本文關(guān)鍵詞：基于改進模擬植物生長算法的雙目標(biāo)逆向物流選址研究　出處：《中北大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 模擬植物生長算法 經(jīng)濟成本 社會成本 逆向物流選址模型 穩(wěn)健性理論

【摘要】：隨著人類科學(xué)技術(shù)水平的提高,物質(zhì)文明文化得到飛速的發(fā)展,但與之相伴隨的就是資源的匱乏和能源的消耗殆盡,并且其中大部分的資源和能源是不可再生的。在這種處于危急關(guān)頭的大背景下,如何將有限的資源和能源重新回收再利用,減小對于資源和能源的消耗是人們十分關(guān)注的問題之一。逆向物流選址在解決資源和能源消耗、實現(xiàn)可持續(xù)發(fā)展的目標(biāo)中的作用日益顯著。本文對模擬植物生長算法進行詳細(xì)介紹后,對模擬植物生長算法存在的問題——對初始值的過度依賴性與得到最優(yōu)解后無法迅速判斷并自行終止問題進行詳細(xì)剖析,并針對這些問題提出相應(yīng)的改進方法,即在尋優(yōu)過程中有效擇優(yōu)判斷優(yōu)秀的可生長點、將原算法的固定搜索步長進行智能化改變以及利用聚類法緩解算法對初始生長點取值的依賴性,詳細(xì)解釋了三種改進方法,并對改進后的模擬植物生長算法步驟及流程進行定義及解釋說明。最后引入算例分情況討論了改進方法并驗證其有效性。證明改進算法采用的擇優(yōu)方法、搜索步長的變化以及利用聚類方法設(shè)置初始生長點均有助于解決模擬植物生長算法存在的優(yōu)化效率較低、算法不能有效自行終止的局限性。在建立廢棄物逆向物流選址模型時,本文考慮回收率確定、回收率不確定兩種情況。在回收率確定情形下,假設(shè)廢棄物如數(shù)回收,并采用斯坦福(Stanford)模型對廢棄物產(chǎn)生量進行估算,考慮逆向物流選址設(shè)計中的經(jīng)濟成本與社會負(fù)成本,構(gòu)建最小化經(jīng)濟費用和社會負(fù)效用的雙目標(biāo)函數(shù)模型,形成了一個綜合考慮經(jīng)濟利益、社會利益的雙目標(biāo)整數(shù)線性規(guī)劃,在建立模型后,利用改進模擬植物生長算法對選址模型進行求解。隨后引入算例進行模型求解�？紤]實際中存在回收率不確定的情形下在區(qū)域內(nèi)構(gòu)建廢舊產(chǎn)品逆向物流選址的穩(wěn)健模型,利用改進模擬植物生長算法對模型進行迭代求解最終得出經(jīng)濟成本、環(huán)境負(fù)效應(yīng)最小的目標(biāo)函數(shù)值以及回收處理中心的合適數(shù)量及位置。最后通過數(shù)值算例的引入驗證了穩(wěn)健模型在回收率不同的情況下的最優(yōu)解和模型的穩(wěn)健性。同時對回收率進行敏感度數(shù)值分析,得出隨著回收率的增大雙目標(biāo)總成本增加,但是總體決策并沒有發(fā)生改變。也就是說回收率的不同僅僅使目標(biāo)函數(shù)最優(yōu)值發(fā)生了變化,但最優(yōu)的決策并沒有發(fā)生改變,即選址模型決策中關(guān)于回收處理中心數(shù)量以及分配決策并沒有隨著回收率的增加而變化。
[Abstract]:With the improvement of the level of science and technology, material civilization and culture have been developing rapidly, but it is accompanied by the scarcity of resources and the depletion of energy, and most of them are non renewable resources. Under such a background of crisis, how to reclaim and reuse limited resources and energy and reduce consumption of resources and energy is one of the issues that people are very concerned about. The location of reverse logistics is becoming more and more important in the goal of solving resources and energy consumption and achieving sustainable development. This paper introduces the algorithm for the simulation of plant growth, the plant growth simulation algorithm to the initial value problems, excessive dependence and the optimal solution can be obtained after the detailed analysis and judgment to quickly terminate itself, aiming at these problems put forward corresponding improving method, namely in the process of optimization and effective judgment of outstanding merit the growth of fixed point, the search step of the original algorithm for intelligent clustering algorithm and the use of change point to the initial growth to alleviate the dependence, a detailed explanation of the three methods, and the improved algorithm simulation of plant growth steps and process definition and explanation. Finally, an example is introduced to discuss the improved method and verify its effectiveness. It is proved that the improved algorithm adopts the preferred method, the change of search step size and the initial growth point set up by clustering method, which is helpful to solve the limitation of the simulation plant growth algorithm, which is low efficiency and the algorithm cannot effectively terminate itself. In the establishment of the waste reverse logistics location model, this paper considers two kinds of conditions: the recovery rate is determined and the recovery rate is uncertain. The recovery rate under a certain situation, such as waste recycling and the use of assumptions, Standford (Stanford) model to estimate the amount of waste, the location in the design of economic cost and social cost of negative reverse logistics, establish the double objective function model of economic and social cost minimization of negative utility, forming a double objective integer linear programming is a comprehensive considering the economic benefits, social benefits, in the model, using the improved plant growth simulation algorithm to solve location model. Then an example is introduced to solve the model. Considering the recovery rate in the condition of uncertainty to construct a stable model of waste products reverse logistics location in the region found in practice, using the improved plant growth simulation algorithm for model iteration to solve the economic costs and environmental negative effect of minimum value of objective function and recovery processing center the appropriate number and position finally. Finally, a numerical example is introduced to verify the optimal solution of the robust model and the robustness of the model in the case of different recovery. At the same time, the sensitivity of the recovery rate is analyzed, and the total cost of the double target increases with the increase of the recovery rate, but the overall decision has not changed. That is to say, the difference of the recovery rate only changes the optimal value of the objective function, but the optimal decision has not changed. That is, the number of recovery centers and the allocation decisions have not changed with the increase of the recovery rate.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;F252

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