Pergamon 0968349(95)00014-3 Loculion S<Qnw, Vol. 3. No. 3. pp. 167-185. 1995 Copyright @ 1996 Elsevier Scmce Ltd Printed in Great Britain. All rights reserved 0966.8349196 $15.00 + 0.00 PROACTIVE OPTIMIZATION OF TOXIC WASTE TRANSPORTATION, LOCATION AND TECHNOLOGY MAX M. WYMAN Center for Advanced Transportation Systems Research, College of Engineering, Arizona State University. Tempe, AZ 85287, U.S.A. and MICHAEL KUBY Department of Geography, Arizona State University, Tempe, AZ 85287-0104, U.S.A. Abstract-Many models of real world problems, such as the toxic waste transportation and location problem, produce solutions that “make the best of a bad situation”. Yet in many cases, giving the model better choices with which to work could produce far superior results. We introduce a framework for proactive optimization, defined as identification of the structural parameters within an OR problem that cause optimal solutions to be less than satisfactory, followed by an exogenous search for better options to add to the model. The proactive methodology is illustrated by a multiobjective, mixed-integer, location-allocation model with technology choice variables. A new technology, solar-driven waste detoxification, is compared with toxic waste incineration on three traditionally conflicting criteria: cost, a new risk measure @g/m3 person hrs), and a new disequity measure (MinMaxSum kg*km). The solar process is found to improve all three objectives considerably. Sensitivity analysis indicates the robustness of the results in terms of cost, risk, and sunlight availability. Keywords: Proactive optimization, multiobjective, technology, toxic waste, location, equity. 1. INTRODUCTION This paper describes a proactive methodology for designing spatial systems, and illustrates its importance through a case study on locating hazardous materials (HAZMAT) facilities. We define proactive optimization as the identification of the structural parameters within an OR problem that cause optimal solutions to be less than satisfactory, followed by an exogenous search for better options to add to the model (Wyman and Kuby, 1995). This process is a fundamental improvement over the traditional process of quantitative analysis defined in most basic MS/OR textbooks, in which a problem is defined, translated into a mathematical model, validated, and solved, as shown in the dashed box in Fig. 1 (Anderson et al., 1985). We contend that, at least in the HAZMAT field, the traditional process has failed to provide good solutions because the feedback loop does not go back far enough. If the optimal solution found according to the traditional process falls short, the modeling team should proactively question the problem’s given initial conditions, return to the basic science, and identify potentially better technologies, as illustrated in Fig. 1. The methodology can be applied at three distinct levels of proactiveness. First, existing technologies may be compared endogenously using decision models that integrate the complete system. Second, unproven laboratory technologies can be modeled to see how they would perform in a real spatial system, as in this study. Third, proactive optimization 167