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以代理人為基礎的模擬準時生產(chǎn)物料搬運系統(tǒng)
作者：郝啟和沈偉明
組織：集成制造技術研究所
加拿大國家研究委員會
摘要：在大多數(shù)組裝廠資料處理是一個松散的環(huán)。Just-in-time準時制(JIT)是一種在正確的地點,正確的時間的管理理念,努力消除生產(chǎn)制造廢物的來源。在組裝廠我們提出應用JIT原則處理材料。介紹了材料看板作業(yè)作為一種有效的手段去控制物理材料和部分工廠物流。實現(xiàn)了一個基于主體的關于AnyLogic仿真原型的使用。在工廠地板，以代理人為基礎的彈性方法便于仿真各種"假設情況”場景，包括不同的布圖設計,客觀參數(shù)和動態(tài)情況。
1.介紹
物料處理是一個松散的環(huán)境,通常是被忽略的大部分生產(chǎn)工廠。從我們的觀察來看,甚至在一個設計良好的流水線,在整個生產(chǎn)線的條件下,在它的布局、流程優(yōu)化、緩沖、進度、運營、物料搬運仍然有不合理的控制。經(jīng)理花費了大量的寶貴時間去找出錯誤的地方然后再合理的布置流程過程。他們沒有意識到的物料搬運或交貨計劃及相關的資源信息(數(shù)量和利用的資源,比如叉車和司機)。作為一個結果,材料處理成為主要障礙,導致生產(chǎn)效率低,低效率,低質(zhì)量的生產(chǎn)體系。準時生產(chǎn)是一種管理理念,可以提高利潤和投資回報減少庫存,減少浪費,提高產(chǎn)品質(zhì)量,降低了生產(chǎn)和交付訂貨至交貨的時間,以及減少其他費用(例如那些相關的設置和設備故障的機器)。特別介紹了拉動式,通過看板的控制,使得JIT生產(chǎn)優(yōu)化生產(chǎn)過程中獲益的生產(chǎn)資源的減少。對工廠而言,早就在運行機制下的需求,材料處理也應該采用拉動式,而不是推動式的。
基于此,本文提出了基于物料搬運系統(tǒng)拉動式準時生產(chǎn)原則。在這樣一個系統(tǒng)中,材料的運輸工廠地板被認為是獨立完成的任務。物料看板在工廠的地板上，材料被引入作為載體的交貨任務是一種有效的手段去控制和平衡的物理處理材料流動?；痉结?是一項任務所產(chǎn)生的聯(lián)合站生產(chǎn)單元要求占領一個物料看板一樣呈交。
另一種技術應用于該材料處理系統(tǒng)就是代理。代理技術的演變而來的研究領域是20世紀90年代的分布式人工智能。從它的產(chǎn)生、代理技術被認為是一種很有前途的范式形式下的設計和制造系統(tǒng)(沈偉明等,2001年)。在JIT物料搬運仿真系統(tǒng)中,實現(xiàn)多個代理商,為協(xié)同解決問題的環(huán)境。例如,每一個交通工具是封裝的代理,以便它是可以在自己的參數(shù)和行為,如速度、當?shù)氐臅r間表,和相關的調(diào)度,路由和沖突化解規(guī)則。司機的交通車輛可以關閉一輛汽車從系統(tǒng)承擔個人活動或當汽車故障需要修理的時候。此外,運輸任務的分配的談判來完成一個雖然看板調(diào)度代理和大量的汽車代理商。這樣的能力,該系統(tǒng)能夠模擬動態(tài)的情況,而且還能得到更多非常準確的信息,是一般的資源的交通工具。
這篇文章的其余部分組織如下:第2部分進行了簡單的回顧和文學的背景知識;第三節(jié)標識本研究樣本準時生產(chǎn)物料搬運問題,敘述了其相應的要求提出了一個基于主體的轉(zhuǎn)機;第四節(jié)的建筑準時生產(chǎn)物料搬運系統(tǒng),并討論了兩個主要方面:生產(chǎn)仿真設計和原材料處理模擬。
2.技術評論
有兩種分類的生產(chǎn)控制系統(tǒng),即拉動式和推動式。原材料需求計劃(MRP)系統(tǒng)和看板管理系統(tǒng)都是最常用的，兩種實現(xiàn)策略的方法分別是拉式和推式。在推動式生產(chǎn)中,為了縮小不準確的預測的交貨期、庫存記錄,變更后的的生產(chǎn)計劃和可疑材料明細表(BOM),一般包含了安全前置時間和安全的系數(shù)。然而,在實踐中,MRP可能導致中的一個嚴重問題（見Shirk 1998，Hopp和斯皮爾曼,1996)。庫存水平和訂貨至交貨的時間經(jīng)過放大在整個供應鏈中,從最終的經(jīng)銷商一直到每個層次的供應商。
