Endüstri Mühendisliği

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A bilevel P-median Problem for location and protection planning of critical facilities
(Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2009., 2009.) Piyade, Nuray.; Aras, Necati.; Aksen, Deniz.
In this thesis, we focus on the problem of location and protection planning of critical facilities. This problem involves a Stackelberg game between a system planner (defender) and a potential attacker. The system planner aims to both find the locations of p critical service facilities and determine the ones among them that should be protected. Following this twofold action, the attacker decides which facilities to interdict having the location and protection information of the opened facilities. This problem involves strategic decisions which can be taken either sequentially or simultaneously. In this study, we consider both of these cases. In the first case, the system planner first decides on the locations and then determines the protection plan of these facilities. In the second case however, the system planner gives concurrent decisions about location and protection of the facilities. Both cases are of a bilevel nature. Therefore, we formulate this problem as a bilevel mixed-integer programming problem. We propose two solution methods. The first one is a two-phase tabu search heuristic for the case which involves concurrent decision process and a sequential solution method for the second case where the system planner prefers to give sequential decisions. Both of the methods include a binary search tree embedded into it. The efficiency of the proposed algorithms is tested on an extensive amount of randomly generated test instances each with two budget levels, namely low and high. We also consider another case where the system planner does not have any financial resources to protect the facilities from an attack. This line of vision helps system planner to determine the critical facilities from the attacker’s perspective. The results show that the protection budget plays a significant role in maintaining the service accessibility after a possible attack.
A bilevel partial interdiction problem with capacitated facilities and demand outsourcing
(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2011., 2011.) Akca, Sema Şengül.; Aras, Necati.
The bilevel partial interdiction problem with capacitated facilities and demand outsourcing involves a static Stackelberg game between a system planner and a potential attacker. The system planner (defender) is responsible for satisfying the overall demand of customers in an existing service network and aims at minimizing the total demand-weighted transportation cost while serving customers from the capacitated facilities. Simultaneously, he should consider the possible capacity reduction of some facilities in the wake of a destructive attack while the attacker's objective is to cause maximum disruption in the service level. The number of facilities to be attacked cannot be known a priori but heavily depends upon the attacker's interdiction budget. Regarding the partial interdiction concept, this defender-attacker relationship is formulated as a bilevel programming model. The attacker takes on the leader role, and forces the system planner, who acts as the follower, to meet customer demands with a higher outsourcing cost. Two di erent methods are proposed in this study. The rst method is a progressive grid search which is impracticable on large-sized problems. The second method is a multi-start revised simplex search heuristic which is based on the Nelder-Mead simplex search method and is developed to overcome the exponential time complexity of the rst method. We also develop an exhaustive search to solve all combinations of the full interdiction of the facilities to assess the bene t of partial interdiction from the perspective of the attacker. Our test results indicate that it would be more bene cial to disrupt facility capacities partially rather than totally from the perspective of the attacker.
A column generation approach for evaluating delivery efficiencies of collimator technologies in IMRT treatment planning
(Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2014., 2014.) Gören, Merve.; Taşkın, Zeki Caner.
Intensity Modulated Radiation Therapy (IMRT) is a form of cancer treatment which delivers radiation beams to the patient from several directions by using a linear accelerator and a collimator. At the leaf sequencing optimization step of the IMRT treatment planning, the intensity matrices are decomposed into a set of deliverable apertures and their associated intensities. Collimator systems used in IMRT can form di erent geometric shapes of apertures depending on their physical capabilities. Hence, comparing the delivery e ciency of di erent collimator technologies is important to determine the value added by the di erent technologies. In this thesis, we compare the e ciency of using regular, rotating and dual multileaf collimator (MLC) systems under di erent combinations of consecutiveness, interdigitation and rectangular constraints and a virtual freeform collimator. We formulate the problem of minimizing total beam-on time (BOT) as a large-scale linear programming problem. To deal with its dimensionality, we propose a column generation approach. Although there exists a general master problem structure, subproblem depends on the used collimator system technology. Therefore, we model each subproblem individually and apply a di erent solution method to each of them. We test our approach on a set of clinical problem instances. Our results indicate that the dual MLC under consecutiveness constraint yields very similar beam-on time as a virtual freeform collimator which can form any possible segment shape by opening or closing each bixel independently. Our approach provides a ranking between other collimator technologies in terms of their delivery e ciencies.
