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THIẾT KẾ ĐA MỤC TIÊU CỦA ĐỆM LÀM VIỆC TRONG QUÁ TRÌNH CHO TIẾN ĐỘ DỰ ÁN XÂY DỰNG LẶP ĐI LẶP LẠI Sự thay đổi trong sản xuất là một trong những yếu tố lớn nhất tác động tiêu cực đến việc thực hiện dự án xây dựng. Thực tế Xây dựng thông thường để bảo vệ các hệ thống sản xuất từ sự thay đổi là việc sử dụng đệm (Bf). Các nhà chuyên môn Xây dựng và các Nhà nghiên cứu đã đề xuất phương pháp đệm cho các tình huống sản xuất khác nhau, nhưng những phương pháp đó đã phải đối mặt với những hạn chế thực tế trong ứng dụng của họ. Mô hình phân tích đa mục tiêu (MAM) được đề xuất để phát triển một giải pháp đồ họa cho thiết kế Làm việc Trong Quá trình (WIP) của Bf để vượt qua những giới hạn thực tế để ứng dụng Bf, được thể hiện thông qua việc lập kế hoạch của các dự án xây dựng lặp lại. Mô hình phân tích đa mục tiêu dựa trên mô hình Mô phỏng Tối ưu (SO) và khái niệm Pareto Fronts. Cơ cấu làm việc của Mô phỏng Tối ưu hóa sử dụng Chiến lược Tiến hóa (ES) như phương pháp tìm kiếm tối ưu hóa, trong đó cho phép thiết kế tối ưu kích thước WIP Bf bằng cách tối ưu các mục tiêu dự án khác nhau (chi phí dự án, thời gian và năng suất). Cơ cấu làm việc được kiểm tra và xác nhận ở hai dự án xây dựng lặp đi lặp lại. Cơ cấu làm việc SO thì khi được khái quát hóa thông qua các khái niệm Pareto Front, tính đến sự phát triển của MAM như các đồ thị cho sử dụng thực tế. Những lợi thế của việc ứng dụng của MAM được biểu thị thông qua một ví dụ lập tiến độ dự án. Các kết quả chứng minh sự cải tiến việc thực hiện dự án và thiết kế hiệu quả và thực tế hơn với WIP Bf. Ngoài ra, chiến lược sản xuất dựa trên WIP Bf và nguyên tắc sản xuất tinh gọn trong xây dựng được thảo luận
THIT K A MC TIấU CA M LM VIC TRONG - QU TRèNH CHO TIN D N XY DNG LP I LP LI LECTURER : LNG C LONG (Ph.D) NHểM : 10 NGUYN HUNH TRC : 1670508 NGUYN NGC CNG : 1670125 CU QUC THUN : 1670157 HONG V TNG : 1670164 Abdtract Li dn Variability in production is one of the largest factors that negatively impacts construction project performance A common construction practice to protect production systems from variability is the use of buffers (Bf) Construction practitioners and researchers have proposed buffering approaches for different production situations, but these approaches have faced practical limitations in their application A multiobjective analytic model (MAM) is proposed to develop a graphical solution for the design of Work-In-Process (WIP) Bf in order to overcome these practical limitations to Bf application, being demonstrated through the scheduling of repetitive building projects Multiobjective analytic modeling is based on SimulationOptimization (SO) modeling and Pareto Fronts concepts Simulation Optimization framework uses Evolutionary Strategies (ES) as the optimization search approach, which allows for the design of optimum WIP Bf sizes by optimizing different project objectives (project cost, time and productivity) The framework is tested and validated on two repetitive building projects The SO framework is then generalized through Pareto Front concepts, allowing for the development of the MAM as nomographs for practical use The application advantages of the MAM are shown through a project scheduling example Results demonstrate project performance improvements and a more efficient and practical design of WIP Bf Additionally, production strategies based on WIP Bf and lean production principles in construction are discussed S thay i sn xut l mt nhng yu t ln nht tỏc ng tiờu cc n vic thc hin d ỏn xõy dng Thc t Xõy dng thụng thng bo v cỏc h thng sn xut t s thay i l vic s dng m (Bf) Cỏc nh chuyờn mụn Xõy dng v cỏc Nh nghiờn cu ó xut phng phỏp m cho cỏc tỡnh sn xut khỏc nhau, nhng nhng phng phỏp ú ó phi i mt vi nhng hn ch thc t ng dng ca h Mụ hỡnh phõn tớch a mc tiờu (MAM) c xut phỏt trin mt gii phỏp cho thit k Lm vic Trong Quỏ trỡnh (WIP) ca Bf vt qua nhng gii hn thc t ng dng Bf, c th hin thụng qua vic lp k hoch ca cỏc d ỏn xõy dng lp li Mụ hỡnh phõn tớch a mc tiờu da trờn mụ hỡnh Mụ phng Ti u (SO) v khỏi nim Pareto Fronts C cu lm vic ca Mụ phng Ti u húa s dng Chin lc Tin húa (ES) nh phng phỏp tỡm kim ti u húa, ú cho phộp thit k ti u kớch thc WIP Bf bng cỏch ti u cỏc mc tiờu d ỏn khỏc (chi phớ d ỏn, thi gian v nng sut) C cu lm vic c kim tra v xỏc nhn hai d ỏn xõy dng lp i lp li C cu lm vic SO thỡ c khỏi quỏt húa thụng qua cỏc khỏi nim Pareto Front, tớnh n s phỏt trin ca MAM nh cỏc th cho s dng thc t Nhng li th ca vic ng dng ca MAM c biu th thụng qua mt vớ d lp tin d ỏn Cỏc kt qu chng minh s ci tin vic thc hin d ỏn v thit k hiu qu v thc t hn vi WIP Bf Ngoi ra, chin lc sn xut da trờn WIP Bf v nguyờn tc sn xut tinh gn xõy dng c tho lun Introduction Gii thiu Variability in production is one of the largest factors that negatively impacts construction project performance It can induce dynamic and unexpected conditions, unsteadying project objectives and obscuring the means to achieve them To understand the effect of variability on production processes, Hopp and Spearman [1] distinguished two kinds of variability in manufacturing systems: 1) the time process of a task and 2) the arrival of jobs or workflow at a workstation Koskela [2] propose a similar classification to variability in construction systems, where the processes duration and the flow of preconditions for executing construction processes (space, equipment, workers, component and materials, among others) are understood as variable production phenomena From a practical standpoint, construction practitioners everyday observe this behavior in the project environment through varying production rates, labor productivity, schedule control, cost control S thay i sn xut l mt nhng nhõn t ln nht tỏc ng tiờu cc n thc hin d ỏn xõy dng Nú cú th to ng lc v nhng iu kin khụng mong i, nhng mc tiờu d ỏn khụng n nh v lm lu m ý ngha t c ca chỳng hiu c nh hng ca s thay i quy trỡnh sn xut, Hopp v Spearman [1] ó phõn bit hai loi bin i nhng h thng sn xut: 1) quỏ trỡnh thi gian ca mt nhim v v 2) s xut hin ca vic lm hoc lung cụng vic ni lm vic Koskela [2] xut mt phõn loi tng t cho s thay i h thng xõy dng, ni m thi gian ca cỏc quy trỡnh v dũng chy ca cỏc iu kin trc cho thc hin cỏc quy trỡnh xõy dng (khụng gian, thit b, nhõn cụng,vt liu, s nhng th khỏc) thỡ c hiu nh bin ca hin tng sn xut T quan im thc t, nhng nh chuyờn mụn xõy dng quan sỏt cỏch c x hng ngy mụi trng d ỏn thụng qua cỏc t l sn phm khỏc nhau, nng sut lao ng, kim soỏt tin , kim soỏt chi phớ Several researchers have shown that variability is a well-known problem in construction projects, which leads to a general deterioration of project performance on dimensions such as: cycle time, labor productivity, project cost, planning efficiency, among others A way to deal with variability impacts in production systems is through the use of buffers (Bf) By using a Bf, a production process can be isolated from the environment as well as the processes depending Mt s nh nghiờn cu ó a rng s thay i l bit n nhiu cỏc d ỏn