Design of a real-time AND/OR assembly scheduler on an optimization neural network

The problem of finding an AND/OR precedence-constraint assembly schedule using optimization neural computation is presented. The precedence relationships of assembly operation result from the geometric constraints of subtasks. Because of the existence of geometric constraints among assembly subtasks, the assembly operation involves AND/OR precedence relationships; that is, the order of assembly crucially determines whether the desired task can be achieved. A feasible assembly schedule is a schedule that satisfies these AND/OR precedence constraints.It has been shown that all the feasible assembly schedules can be generated by transforming geometric constraints of subtasks to the pattern-matching operation. Using the question-answer pattern and pattern-matching operation, the assembly scheduling problem can be transformed into an AND/OR precedence-constrained traveling salesman problem (TSP). Two precedence-constrained TSPs, cost-constrained TSP (CCTSP) and state-constrained TSP (SCTSP), are discussed. The CCTSP artificially sets the cost of the prohibited moves to a very large value which ensures that the constraints are satisfied, while the SCTSP restricts the movement of next assembly subtasks. The advantage of the SCTSP over CCTSP in the generation of the assembly schedule will be illustrated.A novel method proposed here is to obtain the best AND/OR precedence-constraint assembly schedule using neural network computation. The geometric constraints of an assembled object are transformed into the elements of the connection matrix which specifies the connection strength among neurons. A modified Hopfield network is used to tackle the AND/OR precedence-constraints assembly scheduling problem. Multirobot assembly sequences generation is also discussed. The designed algorithm can accommodate various constraints and applications. Detailed algorithms, examples and experiments are presented.     

Integrated aircraft-path assignment and robust schedule design with cruise speed control

Assignment of aircraft types, each having different seat capacity, operational expenses and availabilities, critically affects airlines’ overall cost. In this paper, we assign fleet types to paths by considering not only flight timing and passenger demand, as commonly done in the literature, but also operational expenses, such as fuel burn and carbon emission costs associated with adjusting the cruise speed to ensure the passenger connections. In response to flight time uncertainty due to the airport congestions, we allow minor adjustments on the flight departure times in addition to cruise speed control, thereby satisfying the passenger connections at a desired service level. We model the uncertainty in flight duration via a random variable arising in chance constraints to ensure the passenger connections. Nonlinear fuel and carbon emission cost functions, chance constraints and binary aircraft assignment decisions make the problem significantly more difficult. To handle them, we use mixed-integer second order cone programming. We compare the performance of a schedule generated by the proposed model to the published schedule for a major U.S. airline. On the average, there exists a 20% overall operational cost saving compared to the published schedule. To solve the large scale problems in a reasonable time, we also develop a two-stage algorithm, which decomposes the problem into planning stages such as aircraft-path assignment and robust schedule generation, and then solves them sequentially.     

Optimal Path Problems with Second-Order Stochastic Dominance Constraints

This paper studies optimal path problems integrated with the concept of second order stochastic dominance. These problems arise from applications where travelers are concerned with the trade off between the risks associated with random travel time and other travel costs. Risk-averse behavior is embedded by requiring the random travel times on the optimal paths to stochastically dominate that on a benchmark path in the second order. A general linear operating cost is introduced to combine link- and path-based costs. The latter, which is the focus of the paper, is employed to address schedule costs pertinent to late and early arrival. An equivalent integer program to the problem is constructed by transforming the stochastic dominance constraint into a finite number of linear constraints. The problem is solved using both off-the-shelf solvers and specialized algorithms based on dynamic programming (DP). Although neither approach ensures satisfactory performance for general large-scale problems, the numerical experiments indicate that the DP-based approach provides a computationally feasible option to solve medium-size instances (networks with several thousand links) when correlations among random link travel times can be ignored.     

