Aspiration/reservation‐based decision support—a step beyond goal programming

Abstract

Real‐life decision problems are usually so complex that they cannot be modelled with a single objective function, thus creating a need for clear and efficient techniques for handling multiple criteria to support the decision process. A widely used technique and one commonly taught in general OR/MS courses is goal programming, which is clear and appealing. On the other hand, goal programming is strongly criticized by multiple‐criteria optimization specialists for its non‐compliance with the efficiency (Pareto‐optimality) principle. In this paper we show how the implementation techniques of goal programming can be used to model the reference point method and its extension, aspiration/reservation‐based decision support. Thereby we show a congruence between these approaches and suggest how the GP model with relaxation of some traditional assumptions can be extended to an efficient decision support technique meeting the efficiency principle and other standards of multiobjective optimization theory.

Ordered weighted enhancement of preference modeling in the reference point method for multiple criteria optimization

“…0 þ ; which results in lexicographic order applied to two separate terms of function (2). That means, the regularization can be implemented with the second level lexicographic optimization (Ogryczak and Lahoda 1992). Therefore, RPM may be also considered as the following lexicographic problem (Ogryczak 2001 and references therein):…”

“…Note that instead of (12), the interval achievements may be defined with the goal programming modeling techniques (Ogryczak and Lahoda 1992):…”

“…The best formalization of the quasi-satisficing approach to multiple criteria optimization was proposed and developed mainly by Wierzbicki (1982) as the reference point method (RPM). The reference point method was later extended to permit additional information from the DM and, eventually, led to efficient implementations of the so-called aspiration/reservation-based decision support (ARBDS) approach with many successful applications (Granat and Makowski 2000;Lewandowski and Wierzbicki 1989;Malczewski and Ogryczak 1990;Ogryczak and Lahoda 1992;Wierzbicki et al 2000).…”

Interactive specification and analysis of aspiration-based preferences

“…The relations between ARBDS and other approaches to multi-criteria optimization are discussed in more detail in Makowski (l 994b). AR-BDS can be also considered (see Ogryczak and Lahoda, 1992) as an extension of Goal Programming (see e.g. Charnes and Cooper (1967) • First, a core model is specified and generated.…”

Improving the computational efficiency in a global formulation (GLIDE) for interactive multiobjective optimization

“…In this line, Steuer (1994a, 1994b) suggested a unified algorithm which contains from nine to thirteen different methods, although its implementation turns out to be a hard task. In Ogryczak and Lahoda (2006), goal programming implementation techniques are used to modelize the aspiration-reservation-based decision support approach, and in Romero (1993), a general optimization structure is proposed for several variants of goal programming as well as some other methods.…”