xõy dng, m dn n mt s gim giỏ tr ca vic thc hin d ỏn v kớch thc nh : chu k, nng sut lao ng, chi phớ d ỏn, hiu qu ca k hoch v nhiu th khỏc Mt cỏch i phú vi tỏc ng ca s thay i h thng sn xut l thụng qua s dng m (Bf) Bng vic s dng Bf, mt quy trỡnh sn xut cú th b cụ lp on it Buffers can circumvent the loss of throughput, wasted capacity, inflated cycle times, larger inventory levels, long lead times, and poor customer service by shielding a production system against variability Hopp and Spearman define three generic types of Bf for manufacturing, which can be applied in construction as: mụi trng cng nh l nhng quy trỡnh ph thuc vo nú Cỏc m cú th phỏ v s tn tht ca s lng vt liu a vo mt quỏ trỡnh, lóng phớ nng lc, gia tng chu k, lng hng tn kho ln hn, thi gian hng dn di v dch v khỏch hng kộm bi vic bo h h thng sn xut chng li s thay i Hopp v Spearman nh ngha ba dng tng quỏt ca Bf cho sn xut, m cú th c ng dng xõy dng nh: Inventory: In-excess stock of raw materials, Work in Process (WIP) and finished goods, categorized according their position and purposes in the supply chain Time: Reserves in schedules as contingencies used to compensate for adverse effects of variability Float in a schedule is analogous to a Bf for time since it protects critical path from time variation in noncritical activities Hng húa tn kho: s d tha kho hng ca nguyờn liu thụ, Lm vic Trong Quỏ trỡnh (WIP) v thnh phm, c phõn loi theo v trớ v mc ớch ca chỳng chui cung ng Nng sut: phõn cụng lao ng, nng sut mỏy múc v thit b s d tha cho chỳng cú th hp th cỏc nhu cu sn xut thc t 3.Thi gian: D tr cỏc tin nh d phũng c s dng bự li cho nhng nh hng bt li ca s thay i S th ni tin thỡ tng t Bf cho thi nú bo v ng ti hn t s bin i thi gian hot ng khụng ti hn Theoretically, the analysis of Bf in this paper is based on lean production principles Lean production is a management philosophy focused on adding value from raw materials to finished product It allows avoiding, eliminating and/or decreasing waste from this so-called value stream Among this waste, production variability decreasing is a central point within the lean philosophy from a system standpoint Lean production, as applied in construction, focuses mainly on: i) decreasing non-value-adding V mt lý thuyt, s phõn tớch ca Bf bi bỏo ny c da trờn cỏc nguyờn lý sn xut tinh gn Sn xut tinh gn l mt trit lý qun lý trung vo giỏ tr gia tng t nguyờn liu n thnh phm Nú cho phộp trỏnh, loi b v /hoc gim lóng phớ t cỏi gi l chui giỏ tr Trong s lóng phớ ny, gim s thay i sn xut l mt im trung tõm trit lý tinh gn t mt quan im h thng Sn xut tinh gn, nh c ỏp dng xõy dng, trung ch yu vo: Capacity: Allocation of labor, plants and equipment capacity in excess so that they can absorb actual production demand problems activities or waste (e.g wait times); ii) increasing value-adding activities efficiency (process duration); iii) decreasing variability; and iv) optimizing the production system performance as a whole i) gim cỏc hot ng khụng cú giỏ tr gia tng hoc lóng phớ (vớ d: thi gian ch i); ii) gia tng cỏc hot ng cú giỏ tr gia tng hiu qu (vớ d: thi gian quy trỡnh); iii) gim s thay i; v iv) ti u húa hiu sut h thng sn xut In construction, current buffering practices generally follow an intuitive and/or informal pattern, leading to poor variability control Recently, several researchers and practitioners have proposed new Bf approaches to manage variability in construction, which have allowed industry to partially avoid informal and intuitive methods of designing and managing Bf in construction However, these methods have been either too theoretical in design or too difficult to apply in practice In Fact, there is limited evidence showing any use of practical buffering design approaches in construction practice Trong xõy dng, hin thc hnh m thng theo mt cỏch trc giỏc v/hoc hỡnh thc, dn n hn ch vic kim soỏt s thay i Gn õy, mt s nh nghiờn cu v nh chuyờn mụn ó xut cỏc phng phỏp Bf mi qun lý s thay i xõy dng, cho phộp ngnh cụng nghip trỏnh mt phn cỏc phng phỏp hỡnh thc v trc giỏc ca thit k v qun lý Bf xõy dng Tuy nhiờn, cỏc phng phỏp ny hoc l thun lý thuyt hoc l quỏ khú ỏp thc t Trờn thc t, cú bng chng hn ch cho thy bt k vic s dng tht s ca cỏc phng phỏp thit k m thc t xõy dng This paper presents a buffering approach that is applicable for Work-In-Process(WIP) in repetitive building projects In construction, WIP can be defined as the difference between cumulative progress of two consecutive and dependent processes, which characterizes work units ahead of a crew that will perform work (e.g., work units that have not been processed yet, but that will be) This definition of WIP is clearer in repetitive projects where processes are repeated continuously (highways, railways, pipelines, sewers, etc.) or in discrete repeated units (highrise buildings, multistorey building, and repetitive residential projects, etc.) Existing research explores, the use of WIP Bf in repetitive projects, both implicit and explicitly, and demonstrates the Bi vit ny gii thiu mt phng phỏp m m cú th ỏp dng cho Lm vic Trong Quỏ trỡnh (WIP) d ỏn xõy dng lp i lp li Trong xõy dng, WIP cú th c nh ngha nh l s khỏc gia cỏc tin trỡnh tớch ly ca hai quỏ trỡnh liờn tip v ph thuc, m cỏc c trng n v lm vic trc ca mt i (nhúm) ú s thc hin cụng vic (vớ d: n v cụng vic m cha c thc hin, nhng cụng vic ú s c lm) nh ngha ny ca WIP thỡ rừ rng hn d ỏn lp i lp li ni m cỏc quy trỡnh c lp li liờn tc (vớ d: ng cao tc, ng st, ng ng, cng thoỏt nc v.v ) hoc cỏc n v ri rc c lp i lp li (cỏc nh cao tng, d limitations of its application This body of research suggests opportunities to improve the use of WIP Bf and to overcome practical limitations in current buffering approaches ỏn khu dõn c lp i lp li) Theo cỏc nghiờn cu kho sỏt hin cú, vic s dng WIP Bf d ỏn lp li, c tim n v rừ rng, chng minh s hn ch ng dng ca nú Bn thõn ca nghiờn cu ny gi ý c hi ci tin vic s dng WIP Bf v vt qua gii hn thc hnh cỏc phng phỏp m hin However, WIP Bf application in a production system is neither an apparent nor a direct task The use of WIP Bf is controversial from a lean production perspective since the lean ideal suggests that zero inventories, or non-buffered production systems, are desirable Nevertheless, a production system without WIP implies a production system without throughput Hopp and Spearman recognize this issue and state that pull mechanisms in a production system not avoid the use of buffers However, the use of large WIP Bf to ensure throughput in production systems will inherently increase cycle times and costs Therefore, it appears that a balance problem exists between the use of WIP Bf to reduce variability impacts and overall production system performance based on lean principles Tuy nhiờn, s ỏp dng WIP Bf mt h thng sn xut thỡ khụng rừ rng m cng khụng l nhim v trc tip Vic s dng WIP Bf gõy tranh cói t trin vng sn xut tinh gn ý tng tinh gn gi ý rng hng tn kho bng khụng, hoc cỏc h thng sn xut khụng m, thỡ ỏng mong mun Tuy th, mt h thng sn xut m khụng WIP bao gm mt h thng sn xut m khụng vt