A database approach towards flexible manufacturing: A conceptual framework

Evaluating non-core technologies: Contrasting external and internal views on corporate research results

App stores or application distribution platforms allow users to present their sentiments about apps in the forms of ratings and reviews. However, selecting the “best one” from available apps that offer similar functionality is difficult task - especially, if the selection process only uses the average star rating of the apps. To address this challenge, we have introduced a trust-based selection and ranking system of similar apps by combining the programmatic view (“internal view”) and the sentiments based on users reviews (“external view”). The rankings based on the average star ratings are compared with the rankings generated by our approach. We empirically evaluate our approach by using the publically available apps from the Google Play Store. For this study, we have chosen a dataset of 250 apps with total 114,480 reviews from top 5 different categories - of which we focused our experiments on 90 apps that have at least 1000 reviews. Our experiments indicate that proposed holistic ranking that encompasses both the internal and external views is a better alternative than any ranking that focuses only on the internal or external view.

During the last decade (from the late 1970s to the beginning of the 1990s), manufacturing systems have significantly changed. Their function and performance have altered in a period of rapid development, mainly as a result of the application of the techniques of computer science. The new systems are termed either flexible manufacturing systems (FMS) or computer- integrated manufacturing (CIM). The advance of flexible manufacturing and its related technologies has been accompanied by a number of related developments, such as flexible manufacturing of a global network type, CIM incorporating personnel functions, skill-based manufacturing, and compact flexible manufacturing cells (FMC). These all have notable influences on the system configuration and operational technologies of flexible manufacturing: perhaps as a result, there is some confusion about their terminology. A number of differing nomenclatures are used (such as FMC, FMS, flexible or factory automation (FA), CIM and enterprise automation (EA), for the same fundamental technology.

Derived from epistemological fundamentals, a new concept of process is developed and applied to biological evolution. The new conception is based on a disctinction between external and internal views of processes. Dynamical equations are associated with the external view, whereas the observation of processes is shown to require an internal approach. Conventional models of dynamics are found to show properties (such as time-independence) that cannot be grounded for an important class of systems viewed internally. The processes that require a new approach are argued to include evolutionary phenomena and Whiteheadian instances of change. Problems of motion such as Zeno’s paradoxes are recognized as issues of externally represented static systems versus internally represented changing systems. The basic notions of an ontology for internal processes of a system are developed. Evolutionary models are discussed in the light of these concepts, and a new simulation framework, based on internally viewed processes that can alter themselves, is presented.

The view ahead influences the motion sickness of car passengers but has been found to have little influence on the sickness caused by low frequency fore-and-aft oscillation. Acceleration and deceleration of vehicles is accompanied by pitch motions that may influence sickness. It was hypothesized that: (1) a visual scene would influence sickness caused by combined fore-and-aft and pitch oscillation; and (2) sickness would be dependent on the phase between the fore-and-aft oscillation and the pitch oscillation. While viewing one of three visual scenes (internal view, blindfold, or external view), 6 groups of 20 subjects were exposed for 30 min to 1 of 2 motions (in-phase or out-of-phase combinations of 0.1 Hz fore-and-aft and pitch oscillation). The 0.1-Hz fore-and-aft oscillation at +/- 1.26 ms(-2) rms (displacement of +/- 3.18 m) was combined with +/- 3.69 degrees pitch oscillation either in phase (so the pitch increased acceleration in the plane of the seat to +/- 1.89 ms(-2)) or out of phase (to reduce acceleration to +/- 0.63 ms(-2)). With both types of motion (in-phase and out-of-phase oscillation) there was significantly less sickness with an external view than with an internal view or a blindfold. There was evidence of an interaction between the effects of viewing condition and the effect of the phase between the fore-and-aft and the pitch oscillation consistent with blindfolded subjects experiencing less sickness when they experienced greater forces. Motion sickness caused by combined fore-and-aft and pitch oscillation depends on both the visual scene and the phase between the fore-and-aft and pitch motions. The minimization of sickness arising from such motions should involve the optimization of both the visual environment and the phase.

New jobs regeneration methods for computer integrated manufacturing environment

S.121-154

Recent simulation literature shows a growing interest in the integration of artificial intelligent concepts with computer simulation to automate the simulation phases of model construction and translation. Little attention has been paid to aiding modellers in the use of output data over the life cycle of a simulation model associated with a manufacturing system study. In modern computer integrated manufacturing (CIM) environments, information from related areas ideally should cohabit a common database system. In this paper, relational database structures for computer simulation results over the life cycle of simulation models are developed by classifying different types of model structures, user goals, and associated experimental design conditions common to all manufacturing systems. The structures provide an intermediate step between a pure simulation environment and a complete CIM relational database system and serve as a formal basis to create an automated database structure building system. Several simulation environments have been constructed with this approach and have proven successful.