相反,使用拉動式策略,而不是使用臨床應用準時生產(chǎn)系統(tǒng)容量的緩沖庫存，以此來避免可能會出現(xiàn)的問題。在反應生產(chǎn)發(fā)起真正的客戶訂單及消除物品從最終的經(jīng)銷商緩沖器觸發(fā)來補充庫存生產(chǎn)上游一層層地耗盡。維修人員孫俐在2004年進行定量比較的表現(xiàn),MRP和看板為多級、多產(chǎn)品的生產(chǎn)制造系統(tǒng)。為他們的客戶，拉策略是有生產(chǎn)設施,產(chǎn)生了許多不同的產(chǎn)品,具有獨特的要求和處理的要求,以及設施,使高度工程產(chǎn)品小批量化(即使是獨一無二的)。
理想的行業(yè)應用準時生產(chǎn)生產(chǎn)范圍包括汽車,因為它是準時生產(chǎn)概念的起源。汽車工業(yè)是具有較低的產(chǎn)品品種、大批量生產(chǎn)。在一個汽車生產(chǎn)線上,雖然有一些物流線用推動式的(有時稱為混合生產(chǎn)),如身體車間、噴漆車間,引擎的線條,然而,一旦汽車正在排隊得到處理主要的流水線,生產(chǎn)的控制下一個單一的拉動式。緩沖器設置在離生產(chǎn)線的地點靠不確定性物流線發(fā)揮作用,更好地優(yōu)化了主要生產(chǎn)線的生產(chǎn)速率。
準時生產(chǎn)的概念和看板不是一開始就有的。首先由豐田在1950年贊助準時生產(chǎn)時開發(fā)的,然后1980年在美國被采用。準時生產(chǎn)和精益企業(yè),聚居區(qū),在西方國家,也進化基于準時生產(chǎn)的原則。許多中小企業(yè)和一些大公司已經(jīng)接受了這些概念,像曼莉瓊絲、奔馳、普拉特和惠特尼,保時捷和通用電氣等。1996年，特和瓊斯在他們基于價值的業(yè)務系統(tǒng)中，運用豐田模式 提供了周到的擴張。一路上,他們根據(jù)他們的行動計劃進行新的研究和不斷增強的全球化,他們重新分析它們的一些關鍵案例，研究來自于汽車、航空航天、等制造業(yè)的問題。
許多的分析像在數(shù)學或?qū)嶒災Ｐ偷幕A上,提出了在解決看板的基礎操作計劃和控制問題(具體見Berkley和Martin-Vega 1992;,1990)。到目前為止,模擬的方法論的研究選擇在多數(shù)文獻報告。從理論上講,看板的數(shù)量和配置系統(tǒng)中顯著地影響看板拉動式系統(tǒng)的性能。而不是優(yōu)化這些看板安排導致固定數(shù)量的看板,馬丁斯和Lewandrowski 在1999年提出了一個數(shù)學緩沖區(qū)的方法，使用一個動態(tài)數(shù)據(jù)計量看板的策略。古普塔和Al-Turki 在1998年的關于性能比較傳統(tǒng)看板系統(tǒng)和一個靈活的看板系統(tǒng)(FKS)。通過仿真模型的兩個簡單的準時生產(chǎn)模型,他們證明了FKS優(yōu)于在實時制造環(huán)境下的傳統(tǒng)看板系統(tǒng),如突然破裂材料處理系統(tǒng)。
從應用的角度上說,研究的拉動式系統(tǒng)可劃分分為3大類:(1)生產(chǎn)控制;2)存貨管理;3)供應鏈管理。材料處理是主題被先前的研究在文獻(見 古普塔和Al-Turki雜志,2002;Askin高慶宇,1999年;Venkataramanaiah等,2001年)。然而,他們都在處理自動資料(確切地說,是線上的庫存,屬于生產(chǎn)線之間轉(zhuǎn)移問題本身)生產(chǎn)的單元。在這里我們強調(diào)的材料并沒有提及在制品經(jīng)過的流水線,確切地說,它是流動的物質(zhì)供應或部分服從主要生產(chǎn)線。目前為止，還沒有已知的文獻分析過關于這方面的物料搬運系統(tǒng)。
