The writers express their great appreciation to the authors for comprehensive presentation of the complex issue of evaluating multiobjective programing techniques. The purpose of this comment is to discuss several human factor problems related to multiobjective interactive programing approaches that were not considered in the authors' paper. CONTINUOUS OR DISCRETEThe authors made a crucial assumption for the evaluation that 'water resource problems... are usually characterized by an infinite number of possible alternatives .... '[Cohon and Marks, 1975, p.• 215]. In fact, real world planning problems may be discrete as well as continuous. The continuous representations are often used in sizing and scheduling the operation of systems with known structure; but the discrete approach is almost 'classical' in structuring large systems, where the number of possible alternatives is almost always finite. Then, geography and orography allow only a small discrete

Reliability in water supply reservoir operation is commonly thought of as the probability of failing to achieve some target release. Here we explore two additional proposed descriptions of reservoir performance: the maximum shortfall from the target (system vulnerability) and the maximum number of consecutive periods of deficit during a record (system resilience). The larger the maximum shortfall, the greater the vulnerability. The shorter the maximum length of deficits, the more resilient the system. Using multiobjective mixed‐integer, linear programming, the tradeoffs between reliability, vulnerability, and resilience are examined. It is found that as reliability is increased or as the maximum length of consecutive shortfalls decreases (resilience increases), the vulnerability of the water system to larger deficits increases.

The development of a system of storage facilities for spent fuel rods from commercial nuclear reactors requires solution of a model which simultaneously sites the storage facilities, assigns reactors to those facilities and chooses routes for the shipment of the spent fuel. The problem is multiobjective in its transportation component because shipment is made under two criteria: minimum transportation burden and minimum perceived risk. We blend methods of shortest paths, a zero-one mathematical program for siting, and the weighting method of multiobjective programming to show how to derive optimal solutions to this problem. Applications of the methodology demonstrate how transportation burden and risk influence location decisions and the dual role of siting/routing models in transportation policy analysis. The model is a prototype for the shipment and storage of any hazardous waste whose characteristics make the process of shipment itself an activity with risk.

No abstract

Animal foraging may be influenced by multiple demands simultaneously (eg., nutrient gain and predator avoidance). Conventional approaches to understand the trade-ofii between these demands require cramming them in ffmilar currencies, which is impractical in many field situations. We introduce a new method, called multiobjective programming, as a framework to explore how animals balance conflicting demands. Multiobjective programming allows one to explore die influence of foraging demands directly, without explicit assumptions about how they enter into fitness and without conversion to some common currency. Using multiobjective programming, we show that, as foraging demands change, animait may adaptivety adjust their behavior, even if the constraints on feasible behavior are unaffected (contrary to die predictions of the conventional models). Hence, we may see a variable response in foraging that is consistent with adaptive behavior. We used an empirical test with herbivore grasshoppers and predator spiders to evaluate die utility of multiobjective programming Our experiments show that grasshoppers are able to optimally balance die foraging objectives of energy intake and vigilance under changing levels of predation risk. The multiobjective model is used both to evaluate die biological «ignifiranrf of the broad variation that was observed in die grasshoppers' foraging behavior and to quantify explicitly die trade-off between energy intake and predator avoidance.