A column generation approach to solve ranking problems
(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2020., 2020.) Özcan, Erhan Can.; Baydoğan, Mustafa Gökçe.
Many traditional classification approaches focus on the minimization of misclassification rate. However, this is not a suitable metric in case of imbalance in class distribution and unknown misclassification costs. In such cases, Area under Receiver Operating Characteristics Curve (AUC) is an effective metric, which also quantifies the ranking quality of a classifier. Although this metric can be optimized directly by employing some mixed integer programming models, it is challenging to solve these models due to large number of binary variables. Some alternative formulations such as margin maximizing approaches optimizing surrogate objectives are proposed to solve this problem approximately. These methods extend classical Support Vector Machine (SVM) formulation and aim at minimizing ranking error while penalizing the model coe cients with a cost parameter in the objective. In these approaches, the cost and kernel-related parameters (i.e., type, degree and etc.) must be determined by parameter tuning operations since the test performance is highly reliant on these parameters. Primary aim of this study is to avoid the repetitive experiments to tune the parameters of margin-maximization approaches. We propose a linear programming model and a column generation approach, namely Ranking-CG, to select relevant features in an iterative way to decrease the number of features in the model. Additionally, kernel selection is avoided using the Euclidean distances between points as features to learn the non-linear relations. Ranking-CG is modified slightly to obtain faster convergence by solving a non-linear subproblem at each iteration to find the vector (i.e. prototype) in the feature space that violates dual feasibility the most. Our experiments show that the modified approach, Ranking-CG Prototype, provides competitive results with significantly less number of features compared to margin-maximization approaches.
A comparative analysis of different expectation models for the El Farol Bar problem
(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Çetiner, Burak.; Yaşarcan, Hakan.
The El Farol Bar Problem is first discussed by W. B. Arthur (1994) and he presents this problem to introduce a new field that he names as “complexity economics”. The El Farol Bar Problem is widely used in the literature, especially in congestion and coordination studies. In this study, we model the El Farol Bar Problem using the agent-based modelling methodology and Python computer language. We create different agent types with distinctive expectation models. The emergent behaviors of different agent types are compared with respect to several performance measures. After several experiments with different agent types, we reach two important conclusions: The heterogeneity of the decisions is the key factor in obtaining low standard deviation of attendance values and the assumption of knowing the bar capacity value is crucial for a good performance. Agents who make expectations randomly generate the highest heterogeneity in the attendance values, which is consistent with the findings in the literature. In this thesis, we also introduce agents who use exponential smoothing method in forming expectations. They create low heterogeneity in decisions and a poor performance compared to other agents. Nevertheless, the exponential smoothing method works well in learning the capacity value. Accordingly, we introduce an agent type that combines random attendance expectations with the exponential smoothing method in estimating the capacity. When the bar capacity is unknown, this agent type produces mean attendance values gravitating towards the bar capacity ensuring the heterogeneity in the decisions. Lastly, we develop Yasarcan-Çetiner agents that do not use expectation models, but a hysteresis structure in decision-making. Although, they do not have explicitly coded capacity learning mechanism in their algorithms, they still learn the bar capacity as a swarm according to their emergent collective behavior.
A comparative analysis on modification of social networks over links with mechanism combinations
(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2021., 2021.) Ünal, Merve.; Yücel, Gönenç.
In this study, the problem of preserving the characteristics of social networks changing dynamically is investigated. Distinct mechanism combinations have been implemented on the networks selected. Mechanism combinations providing to be preserved its properties as the small world network modifies are discovered. In total, 36 experiments are carried out considering two network classes, six link addition and three link deletion mechanisms. In the experiments, the structural properties of the network such as the average shortest path, average clustering coefficient and degree distribution of the snapshots of each network are analyzed and compared with the results of initial networks. It is seen that experiments related to some mechanism combinations implemented on the small world network yield similar results to the properties of initial small world network. In addition, dynamic social networks with different characteristics are created by making changes over the links in the experiments carried out. Moreover, mechanism combinations that fragment or randomize the structure of the network have been revealed in the experiments.