Supply management for cost minimization in assembly systems with random component yield times

This paper is concerned with inventory control in assembly systems for minimizing production costs. The system manufactures multiple products assembled from various components, and it operates according to a cyclic schedule. At the start of each cycle time, two decisions are made: the product volumes to be assembled during the current cycle, and the component-stock levels to be ordered. For a given decision, there is an associated cost incurred by backlogging of the finished products on one hand, and the component inventory holding cost, on the other hand. The objective here is to balance the two costs so as to minimize their sum. One complicating factor stems from uncertainties in both product demand levels and components yield times. These uncertainties can be modelled by probabilistic means, and hence the cost minimization problem becomes a stochastic problem. This problem can be quite difficult due to the nonlinearity of the equations involved, the mix of integer and continuous parameters, and their large number in moderate-size problems. Our approach in this paper is to first define certain control parameters and thus reduce the number of the variables involved in the optimization problem, and then solve the latter problem by using sophisticated optimization techniques in conjunction with heuristic modelling. We will demonstrate, by numerical means, the resolution of fairly difficult problems and thus establish the viability of the proposed numerical techniques.     

A pipeline-based approach for scheduling video processing algorithms on NOW

Network Of Workstations (NOW) platforms put together with off-the-shelf workstations and networking hardware have become a cost effective, scalable, and flexible platform for video processing applications. Still, one has to manually schedule an algorithm to the available processors of the NOW to make efficient use of the resources. However, this approach is time-consuming and impractical for a video processing system that must perform a variety of different algorithms, with new algorithms being constantly developed. Improved support for program development is absolutely necessary before the full benefits of parallel architectures can be realized for video processing applications. Toward this goal, an automatic compile-time scheduler has been developed to schedule input tasks of video processing applications with precedence constraints onto available processors. The scheduler exploits both spatial (parallelism) and temporal (pipelining) concurrency to make the best use of machine resources. Two important scheduling problems are addressed. First, given a task graph and a desired throughput, a schedule is constructed to achieve the desired throughput with the minimum number of processors. Second, given a task graph and a finite set of available resources, a schedule is constructed such that the throughput is maximized while meeting the resource constraints. Results from simulations show that the scheduler and proposed optimization techniques effectively tackle these problems by maximizing processor utilization. A code generator has been developed to generate parallel programs automatically. The tools developed in this paper make it much easier for a programmer to develop video processing applications on these parallel architectures.     

Modularity the key to system growth potential

General Electric's Apollo Systems has developed several information systems over the last eight years in support of the Apollo project. The expertise gained in these development efforts has shown the efficiency of a modularized approach to retrieval system design Systems analysts and programmers who design and build these systems should follow the normal systems engineering approach of requirements definition, system design, system implementation, test and verification and operational installation. Short-cutting any one of these phases leads to greater effort in one of the later phases, usually with a longer over-all schedule or greater developmental cost Project management, too, is a major factor in the success of such systems. The tracking of critical milestones in the schedule, consistent and up-to-date documentation, and comprehensive test and verification plans are necessary to a controlled approach to systems implementation Finally, the benefits of such an approach are reduced cost and implementation time, along with simplification of system maintenance, standardized software, adaptability to new environments, and a potential for continued growth to meet users' ever-expanding needs.     

Practical design of robots operating in radiation environments

Nuclear robots must be hardened against radiation for effective operation in high-radiation environments. These robots require special components that usually have few functions, high costs and long delivery times. In the present study, we propose a modulated and hierarchical electrical circuit system for robot components such as actuators. The hierarchical system is set into a design that can withstand radiation without reducing or limiting the functionality of the robot. The hierarchical system is achieved by minimizing the variety of integrated circuit (IC) components to be used and selecting specific ICs that require radiation testing. These ICs are defined by spotted ICs with regard to the integration density and operational speed. In some cases, it is possible to select off-the-shelf IC components, but certain key IC components are needed for on-line irradiation tests, depending on robot operation reliability and the radiation environment. Since the serial numbers of off-the-shelf IC components are random, it is important that a sufficiently large number of IC components are tested under the operating conditions for statistical evaluation. If the radiation lifetime is random, then the radiation lifetime of the sorted IC components can be estimated statistically. Here, the robot user decides the operation based on this lifetime. By doing so, the experimental cost can be minimized in accordance with the proposed method at the user's operational risk. Of course, if on-line irradiation tests are performed for various types of IC components, the radiation lifetime of the robot can be predicted with a relatively high accuracy. As a result, the design of a robot that operates under certain radiation conditions, while having a certain lifetime and cost, is possible. This design technique was applied to a nuclear disaster prevention robot and the radiation lifetime of the spotted IC components was shown to be distributed randomly, indicating the validity of the proposed method.     

