【正文】 sen , 5 billion people have been affected by disasters with an estimated damages of about trillion dollars Although most of these disasters could not have been avoided, significant improvements in death counts and reported property losses could have been made by efficient distribution of supplies here could mean personnel, medicine and food which are critical in emergency supply chains involved in providing emergency services in the wake of a disaster are referred to as humanitarian relief supply relief supply chains are formed within short time period after a disaster with the government and the NGO’s being the major drivers of the supply chain. Clearly, emergency logistics is an important ponent of humanitarian relief supply chains. Most literature in emergency logistics focuses on generating transportation plans for rapid dissemination of medical supplies inbound to the disaster hit regionThere is, however, another aspect of emergency logistics which is often ignored – outbound logistics. The outbound logistics considers a situation where people and emergency supplies (. medical facilities and services for special need evacuees) need to be sent from a particular location affected by disaster within a given time horizon. In the outbound emergency logistics, the demand of traffic flows is usually highly uncertain and depends on a number offactors including the nature of disaster (natural/manmade) and time of uncertainty in the demand causes disruptions in emergency logistics and hence disruptions in humanitarian relief supply chains leading to severe suboptimality or even infeasibility which may ultimately lead to loss of life and order to mitigate the risk of uncertain demand, we study the problem of generating evacuation transportation plans which are robust to uncertainty in outgoing demand. More specifically, we solve a dynamic (multiperiod) emergency response and evacuation traffic assignment problem with uncertain demand at source nodes. Researchers and practitioners in the field of transportation are concerned with multiperiod management problems with an inherent time dependent information dynamic optimization approaches for dealing with uncertainty(. stochastic and dynamic programming) usually require the probability distribution for the underlying uncertain data to obtain expected , in many cases, it may be very difficult to accurately identify the distribution required to solve a , in many cases, it may be very difficult to accurately identify the distribution required to solve a problem. In addition, the robust solution guaranteeing the feasible evacuation plan is important since infeasible solutions may cause the potential loss of life and property in extreme events. We explore the potential of robust optimization (RO) as a general putational approach to manage uncertainty, feasibility, and tractability for plex transportation approach has been originally developed to deal with static problems formulated as linear programming (LP) or conicquadratic problems (CQP), using crude uncertainty with hard means that uncertainty is assumed to reside in an appropriate set and RO guarantees the feasibility of the solution within the prescribed uncertainty set by adopting a min–max RO technique has been successfully