A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.     

A dynamic decision support system to predict the value of customer for new product development

In recent years, firms have focused on how to enter markets and meet customer requirements by improving product attributes and processes to boost their market share and profits. Consequently, market-driven product design and development has become a popular topic in the literature. However, past research neither covers all of the major influencing factors that together drive customers to make purchase decisions, nor connects these various influencing factors to customer purchasing behavior. Past studies further fail to take the time value of money and customer value into consideration. This study proposes a decision support system to (a) predict customer purchasing behavior given certain product, customer, and marketing influencing factors, and (b) estimate the net customer lifetime value from customer purchasing behavior toward a specific product. This will not only enable decision-makers to compare alternatives and select competitive products to launch on the market, but will also improve the understanding of customer behavior toward particular products for the formulation of effective marketing strategies that increase customer loyalty and generate greater profits in the long term. Decision-makers can also make use of the system to build up confidence in new product development in terms of idea generation and product improvement. The application of the proposed system is illustrated and confirmed to be sensible and convincing through a case study.     

Empirical evaluation of a decision support model for adopting software product line engineering

ContextThe software product line engineering (SPLE) community has provided several different approaches for assessing the feasibility of SPLE adoption and selecting transition strategies. These approaches usually include many rules and guidelines which are very often implicit or scattered over different publications. Hence, for the practitioners it is not always easy to select and use these rules to support the decision making process. Even in case the rules are known, the lack of automated support for storing and executing the rules seriously impedes the decision making process.ObjectiveWe aim to evaluate the impact of a decision support system (DSS) on decision-making in SPLE adoption. In alignment with this goal, we provide a decision support model (DSM) and the corresponding DSS.MethodFirst, we apply a systematic literature review (SLR) on the existing primary studies that discuss and present approaches for analyzing the feasibility of SPLE adoption and transition strategies. Second, based on the data extraction and synthesis activities of the SLR, the required questions and rules are derived and implemented in the DSS. Third, for validation of the approach we conduct multiple case studies.ResultsIn the course of the SLR, 31 primary studies were identified from which we could construct 25 aspects, 39 questions and 312 rules. We have developed the DSS tool Transit-PL that embodies these elements.ConclusionsThe multiple case study validation showed that the adoption of the developed DSS tool is justified to support the decision making process in SPLE adoption.     

A system for design and concurrent engineering under imprecision

This paper proposes an approach to handling imprecision in design and concurrent engineering systems by using interval analysis and constraint networks. By allowing design parameters to be specified with intervals rather than exact points, this approach permits designers to iteratively transform vague conceptual designs into detailed final designs. When a designer changes a variable's interval or assigns a value, the results are propagated through constraints and the resulting feasible interval for all other dependent variables is pruned. The interval constraint network approach described in this paper extends previous work by allowing the representation of and reasoning about complex constraints involving conditions, conjunctions and disjunctions, as well as both symbolic and numeric variables. Many concurrent engineering constraints cannot be modeled without this sort of representational flexibility. A prototype of this approach has been implemented in a system called SPARK-IP. The operation of SPARK-IP is demonstrated through a concurrent engineering design problem involving printed wiring boards.     

A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology

We propose an inventory classification system based on the fuzzy analytic hierarchy process (AHP), a commonly used tool for multi-criteria decision making problems. We integrate fuzzy concepts with real inventory data and design a decision support system assisting a sensible multi-criteria inventory classification. We report on a study conducted in a small electrical appliances company and validate the design of the proposed multi-criteria inventory classification system and its underlying fuzzy AHP model.     

A decision support system for automotive product planning and competitive market analysis

A decision support system (DSS) for automotive product marketing, design and manufacturing in China is presented in this paper. The DSS is developed as a tool to support product planning, competitive market analysis, supply chain analysis and subsequent manufacturing systems planning and deployment. The system consists of a number of automotive related databases which provide information about manufacturers' performance in each market segment as well as production information of all existing market players in the Chinese auto industry. Product planning, one of the key modules of the DSS prototype, is highlighted in this paper. It supports decision makers in determining suitable strategies for market entry by analyzing existing competitors' status, growth estimation of each market segment, and competitive market analysis for new vehicle products. A case study for new market entry is included here to demonstrate the feasibility and effectiveness of the proposed methodology.     

A conceptual framework for society-oriented decision support

Inspired by the operation of human social organisation, this paper presents a new architecture—a pyramid-committee—for developing society-oriented intelligence, whose structure imitates the organisation of human society in its decision making. The system takes a pyramid-like hierarchical structure with links in the pyramid forming a semi-lattice, which relate not only to nodes in the same layer, but also to others in different layers. The output of the system is a result of the negotiation and balancing of different interests. For such a system to function, the main difficulties concern the complicated relationships between different factors or agents. Focussing on the airport environment audit, we discuss the development of a model framework and the role of neural networks.     