此外,在大多數(shù)工廠中,材料存貨只是有過關于完全控制下的在企業(yè)層面的MRP II系統(tǒng)或看板系統(tǒng),但不是實在的動態(tài)工廠地板。錯缺件,零件不交付的,一部分是正確的地點,正確的時間中的常見現(xiàn)象,幾乎所有的主流生產(chǎn)工廠,包括通用、福特、純正的卡車。對于經(jīng)理而言材料處理是一項令人頭痛的問題。生產(chǎn)部經(jīng)理感到緊張,每天被指責是缺乏控制制造的過程能力。所以,分析物料處理及動態(tài)模擬對制造行業(yè)會很有幫助。
3. 一個簡單裝配線的物料搬運系統(tǒng)
在拉動式生產(chǎn)中，物料搬運系統(tǒng)的模擬是研究的主要目的。一個基于看板材料處理等問題將會被分析,使其符合拉動式生產(chǎn)線。為方便有較容易的理解,選擇一個工廠案例作為背景研究問題,如圖所示。（圖略）
分析物料搬運方式的時候,我們從基礎準時生產(chǎn)相似原理提出了一種類似的庫存管理: 正確的材料輸送是在正確的時間和正確的量從離開庫存區(qū)到右邊的生產(chǎn)現(xiàn)場。在這里,物資運輸?shù)墓S被認為是獨立完成任務。一項任務需要一個資料看板被遞送。在圖中,材料的請求信號首先由產(chǎn)生的裝配車間發(fā)出去的，這部分的材料供應,經(jīng)過看板,請求被播出了一系列的車輛正常行駛范圍內(nèi)的無線網(wǎng)絡,然后通過調(diào)節(jié),任務是經(jīng)車輛和正在交付最后到右邊站。
在我們看來,根據(jù)系統(tǒng)分析，準時生產(chǎn)的物料搬運基于看板概念并不僅僅是一種純粹的事件。這一事件系統(tǒng)中,一個事件立即要求系統(tǒng)響應;而經(jīng)看板比較,根據(jù)系統(tǒng)所產(chǎn)生的事件處理,得到僅在獲得一個物理對象的物料看板。換句話說,加工物質(zhì)需求的事件保持，直到系統(tǒng)釋放另一種物料看板與該事件是相符的而代替這個看板所有的其他事件。在看板控制機制的基礎上,我們認為在資料處理系統(tǒng)是能夠達成一個自然平衡的材料要求和運輸活動通過精致的安排和管理看板。除了標準函數(shù)在一個模擬環(huán)境,如離散事件的解,仿真時鐘的生成和一個動畫的界面,該系統(tǒng)應具有特殊功能模塊,試圖模型和模擬動力學在工廠的地板上。三個組的功能構成了準時生產(chǎn)物料搬運系統(tǒng)，典型場景生成,模擬器和圖形用戶界面。典型場景生成保存大量有關各種配置信息: 1)靜態(tài)場景,如廠房的布置,移動的軌跡,中心部分庫存; 2)仿真參數(shù),如變幻莫測的車輛數(shù)目和數(shù)量的看板。模擬器為核心的軟件,它控制了模擬生產(chǎn)和物料搬運過程中雙方的過程。每一個交通工具都有一個獨立的車輛仿真模塊,使自己的決定,包括任務序列,任務時間表,移動控制、裝卸操作,如果必要的話還要加上碰撞分析。生產(chǎn)單位仿真是模塊來模仿一個簡化生產(chǎn)過程中考慮到只有消費和補貨的材料或零件的活動。模擬緩沖和循環(huán)(產(chǎn)品全生命周期管理的物料看板)。它使兩種決策1)配置信號空物料看板物質(zhì)需求的;2)對車輛的配置。在模擬器系統(tǒng)中的地位、基本模擬設備如定時器和隨機數(shù)生成器,應提供模擬同步事件或離散事件。圖形用戶界面應該及時更新圖形仿真、系統(tǒng)事件,例如,及時響應用戶的要求在任何一種仿真和統(tǒng)計數(shù)據(jù)。
它直接的一個鮮明特點是“準時生產(chǎn)物料搬運模擬”。該仿真系統(tǒng)具有超越傳統(tǒng)模擬的功能。其一,其最顯著的能力是便于生產(chǎn)的時候重組。例如,裝配任務的安排生產(chǎn)電臺將調(diào)整執(zhí)行過程中仿真,從而瓶頸處和系統(tǒng)反應,可以不斷變化著的。另一個例子是每個組成部分是可控制的,不但在其配置參數(shù),而且在它的行為可控單獨(如,每一個汽車能夠使時間表,控制地位,選擇自己的交貨路線)。相比之下,其他仿真系統(tǒng)是在這之前處理了各文件各仿真信號的發(fā)射。人們難以分析的動力系統(tǒng)行為也通過改變系統(tǒng)配置在不同的模擬發(fā)射。
4. 以代理為基礎的準時生產(chǎn)物料搬運的模型