applied in some plex and large scale engineering design and optimization problems similar as robust control in control theory. The original RO approach considers static problems. The underlying assumption of RO is “here and now” decisions, and all decision variables need to be determined before any uncertain data are is not typical in many transportation management problems that have the multiperiod nature. In multiperiod transportation problems such as dynamic traffic assignment, “wait and see” decisions are made, which means some decision variables are “adjustable” and affected by part of the realized the need to account for such dynamics, BenTal et al. (2004) have extended the RO approach and developed an affinely adjustable robust counterpart (AARC) approach to consider “wait and see” decisions. To demonstrate the use of AARC to emergency transportation management settings, in this paper we consider a system optimum dynamic traffic assignmen。另外，在校圖書館查找資料的時候，圖書館的老師也給我提供了很多方面的支持與幫助。隨著研究的不斷深入，隨著計算工具的日益成熟，還有很多未深入探討的領(lǐng)域有待進一步的研究，主要包括：（1）截至目前為止的研究論文，都把應(yīng)急物流的決策活動作為一項災(zāi)前的運營活動，而實際上，在一個動態(tài)的、非確定的災(zāi)害條件下，把應(yīng)急物流作為一個災(zāi)后的決策活動進行優(yōu)化研究更有實際意義，更能夠反映應(yīng)急救援的時效性和緊迫性，因此，救援時間最短可以作為優(yōu)化目標(biāo)函數(shù)，而不是如前文大多數(shù)文獻以經(jīng)濟性指標(biāo)作為優(yōu)化目標(biāo)。 上圖為救災(zāi)物資配送流程示意圖。（3） 在突發(fā)自然災(zāi)害應(yīng)急物流配送網(wǎng)絡(luò)系統(tǒng)中，救災(zāi)物資和各類儲備網(wǎng)點的初始位置及其種類，數(shù)量可以認為是確定的。但由于突發(fā)性事件發(fā)生的時間、地點是無法預(yù)測的。西安地區(qū)的總面積為9983平方公里,A=9983平方公里。0:平均配送距離參數(shù)。因此物流節(jié)點的建設(shè)費用可用下式表示:式中:Cp:物流節(jié)點的建設(shè)費用。 配送的主要內(nèi)容包括配貨、裝卸、運送、卸載、返回等幾個過程,因此,可將其分為兩類:一類是與配送距離無關(guān),只與物流量相關(guān)的,如配貨、裝載、卸載。但由于在物流量的預(yù)測過程中,所依據(jù)的預(yù)測模型往往難于完全考慮城市和區(qū)域經(jīng)濟發(fā)展等復(fù)雜因素,因此在物流中心布局方案決策時,除對物流量進行定量分析外,還應(yīng)與定性分析結(jié)合起來,即需要綜合考慮其他方面的影響因素,確定物流中心的布局數(shù)量。因此，無論是城市發(fā)展還是古遺址的保護，均不利于城市在現(xiàn)狀的基礎(chǔ)上向兩蔓延。以西安市為例，其空間布局圖如下所示： 西安的城市空間布局主要受城市總體規(guī)劃的影響，將兩安第一次、二次、三次、四次總體規(guī)劃確定的城市空間格局進行分析總結(jié)如下圖所示：“九宮格局、棋盤路網(wǎng)、軸線突出、一城多心”的空間格局；“一高、一繞、兩軸、三環(huán)、六縱、七橫、八射線加旅游環(huán)線”的道路網(wǎng)格局；高標(biāo)準(zhǔn)的水、電、氣等及配置齊全的醫(yī)院、學(xué)校等基礎(chǔ)公共設(shè)施建設(shè)；八水繞城和秦嶺綠色屏障形成的山水城市格局。④多重共線性問題得到解決。特別說明一點,由于總?cè)丝跀?shù)、國民生產(chǎn)總值等因素單位不統(tǒng)一,所以在計算尸值時這些都必須采用標(biāo)準(zhǔn)化之后的值。但是,我們發(fā)現(xiàn)自變量之間相關(guān)度也很高,說明自變量之間存在多重共線性。上述內(nèi)容就是目前我國比較常見的應(yīng)急資源分類情況,其中尤其是以國家發(fā)改委公布的按照用途進行分類的《應(yīng)急物資分類及產(chǎn)品目錄》最為全面和標(biāo)準(zhǔn),且最具有權(quán)威性。爆炸設(shè)備如防暴服等。嚴重級指對減輕災(zāi)害損失,縮小災(zāi)情范圍并對應(yīng)急救災(zāi)工作能夠發(fā)揮重要作用,非常必要且重要的物資,如救援運輸、防護類物資。目前比較常見的分類方式主要有以下幾種：(l)按應(yīng)急物資的使用范圍分類應(yīng)急物資可劃分為兩類,即通用類和專用類。應(yīng)急物資需求分類是研究應(yīng)急物資采購、儲備管理的前提條件,只有對物資的需求種類進行深入研究,才能夠明確應(yīng)該采購和儲備哪些物資。物資的需求數(shù)量是當(dāng)突發(fā)事件發(fā)生后,為了有效地應(yīng)對這些事件所必需的最小的物資需求數(shù)量。不僅如此,由于突發(fā)事件涉及的受災(zāi)面積大、受災(zāi)人口多、受災(zāi)環(huán)境復(fù)雜多變等原因,處理同一類突發(fā)事件需要的應(yīng)急資源種類也非常多?！　?1．地震災(zāi)害（共1種）。主要包括：①危險化學(xué)品事故；②礦山事故；③核事件和輻射事故；④污染物排放；⑤鍋爐、壓力容器、起重機械、電梯等八大類特種設(shè)備事故。①群眾上訪；②民族糾紛及宗教沖突；③非法集會。包括口蹄疫、瘋牛病、高致病性禽流感、布魯氏桿菌病、牛結(jié)核病、狂犬病和植物危險性、突發(fā)性、有害生物的入侵等動植物疫?。ㄒ咔椋? 1．重大氣象災(zāi)害（共7種）。突發(fā)動物疫情在未來一段時期仍存在復(fù)發(fā)、多發(fā)的可能性。交通事故起數(shù)與死亡人數(shù)居高不下。 思路：本文主要以西安市為例，研究應(yīng)急物流需求種類與數(shù)量分析、西安市自然災(zāi)害風(fēng)險評估、應(yīng)急物流需求量測算、西安市應(yīng)急物流的空間布局規(guī)劃原理與方法、Flexsim軟件模擬等流程來得到基于Flexsim下應(yīng)急物流的優(yōu)化方案，最終能在真正需要應(yīng)急時收益最大化。應(yīng)急物流機制和體系的建立，