The use of semantic networks to support concurrent engineering in semiconductor product development

The design of semiconductor devices is an extremely complex and costly process. Numerous design and test iterations are typically necessary to finally complete a successful device. Competition in the industry has forced semiconductor manufacturers to reduce design cycle times and costs. One method now being used to accomplish these objectives is concurrent engineering. This paper will review how concurrent engineering is being integrated into semiconductor device development and how artificial intelligence-based models will support concurrent engineering implementation. Major changes are needed in design simulation, methods of knowledge sharing, and incorporating best practices. A semantic network is proposed that retains the knowledge of a product in a central repository as various engineers contribute to the product's development. The knowledge contained in this central repository can be referenced for applicability by engineers during product design, development, and production.     

A text-based decision support system for financial sequence prediction

Although most quantitative financial data are analyzed using traditional statistical, artificial intelligence or data mining techniques, the abundance of online electronic financial news articles has opened up new possibilities for intelligent systems that can extract and organize relevant knowledge automatically in a usable format. Most information extraction systems require a hand-built dictionary of templates and thus need continual modification to accommodate new patterns that are observed in the text. In this research, we propose a novel text-based decision support system (DSS) that (i) extracts event sequences from shallow text patterns, and (ii) predicts the likelihood of the occurrence of events using a classifier-based inference engine. The prediction relies on two major, but complementary, feature sets: adjacent events and a set of information-theoretic functions. In contrast to other approaches, the proposed text-based DSS gives explanatory hypotheses about its predictions from a coalition of intimations learned from the inference engine, while preserving robustness and without indulging in formalism. We investigate more than 2000 financial reports with 28,000 sentences. Experiments show that the prediction accuracy of our model outperforms similar statistical models by 7% for the seen data while significantly improving the prediction accuracy for the unseen data. Further comparisons substantiate the experimental findings.     

Problem-centered design in concurrent engineering

This paper introduces the concept of incorporating the human and computerised elements of a design team into a single co-operating system, moving away from the model of a computer system supporting a single user towards being part of a heterarchy of agents. This augments classic CSCW where the computer is a facilitator to that where the computerised elements are active participants.

Within that collection of agents the humans are not only using the computers to solve their own problems but are deployed to satisfy the computers requirements; the whole system being designed to satisfy the customers' requirements. In a design and production environment the primary user is the problem which interfaces to the agents who combine to deliver a solution.

Relevance to industry

Rather than take a computer- or user-centred viewpoint, we argue for a holist cybernetic approach integrating both with the focus on the problem rather than any particular component. Where systems are largely user-driven, such as word processing, then the problem will naturally lean towards a user-centred design. In contrast the solution of large sets of differential equations requires little user involvement and so the focus leans naturally towards the computational aspects. Most systems lie between these extremes. Concurrent engineering is concerned with integrating many aspects of design and manufacture and so requires such a holistic approach to succeed.     

A framework for systems analysis for decision support systems

Comprehensive and elaborate systems analysis techniques have been developed in the past of routine and operational information systems. Developing support systems for organizational decision-making requires new tools and methodologies. We present a new framework for data collection and decision analysis which is useful for developing decision support systems. This task analysis methodology encompasses (1) event analysis, (2) participant analysis, and (3) decision content analysis. With a proper coding manual, it provides a framework for collecting relevant and detailed information required for decision support design and implementation. Further research is suggested for application and evaluation of the methodology in real-life DSS environments.     

Re-engineering of the design process for concurrent engineering

According to the requirements of concurrent engineering, three interdependency relationships (uncoupled relationship, coupled relationship and decoupled relationship) between design activities are presented in this paper. And the coupled relationship plays an important role in concurrent product design process. To represent the precedence relationships among design activities, a directed graph is used to describe the design process. And the interdependency relationship between activities is illustrated by a Design Structure Matrix which is the transpose of the accessibility matrix of the corresponding graph. Using the DSM, an algorithm of recognizing the coupled activities during the design process is presented. Moreover, an algorithm to figure out the order levels of activities during the design process is proposed. And both algorithms are illustrated with a die design example.     

Knowledge engineering concepts for decision support system design and implementation

The design of computer-based systems that simulate expert human consulting by drawing on large amounts of task-specific knowledge has been a major research activity of applied artificial intelligence over the last ten years. Building decision support systems that incorporate aspects of this research is a promising new field. The purpose of this paper is to discuss concepts of “knowledge engineering” that are most relevant in designing and building knowledge-based decision support systems.     