FLEXIBLE SKILLS FOR FLEXIBLE MANUFACTURING

Computer-integrated manufacturing (CIM) has not yet provided the expected benefits for small-to-medium volume discrete parts manufacturing operations. Typically, these systems come in over budget and yet do not provide the promised flexibility. This can be directly attributed to the high cost of control software development for integrated systems. Reusable software has been proposed as a solution to the high software development and maintenance costs. One paradigm for reusable software, object-oriented modeling and programming, has emerged as an important method for specifying, creating, and reusing software modules. This paper describes an object-oriented approach to specifying and developing shop floor controllers for automated manufacturing environments. Specifically, a shop floor controller class hierarchy has been developed based on the behaviour of specific shop floor equipment and is described in this paper. User experience has shown that software development time can be significantly reduced through the use of this controller class.

A shop floor control system (SFCS), which is the most important component of any production control system, ff has a direct e ect on the productivity of a tool room. The SFCS presented in this paper aims at streamlining various tool room activities, such as process planning, operations scheduling, loading, progress monitoring, and performance monitoring. The system discussed in this paper is characterized by simplicity and is user friendly. Basic information, consisting of three types of information (order details, process details, and part details), is input through interactive dialogue sessions. On-line updating of the process information is carried out, again through interactive dialogue sessions. The basic input to the system along with the on-line updating enables a number of useful and important reports and documents to be generated ff by the system for e ective control of the shop floor. The reports generated include operations scheduling, process sheet, part list, ffi job card, load pending report, e ciency report, etc. This SFCS is developed for a medium sized tool room in an automotive company and its successful implemention reduced paperwork and improved resource management and has resulted in productivity. It can be said that this system offers a first venture into computer integrated manufacturing (CIM) for a tool room.

In the implementation of computer integrated manufacturing (CIM), there are varying degrees of integration. Within the semiconductor wafer manufacturing industry, only a few companies have installed a comprehensive and fully automated CIM system in their facilities. Thus, compared to other industries, wafer fabs continue to require significant numbers of production personnel. This makes work-in-process (WIP) control and yield problems more complex and difficult. This paper describes the key points of a state-of-the-art automation system, called FABCON, that was developed and is being implemented in an ASIC wafer fabrication facility (fab) in Japan. This system was created through the collaboration of both Japanese and American companies. Along with the traditional CIM functions of automatic lot tracking, recipe setting, and process data collection, this system also creates the production schedule automatically, and controls the transport of almost all wafer carriers throughout the fab by robots. Specifically, this paper discusses a distributed fab control system based on ISA standard 386 and 486 based PC's, a dynamic shop floor scheduler/dispatcher, wafer carrier transportation robotics, feedback/feedforward process control, and standardization of equipment interfaces. In addition, this paper describes some of the experiences in the course of development and implementation of the system. >

This paper addresses a crane scheduling and machine layout problem in a Computer Integrated Manufacturing (CIM) Environment. A single crame is used to move all the Work-in-Process (WIP) in the system. The overall system objective is to maximize the yield rate subject to the flow time limit of the WIP. We formalize the problem, and analytically and empirically show that cyclic scheduling provides a near optimal solution, which is superior to dispatching rules. First, we illustrate the optimality and benefits of cyclic scheduling in a simple environment. Then, for multiple-product problems, we show that for a given sequence, finding the minimum cycle time becomes the maximum cost circular network flow problem in a graph. Based on the insights developed, a heuristic for sequencing product types in a cycle is derived that approximately minimizes the cycle time over all sequences. Finally, computational experiments are reported and various assertions made in the paper are empirically verified.

The key to the successful "factory of the future" will be the effective coordination of managerial functions and production operations. The research presented in this dissertation addresses this "coordination" issue, especially in terms of understanding the impact and possibilities which exist in an information-rich environment. Such an environment is often referred to in the literature as being a computer-integrated manufacturing (CIM) environment. An interactive simulation modeling program, MOSES (Manufacturing Organization Simulation and Evaluation System), was developed for the purpose of describing, analyzing, and understanding such manufacturing. MOSES is a copyrighted computer program, a menu-driven, interactive, simulation modeling tool. MOSES users are able to define the manufacturing environment to be simulated by keying in data values (e.g., products, bills of material, and production rates) which describe the organization. MOSES then uses these data values to "build the manufacturing simulation model." That is, once the manufacturing parameters have been defined, MOSES can simulate the manufacturing organization (from orders through distribution) without the user having to write any computer code or having to understand the principles of simulation modeling. MOSES users are then able to interact with the manufacturing organization as "managers" in the dynamic simulation environment. This interactive capability allows MOSES users to test various manufacturing scenarios, watch the model's progress from the perspectives of different organizational functions (e.g., from marketing or from production), override model-generated decisions (e.g., by changing the demand forecast), and view the results. The MOSES approach to simulation modeling differs from traditional approaches. In short, MOSES uses both mathematic and logical modeling principles, employs traditional simulation modeling techniques but in combination with database technology, and includes the user as an integral component in the simulation process.

