Unbalanced Workload Research Articles

Economic Life of Capital Equipments in a Cellular Manufacturing System

This paper is aimed to investigate the economic life of the capital equipment in a cellular manufacturing system (CMS). The capital equipment is prone to degradation during the usage and this degradation can be measured by increase in the operation and maintenance (O&M) costs. Economic life refers to the optimal replacement age of the equipment in which the total discounted costs is at its minimum value. To our best knowledge, the traditional cell formation methods are built on the principle of economy of scale without considering the economic life and discounted costs of the capital equipment. On the other hand, due to a known drawback of the CMSs of imbalance cell utilization (Rheault, 1995), resulting from the unbalance workload distributed between machines within the cells, the machines within different cells have various degradation rates and consequently different economic lives. A mathematical programming-based approach is used to form the cells considering the life cycle costs of the machines and material handling equipment over a long horizon. The performance of the proposed model is verified by a comprehensive numerical example.

Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system

In China, the tobacco distribution is organized by fixed districts and routes with low efficiency and high distribution cost due to its unbalanced workload. And, though the dynamic method adopting vehicle routing algorithms produces optimal routes, the unstable routes increase the delivery time and managerial cost. So it is urgent and feasible to break the fixed districts to provide periodic balanced partitions for tobacco distribution. In this paper, a multi-criteria balanced partition model is built, whose minimal objectives including total number of tours in all districts, the travel distance and time of all tours, and the balance objectives include number of tours, total demand, traveling distance and time of each district are considered. An immune co-evolutionary algorithm with two stages is designed to search the optimal balanced partitions. In the first stage, the initial balanced partitions are produced. In the second stage, the clonal selection procedure, with partition proliferation, selection and elimination, and cooperative searching among districts, is adopted to achieve balanced partition. The experiments on Lenfen city in China as a practical application reveal the efficiency of the proposed model and algorithm. First, the searching processes are analyzed by exploring the partitions and Pareto partitions. Second, the evolutionary process of the algorithm is shown by the varying of the multiple objectives. Finally, three methods including fixed districts and routes, dynamic VRP scheduling and periodic balanced partition are compared to show the value of the study.

Performance Evaluation of a Multi-Zone Application in Different OpenMP Approaches

We describe a performance study of a multi-zone application benchmark implemented in several OpenMP approaches that exploit multi-level parallelism and deal with unbalanced workload. The multi-zone application was derived from the well-known NAS Parallel Benchmarks (NPB) suite that involves flow solvers on collections of loosely coupled discretization meshes. Parallel versions of this application have been developed using the Subteam concept and Workqueuing model as extensions to the current OpenMP. We examine the performance impact of these extensions to OpenMP and compare with hybrid and nested OpenMP approaches on several large parallel systems.

Simulation of manufacturing cells with unreliable machines

Cellular manufacturing is an application of a group technology used to improve the performance of manufacturing systems. A number of factors, including vulnerability to machine breakdown, under utilization of resources and eventual unbalanced workload distribution in a multi-cell plan disturb the smooth working of the factory when using the group technology concept. This paper focuses on a manufacturing cell composed of unreliable machines. We are interested in the problem of cell production availability facing unexpected circumstances due to an internal perturbation caused by machine breakdown. We consider a policy of intercellular transfer in the event of breakdown to improve the availability of the cells. We examine through simulation the performance of the system and evaluate the intercellular transfer policy in terms of some selected criteria. The results indicate, under the assumed conditions, that the developed policy improves the performance of the production cells. (Received in November 2005, accepted in August 2006. This paper was with the authors 2 months for 1 revision.)

A HYBRID HOPFIELD NEURAL NETWORKS BASED SIMULATION APPROACH FOR OPTIMISATION OF MANUFACTURING GROUP SCHEDULING

ABSTRACT A hybrid Neural Networks have been developed and the simulation techniques have been used to solve complex group scheduling problems. The objectives are to find the best performance criterion optimum for a given number of jobs and machines in order to satisfy different production constraints. The solution methods proposed in this paper, have their roots in both operational research and artificial intelligence. The use of combining Hopfield Neural Networks (HNN) and Tabu (local search) approach to define optimal groups of operations which will facilitate the generation of the route sheet has been discussed and presented in this paper. For each one of the operations, the (HNN) will be used to generate the feasible manufacturing alternatives, and the Tabu search technique will be applied to identify the best schedule. The proposed approach, Hybrid Hopfield Neural Networks (HHNN) and the simulation techniques, save time and minimise unbalanced workloads among the operations. The potential ability of the proposed approach is shown using a numerical case example of a big size of group scheduling problem.

