there has been a growing literature on biologically inspired algorithms, particularly genetic algorithms and genetic programming, applied to supply chain modelling and inventory control optimisation. Due to the rigidity of the genetic algorithms approach, it is difficult to change the underlying model logic and add richness to the supply chain. While genetic programming provides a more flexible approach than that provided by genetic algorithms, to date its application has been limited to small supply chain modelling problems in relation to optimal inventory policies. This research applies Grammatical Evolution, a relatively new biologically inspired algorithm, to the field of supply chain optimisation, employing human readable rules called grammars. These grammars provide a single mechanism to describe a variety of complex structures and can incorporate the domain knowledge of the practitioner to bias the algorithm towards regions of the search space containing better solutions. Results are presented showing Grammatical Evolution is at least competitive in cost terms, and superior in flexibility, with these methods applicable to any supply chain of the serial or distribution type. Furthermore, Grammatical Evolution shows an adaptive ability that augurs well for supply chains in dynamic environments, such as disruption.     

Lean buffering in serial production lines with non-exponential machines

In this paper, lean buffering (i.e., the smallest level of buffering necessary and sufficient to ensure the desired production rate of a manufacturing system) is analyzed for the case of serial lines with machines having Weibull, gamma, and log-normal distributions of up- and downtime. The results obtained show that: (1) the lean level of buffering is not very sensitive to the type of up- and downtime distributions and depends mainly on their coefficients of variation, CV _up and CV _down ; (2) the lean level of buffering is more sensitive to CV _down than to CV _up but the difference in sensitivities is not too large (typically, within 20%). Based on these observations, an empirical law for calculating the lean level of buffering as a function of machine efficiency, line efficiency, the number of machines in the system, and CV _up and CV _down is introduced. It leads to a reduction of lean buffering by a factor of up to 4, as compared with that calculated using the exponential assumption. It is conjectured that this empirical law holds for any unimodal distribution of up- and downtime, provided that CV _up and CV _down are less than 1. Correspondence to: Semyon M. Meerkov