计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2014年
11期
183-188
,共6页
选址-库存问题%设施选址%库存控制%多目标优化%非支配排序遗传算法Π%混合整数规划
選阯-庫存問題%設施選阯%庫存控製%多目標優化%非支配排序遺傳算法Π%混閤整數規劃
선지-고존문제%설시선지%고존공제%다목표우화%비지배배서유전산법Π%혼합정수규화
Location-inventory Problem( LIP)%facility location%inventory control%multi-objective optimization%No-dominated Sort Genetic Algorithm II( NSGAII)%mixed integer programming
针对某些特殊物资的物流网络设计问题，以系统总成本最小与系统实时性程度最高为目标，建立一个考虑随机需求、设施容量约束、客户时限约束、带提前期的选址-库存问题( LIP)模型。该模型被描述为一个双目标的非线性离散混合整数规划模型。针对该模型，基于小生境技术设计一种改进的非支配排序多目标遗传算法Π( NSGAΠ)，以丰富非支配解的数量。算例与对照实验结果表明， NAGAΠ可得模型的 Pateto 前沿解集，与标准NSGAII相比具有明显的优势，该模型及算法可应用于血站或者某些应急药品仓库的选址布局与库存决策。决策者可根据实际需要及偏好在一簇Pateto解中选择合适的优化决策方案。
針對某些特殊物資的物流網絡設計問題，以繫統總成本最小與繫統實時性程度最高為目標，建立一箇攷慮隨機需求、設施容量約束、客戶時限約束、帶提前期的選阯-庫存問題( LIP)模型。該模型被描述為一箇雙目標的非線性離散混閤整數規劃模型。針對該模型，基于小生境技術設計一種改進的非支配排序多目標遺傳算法Π( NSGAΠ)，以豐富非支配解的數量。算例與對照實驗結果錶明， NAGAΠ可得模型的 Pateto 前沿解集，與標準NSGAII相比具有明顯的優勢，該模型及算法可應用于血站或者某些應急藥品倉庫的選阯佈跼與庫存決策。決策者可根據實際需要及偏好在一簇Pateto解中選擇閤適的優化決策方案。
침대모사특수물자적물류망락설계문제，이계통총성본최소여계통실시성정도최고위목표，건립일개고필수궤수구、설시용량약속、객호시한약속、대제전기적선지-고존문제( LIP)모형。해모형피묘술위일개쌍목표적비선성리산혼합정수규화모형。침대해모형，기우소생경기술설계일충개진적비지배배서다목표유전산법Π( NSGAΠ)，이봉부비지배해적수량。산례여대조실험결과표명， NAGAΠ가득모형적 Pateto 전연해집，여표준NSGAII상비구유명현적우세，해모형급산법가응용우혈참혹자모사응급약품창고적선지포국여고존결책。결책자가근거실제수요급편호재일족Pateto해중선택합괄적우화결책방안。
Based on the characteristics of logistic network design problem of some special materials,a joint Location-inventory Problem(LIP) model with lead-time is built,considering stochastic demands,facility capacity constraints and the client time constraints. The goal is to minimize system cost and maximize system timeliness. A discrete nonlinear mixed integer programming model with 2 goals is built to describe the problem. An improved NSGAII based on niching technology is worked out to solve the model, in order to enrich the number of non-dominated solutions. Numerical example and control experiment indicate that the Pateto front solution set can be obtained and the improved NSGAII has obvious advantages compared with standard NSGAII. The model and algorithm can be used to make location and inventory decision of blood banks or other emergency medicine warehouses. And optimal decision schemes can be selected from a cluster of Pateto solutions according to the preferences and actual needs of decision makers.
