Modified iterative aggregation procedure for maintenance optimisation of multi-component systems with failure interaction

This paper studies maintenance policies for multi-component systems which have failure interaction among their components. Component failure might accelerate deterioration processes or induce instantaneous failures of the remaining components. We formulate this maintenance problem as a Markov decision process (MDP) with an objective of minimising a total discounted maintenance cost. However, the action set and state space in MDP exponentially grow as the number of components increases. This makes traditional approaches computationally intractable. To deal with this curse of dimensionality, a modified iterative aggregation procedure (MIAP) is proposed. We mathematically prove that iterations in MIAP guarantee the convergence and the policy obtained is optimal. Numerical case studies find that failure interaction should not be ignored in a maintenance policy decision making and the proposed MIAP is faster and requires less computational memory size than that of linear programming.     

Non-myopic vehicle and route selection in dynamic DARP with travel time and workload objectives

In dial-a-ride problems, a fleet of n vehicles is routed to transport people between pick-up and delivery locations. We consider an elementary version of the problem where trip requests arrive in time and require an immediate vehicle assignment (which triggers an appropriate route update of the selected vehicle). In this context, a relatively general objective can be stated as a weighted sum of the system's effort and the customers' inconvenience. However, optimizing almost any objective in this immensely complex stochastic system is prohibitively difficult. Thus the earlier work has largely resorted to heuristic cost functions that arise, e.g., from the corresponding static systems. By using the framework of Markov decision processes and the classical M/M/1 queue as a highly abstract model for a single vehicle, we explain why certain intuitive cost functions indeed give satisfactory results in the dynamic system, and also give an explicit interpretation of different components appearing in a general cost function. The resulting family of heuristic control policies is demonstrated to offer a desired type of performance thus justifying the assumed analogy between a multi-queue and dial-a-ride systems.     

面向普适计算环境的嵌入式系统研究

服务提供与用户界面自适应问题是普适计算中的重要研究课题。嵌入式系统提供的服务对普适计算的服务起到了重要的支持作用;普适计算任务要获取用户的服务也需要自适应的用户界面来显示服务项目,嵌入式系统的自适应界面是合适的选择。但是,传统的嵌入式系统服务软件不能很好地实现上述目标。针对传统嵌入式系统服务模型的不足,首先总结了面向普适计算的嵌入式系统硬件结构,并将这种结构命名为计算元。随后提出了一种新的嵌入式系统服务提供模型,统一了用户上下文交互的标准信息格式。最后研究了该服务模型的实现。     

Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems

The key to achieving optimum ship system reliability and safety is to have a sound maintenance management system in place for mitigating or eliminating equipment/component failures. Maintenance has three key elements; risk assessment, maintenance strategy selection and the process of determining the optimal interval for the maintenance task. The optimisation of these three main elements of maintenance is what constitute a sound maintenance management system. One of the challenges that marine maintenance practitioners are faced with is the problem of maintenance selection for each equipment item of the ship machinery system. The decision making process involves utilising different conflicting decision criteria in selecting the optimum maintenance strategy from among multiple maintenance alternatives. In tackling such decision making problems the application of a multi-criteria decision making (MCDM) method is appropriate. Hence in this paper two hybrid MCDM methods; Delphi-AHP and Delphi-AHP-PROMETHEE, are presented for the selection of appropriate maintenance strategies for ship machinery systems and other related ship systems. A case study of a ship machinery system maintenance strategy selection problem is used to demonstrate the suitability of the proposed methods.     

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.     

An integrated model for the design of end-of-aisle order picking system and the determination of unit load sizes of AGVs

In this paper, we deal with an assembly line production system in which an automated guided vehicle (AGV) delivers parts to each workstation of assembly lines from a miniload automated storage/retrieval system (AS/RS). Each assembly production line is characterized by a sequence of workstations linked together for the production of a given type of product. We consider simultaneously the design problem of miniload AS/RS and the problem of determining a unit load size of the AGV. First, a non-linear mathematical model is formulated under a proposed AGV dispatching policy. And then, we determine an appropriate sequence of lines of the vehicle visits. Finally, based on the characteristics of the objective function and feasible region of the decision variables, a heuristic solution procedure is developed to find a near optimal solution. To examine the validity of the model, an example problem is solved.     