UM-RTCOM: An analyzable component model for real-time distributed systems

Component-based development is a key technology in the development of software for modern real-time systems. However, standard component models and tools are not suitable for this type of system, since they do not explicitly address real time, memory or cost constraints. This paper presents a new predictable component model for real-time systems (UM-RTCOM) together with a set of tools to support it. The environment allows new components to be developed which can then be assembled to build complete applications, including hardware interaction. The model includes support for real-time analysis at the component and application level. The analysis is achieved by combining component meta-information in the form of an abstract behaviour model and a method to measure worst-case execution times in the final platform. Additionally, we propose an implementation model based on RT-CORBA where the developer uses the UM-RTCOM components and a set of tools to map these elements to elements of the desired platform. In order to apply our proposals, we have used the model and tools in real applications specifically in the context of nuclear power plant simulators.     

Controlled multibatch self-assembly of microdevices

A technique is described for assembly of multiple batches of micro components onto a single substrate. The substrate is prepared with hydrophobic alkanethiol-coated gold binding sites. To perform assembly, a hydrocarbon oil, which is applied to the substrate, wets exclusively the hydrophobic binding sites in water. Micro components are then added to the water, and assembled on the oil-wetted binding sites. Moreover, assembly can be controlled to take place on desired binding sites by using an electrochemical method to deactivate specific substrate binding sites. By repeatedly applying this technique, different batches of micro components can be sequentially assembled to a single substrate. As a post assembly procedure, electroplating is incorporated into the technique to establish electrical connections for assembled components. Important issues presented are: substrate fabrication techniques, electrochemical modulation by using a suitable alkanethiol (dodecanethiol), electroplating of tin and lead alloy and binding site design simulations. Finally, we demonstrate a two-batch assembly of silicon square parts, and establishing electrical connectivity for assembled surface-mount light emitting diodes (LEDs) by electroplating.     

Evaluating vehicle painting plans in an automobile assembly plant using an integrated AHP‐PROMETHEE approach

The painting activity is one of the most complex and important activities in automobile manufacturing. The inherent complexity of the painting activity and the frequent need for repainting usually turn the painting process into a bottleneck in automobile assembly plants, which is reflected in higher operating costs and longer overall cycle times. One possible approach for optimizing the performance of the paint shop is to improve the efficiency of the color planning. This can be accomplished by evaluating the relative merits of a set of vehicle painting plans. Since this problem has a multicriteria nature, we resort to the multicriteria decision analysis (MCDA) methodology to tackle it. A recent trend in the MCDA field is the development of hybrid approaches that are used to achieve operational synergies between different methods. Here we apply, for the first time, an integrated approach that combines the strengths of the analytic hierarchy process (AHP) and the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE), aided by Geometrical Analysis for Interactive Aid (GAIA), to the problem of assessing alternative vehicle painting plans. The management of the assembly plant found the results of value and is currently using them in order to schedule the painting activities such that an enhancement of the operational efficiency of the paint shop is obtained. This efficiency gain has allowed the management to bid for a new automobile model to be assembled at this specific plant.     