我們用代理技術的主要是對準時生產(chǎn)的物料搬運仿真系統(tǒng)模型。代理是先進的計算機程序,自主代表它們的用戶的行為,在合作和分布式環(huán)境中,開放的解決越來越多的復雜問題。在模擬器中有四種類型的代理設計。
1）主要控制商(MCA)
主要控制商(MCA)負責模擬、仿真終止時，初始化(線程)管理、線程同步。MCA還包括一個定時器和事件發(fā)電機及它的主線程。
2）車站代理（SA）
車站代理(SA)是一種處于運行中的線程模擬物質(zhì)需求的活動在車站。它是動態(tài)的生成和破壞的大腦中主動脈。一個簡單生產(chǎn)速度的裝配線上，他們所需配件數(shù)量一定。所以，同進步的一個生產(chǎn)步伐,物料平衡在電臺可能會達到環(huán)境保護的要求水平或緊急的水平。在極端的場合,材料可能感到疲憊引起整個裝配線停止。
3）看板時間表代理公司(KSA)
看板調(diào)度代理公司(KSA)是一個獨立的線程的職責是照顧1)資料要求的優(yōu)化調(diào)度問題的M-看板,(2)轉(zhuǎn)讓物料看板到交通工具。動態(tài)創(chuàng)建和銷毀KSA是由大腦中動脈決定。適用于普通及緊急調(diào)度策略。常規(guī)調(diào)度是實現(xiàn)由議付KSA之間進行,并參與。
4）車輛代理(VA)
每個看板分配到一個被懷疑是其車輛VA和所服務的VA通過一系列的行動。一輛汽車代理人是能夠處理其在當?shù)氐臅r間表,保持其地位,并控制其運動,修理和簡歷的行動。所有的車輛都產(chǎn)生螺紋或毀壞MCA在同一時間內(nèi)。
5．結論
基于要求的工業(yè)合作伙伴,物料搬運被認為是一個在大多數(shù)組裝廠中松散的環(huán)節(jié)。準時制生產(chǎn)是一個普遍范式實施在現(xiàn)今的制造工廠,奮力將生存在一個全球競爭。JIT的原則,提出了一種優(yōu)化的生產(chǎn)環(huán)境和運行機制,減少庫存浪費。然而,材料處理的問題,特別是物料或配件供應工廠地板水平,是很少提到的研究文學與實踐。在本文中,我們提出一個物料處理仿真系統(tǒng),應用準時生產(chǎn)的原則。物料看板是一個個體,攜帶資料的要求,代表著物資運輸任務。一個以代理人為基礎的模擬環(huán)境的設計與實現(xiàn)了一個原型系統(tǒng)使用。
材料處理的準時生產(chǎn)基礎預計將提出了許多優(yōu)點,如系統(tǒng)的水平在于優(yōu)化生產(chǎn), 在整個工廠地板平衡的交通負荷,獲得可管理性的材料上的操縱性能與準確預測和優(yōu)化運輸資源。最大的不同是,該仿真來自別人的靈活性，以代理人為基礎的模擬方法，便于各種假設情況場景包括不同的布圖設計,客觀參數(shù)和動態(tài)情況,在工廠地板。
AN AGENT-BASED SIMULATION OF A JIT MATERIAL HANDLING SYSTEM
Qi Hao and Weiming Shen
Integrated Manufacturing Technologies Institute
National Research Council, Canada
800 CoUip Circle, London, Ontario N6G 4X8, Canada
[qi.hao; weiming.shen]@nrc.gc.ca
Material handling is a loose loop in most assembly plants. Jiist-in-time (JIT) is amanagement philosophy that strives to eliminate sources of manufacturing waste by producing the right part in the right place at the right time. We propose to apply JIT principles to material handling in assembly plants. Material Kanbans are