A comparison of pull control policies in hybrid production systems
(Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2008., 2008.) Çadırcı, Özge.; Korugan, Aybek.
Environmental concerns trigger companies for utilizing reuse activities as much as possible, gradually. One of these activities is remanufacturing process. When manufacturing process is combined with remanufacturing process, the system is referred to as a hybrid production system. This study concentrates on a hybrid production system that performs both remanufacturing and manufacturing activities under pull type production control. The incoming demand is satisfied by the output of either process, where remanufactured products are assumed to be restored into “as good as new” condition. In the study, four of the most common pull control systems, viz. Kanban Control System (KCS), Base Stock Control System (BSCS), Generalized Kanban Control System (GKCS) and Extended Kanban Control System (EKCS), are compared on the single-stage hybrid production system considered. A stochastic model is developed for each of the four control policies. Then these models are analyzed using methods based on the previous studies available in the literature in order to obtain performance measures of interest. After comparing the outcomes of analytical models with the simulation results to test accuracy, a cost function is defined using the performance measures. Then this cost function is minimized with respect to the control parameters of each control mechanism. Finally, results derived from numerical experimentation are obtained, and conclusions are drawn.
A comprehensive dynamic model of cyclic neuropenia
(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Işık, Yusuf.; Yaşarcan, Hakan.
Neutropenia is a hematological disorder that is defined as having a low level of neutrophils in the bloodstream. Low levels of absolute neutrophil counts leave the body defenseless against and vulnerable to infections. Cyclic neutropenia is a type of neutropenia that is described as the oscillations observed in the level of blood neutrophils. The disorder is mostly treated with a cytokine named recombinant granulocyte colony– stimulating factor, rG–CSF, which is administered via injection. A delicate injection schedule is called for because the treatment procedure is costly. However, treatment experiments on an actual patient require frequent sampling from bone marrow and blood, which simply cannot be allowed as it can be detrimental to the health of the patient. Therefore, modeling is a must to carry out treatment experiments. Accordingly, the main motivation in this thesis is to construct a comprehensive dynamic model of cyclic neutropenia. As human physiology is rich in dynamic complexities, system dynamics is selected as the primary methodology. We first construct a model that represents the regulatory structures of neutrophil production for a healthy person. After validating the model, the neutrophil dynamics of a cyclic neutropenia patient is obtained by simply changing the parameter values, but without changing the model structure. Neutrophil production deficiency is the most mentioned cause of cyclic neutropenia in the literature, which is also confirmed in our study. According to our simulation results, the clearance of the apoptotic neutrophils of CN patients takes longer than normal and apoptotic neutrophils can suppress both the production and effects of G– CSF. As a result of experiments with pathogens, we claim that the oscillatory behavior is a characteristic of the neutrophil–GCSF–pathogen system even for a healthy person. This may shed some light on the periodic symptoms observed in patients with diseases caused by an overactive immune system. We experiment with rG–CSF injections too.
A computer package for decision making in engineering environments
(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 1989., 1989.) Badur, Bertan Yılmaz.; Barbarosoğlu, Gülay.
In this thesis a user oriented package program has been developed for personal computers to solve practical problems about engineering economy and decision theory and to be usefulln educational studies. The program developed enables to perform calcul ations about the alternati ve uses of capital in business and engineering projects. The package consists of five subprograms; Basic Calculation Subprogram, Decision Matrices Subprograml Decision Matrices Imbedded to Basic Calculations Subprogram, DecisionTrees Subprogram and Risk Analysis Subprogram. The program is written with Turbo Pascal Version 4.0.
