2000
DOI: 10.1016/s0304-3800(00)00249-0
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A framework to study nearly optimal solutions of linear programming models developed for agricultural land use exploration

Abstract: In problems related to agricultural land use exploration, nearly optimal solutions of linear programming models constitute alternative land use allocations that result in good, albeit not optimal, levels of satisfaction of objectives. In this paper, we develop a framework to study nearly optimal solutions. The principle is to generate a group of nearly optimal solutions, to summarize the generated solutions by low dimensional vectors called 'aspects of the solutions ' and, finally, to present

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Cited by 42 publications
(26 citation statements)
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“…This is often realised in a finite set of scenarios or through one of the many different predictive computational modelling techniques available (Seppelt and Voinov, 2002). The latter mainly use regular tessellations like regular grids or lattices and support the search for 'optimal' spatial decisions (Martinnez-Falero et al, 1998;Makowski et al, 2000;Seppelt and Voinov, 2002).…”
Section: Gismentioning
confidence: 99%
“…This is often realised in a finite set of scenarios or through one of the many different predictive computational modelling techniques available (Seppelt and Voinov, 2002). The latter mainly use regular tessellations like regular grids or lattices and support the search for 'optimal' spatial decisions (Martinnez-Falero et al, 1998;Makowski et al, 2000;Seppelt and Voinov, 2002).…”
Section: Gismentioning
confidence: 99%
“…Kangas et al, 2002;Figueria and Roy, 2002). In the second category, the techniques of linear programming (Makowski et al, 2000), genetic algorithms (Ines et al, 2006), meta modeling (Mousavi and Shourian, 2010), and goal programming (Foued and Sameh, 2001;Agha, 2006;Al-Zahrani and Ahmad, 2004;Yang and Abbaspour, 2007) are more widely used. The first category might not be relevant in this study because it is interview-based and calls for direct participations of decision makers and other stakeholders.…”
Section: Introductionmentioning
confidence: 99%
“…The input values and model structure applied in this study have been validated through peer review [22,47]. c. The capacity of the model to report useful results compared with expected output has been investigated using near-optimal solution space analysis [47,48]. d. The ability of the model to report results that are consistent with real-world observations is presented in Section 4.1.…”
Section: Parameter Valuesmentioning
confidence: 99%