Human-factors engineering for smart transport: design support for car drivers and train traffic controllers

The theme Smart Transport can be described as adequate human-system symbiosis to realize effective, efficient and human-friendly transport of goods and information. This paper addresses how to attune automation to human (cognitive) capacities (e.g. to take care of information uncertainty, operator trust and mutual man-machine adaptations). An introduction to smart transport is presented, including examples of best practice for engineering human factors in the vehicle ergonomics and train traffic control domain. The examples are representative of an ongoing trend in automation and they show how the human role changes from controller to supervisor. Section 2 focuses on the car driver and systems that support, or sometimes even take over, critical parts of the driving task. Due to the diversity of driver ability, driving context and dependence between driver and context factors, there is a need for personalised, adaptive and integrated support. Systematic research is needed to establish sound systems. Section 3 focuses on the train dispatcher support systems that predict train movements, detect potential conflicts and show the dispatcher the possibilities available to solve the detected problems. Via thorough analysis of both the process to be controlled and the dispatcher's tasks and cognitive needs, support functions were developed as part of an already very complex supervision and control system. The two examples, although from a different field, both show the need for further development in cognitive modelling as well as for the value of sound ergonomics task analysis in design practice.     

A knowledge system for integrated product design

This paper discusses the architecture and implementation issues for a knowledge system to assist in product design. The goals of the concurrent Design Advisor (CODA) are to enhance the quality of designs by 25 percent and the efficiency by a factor of 10. The improvement springs from the integration of diverse knowledge bases, ranging from customer needs to product evaluation, and from process configuration to production control. One source of efficiency is the automation of many routine tasks, thereby increasing user productivity. Another source is the increase in the quality of initial designs, which obviates the need for numerous iterations in the design process due to poor manufacturability. CODA is based on the general architecture of the Creativity Support System, an expert system for assisting users in specific domains requiring creative solutions. The bilevel structure of the system consists of a domain-independent module containing general tools and techniques for creative problem-solving, and a domain-dependent module incorporating knowledge specific to particular fields of application. The utility of this approach is illustrated in the realm of concurrent product design by demonstrating a CODA within the general architecture of the system.     

A knowledge based decision support system for computer performance management

Computer systems managers make decisions about hardware and software selection, performance evaluation, capacity planning, and other resource variables on the basis of factual data, accounting data, subjective judgements, and assumptions about the resource consumption of the jobs being run. The importance of computer resource planning calls for effective support methods. A Knowledge-Based DSS (KBDSS) will be able to assist managers in making these policy decisions by utilizing knowledge of the existing configuration and its capabilities, the organizational computing environment, available external resources, and their suppliers. Combining procedural and declarative methods, such a KBDSS may provide early warning of possible bottlenecks, forecast growth of hardware usage, and employ knowledge based inferencing to suggest suitable remedial actions to the systems manager. This paper presents a KBDSS for supporting computer resource planning decisions using a procedural/declarative framework, and illustrates the system's usage aspects.     

A decision support system for optimizing dynamic courier routing operations

In this paper, we propose a prototype of a decision support system (DSS) that integrates a hybrid neighborhood search algorithm to solve the offline and online routing problems arising in courier service. In the dynamic operational environment of courier service, new customer orders and order cancellations continually arrive over time and thus disrupt the optimal routing schedule that was originally designed. This calls for the real-time re-optimization of routes. As service level is sensitive to whether allowable service time intervals are wide or narrow, it is valuable to study how adjustable and flexible time windows influence the courier service efficiency in a dynamic environment. To capture these dynamic features, a dynamic vehicle routing problem (DVRP) that simultaneously considers new customer orders and order cancellations is investigated in this study. Meanwhile, fuzzy time windows are formulated in the DVRP model to quantify the service level and explore the service efficiency. To tackle the new problem, we propose a competitive hybrid neighborhood search heuristic for (re)optimizing the offline and online routes. Numerical computational experiments and the comparison with results from Lingo show that our algorithm is capable of re-optimizing dynamic problems effectively and accurately in a very short time. The proposed model and algorithms are able to enhance courier service level without further expense of a longer traveling distance or a larger number of couriers.     

Computing agents for decision support systems

In decision support systems, it is essential to get a candidate solution fast, even if it means resorting to an approximation. This constraint introduces a scalability requirement with regard to the kind of heuristics which can be used in such systems. As execution time is bounded, these algorithms need to give better results and scale up with additional computing resources instead of additional time. In this paper, we show how multi-agent systems can fulfil these requirements. We recall as an example the concept of Evolutionary Multi-Agent Systems, which combines evolutionary and agent computing paradigms. We describe several possible implementations and present experimental results demonstrating how additional resources improve the efficacy of such systems.     

Fuzzy methods for case-based recommendation and decision support

The paper proposes two case-based methods for recommending decisions to users on the basis of information stored in a database. In both approaches, fuzzy sets and related (approximate) reasoning techniques are used for modeling user preferences and decision principles in a flexible manner. The first approach, case-based decision making, can principally be seen as a case-based counterpart to classical decision principles well-known from statistical decision theory. The second approach, called case-based elicitation, combines aspects from flexible querying of databases and case-based prediction. Roughly, imagine a user who aims at choosing an optimal alternative among a given set of options. The preferences with respect to these alternatives are formalized in terms of flexible constraints, the expression of which refers to cases stored in a database. As both types of decision support might provide useful tools for recommender systems, we also place the methods in a broader context and discuss the role of fuzzy set theory in some related fields.