A computerized mathemetical approach to menu planning
(Thesis (M.S.)- Bogazici University. Institute for Graduate Studies in Science and Engineering, 1986., 1986.) Gürdal, Hakan.; Or, İlhan.
In this thesis it is aimed to develop monthly or bimonthly menu planning methodologies for institutions serving three meals per day. The menus generated are to satisfy certain nutritional, structural and variety requirements at least possible cost. For the purposes stated above multistage and single stage linear and integer programming models are developed and solved using B.U. CDC-Cyber computer and TUBITAK VAX-7BO computer. Data for these models (Dish selections and their nutrient contents, human nutritional requirements etc.) have been obtained from Nutrition and Food Technology Division of TUBITAK. In order to include subjective evaluations of decision maker and food system manager and nonquantifiable factors (such as taste and suitability) interactive modules are added to the developed system.
A control point policy approach for the remanufacturing workshop
(Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2005., 2005.) Erkuş, Oral Mert.; Korugan, Aybek.
The main environmental problems that most industrialized countries are facingnowadays are the increasing waste and the depletion of raw materials. To slowdown theincrease of waste, one effective method is the recovery of materials from used products at the end of their useful lives. Remanufacturing is one of the essential industrial processes torecover the products at the end of their useful lives. This process starts with disassembly ofthe returned product into primary parts. Then every part is renewed or substituted with anewer one and finally assembled in order to obtain a final good. Even if we produce with decreased costs in remanufacturing, cost efficiency is notassured due to the high variation of the reusability percentage of a returned product. Due tothe difficulty of forecasting the state of the returned goods, it becomes difficult to makedecisions about the control of the system. In our thesis, we propose a solution to obtain cost efficiency in a remanufacturingenvironment considering the impact of disposing off the disassembled parts. Weimplement a control mechanism on a sample remanufacturing job-shop represented by aqueuing network topology that aims to minimize the estimated total cost function of our system. We construct the remanufacturing system with unreliable machines andcapacitated buffers and we consider outsourcing and disposal of one specific part type. Webuild the mathematical model of our system, conduct a numerical analysis and comparewith a simulation the model. The resulting methodology provides necessary information to make appropriate decisions about the control of a remanufacturing environment to reachthe optimal cost levels.
A data adaptive categorical time series representation for supervised learning
(Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2016., 2016.) Çakın, Hande.; Baydoğan, Mustafa Gökçe.
A vast majority of the studies in machine learning focus on time-directed or in other words sequential processes. Objectives of these studies vary from classi cation to prediction and clustering to segmentation. Since the dimension of these datasets could be very high as a corollary of sequential process, it is required to map the sequences to a lower dimensional representation for learning tasks. Probabilistic and data adaptive representation approaches are prominent in the literature. This thesis provides a new data adaptive representation method for categorical time series to apply any supervised learning algorithm. The proposed method, namely SW-RF (Sliding Window-Random Forest), requires two main steps to learn a representation for categorical time series. The initial representation is constituted with a sliding window algorithm by using a predetermined window size. Then, this simple representation is trained with a decision tree classi er and a numerical vector representation is gathered by using the frequency of subsequences on the leaf nodes of decision trees for each sequence. Categorical sequences of varying length and missing values are handled e ciently by the tree learners in SW-RF. It is able to perform e ciently even the number of symbols in the sequence is high. Classi cation accuracy of the SW-RF is compared with k-mers and Hidden Markov Model representations, since these two are common representation methods in the literature. Experiments show that proposed approach provides signi cantly better results in terms of accuracy on both synthetic data and DNA promoter sequence data.
A data mining approach to predicting patient-based laser machine settings for kidney stone treatment operation
(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2020., 2020.) Eser, Fidan.; Aras, Necati.; Tozan, Hakan.