A Compiler-Directed Approach to Network Latency Reduction for Distributed Shared Memory Multiprocessors

In distributed shared memory multiprocessor systems, parallel tasks communicate through sharing memory data. As the system size increases, such communication cost becomes the main factor that limits the overall parallelism and performance. In this paper, we propose a new solution to the problem through judiciously managing the relevant resource, namely, the shared data and the interconnection network (IN) through which the sharing is carried out. In this approach, communication cost is minimized by means of data migration/allocation which is based on analyzing general layered task graphs, sharing behavior of parallel tasks, and network topology. Our method is not applicable for read only variables. Further, for the time being, the usefulness of the method is limited to multiprocessors where no cache coherence mechanism is implemented. Four typical interconnection topologies for multiprocessors are considered, namely, shared-bus, hierarchical-bus, 2-D mesh, and fat-tree structures. Efficient data allocation algorithms for each of the four network topologies are developed that make decision on data allocation/migration at the compile time. The complexity of one algorithm isO(np) for shared-bus andO(n²p) for the remaining three in a system withnprocessors executing ap-layer task graph for one shared variable. We have also given an algorithm to determine optimal allocation/migration scheme for multiple shared variables. However, the cost of the algorithm become prohibitive when the number of shared variables is high. Therefore, a heuristic of low complexity is suggested. The heuristic is optimal for some topologies.     

一种轻量级嵌入式GUI系统及其实现

基于图形用户界面（GUI）的嵌入式应用越来越广泛。常见的通用GUI产品往往尺寸大，开发细节多，不适合嵌入式场合的成本和开发周期要求。文中提出了一种GUI系统结构简洁，相对现有GUI产品尺寸更小，同时由于采用了面向对象的模块化设计与仿真器技术，开发更为方便快捷。以会议电视终端为例，介绍了该系统的实现。在会议电视终端的应用中，该系统小巧灵活，同时开发过程只需要很短的时间，有效的降低了产品成本和开发成本。     

An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, job-cluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.     

A storage assignment heuristic method based on genetic algorithm for a pick-and-pass warehousing system

An order storage assignment problem (SAP) is to find an effective way to locate products in a warehouse in order to improve the operational efficiency of order picking. Since SAP is an NP-hard problem, many heuristic algorithms have been proposed. Most of previous researches focused on picker-to-parts warehousing systems or automated storage and retrieval systems. However, pick-and-pass systems play an important role for the faster delivery of small and frequent orders of inventory with the rise of e-commerce and e-business in the global supply chain. Two factors lead to idle time of pickers in a pick-and-pass system: picking line imbalance and shortage replenishment of products. This paper develops a genetic based heuristic method to solve SAP for a pick-and-pass system with multiple pickers to determine the appropriate storage space for each product and balance the workload of each picking zone so that the performance of the system can be improved. A simulation model based on FlexSim is used to implement the proposed heuristic algorithm and compare the throughput for different storage assignment methods as well. The results indicate that the proposed heuristic policy outperforms existing assignment methods in a pick-and-pass system.     

Heuristic search for scheduling flexible manufacturing systems using lower bound reachability matrix

For scheduling flexible manufacturing systems efficiently, we propose new heuristic functions for A^* algorithm that is based on the T-timed Petri net. In minimizing makespan, the proposed heuristic functions are usually more efficient than the previous functions in the required number of states and computation time. We prove that these heuristic functions are all admissible and one of them is more informed than that using resource cost reachability matrix. We also propose improved versions of these heuristic functions that find a first near-optimal solution faster. In addition, we modify the heuristic function of Yu, Reyes, Cang, and Lloyd (2003b) and propose an admissible version in all states. The experimental results using a random problem generator show that the proposed heuristic functions perform better as we expected.     

