Development of a decision support system for supplier evaluation and order allocation

This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.     

An M-ary detection approach for asset allocation

We develop a continuous-time asset allocation model which incorporates both model uncertainty and structural changes in economic conditions. A “dynamic” M-ary detection framework for a continuous-time hidden Markov chain partially observed in a Gaussian process is used to model the price dynamics of the risky asset and the hidden states of an economy. The goal of an investor is to select an optimal asset portfolio mix so as to maximize the expected utility of terminal wealth. Filtering theory is used first to turn the problem into one with complete observations and then to derive M-ary detection filters for the hidden system. The Hamilton-Jacobi-Bellman dynamic programming approach is used to solve the asset allocation problem with complete observations. An explicit solution is obtained for the power utility case.     

Strategic asset allocation and market timing: a reinforcement learning approach

We apply the recurrent reinforcement learning method of Moody, Wu, Liao, and Saffell (1998) in the context of the strategic asset allocation computed for sample data from US, UK, Germany, and Japan. It is found that the optimal asset allocation deviates substantially from the fixed-mix rule. The investor actively times the market and he is able to outperform it consistently over the almost two decades we analyze.     

企业固定资产信息化管理体系

核电企业具有高投入、高产出的特点。高投入转化为价值高、数量大、种类多的固定资产,高产出的前提是机组的安全运行,这就要求对企业已有的固定资产进行更新改造,同时不断扩大对新的固定资产的投入。因此,如何对固定资产进行有效地管理和控制,就成为大亚湾核电站保证安全运行、提高经济和管理效益的重要手段之一。对于固定资产管理体系的重要组成部分——生产离线固定资产的信息系统的开发和实施也成为企业固定资产信息化管理体系的重要内容之一。     

基于RFID远程识别技术的涉密资产进出管理系统研究

针对当前涉密资产管理系统中存在的效率低下、资产被遮挡导致不能准确监测、难以进行进出门判断等问题,设计了一种基于RFID技术的涉密资产进出监控管理系统。该系统利用2.4 GHz RFID远程识别技术对涉密资产进行实时监测,以准确判断资产动向,防止涉密资产非法流出。通过测试表明,该系统能够有效监管涉密资产的出入,比传统涉密资产管理方式更加准确,可提高系统工作效率,并具有较高的可靠性和实用性。     

A GEP-based spatial decision support system for multisite land use allocation

Multisite Land Use Allocation Problem (MLUA) refers to the problem of allocating more than one land use type in an area. MLUA problem is one of the truly NP Complete (combinatorial optimization) problems. To cope with this type of problems, intelligent techniques such as genetic algorithms, and simulated annealing, have been used. Research in the area of Spatial Decision Support Systems (SDSS) for resource allocation issues, a new scientific area of information system applications developed to support semi-structured or unstructured spatial decisions, has recently generated attention for integrating Artificial Intelligence (AI) techniques with Geographic Information Systems (GIS). In this paper we demonstrate how GIS can be integrated with Gene Expression Programming (GEP), a recently developed AI approach, for solving MLUA problems. The feasibility of the proposed approach in solving MLUA problems was checked using a fictive case study. The results indicated that the proposed approach gives good and satisfactory results.     

Modelling and asset allocation for financial markets based on a stochastic volatility microstructure model

On the basis of the market microstructure theory, a continuous time microstructure model is proposed for describing the dynamics of financial markets with stochastic volatility property. From the microstructure model, one may obtain the estimates of two state variables, which represent the market excess demand and liquidity respectively but cannot be directly observed. Based on the indirectly obtained excess demand information instead of the prediction of price, a simple asset dynamic allocation approach is investigated. The local linearization method, nonlinear Kalman filter and maximum likelihood method-based estimation approach for the microstructure model proposed is presented. Case studies on the financial markets modelling and the estimated model-based asset dynamic allocation control for the JPY/USD (Japanese Yen/US Dollar) exchange rate and Japan TOPIX (Tokyo stock Price IndeX) show a satisfactory modelling precision and dynamic allocation performance.     

An intelligent agent-based architecture for strategic information system applications

Strategic information systems (SIS) focus on the use of information system (IS) and information technology (IT) in the strategic management process in business organizations. The emphasis is on the strategic view of IS and IT and their impact on organizational strategy. Increased competition and advances in information technologies push for considerable structural changes in SIS. Agents, as autonomous entities which either work on their own or cooperate with others, and agent architectures have enormous potentials to be applied in such critical systems. In this article, first we investigate the very fundamental concepts of strategic information systems and intelligent agent technology. Then, the discussion continues on the specification of the characteristics and implementation issues of a typical SIS. Afterwards, we make use of these concepts and integrate them into a state-of-the-art, intelligent architecture for strategic information systems, called intelligent agent-based SIS. This is a comprehensive framework for a SIS in IT era which may be put into practice by a team of professionals in the near future. The graphical representation of this model is intended to help the reader understand the concept much better. After explaining the suggested model in full details, we introduce some support agents and specify their corresponding roles in an intelligent agent-based SIS architecture. Discussions and concluding remarks regarding the proposed system are provided at the end of the paper.     

A neural networks-based approach for strategic planning

We establish a systematic approach that incorporates neural networks in conjunction with portfolio matrices to assist managers in evaluating and forming strategic plans. Based on the principle of dispersing risks, we also provide a linear integer programming model, which helps in allocating the annual budget optimally among proposed strategies. The approach has been successfully implemented for a major food industry leader in Taiwan for its annual strategic planning. Although a particular portfolio matrix model was adopted in our approach, the framework proposed here can be modified to incorporate other strategy-evaluation measures.     