liu c a vo mt quỏ trỡnh Hopp v Spearman nhỡn nhn ny v tuyờn b rng c cu mt h thng sn xut khụng trỏnh vic s dng Bf Tuy nhiờn, vic s dng rng rói WIP Bf m bo vic a nguyờn liu vo mt quỏ trỡnh h thng sn xut s gn lin vi gia tng s ln quay vũng v chi phớ Vỡ th, nú xut hin mt cõn bng tn ti gia vic s dng WIP Bf gim tỏc ng s thay i v hiu sut h thng sn xut tng th da vo cỏc nguyờn tc tinh gn Simulation Optimization (SO) modeling can address this balance problem Simulation Optimization modeling can help to design appropriate WIP Bf sizes by addressing the trade-off between decreasing variability through larger WIP Bf sizes and increasing production system performance by lowering WIP Bf sizes to the theoretical limit of zero In designing optimal Mụ hỡnh mụ phng ti u húa (SO) cú th gii quyt cõn bng ny Mụ hỡnh mụ phng ti u húa cú th giỳp thit k phự hp kớch c WIP Bf bng cỏch gii quyt s tha hip gia vic gim s thay i thụng qua kớch c WIP Bf ln hn v vic tng hiu sut h thng sn xut bi vic h thp kớch c WIP Bf n gii hn lý thuyt ca zero WIP Bf sizes, SO modeling must account for different project objectives (project cost, time and/or productivity) Computer simulation is being actively applied as a research tool to investigate how buffering strategies affect construction production systems.To date, research has only addressed specific cases of buffering strategies and it has not effectively addressed the balance problem The first application of SO to model Bf in construction was proposed by [5], and a similar SO approach to model Bf in a construction scheduling context was also developed by [33] Though both explicitly addressed the balance problem in theory, the research was not applied to an actual WIP Bf design in construction Trong vic thit k ti u kớch c WIP Bf, mụ hỡnh SO phi tớnh toỏn cho nhng mc tiờu d ỏn khỏc (chi phớ d ỏn, thi gian v/hoc nng sut) Mụ phng mỏy tớnh ang c tớch cc ỏp dng nh mt cụng c nghiờn cu iu tra chin lc m nh hng h thng sn xut xõy dng nh th no n nay, nghiờn cu ch mi gii quyt cỏc trng hp c th ca chin lc m v nú khụng gii quyt hiu qu cõn bng ng dng u tiờn ca SO n mụ hỡnh Bf xõy dng thỡ c xut bi [5], v mt phng phỏp SO tng t n mụ hỡnh Bf bi cnh tin xõy dng thỡ ó cng c phỏt trin bi [33] Mc dự c hai cỏch c gii quyt rừ rng cõn bng lý thuyt, nghiờn cu ó khụng c ỏp dng thit k WIP Bf thc t xõy dng Research objective Mc tiờu nghiờn cu The main goal of this research is to propose and validate a simple graphical approach to design WIP Bf in repetitive building projects Accomplishment of this goal requires the development of a multiobjective analytic model (MAM) based on SO modeling which uses Evolutionary Strategies (ES) as the optimization search approach and Pareto Front concepts To be practically applicable, this MAM should result in nomographs to facilitate its use in the process of WIP Bf design The paper addresses the development, testing and validation of SO approach and resultant MAM and the proposed graphical approach to design WIP Bf Mc tiờu ch yu ca nghiờn cu ny l xut v xỏc nhn mt phng phỏp th n gin thit k WIP Bf cỏc d ỏn xõy dng lp li Hon thnh mc tiờu ny ũi hi s phỏt trin ca mụ hỡnh phõn tớch a mc tiờu (MAM) da trờn mụ hỡnh SO m s dng Chin lc Tin húa (ES) nh l phng phỏp tỡm kim ti u v khỏi nim Pareto Front cú th ỏp dng thc t, MAM ny a n th toỏn hc thun tin s dng nú quỏ trỡnh thit k WIP Bf Bi bỏo cp n s phỏt trin, th nghim v xỏc nhn ca phng phỏp SO v kt qu MAM v xut phng phỏp th thit k WIP Bf Research methodology Phng phỏp lun nghiờn cu The research methodology consists on three stages: 1) definition of the SO framework to design WIP Bf; 2) testing and validation of the SO frame; and, 3) development and application of the MAM to design WIP Bf A discrete event simulation modeling architecture is employed as a basis for developing the SO framework The SO framework is then applied to two multifamily residential building projects for testing and validation The application includes the construction of discrete event simulation models for repetitive processes, SO modeling to design optimum WIP Bf sizes, and the development and implementation of buffered construction schedules Finally, using the SO framework and Pareto Front concepts, this research develops the MAM for practical application of the concepts, thereby achieving its goal for a simple and practical tool to design WIP Bf in repetitive building projects Multiobjective model development involves: i) the definition of multiobjective nomographs to address the design WIP Bf sizes with various project objectives; ii) sensitivity analysis and selection of WIP Bf sizes according to project preferences; iii) development of buffered construction schedules; and iv) application on a construction project example Phng phỏp lun nghiờn cu bao gm giai on: 1) nh ngha ca c cu lm vic SO thit k WIP Bf; 2) th nghim v xỏc nhn C cu SO v 3) s phỏt trin v ỏp dng ca MAM thit k WIP Bf Mt kin trỳc mụ hỡnh mụ phng cỏc s vic ri rc c dựng nh nn tng cho vic phỏt trin c cu lm vic SO C cu lm vic SO c ỏp dng cho hai d ỏn xõy dng khu dõn c nhiu gia ỡnh kim tra v xỏc nhn Vic ỏp dng bao gm xõy dng cỏc mụ hỡnh mụ phng cụng vic ri rc cho cỏc tin trỡnh lp li, mụ hỡnh SO thit k ti u kớch c WIP Bf, s phỏt trin v thc hin ca m tin xõy dng Cui cựng, vic s dng c cu lm vic SO v khỏi nim Pareto Front, nghiờn cu ny phỏt trin MAM cho cỏc ng dng thc t ca cỏc khỏi nim, t ú t c mc tiờu ca nú cho mt cụng c ng dng n gin v thit thc thit k WIP Bf cỏc d ỏn xõy dng lp i lp li S Phỏt trin mụ hỡnh a mc tiờu bao gm: i) xỏc nh ca th toỏn hc a mc tiờu gii quyt thit k kớch c WIP Bf vi cỏc mc tiờu d ỏn khỏc nhau; ii) phõn tớch nhy v la chn kớch c WIP Bf theo d ỏn u tiờn; iii) phỏt trin m cho tin xõy dng; v iv) ỏp dng trờn mt d ỏn xõy dng mu Describing WIP Bf in repetitive construction proceeses Miờu t WIP Bf quỏ trỡnh xõy dng lp i lp li In repetitive projects, WIP Bf can be characterized by a Linear Scheduling Diagram Fig shows the diagram for n processes in a repetitive project with their different production parameters Let repetitive and sequential processes P1, P2,, Pn 1, Pn with average Trong cỏc d ỏn lp i lp li, WIP Bf cú th c c trng bi mt th tin tuyn tớnh H1 biu din th cho n quỏ trỡnh mt d ỏn lp i lp li vi cỏc thụng s sn xut khỏc ca chỳng quỏ trỡnh lp i lp li v theo tun t P 1, P2, , Pn 1, Pn production rates and standard deviation called m1, m2,, mn 1, mn (units/day) and SD1, SD2,, SDn 1, SDn (units/day), respectively Production rates (mi) for each process are an average value with a certain variation (SDi) This variable behavior can be mathematically captured by means of probability density functions (PDF) of duration by production unit or daily production rate (see Fig 1a and 1b) Fig 1a shows the duration PDF (f(x)), with an expected duration by production unit (D) and a certain standard deviation (D) for actual cumulative progress Fig 1b shows production rate PDF (f(y)), with an expected progress or production rate by day (PR) and a certain tandard deviation (PR) for actual time vi tc sn xut