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.     

Stochastic skill-based manpower allocation in a cellular manufacturing system

In this paper, stochastic skill-based manpower allocation problem is addressed, where operation times and customer demand are uncertain. A four-phased hierarchical methodology is developed. Egilmez and Süer's [1] stochastic general manpower allocation problem is extended such that each worker's individual performance is considered for a more accurate manpower allocation to manufacturing cells to maximize the production rate. The proposed methodology optimized the manpower levels, product-cell formations and individual worker assignment hierarchically with respect to a specified risk level. Three stochastic nonlinear mathematical models were developed to deal with manpower level determination, cell loading and individual worker assignment phases. In all models, processing times and demand were assumed to be normally distributed. Firstly, alternative configurations were generated. Secondly, IID sampling and statistical analysis were utilized to convert probabilistic demand into probabilistic capacity requirements. Thirdly, stochastic manpower allocation was performed and products were loaded to cells. In the final phase, individual worker assignments were performed. The proposed methodology was illustrated with an example problem drawn from a real manufacturing company. The hierarchical approach allows decision makers to perform manpower level determination, cell loading and individual worker assignment with respect to the desired risk level. The main contribution of this approach is that each worker's expected and standard deviation of processing time on each operation is considered individually to optimize the manpower assignment to cells and maximize the manufacturing system production rate within a hierarchical robust optimization approach.     

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。     

On capacity allocation for operating rooms

This paper studies capacity planning decisions that allocate surgical specialties to operating-room (OR) days with the objective of minimizing total expected costs due to penalties for any patients who are not accommodated and for under- (i.e., idleness) and over- (i.e., overtime) usage of OR capacity. It presents a prototypical non-linear, stochastic programming model to structure relevant and practical features of the problem and four adaptations, along with associated solution approaches, with the goal of facilitating solution by overcoming the computational disadvantages of the prototype. Each of these adaptations offers advantages but is also attended by disadvantages. Computational tests compare the four adaptations and solution approaches with respect to solution quality and run time.     

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.     

Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions

Large sized transformers are an important part of global power systems and industrial infrastructures. An unexpected failure of a power transformer can cause severe production damage and significant loss throughput the power grid. In order to prevent power facilities from malfunctions and breakdowns, the development of real-time monitoring and fault prediction tools are of great interests to both researches and practitioners. This research develops an intelligent engineering asset management system for power transformer maintenance. The system performs real-time monitoring of key parameters and uses data mining and fault prediction models to detect transformers’ potential failure under various operating conditions. Principal component analysis (PCA) and a back-propagation artificial neural network (BP-ANN) are the algorithms adopted for the prediction model. Historical industrial power transformer data from Taiwan and Australia are used to train and test the failure prediction models and to verify the proposed general methodology as comparative case studies. The PCA algorithm reduces the number of the primary dissolved gasses as the key factor values for BP-ANN prediction modeling inputs. The system yields effective predictions when verified using various operating condition data from Australia and Taiwan power companies. The accuracy rates are much higher when compared to the fault prediction results without using PCA. The intelligent system combining PCA and BP-ANN algorithms, developed in this research, can be adopted by asset managers in different regions to develop suitable maintenance and repair strategies for transformer failure preventions.     

Microcomputer based multiple criteria decision support system for strategic planning

There are two major approaches currently used for developing Decision support Systems (DSS) for strategic planning, especially in the objective formulation stage. Several mathematical models have been developed to abstract the decision situation. However, they do not take into account either behavioral aspects of decision making or the presence of multiple and conflicting objectives. A second approach is to consider the several qualitative factors that go into decision making; such considerations are normally situation-dependent and hence it is difficult to provide a system for general managerial situations.The Multiple Criteria Decision Making (MCDM) approach combines the advantages of both the approaches and, therefore, is an excellent alternative for designing DSS. This paper develops an MCDM approach to strategic planning. The model is applied to such a problem in a simulated environment and the problem is solved interactively. Our experience shows that the proposed methodology is a viable approach for solving practical decision problems in strategic planning.     

Multiagent meta-model for strategic decision support

This research describes a meta-model which can generate multiagents system (MAS) of strategic decision support. These MAS profit from new concepts of bargaining with learning to determine the cooperation between agents. They answer an insufficiency of the concept of bargaining in the game theory which is proved in this article. The original framework of the meta-model is characterized by three essential functions provided with a technique of learning: the search for typical plan of decisions adapted to the problem and the search for the coalitions in the spatial and temporal dimension. It allows an adaptation to all the types of strategic decision support.     

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.     

Multi-objective optimization models for patient allocation during a pandemic influenza outbreak

Pandemic influenza has been an important public health concern with several historical outbreaks in 1918, 1957, and 1968. During an influenza pandemic outbreak, hospitals are often overwhelmed by the surge demand of influenza patients. It is important to prepare response plans to react to a pandemic influenza outbreak. Because of the widespread effect of the disease and the increased demand on limited medical resources, collaboration among hospitals both in planning and in response is necessary. This paper focuses on patient and resource allocation among hospitals in a healthcare network. Mathematical models are built to optimize the patient allocation considering two objectives related to patients’ cost of access to healthcare services: (1) minimization of the total travel distance by patients to hospitals; and (2) minimizing the maximum distance a patient travels to a hospital. Moreover, the models help to predict a resource shortage during an outbreak; this prediction will alert decision makers to consider increasing the medical capacity or requesting additional capacity from state or national agencies. In addition, the model aids in the determination of the optimal allocation of the additional resources, when available, among hospitals by considering the above two objectives related to patients’ cost of access to services. A case study from Metro Louisville, Kentucky, is presented to demonstrate how the models would aid in patient allocation during a pandemic influenza outbreak.     

A network flow model for the dynamic and flexible berth allocation problem

This paper presents a new model for the dynamic berth allocation problem (BAP). The model is developed using a berth-flow network modeling approach and is formulated as an integer multi-commodity network flow problem. In addition, an innovative flexible berth-space utilization scheme, based on blocking plans, is incorporated into the proposed model. This is referred to as the dynamic (vessel arrivals) and flexible (berth space) BAP model (or DFBAP), and is designed to better utilize wharf space in a container port. Computational experiments conducted on an instance generated using actual data show that the DFBAP model is more effective and efficient than the method currently used by port authorities. A set of scenario analyses is also performed to obtain insights into important model parameters.