introduced as an effective means to control and balance the physical material/part flow in the plant Jloor. An agent-based simulation prototype is implemented using AnyLogic?. The flexibility of the agent-based approach facilitates the simulation of various "what-if" scenarios including different layout designs, objective parameters and dynamic situations in the plant floor.
1. INTRODUCTION
Material handling is a loose loop that is generally neglected in most production plants. From our observation, even in a well designed assembly line, in condition that the whole line is optimized in its layout, processes, buffering, scheduling, and operations, material handling is still laid outside of the scope of control. Managers spend their precious time hunting everywhere for missing parts and arranging their deliveries. They are unaware of material handling/delivery schedules and the related resource information (amount and utilization of resources, such as forklifts and drivers). As a result, material handling becomes the major barrier that results in low efficiency, production breakdowns, and low quality of a production system. Just-in-time (JIT) is a management philosophy that could improve profits and return on investment by reducing inventory levels, reducing variability, improving product quality, reducing production and delivery lead times, and reducing other costs (such as those associated with machine setup and equipment breakdown). The pull mechanism, especially introduced by the Kanban control of JIT manufacturing, enables an optimized production process that benefits from the cutting down of production resources. For a plant that already operates under a pull mechanism, material handling should also employ a pull mechanism rather than a MRP-based push mechanism.
