Bernard T. Han

Objective quality ranking of computing journals

Citation analysis, which provides a clear picture of actual use of journals and their articles, is an effective way to determine a journals influence.

Are European IS journals under-rated? an answer based on citation analysis

A concern that the quality of European IS journals has been under-rated by existing studies was recently addressed in the IS community. In this study, an objective method based on a Citation Analysis was employed to re-examine the quality of four established European IS journals as compared to 11 other well-recognized IS journals. Extensive citation data were collected to derive eight indices as the measures of journal quality. Our research results indicate that the quality of the four leading European IS journals is quite comparable to that of other well-known IS journals reported in prior studies.

Is information systems a reference discipline

The frequent citations to IS research suggest its role in advancing science.

Multiple-type, two-dimensional bin packing problems: Applications and algorithms

In this paper we consider a class of bin selection and packing problems (BPP) in which potential bins are of various types, have two resource constraints, and the resource requirement for each object differs for each bin type. The problem is to select bins and assign the objects to bins so as to minimize the sum of bin costs while meeting the two resource constraints. This problem represents an extension of the classical two-dimensional BPP in which bins are homogeneous. Typical applications of this research include computer storage device selection with file assignment, robot selection with work station assignment, and computer processor selection with task assignment. Three solution algorithms have been developed and tested: a simple greedy heuristic, a method based onsimulated annealing (SA) and an exact algorithm based onColumn Generation with Branch and Bound (CG). An LP-based method for generating tight lower bounds was also developed (LB). Several hundred test problems based on computer storage device selection and file assignment were generated and solved. The heuristic solved problems up to 100 objects in less than a second; average solution value was within about 3% of the optimum. SA improved solutions to an average gap of less than 1% but a significant increase in computing time. LB produced average lower bounds within 3% of optimum within a few seconds. CG is practical for small to moderately-sized problems — possibly as many as 50 objects.

A dynamic programming-based particle swarm optimization algorithm for an inventory management problem under uncertainty

This article presents a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm for solving an inventory management problem for large-scale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multi-objective fuzzy random dynamic programming model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform fuzzy random parameters into fuzzy variables that are subsequently defuzzified by using an expected value operator with optimistic–pessimistic index. The iterative nature of the authors’ model motivates them to develop a DP-based PSO algorithm. More specifically, their approach treats the state variables as hidden parameters. This in turn eliminates many redundant feasibility checks during initialization and particle updates at each iteration. Results and sensitivity analysis are presented to highlight the performance of the authors’ optimization method, which is very effective as compared to the standard PSO algorithm.

Optimal robot selection and workstation assignment for a CIM system

In this paper, a mathematical program and solution algorithm is developed for optimal robot selection and workstation assignment for a CIM system. In specific, our model considers selection of a proper mix of multiple-type robots such that operational requirements from a given number of work- stations are satisfied at minimal system cost. Each robot is characterized by its fixed charge, and subject to limits on machine time and work envelope. Each workstation has known demands on both robot machine time and work space. The model is formulated as a pure 0-1 mathematical program and is shown harder than two-dimensional bin packing, a well-known NP-hard problem. A three-phase optimization algorithm is implemented and tested by solving 450 randomly generated problems. Computational results indicate the solution algorithm is effective in solving problems of a practical size (i.e., 50 workstations and a maximum of 20 robots) within acceptable computational times. >

Moving towards efficient, safe, and meaningful healthcare: issues for automation.

Correcting problems in healthcare delivery has become a top priority in the USA due to skyrocketing insurance costs and fast growth in national healthcare spending. This paper is aimed at examining how Health Information Technology (HIT) could be used to help the USA move towards efficient, safe, and meaningful healthcare. Our study is focused on healthcare needs in three areas: documentation, standardisation, and design and implementation. Urgent research issues in each area are presented. Our findings maintain that unique needs in the diverse healthcare environments must be incorporated into the convergent HIT system.

An algorithm for storage device selection and file assignment

Abstract Newly available write-once/read-many (WORM) optical storage devices provide the opportunity for storing massive amounts of data online at very low cost. However, the slow random access time of the WORM and its write-once limitation has, in general, restricted its application to archival storage and static files. In this paper we analyze a hybrid storage system which uses a combination of conventional magnetic disks and optical devices for management of files which have moderate volatility. The hybrid system appears to be particularly attractive for databases used for decision support. Cost models and solution algorithms are developed which determine a near-optimum database ‘storage plan’. A storage plan specifies the selection of device types and the assignment of files to devices. The solution approach uses a dynamic programming-based heuristic to obtain an initial solution followed by a set-covering algorithm which employs column generation to both search for an improved solution and to provide a tight lower bound. Computational results indicate that the method produces solutions with an average suboptimality of less than 1 percent. More importantly, this research demonstrates that a storage plan which uses a mix of device types can provide costs savings of up to 70% over a storage plan which is limited to conventional magnetic devices.

An Efficient Heuristic for Solving an Extended Capacitated Concentrator Location Problem

In this paper, a mathematical model and a solution algorithm are developed for solving an extended capacitated concentrator location problem. Our model extends the conventional formulation by simultaneously addressing the two capacity constraints, total connection ports and maximum data processing rate, on each concentrator to be selected for satisfying the communication demands of the given end-user nodes. Since the problem is NP-complete, an efficient and effective Lagrangian heuristic is developed and tested by solving 100 randomly generated test problems with sizes ranging from 30(nodes)×30(concentrators) to150×30. Altogether 58% of the tested problems are solved optimally with an average solution gap 0.36% from the optimality and average solution times are from a few seconds to one half of a minute.

An optimization model to determine appointment scheduling window for an outpatient clinic with patient no-shows

This paper investigates appointment scheduling for an outpatient department in West China Hospital (WCH), one of the largest single point of access hospitals in the world. Our pilot data analysis shows that the appointment system at WCH can be improved through leveraging the scheduling window (i.e., the number of days in advance a patient makes an appointment for future services). To gain full insight into this strategy, our study considers two cases, based on if patients are willing to wait for scheduled appointments or not. We developed a stylized single server queueing model to find optimal scheduling windows. Results show that, when patients are less sensitive to time delay (i.e., patients will wait for scheduled services), levering scheduling windows is not effective to minimize the total cost per day of the appointment system. In contrast, when patients are sensitive to time delay (i.e., patients may find services elsewhere), then our model considers the potential cost of physician idle time. The modeling results indicate that the total cost per day is relatively sensitive to the magnitude of scheduling window. Thus, adopting a proper scheduling window is very important. In addition, our study proves that the cost functions of both cases are quasi-concave, which are also validated by actual data drawn from the Healthcare Information System at WCH. A comparison of numerical results between two cases is made to draw further managerial insights into scheduling policies for WCH. Discussion of our findings and research limitations are also provided.