Cost models for future software life cycle processes: COCOMO 2.0

Current software cost estimation models, such as the 1981 Constructive Cost Model (COCOMO) for software cost estimation and its 1987 Ada COCOMO update, have been experiencing increasing difficulties in estimating the costs of software developed to new life cycle processes and capabilities. These include non-sequential and rapid-development process models; reuse-driven approaches involving commercial off-the-shelf (COTS) packages, re-engineering, applications composition, and applications generation capabilities; object-oriented approaches supported by distributed middleware; and software process maturity initiatives. This paper summarizes research in deriving a baseline COCOMO 2.0 model tailored to these new forms of software development, including rationale for the model decisions. The major new modeling capabilities of COCOMO 2.0 are a tailorable family of software sizing models, involving Object Points, Function Points, and Source Lines of Code; nonlinear models for software reuse and re-engineering; an exponentdriver approach for modeling relative software diseconomies of scale; and several additions, deletions and updates to previous COCOMO effort-multiplier cost drivers. This model is serving as a framework for an extensive current data collection and analysis effort to further refine and calibrate the model's estimation capabilities.     

Creating schedules and computing operating costs for LTL load plans

This paper presents a computational approach for creating an operational schedule for the tactical load plan used by a less-than-truckload (LTL) motor carrier. A load plan determines how freight is routed through an LTL carrier's linehaul terminal network by specifying a sequence of transfer terminals for all freight shipments, but does not provide a timed schedule of trailer, tractor, and driver dispatches required to operate the plan. Developing a detailed operational schedule allows one to more accurately estimate the likely cost of a given load plan, along with other important performance metrics. Given a load plan, the scheduling approach we develop creates loaded truck dispatches between terminals with specified time windows, and then creates cyclic driver schedules to cover all dispatches. A computational study using data from a large U.S. LTL carrier demonstrates that our technology produces schedules that accurately model operational costs, typically within 2% of actual reported costs.     

Analysis of assembly operations’ difficulty using enhanced expert high-level colored fuzzy Petri net model

Majority of the products can be assembled in several ways that means the same final product can be realized by different sequences of assembly operations. Different degree of difficulty is associated with each sequence of assembly operation and such difficulties are caused by the different mechanical constraints forced by the different sequences of operations. In the past, few notable attempts have been made to represent and enumerate the degree of difficulty associated with an assembly sequence (in the form of triangular fuzzy number) by using the concept of assembly graph. However, such representation schemes do not possess the capabilities to model the user's reasoning and preferences. In this research, an intelligent Petri net model that combines the abilities of modelling, planning and performance evaluation for assembly operation is presented. This modelling tool can represent the issues concerning degree of difficulty associated with assembly sequences. The proposed mechanism is enhanced expert high-level colored fuzzy Petri net (EEHLCFPN) that is a hybrid of knowledge-based system and colored Petri net. An example encompassing assembly of subassemblies is considered to efficiently delineate the modelling capabilities of proposed hybrid petri net model.     

知识化制造环境下航空发动机装配线调度及自重构

航空发动机装配工序数量多、工序间装配约束复杂. 当产品需求变化时, 人工调整存在响应速度慢、装配效率低等问题. 以最小化产品完工成本、工序提前期惩罚成本及班组重构成本加权和为目标, 建立了航空发动机装配线调度和装配班组自重构优化模型. 提出一种新的基于工序局部最优排序的分解算法, 将调度问题分解为单个装配组上工序顺序优化问题. 设计了一种工序后向插入搜索策略. 最后提出装配线调度及自重构集成优化算法. 通过数值试验,验证了模型与算法的有效性.     

A refined cost-based approach to assembly scheduling

Increased emphasis on control of work-in-process costs in assembly scheduling of large, complex items leads to increased needs for aids to foremen in dealing with schedule changes. The task is complicated by constraints on resources that often require that activities are begun earlier than a just-in-time schedule would otherwise dictate. A criterion used in prototype tandem knowledge-based decision-aiding systems in the past was based on the assumption that investment costs do not compound. This can provide misleading choices in some cases. The present work refines the criterion previously used by including the compounding costs of holding subassemblies in inventory. A simplified version of the new formula is developed which provides simple rules for deciding which activities to start early if necessary. Numerical comparisons are made between the criteria.     