Urinary tract stone disease is a common health problem that a ects human health and quality of life. The main goal for the treatment of this disease is to reach a complete stone-free status by causing minimal damage to the patient. The advancement in technology has brought many modalities for the treatment of kidney stones. Selecting the best treatment option by considering patient characteristics is an important factor a ecting the success of the treatment. In this study, it is aimed to estimate patientspeci c machine settings for successful completion of an operation performed using the Retrograde Intrarenal Surgery method. With this motivation, the performance of linear regression, regression trees, random forest, and extreme gradient boosting methods in machine setting estimation are analyzed. The study is carried out using a dataset provided by the Urology Department of Istanbul Medipol University Hospital. Since the dataset has many missing values with only few complete observations and imputation methods do not provide good results, synthetic data is generated using the original dataset. Models constructed on synthetic data are tested on the original data. Models established on synthetic data have been found to give better estimates. In addition, the models trained on successful observations and the models trained on unsuccessful observations give di erent estimates, since the data groups they are trained on are di erent.
A decision support system for consumer driven supply networks
(Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2007., 2007.) Ceviz, Ercan.; Ünal, Ali Tamer.
In this thesis, a decision support system is constructed to manage tactical and strategic level decision in a consumer driven supply networks. Decision making process includes many decision makers, multiple objectives, and high complexity most of the time. Therefore, senior and top management seek for a tool to handle these problems. This decision support system should have extended functionality that enable customization, optimization, what- if analysis. Since the investigated problem is multi-objective by nature, Pareto analysis and developing efficient frontiers for different decision measures becomes vital. The motivation of this study is not to develop the best optimization model that solves supply network problems but to design a tool that manages these large scale optimization models for real life applications where the decision-maker has a limited knowledge or time to handle the very much details of the decision making process. In addition to these, synchronizing several functions that define different portions of the entire supply network with their objectives, parameters and constraints is crucial. Our supply network model includes several generic concepts from market planning, promotion planning, rough cut capacity planning, demand planning and material resource planning, Together with these synchronized sub- functions, decision nodes that enable top managers to create their own scenarios at the higher level and to manage the model are constructed.
A decision support system for quality control
(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 1984., 1984.) Aktan, Fikret Ümit.; Ulusoy, Gündüz.
The important role df Quality Control in the economic growth of Turkey has become a well-known fact, especially in the last few years when Turkey has increased her efforts for expanding the exports. However, Quality Control is not understood and utilized effectively as a management tool in most of the Turkish industrial companies. There exists a large gap between the practical applications and the theoretical developments in statistical quality control techniques, due to a lack of understanding of QC by managers. In this study, Decision Support Systems (DSS) are proposed as a tool that would be helpful in filling this gap, by leading the managers towards the use of scientific management approach in making decisions related to quality improvement. Quality Control activities are conducted through various decisions ranging from short-term to long-term taken at different levels of management. In this study, only one of these decisions is taken into consideration and it is aimed to give support to one of the basic activities of QC, namely the feedback mechanism between production and management. Thus, a decision support system is designed to aid the management in fighting with the chronic quality problems, in away to provide a set of capabilities expected· from a DSS, in general. This decision support system which will be called QCDSS, is an in teractive computer-based system which helps the decision-maker in the identification of vital chronic quality problems, and in the evaluation of possible precautions that can be taken to give remedy. QCDSS combines flexible, ad hoc data analysis capabilities with some models. This integration is achieved through a user-friendly dialog component. The problem identification phase of the decision is supported by a systematic Pareto analysis model which will be called "Failure Analysis Module" while a Monte-Carlo simulation model embedded in the "Revision of the Inspection Plan Module" is utilized to support the alternative evaluation phase. QCDSS is developed for a light bulb manufacturing company where production is continuous and controls are by attributes.
A decision-support system for distribution system design
(Thesis (M.S.)- Bogazici University. Institute for Graduate Studies in Science and Engineering, 1985., 1985.) Yılmaz, Fusun.; Kavrakoğlu, İbrahim.
A decomposition approach to solve the selective graph coloring problem
(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2018., 2018.) Şeker, Oylum.; Aşıcı, Tınaz Ekim.; Taşkın, Zeki Caner.