一种面向环境可配置嵌入式系统软件模型研究

嵌入式系统硬件资源是相对固定且有限,为了适应环境,就需要系统从软/硬件上可重构。基于此,提出了一种基于可配置构件面向环境的嵌入式系统模型。它能适应不同环境下,面向任务地对系统进行重构来完成使命。通过可重构缩小了系统成品体积,节省了硬件资源,提高了系统可重用性。该模型在某航空机载设备中得到实际应用,解决了由来已久的问题,提高了系统的灵活性和自适应性。     

Heuristic search for scheduling flexible manufacturing systems using lower bound reachability matrix

For scheduling flexible manufacturing systems efficiently, we propose new heuristic functions for A^* algorithm that is based on the T-timed Petri net. In minimizing makespan, the proposed heuristic functions are usually more efficient than the previous functions in the required number of states and computation time. We prove that these heuristic functions are all admissible and one of them is more informed than that using resource cost reachability matrix. We also propose improved versions of these heuristic functions that find a first near-optimal solution faster. In addition, we modify the heuristic function of Yu, Reyes, Cang, and Lloyd (2003b) and propose an admissible version in all states. The experimental results using a random problem generator show that the proposed heuristic functions perform better as we expected.     

Optimal maintenance policy for multi-component systems under Markovian environment changes

In this paper, we study multi-component systems, which environmental conditions and opportunistic maintenance (OM) involve. Environmental conditions will exert an influence on deterioration processes of the components in the system. For each component, the worse the environmental conditions are, the faster its deterioration speed is. We want to determine when to preventively maintain each component under such environmental influence. Our purpose is to minimize its long-run average maintenance cost. We decompose such a multi-component system into mutually influential single-component systems, and formulate the maintenance problem of each component as a Markov decision process (MDP). Under some reasonable assumptions, we prove the existence of the optimal (n_r, N_r) type policy for each component. A policy iteration method is used to calculate its optimal maintenance policy. Based on the method, we develop an iterative approximation algorithm to obtain an acceptable maintenance policy for a multi-component system. Numerical examples find that environmental conditions and OM pose significant effects on a maintenance policy.     

Material supply scheduling in a ubiquitous manufacturing system

Recently, ubiquitous manufacturing has attracted wide attention in both academia and industry. To create a successful ubiquitous manufacturing system, an efficient material handling system is essential. In accordance with this reason, mobile robots have been used for transporting materials. This paper aims at developing a methodology for scheduling the material supply for a single mobile robot in a ubiquitous manufacturing environment. In this type of environment, the processing rate of the materials along with supply quantity corresponds to the cycle of material supply. The carrying capacity of the robots are limited and thus the problem of determining the material supply quantity and material supply schedule without lack of materials for production or service processes becomes complicated. In this work, a nonlinear program is formulated to schedule the supply of material and determine the required material quantity. A heuristic algorithm based on genetic algorithm is developed to solve the problem. From the numerical experiments conducted in this study, it is observed that the proposed algorithm shows good performance and can also be implemented to solve large scale problems.     

A comparative study on heuristic algorithms for generating fuzzydecision trees

Fuzzy decision tree induction is an important way of learning from examples with fuzzy representation. Since the construction of optimal fuzzy decision tree is NP-hard, the research on heuristic algorithms is necessary. In this paper, three heuristic algorithms for generating fuzzy decision trees are analyzed and compared. One of them is proposed by the authors. The comparisons are twofold. One is the analytic comparison based on expanded attribute selection and reasoning mechanism; the other is the experimental comparison based on the size of generated trees and learning accuracy. The purpose of this study is to explore comparative strengths and weaknesses of the three heuristics and to show some useful guidelines on how to choose an appropriate heuristic for a particular problem.     