A learning optimizer for a federated database management system

Optimizers in modern DBMSs utilize a cost model to choose an efficient query execution plan (QEP) among all possible plans for a given query. The accuracy of the cost estimates depends heavily on accurate statistics about the underlying data. Outdated statistics or wrong assumptions in the underlying statistical model frequently lead to suboptimal selection of execution plans and thus to bad query performance. Federated systems require additional statistics on remote data to be kept on the federated DBMS in order to choose the most efficient execution plan when joining data from different data sources. Wrong statistics within a federated DBMS can cause not only suboptimal data access strategies but also unbalanced workload distribution as well as unnecessarily high network traffic and communication overhead. The maintenance of statistics in a federated DBMS is troublesome due to the independence of the remote DBMSs that might not expose their statistics or use different models and not collect all statistics needed by the federated DBMS. We present an approach that extends DB2’s learning optimizer to automatically find flaws in statistics on remote data by extending its query feedback loop towards the federated architecture. We will discuss several approaches to get feedback from remote queries and present our solution that utilizes local query feedback and remote query feedback to retrieve information needed to compute statistics profiles. We provide a detailed performance study and analysis of our approach, and demonstrate in a case study a potential query execution speedup of orders of magnitude while only incurring a moderate overhead during query execution.

A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm

In this paper, a novel approach using an efficient multi-objective genetic algorithm EMOGA is proposed to solve the problems of production planning of flexible manufacturing systems (FMSs) having four objectives: minimizing total flow time, machine workload unbalance, greatest machine workload and total tool cost. EMOGA makes use of Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. High efficiency of EMOGA arises from that multiple objectives can be optimized simultaneously without using heuristics and a set of good non-dominated solutions can be obtained providing additional degrees of freedom for the exploitation of resources of FMSs. Experimental results demonstrate effectiveness of the proposed approach using EMOGA for production planning of FMSs.

Efficient cache co-ordination scheme for shared web proxy

A cache co-ordination scheme for shared Web proxy that co-ordinates cache contents by considering re-reference likelihood of Web objects and loads of proxies is presented. Trace-driven simulations show that the proposed scheme minimises object fetch cost by exploiting temporal locality of Web access and by coping with unbalanced workloads.

Makespan estimation in batch process industries: A comparison between regression analysis and neural networks

Batch processing is becoming more important in the process industries, because of the increasing product variety and the decreasing demand volumes for individual products. In batch process industries it is difficult to estimate the completion time, or makespan, of a set of jobs, because jobs interact at the shop floor. We assume a situation with hierarchical production control consisting of a planning level and a scheduling level. In this paper we focus on the planning level. We use two different techniques for estimating the makespan of job sets in batch process industries. The first technique estimates the makespan of a job set by developing regression models, the second technique by training neural networks. Both techniques use aggregate information. By using aggregate information the presented techniques are less time consuming in assessing the makespan of a job set compared with methods based on detailed information. Tests on newly generated job sets showed that both techniques are robust for changes in the number of jobs, the average processing time, a more unbalanced workload and for different resource configurations. Finally, the estimation quality of the neural network models appears significantly better than the quality of regression models.

Dynamic Scheduling for a Flexible Processing Network

We study flexible processing systems where there are multiple paths for a certain type of customer to follow through the system. This paper analyzes an open processing network model with discretionary routing. The underlying methodology is the heavy traffic approximation, which has been well publicized by many researchers. Allowing priority sequencing in addition to dynamic routing as our control policy, we show that the balanced workload routing rule does not necessarily give us an optimal solution. In general unbalanced workload routing with priority sequencing is shown to give us better performance than a balanced one. Through some simulation results we compare our recommendation with other scheduling policies for a flexible processing network.

