A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS

Under high uncertainty and risky environments, the future estimations related to project proposalscannot be certain and really materialized values. It is inevitable that there exists a deviation or gap betweenforecasted values and actual values. Thus, project risk level of the proposal should be analyzedin the assessment phase. Simulation based project evaluation approaches enables to make more reliableinvestment decision since they permits including future uncertainty and risk in analyze process. Inaddition, many times, project proposals are evaluated with more than one conflicted criteria. The aimof this paper is to present a new approach that accounts for multiple objectives for evaluating riskyinvestment projects and determining projects risk level. With the proposed simulation based optimizationapproach, necessity values for project parameters are determined to reach the expected profitabilityof the investment with the minimum initial investment cost. Also, there is an illustrative examplegiven in this study as an application of the proposed approach

BELİRSİZ VE RİSKLİ ORTAMLARDA YATIRIM PROJELERİNİN DEĞERLENDİRİLMESİNE YÖNELİK BENZETİM TABANLI BİR YAKLAŞIM

Belirsizlik ve riskin yüksek olduğu ortamlarda, proje önerilerine ilişkin gelecek ile ilgili yapılacak tahminler kesin ve fiilen gerçekleşen değerler olamaz. Gerçekleşeceği tahmin edilen değerler ile fiilen gerçekleşen değerler arasında az ya da çok sapmalar olması kaçınılmazdır. Bundan dolayı, yatırım projeleri değerlendirilirken, proje önerisine ait risk düzeyinin de mutlaka analiz edilmesi gerekir. Benzetimi temel alan proje değerlendirme yaklaşımları, geleceğin belirsizliğini ve riskini analiz sürecine dahil etmeye izin verdiklerinden daha güvenilir yatırım kararlarının alınmasına olanak sağlar. Bununla birlikte, proje önerileri çoğu zaman birbiriyle çelişen birden fazla kriter göz önüne alınarak değerlendirilir. Bu çalışmanın amacı, riskli yatırım projelerinin değerlendirilmesine ve özellikle proje risk düzeyinin belirlenmesine yönelik birden çok proje amacını dikkate alan yeni bir yaklaşım önermektir. Önerilen bu benzetim tabanlı eniyileme yaklaşımıyla, karar vericinin yatırımdan beklediği hedef karlılık değerine en düşük ilk yatırım maliyeti ile ulaşabilmesi için proje parametre değerlerinin ne olması gerektiği belirlenecektir. Çalışmada ayrıca önerilen yaklaşımın nasıl uygulanacağı konusunda bir örnek yer almaktadır

Tren çizelgeleme problemi için bir olurlu tarife üretici benzetim modelleme yapısı ve benzetimle bütünleşik genetik ve melez genetik algoritmalar

Demiryolu sistemlerinin yönetiminde önemli bir problem tren çizelgeleme problemidir (TrnÇzgPrb). Bu bir küme tren için ray kapasitelerini ihlal etmeyen ve eylemsel kısıtları tatmin eden bir tarife belirleme problemidir. Bu tezde, bir olurlu tarife üretici stokastik benzetim modelleme yapısı geliştirilmiştir. Amaç sistemdeki tüm trenler için bir olurlu tren tarifesi elde etmektir. Olurlu tren tarifesi hesaplanmış ortalama tren seyahat süresi ile birlikte tüm ziyaret edilen istasyonlar için tren geliş ve hareket zamanlarını içerir. Bir olurlu tarife elde etmenin yanında, ortalama tren seyahat süresini minimize etmek amacıyla melez algoritmalar geliştirilmiştir. İlk melez, benzetim ve genetik algoritma (GA) bütünleştirilerek elde edilmiştir, diğer üç melez ise üç yerel arama algoritmasından her birinin benzetimle bütünleşik GA içerisine gömülmesiyle elde edilmiştir. Bu tezde geliştirilen benzetim modelleme yapısı gerçek bir demiryolu hat sisteminden esinlenmiş altyapı tabanlı bir tek ray koridorlu TrnÇzgPrb için uygulanmıştır. Benzetim tarafından bulunan olurlu tarifeler kümesi geliştirilen melez GA'ların başlangıç çözüm alanını oluşturmaktadır. Bu melez GA'lar eniyi ortalama tren seyahat süresiyle birlikte bir olurlu tren tarifesi elde etmek için çalıştırılmıştır. Melez GA'lar tarafından bulunan en iyi ortalama tren seyahat süreleri karşılaştırılmış ve sonuçlar tartışılmıştır. Bu tez tren çizelgeleme/tarife oluşturma problemine odaklandığı halde, geliştirilen benzetimle bütünleşik yapı, eğer gerçek zamanlı veriler ile beslenebilirse, aynı zamanda yeniden tren çizelgeleme/sevk etme problemi için de kullanılabilir. An important problem in management of railway systems is train scheduling problem (TrnSchPrb). This is the problem of determining a timetable for a set of trains that does not violate track capacities and satisfies operational constraints. In this thesis, a feasible timetable generator stochastic simulation modelling framework is developed. The objective is to obtain a feasible train timetable for all trains in the system. The feasible train timetable includes train arrival and departure times at all visited stations with calculated average train travel time. In addition to obtaining a feasible timetable, hybrid algorithms are developed with the objective of minimizing the average train travel time. The first hybrid is obtained by integrating simulation and genetic algorithm (GA), and the other three hybrids are obtained by embedding each of three local search algorithms in simulation integrated GA. The simulation modelling framework developed in this thesis is implemented for a TrnSchPrb based on an infrastructure which was inspired by a real railway line system with single track corridor. The set of feasible timetables found by simulation forms the initial solution space of the developed hybrid GAs. These hybrid GAs are run for getting a feasible train timetable with optimum average train travel time. The optimum average train travel times found by the hybrid GAs are compared, and the results are discussed. Although this thesis focuses on train scheduling/timetabling problem, the developed simulation integrated framework can also be used for train rescheduling/dispatching problem if this framework can be fed by real time data

