Joanna R. Baker

Scheduling parallel processors: An integer linear programming based heuristic for minimizing setup time

The problem of scheduling parallel processors in a make-to-stock environment with sequence setup costs is considered. A new algorithm which formulates a series of 0-1 integer sub problems is proposed and contrasted with an earlier formulation (Dearing and Henderson 1982,1984). Parallels between the sub problem formulations and generalized networks are discussed. The efficiency and quality of the solutions provided were tested using previously published data for a loom assignment problem. The heuristic solution was evaluated against the optimal integer linear programming (ILP) solution, and a rounded linear program (LP) approximation to the optimal solution for several sample problems. Results indicate that the heuristic is efficient, provides near optimal solutions to production planning problems and requires significantly less computing capability than previously reported LP, TLP approaches.

The impact of recidivism and capacity on prison populations

Prison crowding currently poses a serious problem for society. This problem is attributable to a failure to anticipate and plan for the increased numbers of individuals sentenced to prison over the last decade. Crowded prisons have forced many jurisdictions to release prisoners earlier than would have been the case with unlimited prison capacity and to initiate expensive prison construction programs. In this paper, we develop a prison population projection model that extends previous work by considering the impact of limited prison capacity on time served, releases, and future admissions. The model was demonstrated for the State of North Carolina. Results suggest the tradeoffs that exist between prison capacity and punitiveness as measured by time served in prison.

Constructing optimal drug-testing plans using a Bayesian acceptance sampling model

Drug testing has become an accepted strategy for controlling drug use, particularly among individuals in the custody of the criminal justice system. Emphasis has been placed on testing those free in the community, either on pretrial release, probation, or parole. The drug-testing strategies applied to these populations-whom and how often to test-have evolved largely on an ad hoc basis. In this paper, we investigate optimal (cost-minimizing) drug-testing strategies as a means of achieving the efficient allocation of scarce resources to meet agency goals and objectives. We propose an analytic model based on individual decision theory and Bayesian acceptance sampling and apply the model to a hypothetical criminal justice population in which drug use is presumed to be highly prevalent.

Redesign of primary response areas for county ambulance services

Abstract This paper describes a modeling approach used to redesign primary response areas (sectors) for an existing county ambulance service. The model employs multicriteria analysis of the problem including goals to minimize and balance travel times and balance workloads among ambulance service units. The model is quite flexible in that optimal designs can be made subject to budgetary and personnel constraints and sector continuousness is maintained. Given inputs such as the location of the ambulance bases and demographic information, the model generates efficient and equitable designs. The model is solved using a modified multicriteria gradient search technique for nonlinear models. The sensitivity of the model to reordering planning priorities is also evaluated.

A problem identification taxonomy for classification and automated formulation of multiple objective linear programming models

This paper presents a knowledge-based Decision Support System (DSS) for classification, formulation and solution of multiple objective linear programming problems. The authors propose a generic taxonomy which is used to classify commonly encountered types of linear programming problems. Classification of the problem, vis-à-vis the proposed taxonomy, is based on the interaction with the user to determine the attributes which best describe the context or setting of the problem. A total of twenty-four problem types are included in the taxonomy. Following classification, a problem type-specific rule base is invoked to assist the user in constraint formulation. A product blending linear programming problem is used to demonstrate this component of the system since these types of problems typically include more varied constraints, including ratio as well as additive types. A second rule base is invoked for formulation of the multiple criteria objective function, model solution and sensitivity analysis. An initial goal prioritization scheme is obtained by use of the Analytical Hierarchy Process [17]; the optimal goal ordering is obtained by interaction with the user in the form of pairwise attribute value tradeoffs. The system developed is intended for use by the OR-naive user who is more familiar with the content of a problem than he/she is with the mathematical tools needed to formulate and solve such models. The system is a model management tool designed to interpret user inputs and translate those inputs into a solvable multiple objective LP. This interface alleviates the technical burdens of content specification and solution. The approach expands previous formulation tools, such as those based on natural language processing [8], to a broader range of problem types in a multiple criteria environment. The system was implemented on a personal computer using VP-Expert, BASIC and LINDO, and is demonstrated on a multiple objective blending problem. The ability of the approach to accurately classify LP problems was tested on thirty-six subjects. Results suggest that correct classification of problems was more likely to occur when the system was used.

A long-range planning model for large capital expenditures: An application to ambulance allocation

Abstract A nonlinear programming model for ambulance addition is proposed and demonstrated. The model is appropriate for use in long-range planning for urban regions where fixed and operating costs for facilities are high and a minimal service level criterion must be met. The approach is unique in that it allows planners to “time phase” ambulance allocation over a 5–10 yr planning horizon while explicitly considering total cost and minimal service level criteria. The model is solved using a PASCAL program which runs on a microcomputer. The simplicity of the formulation and the solution technique are unique among previously reported ambulance planning/allocation models. The utility and flexibility of the model for emergence medical service (EMS) planners is demonstrated using some typical planning scenarios. The results show the model to be an efficient, flexible tool for long-range planning.