This paper intends to propose a pull material handling system based on principles in JIT manufacturing. In such a system, materials transportation in the plant floor is considered as individual tasks. Material Kanban (M-Kanban) is introduced as a carrier of delivery tasks which is an effective means to control and balance the physical material handling flow in the plant floor. The main principle behind is that a task generated by a production station (cell) requires the occupation of an M-Kanban to be delivered. Another technology used in this material handling system is agent. Agent technology is evolved from the research domain of Distributed Artificial Intelligence in 1990s. From its emergence, agent technology is widely recognized as a promising paradigm for the next generation of design and manufacturing systems (Shen et al., 2001). In the JIT material handling simulation system, multiple agents are implemented to facilitate a collaborative problem solving environment. For example, each transportation vehicle is encapsulated as an agent so that it is manageable on its own parameters and behaviors, such as velocity, local schedule, and the associated scheduling, routing and conflict resolving ndes. The driver of a transportation vehicle can deactivate a vehicle from the system to take personal activities or when the vehicle malfunctions and needs a repair. Moreover, the allocation of transportation task is accomplished though the negotiation of a Kanban scheduling agent and a number of vehicle agents. With such capacities, the system is able to simulate very dynamic situations and get more accurate information of transportation resources in general.
The rest of this paper is organized as follows: Section 2 reviews the background knowledge and literature of this study; Section 3 identifies a sample JIT material handling problem and describes the corresponding requirements; Section 4 proposes an agent-based architecture of the JIT material handling system and discusses two major design aspects: production simulation and material handling simulation; finally. Section 6 draws our conclusions.
2. A TECHNOLOGY REVIEW
There are two classifications of production control systems, namely push and pull. Material requirement planning (MRP) systems and Kanban control systems are the two most popular implementations of the push and pull strategies respectively. In a push production, in order to buffer inaccurate forecasts, inaccurate lead times, inaccurate inventory records, variable production schedules or questionable bill of materials (BOMs), MRP generally incorporates safety lead times and safe stocks. However, in practice, MRP may result in a serious problem of excessive inventories (Shirk, 1998; Hopp and Spearman, 1996). Stock levels and lead times are amplified down throughout the supply chain, from the final distributor down to each hierarchy of suppliers.
In contrast, using a pull strategy, a JIT system uses underutilized capacity instead of buffer inventories to hedge against problems that may arise. Production is initiated in response to real customer orders and the removal of items from the final distributor buffers triggers production upstream to replenish exhausted inventories layer by layer. Krishnamurthy et al. (2004) quantitatively compares the performance of MRP and Kanban for a multi-stage, multi-product manufacturing system. They reached the conclusion that pull strategies are handicapped for manufacturing facilities that produce a number of different products with distinct demands and/or processing requirements, as well as for facilities that make highly engineered products in small batches (even one-of-a-kind) for their customers.
The ideal industries that JIT production applies include automobile because it is where the JIT concept originated. The automotive industry is characterized by low product variety, and high-volume production. In an automotive assembly line, although there are some sub-lines using push strategies (sometime it is called hybrid production), such as the body shop, paint shop, and engine line, however, once cars are lining up to be processed on the main assembly line, the production is under control of a pure pull mechanism. Buffers are set at offline sites of sub-lines to tickle uncertainties and better serve the optimized production rate of the main assembly line.
The concepts of JIT and Kanban are never new. JIT were firstly developed by Toyota in the 1950's and adopted in the United States in the 1980's. Lean manufacturing and lean enterprise, proliferating in western countries, are also evolved based on JIT principles. Many small and medium sized businesses have embraced these concepts along with some of the major corporations such as, Mercedes/Benz, Pratt & Whitney, Porsche and General Electric to name a few. Womack and Jones (1996) provide a thoughtful expansion upon their value-based business system based on the Toyota model. Along the way they update their action plan in light of new research and the increasing globalization of manufacturing, and they revisit some of their key case studies from the automotive, aerospace, and other manufacturing industries.