trung bỡnh v lch chun tng ng l m1, m2, , mn-1, mn (n v/ngy) v SD1, SD2, , SDn - 1, SDn (n v/ngy) Tc sn xut (mi) cho mi quỏ trỡnh l mt giỏ tr trung bỡnh vi mt s bin thiờn nht nh (SDi) Cỏch ng x ca bin ny cú th c nm bt chớnh xỏc bng cỏc hm mt xỏc sut (PDF) ca thi gian theo n v sn xut hoc tc sn xut hng ngy (xem hỡnh 1a v1b) Hỡnh 1a biu din PDF (f(x)) thi gian, vi thi gian c d kin bi n v sn xut (D) v lch chun nht nh (D) cho tin trỡnh tớch ly thc t Hỡnh 1b biu din PDF (f(y)) tc sn xut, vi mt tin trỡnh c d kin hoc tc sn xut theo ngy (PR) v lch chun nht nh (PR) cho thi gian thc t Fig Graphical representation of model for WIP Bf characterizing n processes: (a) unitary duration PDF, and (b) daily production rate PD H th biu din cỏc mụ hỡnh cho c trng WIP Bf n quỏ trỡnh: (a) PDF thi gian n l, v (b) PDF tc sn xut hng ngy Variability of a process, represented by a PDF for duration or production rate in this case, impacts the succeeding processes For instance, P1 variability impacts P2, P2 variability impacts P3, and so on The production variability has a cumulative effect from upstream processes to downstream processes in repetitive production systems (i.e., a ripple effect) WIP Bf decreases this effect, isolating and protecting downstream processes from upstream processes variability The location and size of WIP Bf for repetitive project can be seen in Fig.1 Let WIP Bf1,2, WIP Bf2,3,,WIP Bfn1,n which have the corresponding Time Bf called T Bf1,2, T Bf2,3,,T Bfn1,n, respectively The main assumption relating to the location and size of WIP Bf within production processes is that these are restrictions applied only at the beginning of processes, which could change during the progression of work between processes S thay i ca mt tin trỡnh, c i din bi mt PDF cho thi gian hoc tc sn xut trng hp ny, cỏc tỏc ng n s cỏc quy trỡnh thnh cụng Chng hn, s thay i P1 tỏc ng P2, s thay i P2 tỏc ng P3, v c tip nh vy Thay i sn xut cú hiu ng tớch ly t u chui quỏ trỡnh n cui chui quỏ trỡnh h thng sn xut lp i lp li (hiu ng gn súng) WIP Bf lm gim hiu ng ny, cụ lp v bo v cui chui quỏ trỡnh t s thay i ca u chui quỏ trỡnh V trớ v kớch c ca WIP Bf cho d ỏn lp i lp li cú th c nhỡn thy H1 cho WIP Bf1,2, WIP Bf2,3, , WIP Bfn-1,n m cú Bf Thi gian tng ng c gi l T Bf1,2, T Bf2,3, , T Bfn-1,n, tng ng Gi thuyt chớnh liờn quan n v trớ v kớch c ca WIP Bf quỏ trỡnh sn xut thỡ cú nhng hn ch ch c ỏp dng lỳc bt u ca quỏ trỡnh, m cú th thay i quỏ trỡnh tin trin ca cụng vic gia cỏc quỏ trỡnh Modeling requires definitions for the various states and boundary conditions relating to WIP Bf sizes Minimum WIP Bf (MWIP Bf) is the minimum amount of work units ahead of a crew, from which the crew can perform its work and avoid any technical problem relating to buffering (e.g., the Bf to avoid crew congestion) This is a boundary condition for modeling and it has a related Time Bf that is defined as Minimum Time Mụ hỡnh ũi hi s nh ngha cho cỏc tỡnh trng khỏc v iu kin biờn liờn quan n kớch c WIP Bf WIP Bf (MWIP Bf) ti thiu l lng ti thiu ca cỏc n v cụng vic trc ca mt i (nhúm), t ú m cỏc i (nhúm) cú th thc hin cụng vic ca mỡnh v trỏnh bt k k thut liờn quan n m (vớ d: Bf trỏnh s tc nghn i) õy l mt iu kin biờn cho mụ hỡnh 10 WIP Bf On the other hand, the average COV of mi is increased for the buffered case and on-site implementation up to 12.34% and 44.34% respectively, in relation to the base case The main reason is the high increment of variability in process P4 In contrast, the remaining processes tend to maintain their variability levels P4 had the higher mi due to the IWIP Bf size impact (8.80 units) induced by higher levels of production rates for type units and lower levels of production rates for type units This range of production rates between type and units produces high levels of buffered and on-site COV for mi cú th c quy cho cỏc WIP Bf Mt khỏc, COV bỡnh quõn ca mi i vi cỏc trng hp khụng m v thc hin trờn cụng trng tng lờn n 12,34% v 44,34% tng ng, liờn quan n cỏc trng hp c s Lý chớnh l s tng cao ca bin i quỏ trỡnh P4 Ngc li, cỏc quỏ trỡnh cũn li cú xu hng trỡ mc a dng ca chỳng P4 cú mi cao hn tỏc ng kớch thc IWIP Bf (8,80 n v) gõy bi cp cao hn mc sn xut cho loi n v v cỏc cp thp hn ca mc sn xut cho loi n v Phm vi ca mc sn xut ny gia loi v loi n v sn xut mc cao cho m v trờn cụng trng COV cho mi 7.4 7.4 Tho lun v th nghim v xỏc nhn SO Discussion of SO testing and validation Site personnel for both projects agreed with the project improvements after WIP Bf implementation They found increasing efficiency of crews and reduction in production system variability Also, they perceived that the SO approach was a reliable tool to design WIP Bf, requiring a minimum effort of implementation, control and measurement supported by scheduling construction process Nhõn viờn cụng trng cho c hai d ỏn ó ng ý vi cỏc ci tin d ỏn sau thc hin WIP Bf H ó tỡm thy s gia tng hiu qu ca thuyn viờn v gim h thng sn xut bin i Ngoi ra, h nhn thy rng cỏch tip cn SO l mt cụng c ỏng tin cy thit k WIP Bf, ũi hi mt n lc ti thiu ca vic thc hin, kim soỏt v measureơment c h tr bng cỏch lp lch trỡnh xõy dng The SO testing and validation showed that the IWIP Bf size for MinTC is between the IWIP Bf sizes for Max ATm and Min TCT respectively Table shows that the average IWIP Bf sizes in Project A for Min TCT, Min TC and Max ATm are 2.3 units, 5.8 and 12.8 units respectively, where the location of the average IWIP Bf size for Min TC is between Min TCT and Max ATm Furthermore, the average IWIP Bf size for MinTC Vic xỏc nhn SO th nghim v cho thy rng kớch thc IWIP Bf cho MinTC l gia cỏc kớch thc IWIP Bf cho Max v Min ATM TCT tng ng Bng cho thy rng kớch thc IWIP Bf trung bỡnh ti d ỏn A cho Min TCT, Min TC v Max ATM l 2,3 n v, 5,8 v 12,8 n v tng ng, ni v trớ ca trung bỡnh kớch thc IWIP Bf cho Min TC l gia Min TCT v Max ATM Hn na, trung bỡnh 44 has time and production rate responses located between the same ones for Min TCT and Max ATm Table shows a similar behavior for Project B, where average IWIP Bf sizes for MinTC (16.0 units) is between the average IWIP Bf sizes for Min TCT (5.0 units) and Max ATm (25.7 units), being the location for its time and production responses similar to Project A Evidence showed that the type of project objectives for nomographs (Figs 3d and 4) and location of IWIP Bf sizes for Min TC were appropriate, demonstrating that the assumptions for the MAM were correct kớch thc IWIP Bf cho MinTC cú thi gian v tc sn xut ỏp ng nm gia nhng ngi cựng cho Min TCT v Max ATM Bng cho thy mt hnh vi tng t i vi D ỏn B, ni m trung bỡnh kớch thc IWIP Bf cho MinTC (16,0 n v) l gia cỏc kớch thc IWIP Bf trung bỡnh cho Min TCT (5,0 n v) v Max ATM (25,7 n v), v trớ cho thi gian ca nú v phn ng sn xut tng t nh d ỏn A Bng chng cho thy rng cỏc loi mc tiờu d ỏn cho nomographs (hỡnh 3d v 4) v v trớ ca cỏc kớch c IWIP Bf cho Min TC l thớch hp, chng minh rng cỏc gi nh cho MAM l ỳng Fig WIP Bf design nomographs for 10 repetitive processes, with an expected duration by unit of DAYS: (A) COVD=25%, (B) COVD=50%, (C) COVD=75%, AND (D) COVD=98% IWIP BF SIZES 45 ARE PRODUCTION UNITS Hỡnh thit k WIP Bf cho 10 quy trỡnh lp i lp li, vi thi gian d kin ca n v ngy: (a) COVD = 25%, (b) COVD = 50%, (c) COVD = 75%, v (d) COVD = 98% IWIP Bf kớch thc l n v sn xut Table 8 MAM application optimum ng dng MAM Sensitivity analysis to choose the IWIP Bf size Bng :Phõn tớch nhy la chn ti u kớch thc IWIP Bf To illustrate the development and application of minh cho s phỏt trin v ng dng the MAM, anWIP example of ATm projectActual schedule is ca MAM, mt vớ d v tin d ỏn c Bf Actual TCT Actual TC ($) AATm ATCT ATCa (units) (units/day) (days) used In doing so, a repetitive building project can s dng Khi lm nh vy, mt d ỏn xõy be tested Fig shows the scheduling network, dng lp i lp li cú th c kim tra 0.44 $2,009,216 planned production parameters and 258 costs The Hỡnh 11.49% cho thy18.36% mng 6.02% li lp lch trỡnh, expected duration for each process is days for thụng s sn xut k hoch v chi phớ Thi 0.48 275 $1,947,261 3.81% 25.98% 2.75% each production unit and the MWIP Bf is unit gian d kin cho mi quỏ trỡnh l ngy i 0.50 346 $1,951,267 0.81%v sn58.71% vi mi n xut v2.96% MWIP Bf l n v 12 0.50 416 $1,972,811 0.06% 90.85% 4.10% 16 $1,999,392 - 0.16%cỏc 124.09% 5.50% To get nomographs for0.50 this project489example, cú c th cho d ỏn ny, mu SO modeling for 10 processes was 560 developed SO cho -0.32% 10 quy trỡnh c phỏt trin (bng 20 0.50 $2,026,231 157.03% 6.92% (equal to the number of processes over Critical s lng ca cỏc quỏ trỡnh trờn ng Estimated as the shown differencein between and planned Path for example Fig actual 7) Using BetaTC.quan trng cho vớ d th hin hỡnh 7) l s khỏc bit gia TC thc t v k hoch PDFs c fortớnhthe duration of these processes, S dng cỏc file PDF Beta sut thi nomographs for variability levels (COVD) of 25%, gian ca cỏc quỏ trỡnh ny, cỏc th cho 50%, 75% and 98% were developed Note that cỏc mc bin thiờn (COVD) l 25%, 50%, the Beta PDFs limited the level of variability to 75% v 98% ó c phỏt trin Lu ý rng 98% due to the nature of the function Expected cỏc file PDF Beta gii hn mc bin i duration by unit of days for each process and n 98% tớnh cht ca hm thi gian d COVD value was estimated, evaluating the Beta kin ca n v ngy i vi mi quỏ trỡnh PDF In practice, nomographs for different v giỏ tr COVD c tớnh, ỏnh giỏ Beta number of processes and variability levels could PDF Trong thc t, cỏc th cho s lng be available A decision maker will need to khỏc ca cỏc quỏ trỡnh v mc bin choose the most appropriate nomographs thiờn cú th cú sn Mt nh sn xut quyt according to project characteristics and/or the nh s cn phi chn cỏc th thớch hp decision maker's preferences nht theo c im ca d ỏn v / hoc s thớch ca ngi quyt nh ca a Initially to develop the nomograph, IWIP Bf Ban u phỏt trin cỏc cỏc th, kớch sizes for Min ATm and Min TCT (nomographs thc IWIP Bf cho Min ATM v Min TCT 46 extreme points) for each COVD were computed through SO processes in the same procedure used to obtain optimum IWIP Bf in the case studies Afterwards, intermediate IWIP Bf sizes were determined for each COVD by simple inspection (i.e inspection of integer values for IWIP Bf sizes contained between extreme points) Final responses over AATm and ATCT after 1000 simulation runs for each case were estimated, stating the Pareto Front lines in Fig (nomographs cc im) cho mi COVD c tớnh toỏn thụng qua cỏc quỏ trỡnh SO cỏc th tc tng t c s dng cú c ti u trng hp IWIP Bf cỏc nghiờn cu Sau ú, kớch thc IWIP Bf trung cp ó c xỏc nh cho tng COVD qua s kim tra n gin (vớ d: kim tra cỏc giỏ tr s nguyờn cho cỏc kớch c IWIP Bf cha gia cỏc im cc) phn ng chớnh thc trờn AATm v ATCT sau 1000 chy mụ phng cho tng trng hp c tớnh, ú bt u ghi nhng dũng Pareto hỡnh By using multivariate linear regression, two kinds of analytical expressions for each nomograph were developed (Fig 8) The first one provides a relationship between AATm and ATCT A decision maker can then estimate both graphic and analytically the expected results for AATm and ATCT For example, Fig 8b shows graphically that for ATCT of 18.4%, there will be a AATm of 11.5% (i.e there will be an increment of TCT and a reduction of average production rates for construction processes, respectively, compared with initial estimations) Additionally, a decision maker can compute with the analytical expression shown in Fig 8b the value of AATm given ATCT The second expression provides the relationship of IWIP Bf size to both AATm and ATCT Similarly, the decision maker can estimate the IWIP Bf size both graphic and analytically For example in Fig 8b, the IWIP Bf is unit All analytic expressions in Fig had a coefficient of determination (R2) higher or equal to 0.98 and a P-value (at a level=0.05) lesser or equal to 0.002, demonstrating their statistical significance Bng cỏch s dng hi quy tuyn tớnh a bin, cú hai loi biu thc gii tớch cho mi nomograph c phỏt trin (Hỡnh 8) Ngi u tiờn cung cp mt mi quan h gia AATm v ATCT Sau ú mt quyt nh cú th c tớnh c cỏc kt qu d kin cho AATm v ATCT v phõn tớch Vớ d, hỡnh 8b hin th m cho ATCT 18,4%, s cú mt AATm 11,5% (ngha l s cú mt s gia ca TCT v gim tc sn xut trung bỡnh i vi quỏ trỡnh xõy dng, tng ng, so vi estimations ban u) Ngoi ra, nh sn xut quyt nh cú th tớnh toỏn vi cỏc biu hin phõn tớch hỡnh 8b giỏ tr ca AATm cho ATCT Biu thc th hai cung cp cỏc mi quan h ca kớch thc IWIP Bf cho c AATm v ATCT Tng t nh vy, ngi quyt nh cú th c tớnh kớch thc IWIP Bf c v phõn tớch Vớ d hỡnh 8b, cỏc IWIP Bf l n v Tt c cỏc biu phõn tớch hỡnh ó cú mt h s xỏc nh (R2) cao hn hoc bng 0,98 v giỏ tr P ( mt mc = 0,05) thp hn hoc bng 0.002, th hin ý ngha thng kờ 47 For this example, the nomograph from Fig 8b applies because it is estimated that processes could reach variability levels of 50% during the execution phase The Actual ATm, TCTand TC can be computed using the IWIP Bf sizes from the nomograph and Eqs (6), (7) and (11) respectively The sensitivity analysis is shown in Table In this case, it has been assumed that a decision maker could be interested in minimizing project cost Therefore, the optimum IWIP Bf size is units (see Table 8) i vi vớ d ny, th t hỡnh 8b ỏp dng bi vỡ nú c c tớnh rng cỏc quỏ trỡnh cú th t c mc thay i ca 50% giai on thc hin ATM thc t, TCTand TC cú th c tớnh bng cỏch s dng cỏc kớch c IWIP Bf t th v EQS (6), (7) v (11) tng ng Cỏc phõn tớch nhy c th hin Bng Trong trng hp ny, nú ó c gi nh rng mt quyt nh cú th c quan tõm vic gim thiu chi phớ d ỏn Do ú, ti u kớch thc IWIP Bf l n v (xem Bng 8) To analyze the impacts of the WIP Bf strategy design, a network schedule IWIP Bf of units was constructed for a hypothetical repetitive building project Two scenarios were taken into account: a base case (with MWIP Bf size equal to unit) and a buffered case The processes duration PDFs are shown in Table The IWIP Bf on the repetitive building project is shown in Table 10 with 1000 simulation runs for each scenario, which indicates improvements in cost using IWIP Bf size equal to units This approach