Remote high-performance visualization and collaboration

Using commercial, off-the-shelf equipment minimizes costs yet supports real-time, full-frame-rate transfers of compressed, high-resolution images to remotely distributed large-format displays. Sandia National Laboratories' engineers and analysts regularly use design centers to design, prototype and test components in a virtual space, and to explore and examine the results of large-scale simulations. Using commercially available video, compression and network equipment and the display quadrant technique, distributed workgroups can now collaborate on tera-scale problem sets. They can take full advantage of high-resolution displays with real-time interactivity. In addition, the system cost and network usage scales linearly for larger resolution displays and/or the addition of new remote sites     

Assessing COTS software in a certifiable safety-critical domain

Abstract.  Commercial off-the-shelf (COTS) software solutions have become commonplace in many domains, including the military, because they can provide standardized functionality with more responsiveness, a shorter time-to-market and at lower costs than custom-made solutions. In one domain, however, that of certifiable safety-critical applications, COTS software has not been adopted. One particular type of certifiable safety-critical domain, the civil air transport industry, is under pressure to reduce cost and time-to-market while simultaneously increasing safety. Therefore, the use of COTS software, rather than exclusive reliance on custom-made software, would appear to be a solution worthy of investigation. This study examines the certifiability of COTS software, its technical feasibility in this environment, and the ability to achieve the expected responsiveness, time-to-market and cost benefits. A detailed evaluation of COTS software and domain-specific certification requirements is used to demonstrate that the certification of COTS-based systems is possible. A prototype COTS-based system (built upon a number of COTS components) is created to illustrate the technical feasibility of such a system in the civil air transport domain. Expected benefits from COTS solutions are evaluated both by examining process artefacts from the development of the COTS-based system and by comparing this development process with the domain's traditional custom-development process.     

R2P: An open source hardware and software modular approach to robot prototyping

Rapid prototyping is a key aspect for the development of innovative robotic applications. A modular, platform-based, approach is the way to obtain this result. Modular approaches are common for software development, but hardware is still crafted often re-inventing solutions every time. As a consequence, the resources that should be invested in the development of a new robot get often drained by the implementation of a physical, working prototype to test the application idea. To overcome this problem, we propose R2P (Rapid Robot Prototyping), a framework to implement real-time, high-quality architectures for robotic systems with off-the-shelf basic modules (e.g., sensors, actuators, and controllers), integrating hardware and software, which can be assembled in a plug-and-play way. R2P provides hardware modules, a protocol for real-time communication, a middleware to connect components as well as tools to support the development of software on the modules. R2P aims at dramatically reduce time and efforts required to build a prototype robot, making it possible to focus the resources on the development of new robotic applications instead of struggling on their implementation. This enables also people with experience in a specific application domain, but with little technical background, to actively participate in the development of new robotic applications. R2P is open-source both in its software and hardware to promote its diffusion among the robotics community and novel business models that will substantially reduce the costs to design a new robotic product.     

ISIS—a knowledge-based system for factory scheduling

Abstract: Analysis of the job shop scheduling domain has indicated that the crux of the scheduling problem is the determination and satisfaction of a large variety of constraints. Schedules are influenced by such diverse and conflicting factors as due date requirements, cost restrictions, production levels, machine capabilities and substitutability, alternative production processes, order characteristics, resource requirements, and resource availability. This paper describes ISIS, a scheduling system capable of incorporating all relevant constraints in the construction of job shop schedules. We examine both the representation of constraints within ISIS, and the manner in which these constraints are used in conducting a constraint-directed search for an acceptable schedule. The important issues relating to the relaxation of constraints are addressed. Finally, the interactive scheduling facilities provided by ISIS are considered.