计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
8期
67-71
,共5页
应急设施选址%多目标%多级覆盖衰减%启发式算法
應急設施選阯%多目標%多級覆蓋衰減%啟髮式算法
응급설시선지%다목표%다급복개쇠감%계발식산법
emergency facility location%multi-objective%multiple gradual coverage%heuristic algorithms
大规模突发事件下应急物资的需求量巨大以及对资源持续需求的特点,考虑设施选址的公平性、效率性及成本等因素,基于多级覆盖和覆盖衰减思想,提出一类应急设施多目标多重覆盖衰减选址模型.基于MATLAB7.0设计贪婪算法、上升算法、遗传算法程序对模型进行求解,以25组不同规模的算例验证了模型的性能和算法的有效性.数值模拟结果表明,该模型较之传统覆盖选址模型可以为需求点提供更高的覆盖满意度;当目标权系数取不同值时对选址结果产生较大影响;对三个算法性能进行比较,遗传算法最优,上升算法次之,贪婪算法最差,上升算法适于求解中小规模的选址问题,而遗传算法更适合于大规模选址问题的求解.
大規模突髮事件下應急物資的需求量巨大以及對資源持續需求的特點,攷慮設施選阯的公平性、效率性及成本等因素,基于多級覆蓋和覆蓋衰減思想,提齣一類應急設施多目標多重覆蓋衰減選阯模型.基于MATLAB7.0設計貪婪算法、上升算法、遺傳算法程序對模型進行求解,以25組不同規模的算例驗證瞭模型的性能和算法的有效性.數值模擬結果錶明,該模型較之傳統覆蓋選阯模型可以為需求點提供更高的覆蓋滿意度;噹目標權繫數取不同值時對選阯結果產生較大影響;對三箇算法性能進行比較,遺傳算法最優,上升算法次之,貪婪算法最差,上升算法適于求解中小規模的選阯問題,而遺傳算法更適閤于大規模選阯問題的求解.
대규모돌발사건하응급물자적수구량거대이급대자원지속수구적특점,고필설시선지적공평성、효솔성급성본등인소,기우다급복개화복개쇠감사상,제출일류응급설시다목표다중복개쇠감선지모형.기우MATLAB7.0설계탐람산법、상승산법、유전산법정서대모형진행구해,이25조불동규모적산례험증료모형적성능화산법적유효성.수치모의결과표명,해모형교지전통복개선지모형가이위수구점제공경고적복개만의도;당목표권계수취불동치시대선지결과산생교대영향;대삼개산법성능진행비교,유전산법최우,상승산법차지,탐람산법최차,상승산법괄우구해중소규모적선지문제,이유전산법경괄합우대규모선지문제적구해.
It has very important significance to research on the location theory for emergency material depositories in response to frequently occurring large-scale emergencies. In allusion to the characteristics of tremendous and continued demands for emer-gency supplies during large-scale emergency occurring, considering the factors of fairness, efficiency and cost for facility loca-tion, a multi-objective multiple gradual coverage location model is proposed based on the idea of multiple coverage and gradual coverage. Three programs of greedy algorithm, ascent algorithm and genetic algorithm based on MATLAB 7.0 are developed to solve the model. A computational experiment is adopted to prove the effectiveness and performance of model and heuristic algo-rithms. The results of simulation prove that the performance of gradual coverage model is better than traditional binary coverage model. Different weight coefficients of objective can affect the location results obviously. Twenty-five different scales data is used to compare the performance of heuristic algorithms, the performance of genetic algorithm is better than ascent algorithm, and greedy algorithm is worst. The ascent algorithm is suitable for the small and medium-sized location problems, and genetic algorithm is suitable for solving large-scale location problems.