华南理工大学学报(社会科学版)
華南理工大學學報(社會科學版)
화남리공대학학보(사회과학판)
JOURNAL OF SOUTH CHINA UNIVERSITY OF TECHNOLOGY(SOCIAL SCIENCE EDITION)
2014年
3期
56-62
,共7页
服务惩罚%双层规划%免疫遗传算法
服務懲罰%雙層規劃%免疫遺傳算法
복무징벌%쌍층규화%면역유전산법
service punishment%bi-level programming%immune genetic algorithm
从系统的角度考虑在配送中心选址决策过程中,处于物流企业上层和下层之间的有机联系,深入的分析了两者所追求的目标、面临的约束以及相互影响,并在此基础上构建了上层以总成本最小为目标,下层以无法及时响应所带来的服务惩罚最小为目标的双层规划模型。根据模型的特点,利用免疫遗传算法求解在不同配送中心方案数目下,上下层模型的最优解,并最终确定双层规划的解。以某电网公司的电力物资配送网络的实际数据和配送中心选址问题为算例验证了模型和算法的有效性和实用性。
從繫統的角度攷慮在配送中心選阯決策過程中,處于物流企業上層和下層之間的有機聯繫,深入的分析瞭兩者所追求的目標、麵臨的約束以及相互影響,併在此基礎上構建瞭上層以總成本最小為目標,下層以無法及時響應所帶來的服務懲罰最小為目標的雙層規劃模型。根據模型的特點,利用免疫遺傳算法求解在不同配送中心方案數目下,上下層模型的最優解,併最終確定雙層規劃的解。以某電網公司的電力物資配送網絡的實際數據和配送中心選阯問題為算例驗證瞭模型和算法的有效性和實用性。
종계통적각도고필재배송중심선지결책과정중,처우물류기업상층화하층지간적유궤련계,심입적분석료량자소추구적목표、면림적약속이급상호영향,병재차기출상구건료상층이총성본최소위목표,하층이무법급시향응소대래적복무징벌최소위목표적쌍층규화모형。근거모형적특점,이용면역유전산법구해재불동배송중심방안수목하,상하층모형적최우해,병최종학정쌍층규화적해。이모전망공사적전력물자배송망락적실제수거화배송중심선지문제위산례험증료모형화산법적유효성화실용성。
From the perspective of system,the paper analyzes the relationship between the upper and lower of logis-tics enterprises in the decision-making process of distribution center location,and carries out in-depth analysis of the objectives,constraints and interaction between these two levels.On the basis of above analysis,the paper builds a bi-level programming model.The objective of the upper model is to minimize the total cost (including re-construction costs and distribution costs),and the objective of the lower model is to minimize the service punish-ment cost caused by the failed service response.According to the model’s characteristics,the above issue is equiva-lently to solve the optimal solutions for the upper and lower under the different numbers of distribution centers,and ultimately determines the solution of bi-level programming model.Due to the fact that bi-level programming problem is a NP hard problem,the paper uses the immune genetic algorithm to solve this problem.Finally,the practicality and effectiveness of the proposed model and the algorithm are verified by an example of a power grid company with actual data.