The work describes a scheduling system based on an opportunistic framework. It exploits the potentiality of a predictive module that provides a-priori information about the conflicts that may arise in a schedule, managing a constraint based approach in problem solving. The system SOS has been designed and realized to apply in real productive environments this theoretical approach. Requirements of efficiency and flexibility of use have been particularly considered. GUIDELINES AND SCHEDULING STRATEGIES IN SOS In this paper we will present an approach to scheduling problems which is focused on the requirements of real manufacturing environments. Therefore we considered aspects that are tied to the managerial perspective of the problem. The SOS system is designed both for computing solutions in scheduling problems and for supporting manager in obtaining schedules that reflect the state of shop-floor and organizational problems in production. Two aspects have been considered in these perspective: • it develops a temporal predictive method [7] to support a preventive analysis of activity interactions and to detect bottlenecks. It exploits an a-priori analysis of the effects of organizational constraints on the scheduling problem, evaluating alternatives as quick as possible. Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517 628 Artificial Intelligence in Engineering • If some unexpected event happens on the job shop, it works as a support tool for the evaluation of different reaction decisions (reactive scheduling [4]). The contribution of Al-based techniques is focused especially on the analysis of the effects that scheduling variables have on solutions and on the definition of adequate reasoning patterns. We based our approach on opportunistic reasoning [5]. This term characterizes a problem solving process in which the focus of attention is constantly directed to those choices that appear the most promising for reaching a solution. This policy is expected to reduce the search space (and therefore the computational complexity), focusing the reasoning on areas that are considered as the more constrained (island driving). The capability to provide a feasible solution in a sufficiently short time is fundamental for manufacturing environments, but we must remember that the existence of at least one feasible solution is not always guaranteed. The evaluation of effects of constraints becomes fundamental to identify a solution. Production managers want to know how the decision to change or to relaxing the value of some constraints (concerning for example orders due-dates or priorities) affects the quality of final schedules and the global productive efficiency. This feature is particularly important when a feasible solution does not exist; in this case, from a managerial viewpoint, it is required to the system to support the user in building a feasible solution. From a mathematical perspective we considered scheduling as a Constraint Satisfaction Problem (CSP) [6], concerned with the assignment of values to variables subject to a set of constraints. The solution of the CSP (a feasible set of values assumed by the variables) depends on the constraints defined on the problem: by changing the constraints, the solution will change, too. In solving a CSP, two decisions have to be made at each cycle, i.e. which variable to instantiate next and which value to assign to that variable. This formalization highlights the importance associated to constraints analysis: • they allow the quick computation of a feasible solution, without exploring the entire problem domain. • Through their analysis we can obtain information about how they can be modified opportunistically during scheduling. Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517 Artificial Intelligence in Engineering 629 We distinguished between two classes of constraints [3], considering the possibility for production managers to change their values in a very short time horizon: Restrictions: they must be always respected and cannot be easily and quickly changed; they can be furtherly divided into causal constraints (they represent conditions to be satisfied before starting an activity), physical constraints (each equipment has specific capabilities that restrict the type and the speed of operations it can accomplish), and resources unavailability. Preferences: restrictions leave scheduling problem underconstrained. Preferences represent the set of managerial constraints and the definition of their values is subject to a decision process. Some examples are: defining orders due date and priority, meeting due-dates, minimizing WIP, maximizing resource utilization, the definition of operational preferences (an order may follow alternative production paths in shop floor), etc. The role of constraints can be underlined considering a second classification, which concerns the links among activities. In a manufacturing environment orders (made of many activities) are processed contemporaneously; moreover many of them may require the same productive resources. These connections cause two kinds of constraints [9]: • intra-order: they represent explicit links existing among activities belonging to an order (for example temporal relations [1]. These constraints are explicitly defined in the problem. • Inter-order: they represent implicit links existing among activities belonging to different orders. For example activities belonging to different orders may require contemporaneously the same productive resource. These constraints remain implicit after the problem definition. THE SOS SCHEDULING PROCESS The goal of our system is to find solutions that satisfies a given set of constraints as quick as possible. If a solution does not exist, the system relaxes only preferences, minimizing the overall effects on final schedule. The scheduling policies followed in SOS can be summarized as follows: 1. Scheduling is made allocating one activities at a time, consider at each step the effects of intraand inter-order constraints; Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517 630 Artificial Intelligence in Engineering 2. Most constrained and least-impact policies are implemented at each step. First the most-constrained policy selects dynamically on which agent must be focused scheduling attention. Then, the least impact policy chooses for that agent a value whose impact on the rest of the non-scheduled agents is as small as possible. The strategic goal is the identification of critical activities that heavily rely on the possession of highly contended temporal intervals or resources because of intra-order and inter-order interactions (look-ahead strategy). These two policies are based on numeric indexes which account for the particular structure of a problem. They give a measure of the interaction among activities and resources in terms of variable looseness (it is a measure of constraint degree) and value goodness (it is a measure of the impact of a scheduling decision on non-scheduled activities) [9]. Finally -we decided to avoid the utilization of backtracking in SOS systems because of the particular field of application that we identified. In real problems, we have no a priori guarantees concerning the existence of a solution: in this case backtracking is completely useless. Moreover, in order to meet our basic requirements, we prefer to look for a satisfying solution in a short time, instead than to wait for the best solution (which may not exist). V)PI ( Predictive pre-analysis 1 constraints modification / jtraints modification W Result and jus sy ' xi' :•:':':•:•::•:: •;::-:•: j @ Sch, Pr X^x:: :.:••::.::.-::: s display jtification stem