Dynamic dispatching rule-pairs for multitasking workers in JIT production systems

The concept of multitasking workers plays a major role in the success of Just-inTime (JIT) implementation. One key element of a JIT system is its ability to use multitasking workers to react to unbalanced workloads where the bottleneck location changes from period to period. In addition to existing rule-pairs (when to move and where to move), this study develops eight new dynamic dispatching rule-pairs especially for assigning multitasking workers within a JIT production environment. The purpose of this study is to evaluate the performance of these rule-pairs using object-oriented discrete-event simulation and response surface methodology.

某醫學中心專任主治醫師薪資滿足感、離職傾向與影響因素關係之研究－線性結構模式分析之應用

國立醫學中心級醫院的薪資制度受限於政府機關法今的規範，薪資制度採年資制及職級制造成不同工同酬的現象，致使績效好的醫師無法在薪酬上得到適度的回饋，因此可能影響其薪資滿足感因而降低服務意願致離職。 本研究假設薪資滿足感受到個人特性、對組織期望因素、及工作績效的影響；離職傾向受到組織期望因素及薪資滿足感的影響。利用線性結構模式分析方法來探討醫師薪資態度及相關行為間的關係。 研究對象為以某國立醫學中心級醫院專任主治醫師388人（回卷率為58.5%）。研究結果發現：影響離職傾向的直接因素：薪資滿足感、「家庭因素」、臨床績效、年薪所得。影響薪資滿足感的因素：「組織氣氛因素」、「薪資因素』、年薪所得、臨床及研究績效。顯示該個案醫院管理者在提高主治醫師留任意願的衆多措施之一，以改善目前薪資制度的缺失，維持及增進組織支持的氣氛，增加工作設計上的彈性，及改善工作量不平均的現象有助淤醫師的留任。

Parallel Algorithms for Perceptual Grouping on Distributed Memory Machines

Perceptual grouping is a key intermediate-level vision problem. Parallel solutions to this problem are characterized by uneven distribution of symbolic features among the processors, unbalanced workload, and irregular interprocessor data dependency caused by the input image. In this paper, we propose two load-balancing techniques for parallelizing perceptual grouping on distributed-memory machines. By using an initial workload estimate, we first partition the computations to distribute the workload across the processors. In addition, we asynchronously perform ongoing task migrations to adapt to the unbalanced workload which may evolve differently from the initial estimate. We also discuss two strategies to manage the irregular interprocessor data dependency. To illustrate our ideas, perceptual grouping steps used in an integrated vision system for building detection are used as examples. Our experimental results show that, given 8K extracted line segments from a 1K × 1K image, both the line and junction grouping steps can be completed in 0.644 s on a 32-node SP2 and in 0.585 s on a 32-node T3D. For the same grouping steps, a serial implementation requires 10.550 s and 10.023 s on a single node of SP2 and T3D, respectively. The implementations were performed using the message passing interface standard and are portable to other high performance computing platforms.

Scheduling customized bag production: an application of the Group Technology concept

Describes the application of the Group Technology concept in scheduling industrial bag production in a woven products division of a bag manufacturer. The labour assignments, determined through a linear goal programming model, minimize unbalanced workloads among the operations. This study shows a new application area of the Group Technology concept which leads to a better solution of labour assignments and provides management with valuable information essential to the development of a strategic competitive advantage.

Production planning of FMS under tool magazine constraints: A dynamic programming approach

Production planning is one of the most important activities for efficient operation of a flexible manufacturing system. This complex acivity is concerned with the decisions related to system set-up, involving solving the problems of selection of a set of part types for simultaneous processing, determining the production ratios, assigning the pallets and fixtures, and assigning operations and tools to machines. In this paper, a dynamic programming algorithm is developed to solve the above problems simultaneously by considering the flexibilities and constraints of the system in order to minimise the unbalanced workload of machines. In this context, various factors such as the tools required for operations, alternative routes available for operations, tool magazine capacity and the limited number of pallets and fixtures are considered. The method is validated with a case study.

