本文選題：選址-路徑　切入點：軟時間窗　出處：《福州大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著全球經濟的飛速發(fā)展,物流企業(yè)面臨著一個復雜多變、市場競爭日益激烈的動態(tài)決策環(huán)境,很多決策因素會隨著時間而發(fā)生變化。因此,根據(jù)決策環(huán)境的動態(tài)變化進行合理的物流系統(tǒng)規(guī)劃已成為一體化物流的發(fā)展趨勢。在物流系統(tǒng)規(guī)劃中,配送中心選址的確立和路徑的安排之間存在相互依賴的關系,對這兩個要素進行集成優(yōu)化是物流系統(tǒng)規(guī)劃的核心問題。國內外已有不少學者對其進行了研究,但主要是靜態(tài)的,不符合目前的動態(tài)決策環(huán)境,導致所做出的決策屬于“短視”的次優(yōu)決策。同時,考慮客戶需求呈現(xiàn)多樣性發(fā)展,客戶對訂單的響應速度要求越來越高,使得企業(yè)處于一個基于時間競爭的市場環(huán)境。因此,研究帶軟時間窗的動態(tài)選址-路徑優(yōu)化問題不僅具有理論價值也有現(xiàn)實意義。本文對配送中心選址的確立和路徑的安排進行集成優(yōu)化,同時考慮物流系統(tǒng)中客戶需求量、配送中心運營成本、重新打開和關閉成本等參數(shù)隨時間推移而變化的動態(tài)性質,建立了動態(tài)的選址-路徑優(yōu)化模型。在一個工廠、多配送中心、多客戶、多時間段的情況下確定了各個時間段配送中心的選址數(shù)量和位置以及配送路徑的安排方案,并確定了整個計劃期內的最優(yōu)方案；在此基礎上,考慮客戶軟時間窗約束,建立了帶軟時間窗的動態(tài)選址-路徑優(yōu)化模型,確定了各個時間段配送中心的選址數(shù)量和位置以及配送路徑的安排方案,并確定了整個計劃期內的最優(yōu)方案。在模型的求解上,針對兩個優(yōu)化模型,本文將其分別分解成靜態(tài)選址-路徑優(yōu)化問題和動態(tài)規(guī)劃以及靜態(tài)帶軟時間窗的選址-路徑優(yōu)化問題和動態(tài)規(guī)劃兩個子問題求解。對于靜態(tài)的選址-路徑優(yōu)化問題和靜態(tài)的帶軟時間窗的選址-路徑優(yōu)化問題分別設計了基于貪心算法的改進微粒群算法(以下簡稱GA-PSO算法)和罰函數(shù)的微粒群算法(以下簡稱PENALTY-PSO算法)進行求解,得出各個時間段的選址和路徑安排方案。最后,用動態(tài)規(guī)劃法求出整個規(guī)劃期內的最優(yōu)方案。實驗結果表明,GA-PSO算法在最低總成本平均值、解的穩(wěn)定性、最優(yōu)解的命中率以及搜索速度上均優(yōu)于P ENALTY-PSO算法。同時加入客戶的軟時間窗約束對最低總成本平均值也有一定的影響。算例結果說明了模型及求解算法是有效的和實用的。最后,對本文的研究成果進行簡要總結,并指出需要進一步研究的方向。
[Abstract]:With the rapid development of the global economy, logistics enterprises are faced with a complex and changeable dynamic decision-making environment in which market competition is increasingly fierce. Many decision-making factors will change with time. According to the dynamic change of decision-making environment, reasonable logistics system planning has become the development trend of integrated logistics. In the logistics system planning, the establishment of distribution center location and the path arrangement are interdependent. The integration optimization of these two elements is the core problem of logistics system planning, which has been studied by many scholars at home and abroad, but it is mainly static and does not conform to the current dynamic decision-making environment. At the same time, considering the diversity of customer demand, the customer response speed is higher and higher, which makes the enterprise in a market environment based on time competition. The study of dynamic site-path optimization with soft time window has not only theoretical value but also practical significance. In this paper, the establishment of distribution center location and the path arrangement are integrated and optimized, and the customer demand in logistics system is taken into account at the same time. The dynamic properties of the operating costs, re-opening and closing costs of distribution centers over time, and a dynamic site-path optimization model is established in a factory, multi-distribution center, multi-customer, and so on. In the case of multiple time periods, the location number and location of distribution center and the arrangement scheme of distribution route in each time period are determined, and the optimal scheme in the whole plan period is determined. On this basis, the soft time window constraint of customer is considered. The dynamic site-path optimization model with soft time window is established, the location number and location of distribution center in each time period and the arrangement scheme of distribution path are determined, and the optimal scheme is determined during the whole planning period. For two optimization models, In this paper, it is decomposed into static site-path optimization problem and dynamic programming problem, as well as static site-path optimization problem with soft time window and dynamic programming. The problem and static site-path optimization problem with soft time window are solved by the improved particle swarm optimization (GA-PSO) algorithm based on greedy algorithm (hereinafter referred to as GA-PSO algorithm) and the penalty function particle swarm optimization algorithm (PENALTY-PSO algorithm), respectively. Finally, the dynamic programming method is used to solve the optimal scheme for the whole planning period. The experimental results show that the GA-PSO algorithm is stable in the mean of the lowest total cost and the stability of the solution. The hit ratio and search speed of the optimal solution are better than that of the P ENALTY-PSO algorithm. At the same time, the soft time window constraint of the customer also has a certain effect on the average minimum total cost. The results of an example show that the model and the algorithm are effective. And practical. Finally, This paper briefly summarizes the research results and points out the direction of further research.
【學位授予單位】：福州大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U116.2;F252

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