Analytical Evaluation of Multi-Criteria Heuristics
提出一种分析性方法，通过将有效解和启发式解转化为价值度量，定义近似误差，并推导出消除非最优解的规则；针对双准则问题给出多项式算法，对一般多准则问题则通过求解一系列线性规划来确定最大近似误差。
This paper considers the problem of evaluating the solution quality of multi-criteria heuristics. By assuming an additive multi-attribute value structure, efficient and heuristic solutions can be translated into value measures that depend only on the relative importance assigned to the criteria of interest. Approximation errors are then defined as the value penalty incurred by approximating an efficient solution with its heuristic alternative. Results are derived which can be used to eliminate solutions that cannot represent the best available alternative among the set of efficient and heuristic solutions. For the bicriterion case, a polynomial algorithm for determining the mean and maximum relative heuristic error for a given problem instance is presented. For more general multi-criteria problems, the maximum relative approximation error can be determined by solving a series of linear programming problems.