Clustering algorithms to optimize the tool handling system management in an FMS

Tool management is recognized as a critical issue in flexible manufacturing facilities management. This article addresses the issue of tool management in a flexible system installed in an avionics components factory. The system is composed of two machining centers equipped with local tool magazines of limited capacity. A tool handling system is in charge of tool movements between the tool room and the two machines. Each machine is able to perform any operation, provided that it is equipped with the suitable tool. In this kind of installation, tool allocation must be determined, and tool movements must be synchronized in order to minimize operating costs, or, equivalently, maximize the productivity of the system. We propose an approach to production planning based on a clustering algorithm, which takes into account the tool requirements of each part program in the production batch. We also propose two different heuristics for the scheduling problem. A case study was conducted on the facility mentioned above. Two conflicting objectives can be identified for this kind of production system: the reduction of tools to be shared among machines and the reduction of workload unbalance. The tests and comparison made demonstrate how the proposed procedure leads to superior results in terms of both objectives.

Machining Economics in FMS by a Fuzzy Approach

In a conventional manufacturing system, cutting parameters have to be fixed in order to pursue the economical and productive target related to each cutting operation. In a Flexible Manufacturing System (FMS), as a consequence of the unbalanced workload due to the mutual interaction among the shared resources, the optimization of each operation does not allow the optimum of the global production system to be reached. A fuzzy programming model is developed in this paper in order to select cutting speeds of the operations in the production plan as a satisfying compromise solution between single operation and the integrated system targets.

Parallel adaptive and time-stabilizing schemes for constant-coefficient parabolic PDE's

New asynchronous and time stabilizing finite-difference methods for constant coefficient parabolic PDEs are presented. These schemes remove the synchronization overhead and are suitable for shared-memory, message-passing, single- or multi-user multi-processors. They generalize previous schemes by allowing adaptive and non-constant time increments. They are characterized by the time-stabilizing property: the derivatives of their time-level with respect to the spatial coordinates are bounded as the number of time-steps increases, and they vanish as the spatial mesh-size approaches zero. Therefore, unlike others, our schemes are suitable for multi-processors with non-identical processors or unbalanced workload, provided that the differences among their speeds/loads are not too large. The consistency, stability, convergence and the time-stabilizing property of our methods are analyzed in detail. When implemented on the shared-memory multi-user Sequent Balance machine, they show to provide an excellent efficiency and in certain cases it is tripled,, compared to other existing schemes. However, their truncation error is of lower order ( O( Δx l )nstead of O( Δx l 2)). Hence, because of the dimensional additivity of the truncation error, these schemes are less accurate especially for multi-dimensional problems. For such problems, if high accuracy is sought, in general one should use higher order schemes. Nevertheless, under certain circumstances, when the spatial increments are relatively large, the performance of our asynchronous schemes (i.e., both their efficiency and accuracy) may be about the same as compared to their synchronous counterparts, and sometimes even better.

Performance evaluation for systems of pooled machines of unequal sizes: Unbalancing versus balancing

This research explores the appropriateness of unbalancing the workload per machine in certain types of flexible manufacturing systems (FMSs) configured as pooled machines of unequal sizes. Studies are conducted to examine the applicability of following an objective to unbalance workloads when solving the FMS planning problems of selecting part types to be machined together and determining their production ratios. Simulation is used to compare unbalancing and balancing on realistic, detailed models of flexible flow system (FFSs). The experiments are constructed to evaluate the impact of operational factors such as blocking, transportation, buffer utilizations, fixture requirements of various types, and different workload distributions among the machine types. The research results indicate that the aggregate and theoretically optimal unbalanced workloads provided by Stecke and Solberg (1981, 1985) using a closed queueing network model can be appropriate in a realistic FMS. Production rate and system and machine utilizations can all be higher when unbalancing workloads in systems of pooled machines of unequal sizes. It is also observed that: (1) system performance in terms of system utilization or production rate is sensitive to the appropriate number of pallets in the system, when either unbalancing or balancing; and (2) unbalanced part mix ratios conversely can lead to balanced machine utilizations among unequally sized pooled machine types. Overall system utilization seems to be more sensitive to the number of pallets in the system when unbalancing than when balancing. Further research needs are also noted.

Modelling and simulation of a flexible manufacturing system with variable production ratios

A flexible manufacturing system (FMS) is an integrated, computer-controlled configuration of automated material handling devices and numerically controlled machine tools that can simultaneously process medium-sized volumes of a variety of part types. This paper addresses the loading problem in an FMS with variable production ratios. A simulation model is developed for investigating the system performance while minimizing the workload unbalance. It is shown that the developed approach is quite efficient and yields reliable results.