Many analytical, mathematical or experimental models are proposed to address the Kanban based operational planning and control issues (Berkley, 1992; Uzsoy and Martin-Vega, 1990). Simulation has been by far the methodology of choice in the majority of studies reported in the literature (Gupta and Al-Turki, 1998). Theoretically, the number of Kanbans and allocation of Kanbans in a system significantly affects the performance of a pull system. Instead of optimization of these Kanban arrangement which leads to a fixed number of Kanbans, Martins and Lewandrowski (1999) proposed a mathematical buffer stocks dimensioning approach using a dynamic kanban strategy. Gupta and Al-Turki (1998) compared the performance of a traditional kanban system (TKS) and a flexible kanban system (FKS). Through the simulation of two simple JIT models, they proved that FKS outperforms TKS under real-time manufacturing environments, such as sudden breakdown of a material handling system.
From application point of view, the researches of pull technologies could be classified in three categories: I) production control; 2) inventory management; and 3) supply chain management (Kusiak, 2000). Material handling is a topic being previously researched in the literature (Gupta and Al-Turki, 1998; Askin, 1999; Venkataramanaiah et al., 2001), however, they all deal with the automatic material (specifically, the Work-In-Process, which belongs to the production line itself) transfer problem between production cells. The material we emphasize here does not refer to the WIP going through the assembly line, rather, it is the supply fiow of material or parts subordinating to the main production line. None of known literature touched the topic of material handling from this aspect.
Moreover, in most plants, material inventories are only virtually under control of either a MRP II system or a Kanban system at the enterprise level, but not physically at the dynamic plant floor. Missing parts, wrong part delivered, parts not at right place at right time are common occurrence in almost all mainstream production plants, including GM, Ford, and Sterling Truck. Material handling is a frustrating problem faced by production managers. Production managers are feeling nervous everyday and are blamed for lack of ability to control the manufacturing process. As a result, analysis of material handling and dynamic simulation will be of great help to industries.
3. MATERIAL HANDLING SPECIFICATION OF A
SIMPLIFIED ASSEMBLY LINE
Simulation of material handling in a pull production setting is the primary purpose of this research. A Kanban-based material handling will be investigated to make it in line with the pull production line. For the convenience of a common understanding, a sample scenario is chosen as the background problem, as shown in Figure 1 （Figure slightly）.
The JIT-based material handling approach we proposed borrows similar principles from JIT-based production control and JIT-based inventory management in that: the right material is delivered from its inventory to the right production site, at the right time and in the right amount. Here, material transportation in the plant floor is considered as individual tasks. A task requires a material Kanban (M-Kanban) to be delivered. In figure 1, a material request signal is firstly generated by an assembly station running out of a part supply; after occupying a material Kanban, the request is broadcasted to a number of vehicles moving in the scope of a wireless network; then through negotiation, the task is confirmed by a vehicle and being delivered finally to the right station.
In our view, JIT material handling based on Kanban concept is not merely a pure event based system. In an event system, an event calls for a system response immediately; while in a Kanban based system, a generated event gets processed only after obtaining a physical object - M-Kanban. In other words, the processing (transportation) of a material requirement event holds until the system releases a M-Kanban and the event is qualified to occupy this free Kanban among all other events. Based on a Kanban control mechanism, we believe that the material handling system is able to reach a natural balancing of material requirements and transportation activities through delicate arrangement and management of Kanbans.
In addition to the standard function in a simulation environment, such as discrete event generation, simulation clock generation and an animation interface, this system should have special functional modules that try to model and simulate the dynamics in the plant floor. Three groups of functions make up the JIT material handling system: scenario generation, simulator, and graphical user interface. Scenario generation maintains a large variety of configuration information relating to: 1) static scenario, such as plant layout, moving tracks, and a central part inventory; 2) changeable simulation parameter, such as number of vehicles and number of Kanbans. Simulator is the core of the software in that it controls the simulation of both the production and material handling processes. Each transportation vehicle has a separate vehicle simulation module to make its own decisions, including task sequence, task schedule, moving control, loading andunloading operations, or even collision resolution decisions if necessary. Station simulation is a module to simulate a simplified production process taking into consideration only the consumption and replenishment activities of materials/parts at each station. Kanban Simulation manages buffering and circulation (life-cycle) of M-Kanbans. It makes two kinds of decisions 1) allocation of material requirement signals to empty M-Kanbans; 2) allocation of M-Kanbans to vehicles. In the simulator, basic simulation facilities such as timer and random number generator should be provided to simulate synchronize events or discrete events. Graphical User interfaces are supposed to timely update the graphical simulation, system event, exceptions, etc, and provide timely response upon users' requests for any kind of simulation and statistical data.
