Parviz Ghandforoush

A DSS to manage platelet production supply chain for regional blood centers

This paper presents a prototype decision support system for platelet production and blood mobile scheduling for a regional blood center. Unlike whole blood cells, platelets have a very short shelf life, which requires matching demand and supply closely. This is achieved by an efficient supply chain DSS that is optimized for delivery of platelets from production centers to transfusion centers, typically hospitals. One of the critical elements of the DSS is an embedded non-convex integer optimization model that assists the regional blood center manager to schedule the shuttle transportation of whole blood from collection sites to the regional processing center. The proposed non-convex integer model is transformed to a linear 0-1 problem using a two-step conversion process. The transformed model is successfully solved and the optimal solution is reached for the test data. An application of the integrated DSS using data from a regional blood center is described. The results suggest that the proposed DSS better meets the daily demand by producing a superior production plan and mobile assignment schedule.

A multi-criteria decision model for the selection of a computerized manufacturing control system

The purpose of this paper is to describe and demonstrate a quantitative procedure for evaluating alternative computerized manufacturing control software packages that are being considered for possible purchase. The procedure considers both quantitative factors (i.e. costs) and qualitative factors (i.e. vendor reputation) that should be considered in the evaluation process. The various evaluation factors and criteria are identified and a procedure for quantifying the factors in commensurable unite is described. The procedure is demonstrated via an example.

Improving prediction of neural networks: a study of tow financial prediction tasks

Neural networks are excellent mapping tools for complex financial data. Their mapping capabilities however do not always result in good generalizability for financial prediction models. Increasing the number of nodes and hidden layers in a neural network model produces better mapping of the data since the number of parameters available to the model increases. This is determinal to generalizabilitiy of the model since the model memorizes idiosyncratic patterns in the data. A neural network model can be expected to be more generalizable if the model architecture is made less complex by using fewer input nodes. In this study we simplify the neural network by eliminating input nodes that have the least contribution to the prediction of a desired outcome. We also provide a theoretical relationship of the sensitivity of output variables to the input variables under certain conditions. This research initiates an effort in identifying methods that would improve the generalizability of neural networks in financial prediction tasks by using mergers and bankruptcy models. The result indicates that incorporating more variables that appear relevant in a model does not necessarily improve prediction performance.

A Surrogate cutting plane algorithm for all-integer programming

Abstract In this paper we present an all-integer cutting plane technique called the surrogate cutting plane algorithm (SCPA), for solving the all-integer (otherwise linear) programming problem. We develop and solve a smaller surrogate problem based on the solution of the LP relaxation, and thereby speed convergence to the optimal solution of the original problem. We exhibit the operation of the SCPA on three small example problems, and discuss computational results on a set of standard test problems.

Optimal allocation of time in a hospital pharmacy using goal programming

Abstract This paper presents a preemptive goal programming model for optimal allocation of personnel time among various tasks in a hospital pharmacy. The model assigns priorities based on daily time intervals, and ensures that the incomplete tasks belonging to the time interval under consideration are fully satisfied in the next interval. We apply the goal programming model to a situation involving a large hospital pharmacy and make a recommendation regarding its staffing requirements.

Microcomputer simulation systems

Abstract This study provides information as to general nature, hardware requirements, price and range of applications of approximately 20 simulation software packages. In addition, more detailed information on the use, documentation, etc., of GPSS/PC, SIMAN, PC SIMSCRIPT II.5 and SLAM II PC is included.

A model for the evaluation of computer software packages

Abstract The tremendous increase in recent years in the demand for, and availability of, computer software packages, has led researchers to investigate better ways of evaluating them for selection. The selection of software packages is confounded because criteria for evaluation are dependent on the system type. For example, decision support systems should be judged largely on the basis of subjective criteria, while transaction processing systems can be evaluated primarily on quantifiable factors. We discuss the use of a well-known model used in site selection, as a means to evaluate software packages according to subjective or objective factors. We illustrate application of the model with a small example.

A Heuristic Algorithm for the Guillotine Constrained Cutting Stock Problem

This paper presents a heuristic algorithm, programmed in PROLOG (a rule-based programming language) and run on a personal computer with 4.77 MHz CPU, for determining the packing of identical rectangles (boxes) onto a larger rectangle (pallet) such that the amount of waste is minimized. The algorithm has the additional property of producing solutions which satisfy the guillotine cut constraint. The rule-based approach takes advantage of the recursive and backtracking properties to expedite the matching operations. The algorithm is tested using 7500 randomly generated test cases and is compared to other cutting stock algorithms. Test results for one set of problems indicate that the rule-based method generates layout solutions that are within one box of the optimal solution 85% of the time. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Heuristic sensitivity analysis in a combinatoric environment: An exposition and case study

Abstract Sensitivity orpost-optimality analysis investigates the effect of parametric changes on heuristic robustness and solution quality. This approach is relatively unexplored for combinatorial optimization problems, and yet is of considerable interest in analyzing performance characteristics of heuristic approaches. The purpose of this paper is to: (1) develop the semantics and rationale of parametric analysis within the combinatoric environment; (2) present as an example the design and implementation of sensitivity analysis procedures for a newly developed heuristic — theVariable-Depth-Search Heuristic (VDSH) — to solve the Generalized Assignment Problem (GAP). The concepts and methodology discussed in this paper may as well be applied to other heuristics, or in developing a heuristic sensitivity analysis procedure for a large-scale optimization method.

Solving allocation and scheduling problems inherent in forest resource management using mixed-integer programming

Abstract The simultaneous choice of land-use allocation and production scheduling on forest lands is a complex management problem that is best addressed using integer programming. This paper presents the general form of the allocation and scheduling problem, provides two empirical examples that reflect two distinct problem types and presents strategies for increasing the efficiency with which one can solve largerscale empirical problems. The mixed-integer linear programming techniques presented in this paper capitalize on the special structure of each problem type and explore the possibility of reducing the solution effort. Inherent in type one problems is a distinct relationship among the allocation variables that can be exploited to help reduce the number of integer variables as well.as the allocation constraints. For problems of type two, however, there exists a set of multiple choice allocation constraints which can also be exploited for better solution performance.