decreases the impacts of variability on labor productivity and stimulates continuous resource utilization It is interesting to comment that variability levels and process durations for stochastic production situations (without and with buffer) were higher than the nomograph of Fig 8b However, the conservative results of the nomograph not negate the beneficial impacts of WIP Bf on the variable production scenarios phõn tớch nhng tỏc ng ca vic thit k chin lc WIP Bf, mt lch trỡnh mng IWIP Bf vi n v c xõy dng cho mt d ỏn xõy dng gi thuyt lp i lp li Hai kch bn c a vo ti khon: mt trng hp c s (vi kớch thc MWIP Bf bng n v) v mt trng hp m Cỏc file PDF thi gian quy trỡnh c th hin Bng IWIP Bf v d ỏn xõy dng lp i lp li c th hin Bng 10 vi 1000 mụ phng chy cho mi kch bn, ú ch nhng ci tin chi phớ s dng kớch thc IWIP Bf bng n v Cỏch tip cn ny lm gim tỏc ng ca bin i v nng sut lao ng v kớch thớch s dng ngun lc liờn tc Nú thỳ v nhn xột rng mc thay i v thi gian quỏ trỡnh sn xut cho cỏc tỡnh ngu nhiờn (khụng cú v vi b m) cao hn so vi th ca hỡnh 8b Tuy nhiờn, kt qu bo th ca th khụng ph nhn nhng tỏc ng cú li ca WIP Bf trờn kch bn sn xut bin In summary, nomographs allow project Túm li, Cỏc th cho phộp cỏc nh sn decision makers to design IWIP Bf sizes for a xut quyt nh d ỏn thit k kớch thc 48 construction schedule following steps: 1) Select a variability level for project through COVD, 2) Determine production responses on cost (ATCT) and production rates (AATm) for each IWIP Bf size defined along Pareto Front lines, 3) Develop sensitivity analysis over actual cost, time and production rates using production responses of abacus (i.e ATCT and AATm) and Eqs.(6), (7) and (11), and (4) Select optimum IWIP Bf size according to decision makers' preferences on production objectives, i.e to minimize cost or time and to maximize production rates Processes P1 P10 P ,P 11. 18 P19,,P23 IWIP Bf cho mt tin xõy dng theo cỏc bc sau õy: 1) Chn mt mc bin i cho d ỏn thụng qua COVD, 2) Xỏc nh phn ng sn xut trờn chi phớ (ATCT) v mc giỏ sn xut (AATm) cho mi kớch thc IWIP Bf c xỏc nh theo cỏc ng ng k Pareto, 3) Xõy dng phõn tớch nhy hn chi phớ thc t, thi gian v mc sn xut bng phn ng sn xut ca bn tớnh (tc l ATCT v AATm) v EQS (6), (7) v (11), v (4 ) Chn ti u kớch thc IWIP Bf theo s thớch ca ngi quyt nh "v mc tiờu sn xut, tc l gim thiu chi phớ v thi gian, ti a húa tc sn xut Table Duration PDFs for processes of project example PDF PDF parameters Type COVD Average duration by unit (days) (%) 2.34 57.39 Beta a = 0.7 b = 1.01 L=0.5 U=5.0 2.15 57.74 56.36 Uniform Gamma a = b = 4.3 a=3.17 3=0.694 2.20 Bng : Hm mt xỏc sut thi gian cho cỏc quỏ trỡnh d ỏ Table 10 Project performance comparisons between WIP Bf scenarios at strategic level (so sỏnh hiu sut d ỏn gia hai kch bn WIP Bf cp chin lc) Production scenario (kch bn sn xut) WIP Bf size (units) AATm ATCT ATC Real case without Bf 23.31% 26.40% 14.54% Buffered case 17.40% 27.58% 10.99% Bng 10 : So sỏnh hiu sut d ỏn gia hai kch bn WIP Bf cp chin l 49 Conclusions Kt lun This research has demonstrated the feasibility of designing WIP Bf strategies for construction projects to decrease the negative impacts of variability in production processes and to increase project performance By doing so, a MAM to design WIP Bf based on SO modeling and Pareto Front concepts was proposed A SO approach was tested and validated by means of two case studies, allowing for different levels of performance improvement after the application of WIP Bf strategies However, the magnitude of the improvements depends on the context of the application (e.g., seasonality, execution complexity, types of processes, variability levels, modeling assumptions, etc.), the project decision makers and site personnel willingness to apply buffering strategies, and the level of supply chain control Nghiờn cu ny ó chng minh tớnh kh thi ca thit k chin lc WIP Bf cho cỏc d ỏn xõy dng gim cỏc tỏc ng tiờu cc ca bin i quỏ trỡnh sn xut v tng hiu sut d ỏn Bng cỏch lm nh vy, mt MAM thit k WIP Bf da trờn SO mụ hỡnh húa v khỏi nim ng thng Pareto ó c xut Mt cỏch tip cn SO ó c th nghim v xỏc nhn bng phng tin ca hai trng hp nghiờn cu, cho phộp mc khỏc ca cỏc ci tin hiu qu sau ỏp dng cỏc chin lc WIP Bf Tuy nhiờn, ln ca nhng ci tin ph thuc vo bi cnh ca ng dng (vớ d, tớnh thi v, thc hin phc tp, cỏc loi quy trỡnh, mc bin i, gi nh mụ hỡnh, vv), cỏc nh sn xut quyt nh d ỏn v nhõn viờn cụng trng sn sng ỏp dng chin lc m, v mc kim soỏt chui cung ng The MAM was developed as nomographs using only two production variables: time and production rates This framework allowed for a simple and practical method of designing WIP Bf for scheduling repetitive building projects with independence of cost The framework is supported by evidence from the SO case studies This statement was demonstrated through cost improvements obtained in the project examples after application of the MAM It was apparent that the use of MAM reduced the interdependencies between processes for a given level of variability This paper provides the first application of the MAM approach to generalize the application of WIP Bf in construction through simple and practical means It is hoped that this approach Cỏc MAM c phỏt trin nh th ch s dng hai bin sn xut: thi gian v tc sn xut Khung ny cho phộp cho mt phng phỏp n gin v thc t ca thit k WIP Bf cho lp k hoch d ỏn xõy dng lp i lp li vi s c lp ca chi phớ Khung c h tr bi bng chng t cỏc nghiờn cu SO Tuyờn b ny ó c chng minh thụng qua ci thin chi phớ thu c cỏc vớ d d ỏn sau ỏp dng cỏc MAM Rừ rng l vic s dng cỏc MAM gim ph thuc ln gia cỏc quỏ trỡnh vi mt mc bin i Bi vit ny cung cp nhng ng dng u tiờn ca cỏch tip cn MAM khỏi quỏt cỏc ng dng ca WIP Bf xõy dng thụng qua cỏc 50 will facilitate the use ofWIP Bf in the construction industry and contribute to reduce the gap between theory and practice in the body of knowledge for the buffer management Because there is variability in construction, more rational use of buffers is necessary In addition, further research is necessary in order to produce more nomographs to design WIP Bf for other production situations and contexts, stimulating its generalization and facilitating its industry adoption as a practical tool phng tin n gin v thit thc Ngi ta hy vng rng phng phỏp ny s to iu kin cho vic s dng ca WIP Bf ngnh cụng nghip xõy dng v gúp phn thu hp khong cỏch gia lý thuyt v thc hnh c th ca kin thc cho vic qun lý b m Bi vỡ cú s thay i xõy dng, s dng hp lý hn b m l cn thit Ngoi ra, nghiờn cu sõu hn l cn thit sn xut nhiu nomographs nhm thit k WIP Bf cho cỏc tỡnh sn xut v bi cnh khỏc, kớch thớch s tng quỏt ca nú v to thun li cho vic ỏp dng cụng nghip nh l mt cụng c thit thc This paper also documents a two-level methodology, both strategic and tactical, to design WIP Bf It is demonstrated in the scheduling process for repetitive building projects The MAM approach can be applied at the strategic level, while the SO approach can be applied at the tactical level This methodology can reduce the management cost and supervision effort