Enhancing the scheduling performance of a knitting mill

Continuously changing customer desires increase the product variety and volatility of the orders and this makes scheduling harder in textile industry. So the companies have to schedule their production effectively to meet the demand of their customers in due dates. In this paper, the effect of different dispatching rules and batching on the production performance of a knitting mill was examined via simulation. While the orders were assigned to the machines with FIFO (first in, first out) rule without batching in current system, a new system was proposed by which the orders were batched and then were assigned to the machines with FIFO, LIFO (last in, first out), LVF (low value first) and HVF (high value first) dispatching rules. The performances of the current and proposed systems were compared in terms of total number of setups, total production time, average machine utilization and average queue length performance cri ter ions. HVF rule with batching system gave the best results for the above determined performance criterions and improved the performance of the current system

A feasible timetable generator simulation modelling framework for train scheduling problem

An important problem in management of railway systems is the train scheduling/timetabling problem. This is the problem of determining a timetable for a set of trains that do not violate track capacities and satisfy some operational constraints. In this study, a feasible timetable generator framework for stochastic simulation modelling is developed. The objective is to obtain a feasible train timetable for all trains in the system. The feasible train timetable includes train arrival and departure times at all visited stations and calculated average train travel time. Although this study focuses on train scheduling/timetabling problem, the developed simulation framework can also be used for train rescheduling/dispatching problem if this framework can be fed by real time data. The developed simulation model includes stochastic events, and can easily cope with the disturbances that occur in the railway system. (C) 2011 Elsevier B.V. All rights reserved

Modelling and optimization of average travel time for a metro line by simulation and response surface methodology

This research presents a modelling and solution approach based on discrete-event simulation and response surface methodology for dealing with average passenger travel time optimization problem inherent to the metro planning process. The objective is to find the headways optimizing passenger average travel time with a satisfactory rate of carriage fullness. Due to some physical constraints, traffic safety and legal requirements, vehicle speeds cannot be raised any further to decrease travel time. But travel time can be optimized by arranging headways (i.e. the time period between the departure times of two consecutive transportation vehicles) in a timetable. In the presented approach, simulation metamodels that best fit the data collected from the simulated experiments are constructed to describe the relationship between the responses (average travel time and rate of carriage fullness) and input factors (headways). Then, the Derringer-Suich multi-response optimization procedure is used to determine the optimal settings of the input factors that produce the minimum value of the average travel time by providing a proper rate of carriage fullness. This methodology is applied for a real metro line, and good quality solutions are obtained with reduced number of experiments that needed to provide sufficient information for statistically acceptable results. (C) 2008 Elsevier B.V. All rights reserved

Scheduling Additional Train Unit Services on Rail Transit Lines

This paper deals with the problem of scheduling additional train unit (TU) services in a double parallel rail transit line, and a mixed integer programming (MIP) model is formulated for integration strategies of new trains connected by TUs with the objective of obtaining higher frequencies in some special sections and special time periods due to mass passenger volumes. We took timetable scheduling and TUs scheduling as an integrated optimization model with two objectives: minimizing travel times of additional trains and minimizing shifts of initial trains. We illustrated our model using computational experiments drawn from the real rail transit line 16 in Shanghai and reached results which show that rail transit agencies can obtain a reasonable new timetable for different managerial goals in a matter of seconds, so the model is well suited to be used in daily operations