A distinctive feature of the designated JIT material handling simulation is "quasi-realism". The proposed simulation system possesses functions that surpass traditional simulations. The most distinguishing one is its ability to facilitate run-time reconfiguration. For example, the arrangement of assembly tasks to manufacturing stations could be adjusted during the execution of a simulation, so that the bottleneck (of the line) and system responses could be constantly changing. Another example is that each component is manageable not only in its configuration parameter, but also controllable in its behaviors individually (for example, each vehicle is able to make schedules, control status, and choose its own delivery route). In contrast, other simulation systems read a batch file before each simulation launch. It is difficult for people to analyze dynamic system behaviors by changing system configurations in separate simulation launches.
4. THE AGENT-BASED JIT MATERIAL HANDLING MODEL
We use agent technology to model major components in the JIT material handling simulation system. Agents are sophisticated computer programs that act autonomously on behalf of their users, collaborate across open and distributed environments, to solve a growing number of complex problems. There are four kinds of agents designed in the simulator.
1) Main Control Agent (MCA)
Main Control Agent (MCA) is responsible for simulation initialization, simulation termination, agent (thread) management, and thread synchronization. MCA also includes a timer and an event generator along with its main thread.
2) Station Agent (SA)
Station Agent (SA) is a running thread simulating material requirement activities at stations. It is dynamically generated and destroyed by the MCA. A simple production rate of the assembly line is set for all stations to consume their required parts in certain amounts. So, with the progress of one production step, the material balances at stations may reach the requirement levels or the urgent levels. In extreme occasion, materials may be exhausted which causes the whole assembly line to stop.
3) Kanban Schedule Agent (KSA)
Kanban Scheduling Agent (KSA) is a separate thread whose role is to take care of 1) the scheduling of material requirements to M-Kanbans, and 2) the assignment of M-Kanbans to vehicles. KSA is dynamically created anddestroyed by the MCA. It applies regular and emergent scheduling strategies. Regular scheduling is fulfilled by the negotiation carried out between KSA and participating VAs.
4) Vehicle Agents (VA)
Each Kanban assigned to a vehicle is confirmed by its VA and served by the VA through a series of actions. A vehicle agent is able to handle its local schedule, maintain its status, and controls its movement, repair, and resume actions. The threads for all vehicles are generated or destroyed by the MCA at the same time.
5. CONCLUSION
Based on requirements of industrial partners, material handling has been recognized as a loose loop in most assembly plants. Just-in-time is a pervasive paradigm implemented in nowadays manufacturing plants that strive to survive in a global competition. The principles of JIT bring forward an optimized production environment and a mechanism for waste less inventory replenishment. However, material handling problem, especially the material/part supply at the plant floor level, is seldom addressed in research literature and in practices. In this paper, we propose a material handling simulation system that applies JIT principles. Material Kanban is an entity that carries a material request and represents a material transportation task. An agent-based simulation environment is designed and a prototype system is implemented using AnyLogic. Many experiments will be performed based on the simulation model build for this purpose.
The JIT-Based material handling is expected to bring forward a number of advantages, such as optimization of stocks levels at production stations / cells, balancing of transportation load in the whole plant floor, obtaining manageability on material handling performance and accurate prediction and optimization of transportation resources. The major difference of this simulation from others is that the flexibility of the agent-based approach facilitates the simulation of various "what-if scenarios including different layout designs, objective parameters and dynamic situations in the plant floor.