Storage space allocation to maximize inter-replenishment times

Given a set of storage spaces and a set of products, with specific space requirements and demand rates, we find the optimal product assignment that will lead to the longest time before any product is depleted. When demand rates are known with certainty, the assignment is found through the solution of a max–min integer program. When demand rates are stochastic with a common law, the assignment is found by solving an integer programme the objective of which is a non-homogeneous partial difference equation of first order. Unlike what common rules of thumb would dictate, our results show that the proportion of spaces assigned to a product is not necessarily equal to its demand frequency. Numerical computations show that employing the latter policy would result in inter-replenishment times that are about 24% shorter than the optimal solution.     

A hybrid approach to asset allocation with simultaneous consideration of suitability and optimality

In this paper, we develop mathematical models for simultaneous consideration of suitability and optimality in asset allocation. We use a hybrid approach that combines behavior survey, cluster analysis, analytical hierarchy process and fuzzy mathematical programming.     

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

One of the major design problems in the context of manufacturing systems is the well-known Buffer Allocation Problem (BAP). This problem arises from the cost involved in terms of space requirements on the production floor and the need to keep in mind the decoupling impact of buffers in increasing the throughput of the line. Production line designers often need to solve the Buffer Allocation Problem (BAP), but this can be difficult, especially for large production lines, because the task is currently highly time consuming. Designers would be interested in a tool that would rapidly provide the solution to the BAP, even if only a near optimal solution is found, especially when they have to make their decisions at an operational level (e.g. hours). For decisions at a strategic level (e.g. years), such a tool would provide preliminary results that would be useful, before attempting to find the optimal solution with a specific search algorithm.     

A simulation-based risk network model for decision support in project risk management

This paper presents a decision support system (DSS) for the modeling and management of project risks and risk interactions. This is a crucial activity in project management, as projects are facing a growing complexity with higher uncertainties and tighter constraints. Existing classical methods have limitations for modeling the complexity of project risks. For example, some phenomena like chain reactions and loops are not properly taken into account. This will influence the effectiveness of decisions for risk response planning and will lead to unexpected and undesired behavior in the project. Based on the concepts of DSS and the classical steps of project risk management, we develop an integrated DSS framework including the identification, assessment and analysis of the risk network. In the network, the nodes are the risks and the edges represent the cause and effect potential interactions between risks. The proposed simulation-based model makes it possible to re-evaluate risks and their priorities, to suggest and test mitigation actions, and then to support project manager in making decisions regarding risk response actions. An example of application is provided to illustrate the utility of the model.     

A decision support system for the selection of computer-integrated manufacturing technologies

The adoption of computer-integrated manufacturing (CIM) offers manufacturing organizations many tangible and intangible benefits which enable them to produce products of high quality at low costs. However, the selection and evaluation of CIM is a complex process as it involves the consideration of many parameters to ensure that the selected technology meets the requirements of individual companies. This paper describes the development of a quantitative/qualitative decision support system for the evaluation of CIM which takes into consideration the objectives and operating characteristics of a company, thus ensuring that the selected technology matches the individual needs of that company. The methodology used in the decision support system is based on a combination of the analytical hierarchy process (AHP) and database technology. The AHP provides a means to consider both the tangible and the intangible benefits of CIM while databases are used to store the knowledge about the various benefits that CIM may offer. The system has been implemented in EXCEL, which fully automates the evaluation process. A case study is also presented to illustrate the capability of the proposed decision support system.     

A strategic decision support system for supply network design and management in the semiconductor industry

This paper presents a strategic decision support system (DSS) which has been conceptualized and designed by SEMATECH^* to assist the large semiconductor manufacturing organization in managing its extensive supply chain network. This DSS has been named “Manufacturing Enterprise Model” or “MEM”. MEM ties each factory and its primary metrics to the rest of the business enterprise to assess how changes in wafer fabrication affect other factories, the distribution system, and customer deliveries. The model is intended to be used to evaluate future factory concepts and to assist business planners in strategic decisions about product allocation and major resource/facility planning.     

A decision support system for cyclic master surgery scheduling with multiple objectives

This paper presents a decision support system for cyclic master surgery scheduling and describes the results of an extensive case study applied in a medium-sized Belgian hospital. Three objectives are taken into account when building the master surgery schedule. First of all, the resulting bed occupancy at the hospitalization units should be leveled as much as possible. Second, a particular operating room is best allocated exclusively to one group of surgeons having the same speciality; i.e., operating rooms should be shared as little as possible between different surgeon groups. Third, the master surgery schedule is preferred to be as simple and repetitive as possible, with few changes from week to week. The system relies on mixed integer programming techniques involving the solution of multi-objective linear and quadratic optimization problems, and on a simulated annealing metaheuristic.     

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 strategic classification support system for brownfield redevelopment

Brownfield redevelopment (BR) is an ongoing issue for governments, communities, and consultants around the world. It is also an increasingly popular research topic in several academic fields. Strategic decision support that is now available for BR is surveyed and assessed. Then a dominance-based rough-set approach is developed and used to classify cities facing BR issues according to the level of two characteristics, BR effectiveness and BR future needs. The data for the classification are based on the widely available results of a survey of US cities. The unique features of the method are its reduced requirement for preference information, its ability to handle missing information effectively, and the easily understood linguistic decision rules that it generates, based on a training classification provided by experts. The resulting classification should be a valuable aid to cities and governments as they plan their BR projects and budgets.     

基于物联网技术的高校资产管理系统设计

现代高校教学科研设备繁多、存放地点分散、管理人员多,传统条形码编码的资产管理方式已经很难适应管理需要。为了提高资产管理的效率,保护资产的安全与完整,防止资产流失,提出了一个在高校资产管理中采用物联网技术的总体解决方案,并给出了其中主要的软硬件的具体设计方法。实际运行效果显示,该系统可以提高高校资产管理的工作效率。