【摘要】：物流中心是現(xiàn)代物流活動的核心部門與場所,訂單分揀是物流中心最為關(guān)鍵的環(huán)節(jié),其成本占到整個物流中心成本的50%以上,以往的研究及實踐表明通過倉儲分布設(shè)計,儲位指派,訂單分批以及路徑規(guī)劃等策略的有效實施能減少揀選人員行走路程,從而減少分揀時間以改善客戶服務(wù)水平。本文旨在通過制定合理的訂單分批策略以改善人工揀選系統(tǒng)中揀選作業(yè)的工作效率。訂單分批通過將多個訂單合成一個批次或更大的訂單以提升揀選設(shè)備的利用率并減少工作量,使得分揀過程得以更有效的實施。訂單分批問題是NP難問題,因此采用一些高效的算法進行求解是國內(nèi)外研究的主要方向。 本文希望通過研究訂單批次分揀策略以求最小化揀選路程,從而節(jié)約揀選時間,使得貨品的流動周期更短,對訂單的響應(yīng)更迅速。本文在以往研究的基礎(chǔ)上提出了三種訂單分批算法,分別是降批次啟發(fā)式算法、基于粒子群的分批算法和基于降批次的遺傳分批算法。降批次算法考慮到揀選過程中批次數(shù)量對于結(jié)果的影響�；诹Ｗ尤旱姆峙惴▌t考慮將粒子群算法用于求解訂單分批問題,為了使算法匹配所要求解的問題,對二進制粒子群算法進行了改進。基于降批次的遺傳分批算法的提出得益于前人的工作,在遺傳分批算法的基礎(chǔ)上引入了降批次過程,使得算法有了更好的搜索特性。遺傳算法具有快速的收斂和高效的尋優(yōu)能力,因此在三種算法中求得的結(jié)果最好�！疄榱吮阌谟嬎惚疚倪�設(shè)計了一種穿越策略下的路徑計算方法。最后利用matlab仿真軟件進行仿真實驗,三種算法與經(jīng)典的啟發(fā)式算法進行了比較,實驗結(jié)果顯示三種算法都有較經(jīng)典的啟發(fā)式算法更好的求解結(jié)果,特別是基于群的算法優(yōu)化性能有大幅提升。
[Abstract]:Logistics center is the core department and place of modern logistics activities, order sorting is the most critical link of logistics center, its cost accounts for more than 50% of the cost of the whole logistics center. The effective implementation of storage assignment, order batching and path planning can reduce the walking distance of the pickers, thus reduce the sorting time and improve the customer service level. The purpose of this paper is to improve the efficiency of sorting in manual sorting system by making reasonable order batch strategy. In order to increase the utilization rate of sorting equipment and reduce the workload, the sorting process can be implemented more effectively by synthesizing multiple orders into one batch or more orders in order to increase the utilization rate of sorting equipment and reduce the workload. Order batch problem is a difficult problem of NP, so it is the main research direction to use some efficient algorithms to solve the problem at home and abroad. This paper hopes to study the order batch sorting strategy in order to minimize the picking path, so as to save the picking time, make the goods flow shorter, and respond more quickly to the order. In this paper, three kinds of order batch algorithms are proposed based on previous studies, namely, reduced batch heuristics, particle swarm optimization and genetic batching based on reduced batches. The batch reduction algorithm takes into account the effect of batch number on the result. Particle Swarm Optimization (PSO) based batch algorithm is considered to solve order batch problem. In order to match the required solution, binary PSO algorithm is improved. Based on the previous work, the genetic batch algorithm based on reduced batch is proposed, and the process of batch reduction is introduced on the basis of genetic batch algorithm, which makes the algorithm have better search characteristics. Genetic algorithm has fast convergence and efficient optimization ability, so the results obtained in the three algorithms are the best. In order to be easy to calculate, this paper also designs a path calculation method based on traversing strategy. Finally, the three algorithms are compared with the classical heuristic algorithm by using the matlab simulation software. The experimental results show that the three algorithms have better results than the classical heuristic algorithm. In particular, the performance of swarm-based algorithm has been greatly improved.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP18

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本文編號：2393998