of labor, due to the fact that labor permanency on site is decreased while its efficiency is increased Alternatively, the increment of labor efficiency can be related to more profits for subcontractors given the reduction of labor permanency in projects As a result, labor can be assigned to other projects This methodology can also reduce on-site waste, decreasing waiting times and stimulating continuous resource utilization The methodology can also contribute to reduction of rework by assuring the quality of WIP for downstream crews (stimulation of value-adding activities) Bi vit ny cng ghi nhn mt phng phỏp hai cp, c hai chin lc v chin thut, thit k WIP Bf Nú c chng minh quỏ trỡnh lp k hoch cho cỏc d ỏn xõy dng lp i lp li Cỏch tip cn MAM cú th c ỏp dng cp chin lc, phng phỏp SO cú th c ỏp dng cp chin thut Phng phỏp ny cú th lm gim chi phớ qun lý v giỏm sỏt n lc lao ng, thc t rng s vnh cu lao ng trờn cụng trng ang gim hiu qu ca nú c tng lờn Ngoi ra, tng hiu qu s dng lao ng cú th liờn quan n li nhun nhiu hn cho cỏc nh thu ph cho vic gim lao ng thng trc cỏc d ỏn Kt qu l, lao ng cú th c giao cho cỏc d ỏn khỏc Phng phỏp ny cng cú th lm gim cht thi ti ch, gim thi gian ch i v kớch thớch s dng ngun lc liờn tc Phng phỏp ny cng cú th gúp phn gim vic lm li bng vic m bo cht lng ca WIP cho i 51 ng bờn di (kớch thớch cỏc hot ng giỏ tr gia tng) 10 Notation 10 Ký hiu The following symbols are used in this paper: Cỏc ký hiu c s dng bi vit : ATm - difference between expexcted and actual ATm - s khỏc bit gia Atm d kin v ATm TC - difference between actual and thc t expected TCT ( budget) TC - s khỏc bit gia TCT (ngõn sỏch) d kin v thc t TCT - difference between actual expected TCT ( schedule) TCT - s khỏc bit gia TCT (lch trỡnh) d kin v thc t ATm average production rate for process Atm tc sn xut trung bỡnh cho gúi quỏ package trỡnh Bf buffer Bf m COV coefficient of variation COV - h s bin thiờn CTi - cycle time CTi - thi gian chu k DC direct cost DC - chi phớ trc tip DOC- daily overhead cost for process package DOC - chi phớ hnh hng ngy cho gúi quỏ trỡnh E [ ] - mong i phn ng ca E [ ] response expectation of EqDCi - chi phớ thit b hng ngy EqDCi equipment daily cost EA - thut toỏn tin húa EA Evolutionary algorithms ES - Chin lc tin húa ES Evolution Strategies FC - chi phớ c nh FC Fix cost IC - chi phớ giỏn tip IC Indirect cost IT Bf m thi gian ban u IT Bf Initial time Buffer IWIP Bf - Cụng vic ban u ti ch m IWIP Bf Initial work in place buffer 52 LDCi labor daily cost LDCi chi phớ lao ng hng ngy MAM multiobjective analytic model MAM - mụ hỡnh phõn tớch a mc tiờu Max ATm Maximize average total production Max Atm - Ti a húa tng tc sn xut rate trung bỡnh mi average production rates mi tc sn xut trung bỡnh Min Tc minimize total cost Min Tc - gim thiu tng chi phớ Min TCT Minimize total cycle time Min TCT - gim thiu tng thi gian chu k MT bf minimum time buffer MT bf - m thi gian ti thiu MUCi material unit cost MUCi - chi phớ n v nguyờn liu MWIP Bf minimum work in place buffer MWIP Bf - cụng vic ti thiu ti ch m n number of processes n - s quy trỡnh PDF Probability density function PDF - Hm mt xỏc sut Pi repetitive and sequential process Pi - quỏ trỡnh lp i lp li v tun t SDi standard deviation of mi SDi - lch chun ca mi SO Simulation Optimization SO - Mụ phng - Ti u húa TC Total cost TC chi phớ tng TCT Total cycle time TCT - tng thi gian chu k TP Total production TP - Tng sn lng VL Processes Variability VL S thay i cỏc quỏ trỡnh W - feasible range of optimization search for Xi W - phm vi kh thi ca vic tỡm kim ti u húa cho Xi WIP work in process D - certain standard deviation for actual cumulative progress WIP - lm vic quỏ trỡnh D - lch tiờu chun nht nh cho tin trỡnh tớch ly thc t PR - certain standard deviation for actual time i - standard deviation of mutation process or mutation strength controlling the step-size 53 PR - lch tiờu chun nht nh cho thi gian thc t i2 i - lch chun ca quỏ trỡnh t bin hoc sc mnh t bin kim soỏt cỏc bc kớch thc - variance of mutation process i - Decision variable for i2 - phng sai ca quỏ trỡnh bin i - offsprings population for a ES search i - parent population for a ES search - qun th cỏi cho mt tỡm kim chin lc tin húa D - expected duration by production unit PR - expected progress or production rate by day à- qun th cha m cho mt tỡm kim chin lc tin húa D - thi gian d kin cho n v sn xut - mixing number for a ES search - Bin quyt nh cho PR - d kin tin hoc sn xut theo - general expression for objective function ngy - trn s cho mt tỡm kim thut toỏn tin húa - biu hin chung ca hm mc tiờu Tham kho [1] W.J Hopp, M.L Spearman, Factory Physics: Foundations of Manufacturing Management, 2nd ed Irwin/McGraw-Hill, Boston, 2000 [2] L Koskela, An Exploration Towards a Production Theory and its Application to Construction Ph.D Dissertation, VTT Building Technology, Helsinki University of Technology, 2000 [3] L.F Alarcún, D.B Ashley, Playing games: evaluating the impact of lean production strategies on project cost and schedule, Proceedings of 7th Annual Conference of International Group for Lean Construction, California, USA, 1999 54 [4] G Ballard, Lean construction and EPC performance improvement, in: L.F Alarcún (Ed.), Lean Construction, A.A Balkema, The Netherlands, 1993 [5] V Gonzỏlez, L.F Alarcún, P Gazmuri, Design of WIP buffers in repetitive projects: a study case, Proceedings of 14th Annual Conference of International Group for Lean Construction, Santiago, Chile, 2006 [6] L.J Shen, D.K.H Chua, Impact of variability on construction schedules, Proceedings of 13th Annual Conference of International Group for Lean Construction, Sidney, Australia, 2005 [7] I.D Tommelein, D.R Riley, G Howell, Parade game: impact of work fow variability on trade performance, J Constr Eng Manag 125 (5) (1999) 304310 [8] H.R Thomas, M.J Horman, U.E Lemes de Souza, I Zavrski, Reducing variability to improve performance as a lean construction principle, J Constr Eng Manag 128 (2) (2002) 144154 [9] H.R Thomas, M.J Horman, R.E Minchin, D Chen, Improving labor fow reliability for better productivity as a lean construction principle, J Constr Eng Manag 129 (3) (2003) 251261 [10] G Ballard, G Howell, Implementing lean construction: stabilizing work fow, in: L.F Alarcún (Ed.), Lean Construction, A.A Balkema, The Netherlands, 1994 [11] L.F Alarcún, S Diethelm, O Rojo, R Calderún, Assessing the impacts of implementing lean construction, Proceedings of 13th Annual Conference of International Group for Lean Construction, Sidney, Australia, 2005 [12] G Howell, G Ballard, Factors affecting project success in the piping function, in: L.F Alarcún (Ed.), Lean Construction, A.A Balkema, The Netherlands, 1995 [13] G Polat, D Arditi, U Mungen, Simulation-based decision support system for economical supply chain management of rebar, J Constr Eng Manag 133 (1) (2007) 2939 [14] M.J Horman, Process Dynamics: Buffer Management in Building Project Operations, Ph D Dissertation, Faculty of Architecture, Building and Planning, The University