Content Replication in a Distributed and Controlled Environment

Recently, we have witnessed a phenomenal growth in the Internet/Intranet coupled with rapid deployment of new services. Information dissemination over the network has become one of the most important activities in our daily life. The existing systems, however, often suffer from notorious long delays experienced by clients, especially during peak hours. This is caused by the combination of a variety of factors, including inadequate link bandwidth, server overload, and network congestion. Content replication has been shown to be one of the most effective mechanisms to cope with this problem. The basic idea is to replicate the information across a network so that clients' requests can be spread out. One of the major issues is which locations inside the network these replications should take place, i.e., where to place the replicated servers. In this paper we investigate the content replication is a controlled and distributed environment. The salient feature of this environment is that the decision where to replicate information can be determined by a single authority, the Intranet being the typical example. We consider the problem of placing multiple replicated servers within a network, given there exist multiple target web servers as information providers. We formulate this as an optimization problem by taking into consideration the characteristics of the network topology. We first show that this is a NP-complete problem, and then we present a number of heuristsic-based algorithms for content replications. Finally, in order to investigate various trade-offs in terms of cost and algorithm complexity, we carry out comparison studies among different heuristic algorithms and, also, with an optimal placement algorithm we recently proposed for the single target web server environment.     

A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

Redundancy allocation problem (RAP) is one of the best-developed problems in reliability engineering studies. This problem follows to optimize the reliability of a system containing s sub-systems under different constraints, including cost, weight, and volume restrictions using redundant components for each sub-system. Various solving methodologies have been used to optimize this problem, including exact, heuristic, and meta-heuristic algorithms. In this paper, an efficient multi-objective meta-heuristic algorithm based on simulated annealing (SA) is developed to solve multi-objective RAP (MORAP). This algorithm is knowledge-based archive multi-objective simulated annealing (KBAMOSA). KBAMOSA applies a memory matrix to reinforce the neighborhood structure to achieve better quality solutions. The results analysis and comparisons demonstrate the performance of the proposed algorithm for solving MORAP.     

Investigating impact of the order activity costing method on product cost calculations

Inaccuracies in calculated product costs have existed since the development of costing systems. A key contributor to the issue is the use of inappropriate bases for the application of overhead costs. This research proposes and provides preliminary evaluation in a virtual environment for a new allocation base that is believed to be better matched to the consumption rate of the indirect costs being allocated. Using a generalized manufacturing operational framework incorporating multi-period simulation, this research investigates the relationship between allocated cost categories and production or sales order activity. The existing cost allocation methods of full absorption and activity based costing (ABC) are used for comparison to the proposed method. Results show that at the aggregate reporting level (that is, income statement), the use of sales order or production order activity as an allocation base tracks closely with performance levels experienced using more traditional allocation bases. However, the results indicate that the impact on calculated product costs would influence decision making within a firm in terms of sales emphasis, mix, and markets in which to expand and from which to exit. This approach toward cost allocation would be equal to other Enterprise Resource Planning system based solutions in terms of simplicity of maintenance while offering product cost accuracies relatively equal with unit-level focused ABC systems, without requiring the substantial maintenance costs.     

On-line maintenance job scheduling and assignment to resources in distributed systems by heuristic-based optimization

A heuristic-based optimization algorithm is proposed in this paper for on-line scheduling and assignment of preventive maintenance jobs to processors, to minimize under availability constraints, on a given time-window, the total cost of the maintenance operations of a distributed system. This algorithm minimizes the cost of discharge of preventive maintenance tasks or jobs, while assigning the tasks along with balancing the processors load. It is shown that the problem is NP-hard. To solve it, the concept of job emergency is introduced and the priority rule for total flow time (PRTF) criterion is used in an adapted heuristic job-scheduling model. In addition, the algorithm considers the constraints of precedence among consecutive standby jobs and their emergency. It is depicted the specific properties of the proposed heuristic allowing jobs scheduling in the right order. Computational results illustrate the efficiency of the approach implemented on different system configurations.