of Melbourne, 2000 [15] J.P Womack, D.T Jones, Lean Thinking: Banish Waste and Create Wealth in your Corporation, Simon & Schuster, New York, N.Y., 1996 [16] L.F Alarcún, R Calderún, Implementing lean production strategies in construction companies, Proceedings Construction Research Congress, Winds of Change: Integration and Innovation of Construction, ASCE, Honolulu, Hawaii, 2003 [17] Construction Industry Institute, Project Materials Management Primer, RS47-1, Prepared by the Material Management Task Force, Austin, Texas, 1988 [18] E.M Goldratt, Critical Chain, North River Press, Great Barrington, Massachusetts, 1997 [19] V Gonzỏlez, L Rischmoller, L.F Alarcún, Design of buffers in repetitive projects: using production management theory and IT tools, Proceedings of 4th 55 International Postgraduate Research Conference, University of Salford, Manchester, UK, 2004 [20] G Howell, G Ballard, J Hall, Capacity utilization and wait time: a primer for construction, Proceedings of 9th Annual Conference of International Group for Lean Construction, Singapore, Korea, 2001 [21] K Kim, J.M De la Garza, Phantom foat, J Constr Eng Manag 129 (5) (2003) 507517 [22] L.P Leach, Schedule and cost buffer sizing: how account for the bias between project performance and your model, Proj Manage J 34 (2) (2003) 3447 [23] I.D Tommelein, Pull-driven scheduling for pipe-spool installation: simulation of lean construction technique, J Constr Eng Manag 124 (4) (1998) 279288 [24] H.R Thomas, T.M Arnold, Overmanning and the effects on labour, Proceedings of the CIB W65 International Symposium for the Organization and Management Construction, Glasgow, UK, 1996 [25] G Ballard, The Last Planner System of Production Control, Ph.D Dissertation, School of Civil Engineering, Faculty of Engineering, The University of Birmingham, 2000 [26] G Howell, A Laufer, G Ballard, Interaction between subcycles: one key to improved methods, J Constr Eng Manag 119 (4) (1993) 714728 [27] H.H Bashford, A Sawhney, K.D Walsh, K Kot, Implications of even fow production methodology for U.S Housing Industry, J Constr Eng Manag.129 (3) (2003) 330337 [28] M Park, F Peủa-Mora, Reliability buffering for construction projects, J Constr Eng Manage 130 (5) (2004) 626637 [29] P.G Ipsilandis, Multiobjective linear programming for scheduling linear repetitive projects, J Constr Eng Manag 133 (6) (2007) 417424 V Gonzỏlez et al / Automation in Construction 18 (2009) 95108 107 [30] T Alves, I.D Tommelein, Simulation of buffering and batching practices in the interface detailing fabrication installation of HVAC ductwork, Proceedings of 12th Annual Con ference of International Group for Lean Con struction, Elsinore, Denmark, 2004 [31] S Lee, F Peủa-Mora, M Park , Reliability and stability buffering approach in concurrent design and construction projects, Proceedings of 11th Annual Conference of International Group for Lean Construction, Virginia, USA, 2003 [32] M Sakamoto, M.J Horman, H.R Thomas , A study of the relationship between buffers and performance in construction, Proceedings of 10th Annual Conference of International Group for Lean Construction, Gramado, Brazil, 2002 [33] C Sri su wanrat , P I oan nou , The investi gati on of lead -time buffering under uncertainty using simulation and cost optimization, Proceedings of 15th Annual Conference of International Group for Lean Con struction, East Lansing, Michigan, 56 USA, 2007 [34] K.D Walsh, A Sawhney, H.H Bashford, Production equation for unsteadystate construction processes, J Constr Eng Manag 133 (3) (2007) 254 261 [35] R Arbulu, I.D Tommelein, K.D Walsh, J.C Hersha uer, Contributors to lead time in constructions upply chains : case of pipe supports used in power plants, Proceedings of 34th Winter Simulation Conference, San Diego, Cal ifornia, USA, 2002 [36] K.D Walsh, J Hershauer, T Walsh, I.D Tommelein, A Sawhn ey, Lead time reduction via pre-positioning of inventory in an industrial construction supply chain, Proceedings of 34th Winter Simulation Conference, San Diego, California, USA, 2002 [37] A M Law, W.D Kelton, Simulation Modeling and Analysis, 3rd ed McGraw-Hill, New York, 2000 [38] Extend v6 User's Guide, Manual User, Imagine That Inc., 2002 [39] J.W Hooper, Strategy related characteristics of discrete-event languages and models, Simulation 46 (4) (1986) 153 159 [40] P Buchholz, A Thỹmmler, Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization, Proceedings of 37th Winter Simulation Con ference, Orlando, Florida, USA, 2005 [41] Y Carson, A Maria, Simulation optimizati on: methods and applications, Proceedings of 29th Winter Simulation Conference, Atlanta, Georgia, USA, 1997 [42] I.F Sbalzarini, S Mỹller, P Koumoutsa kos, Multiobjective optimization using evol utionary al gorithms , Proceedings of the Summer Progra m, Center for Turbulence Research, NASA, 2000 [43] H.G Beyer, H.P Schwefel, Evolution strategies: a comprehensive introduction, Nat Comput (2002) 52 [4 4] L Costa, P Oliveira, An evolution strategy for multiobjective optimization, in: D.D Fogel, et al., (Eds.), CEC'02-IEEE, Vol 1, May 12th-17th 2002, pp 97 102 [45] T Bọck, U Hammel, H.P Schwefel, Evolutionary co mputation: comments on the history and current state, IEEE Trans on Evol Comput Vol 11, N1 (1997) 17 [ 6] E Stomeo, K Tatiana, C Lambert, Mutation rate for evolvable hard ware , Proceedings of World Academy of Science , Engineering and Technology, Vol 7, August 2005, pp 117 124, ISSN 1307-6884 [47] G.W Greenwood, Q.J Zhu , Convergence in evolutionary programs with selfadaptation, Evol Comput (2) (2001) 147 157 [48 ] C Fonseca, P Fleming, An overview of evolutionary algorithms in multiobjective optimization, Evol Comput (1) (1995) 16 [49 ] D.X Zheng, S Thomas , Stochastic time cost optimization model incorporating fuzzy sets theory and nonreplaceable front, J Constr Eng Manag 131 (2) (2005) 176 186 57 [50] S Huband, P Hingston, L While, L Barone, An evolution strategy with probabilistic mutation for multiobjective optimisation, In Congress on Evolutionary Computation (CEC 2003), IEEE Press, Piscataway NJ, December 8th-12th 2003, pp 2284 2291 [51] T Bọck, Evolution Strategies: An Alternative Evolutionary Algorithm, 1996 http://citesser.ist.psu.edu/118370.html [52] D.W Halpin, Luis H Martớnez, Real world applications of construction process simulation, Proceedings of 31st Winter Simulation Conference, Pheoenix Az, USA, 1999 [53] C Feng, L Liu, S Burns, Using genetic algorithms to solve construction time cost trade-off problems, J Comput Civ Eng 11 (3) (1997) 184189 [54] I Yang, Chance-constrained timecost tradeoff analysis considering funding variability, J Constr Eng Manag 131 (9) (2005) 10021012 [55] S.M AbouRizk, D.W Halpin, J.R Wilson, Visual interactive ftting of beta distributions, J Constr Eng Manag 117 (4) (1991) 589605 [56] J Fente, K Knutson, C Schexnayder, Defning a probability distribution function for construction simulation, Proceedings of 31st Winter Simulation Conference, Pheoenix Az, USA, 1999 58