Graph coloring is the problem of assigning a minimum number of colors to all vertices of a graph such that no two vertices that are linked by an edge receive the same color. The selective graph coloring problem is a generalization of the standard graph coloring problem; given a graph with a partition of its vertex set into clusters, the aim is to pick exactly one vertex per cluster so that, among all possible selections, the number of colors needed to color the vertices in the selection is minimum. In this study, we focus on a decomposition based exact solution framework for selective coloring, and apply it rst to some special graph families, and then to general graphs with no particular structure. The special classes of graphs that we consider are perfect graphs and some special subclasses of perfect graphs, which are permutation, generalized split, and chordal graphs. In order to test the performance of our solution approach, we need graph instances from these graph classes, which led us to concentrate on the generation of random graphs from the graph classes under consideration in the second part of this study. We then test the decomposition method on graphs with di erent sizes and densities that we have produced with our generation methods, present computational results and compare them with an integer programming formulation of the problem solved by CPLEX, and a state-of-the-art algorithm from the literature. Our computational experiments indicate that our decomposition approach signi cantly improves the solution performance, especially in low-density graphs in permutation and generalized split graphs, and regardless of the edge-density in the class of chordal graphs. For perfect graphs in their general form, our approach outperforms both of the other two methods. In the case of general graphs, however, further improvements are needed to make our method competitive with the alternative methods we compare with.
A deterministic demand inventory model with advance supply information
(Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2009., 2009.) Küçük, Bilge; Güllü, Refik.
In this study we consider a periodic review, single-item inventory model under non-stationary supply availability with advance supply availability information. We have a dynamic deterministic demand and the objective is to minimize expected cost, including fixed ordering cost, holding and backorder costs, over a finite planning horizon under supply constraints. Supply availability has a binomial structure such that supply is either fully available or completely unavailable. Firstly, the dynamic programming formulation of the model is given and the optimality of state dependent (s, S) policy is shown. A heuristic algorithm for finding a good ordering strategy, which is inspired by Silver-Meal Heuristic, is suggested. Then the model with no fixed ordering cost is analyzed. For this model it is shown that optimal policy is of order-up-to type and various properties of the optimal order-up-to levels are demonstrated. A one- pass algorithm that computes the optimal order-up-to levels over the planning horizon is found. Finally, numerical analysis is given including the value of advance supply information and important managerial insights are provided.
A distributed time stepped simulation approach for analysis and comparison of shop floor control architectures
(Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2006., 2006.) Beşikci, Umut.; Bilge, Ümit.
Different shop floor control architectures (SFCAs) are trying to resolve control problem of Flexible Manufacturing Systems (FMSs) by proposing different paradigms. Hierarchical SFCAs enable a layered hierarchy and try to overcome the complexity of the system by aggregating the problem. Heterarchical approaches see the system as a whole that consists of autonomous and cooperative agents. To develop efficient applications of SFCAs, simulation is an appropriate approach which lets a limitless configuration and test possibility. But the methodology of the simulation application is very important and must be developed with considering the system at hand. When modeling a system composed of entities, which interchange messages, consideration of messaging structure becomes essential. In this respect, Parallel and Distributed Simulation (P/DS) is the most appropriate methodology for these systems. In this thesis, simulation applications for both hierarchical and heterarchical SFCAs are built with a distributed and parallel simulation approach. Thus a test bed for comparing the performances of different architectures and for developing new approaches to decision making, information sharing, or communication within each architecture is obtained. In developing P/DS applications, existing real-time SFCA applications in Bo˘gazi歔ci University Flexible Automation and Intelligent Manufacturing (BUFAIM) laboratory are taken as basis. Applications are developed in a modular and object oriented fashion, to enable easy progression from simulation to real-time control and vice versa. With running each distributed module on a different computer and keeping the messaging real it is aimed to catch the dynamics of the communication structures which are the defining characteristics of the SFCAs.
A DSS on productivity measurement, evaluation and improvement
(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 1986., 1986.) Coşkuner, Sibel.; Kavrakoğlu, İbrahim.
Organizations have control systems for behaviors, costs, prices, information, decisions, financial performance, production, quality and so forth. We can classify them with respect of the type of "organizational system performance" they are attempting to control or manage. Productivity being an important component of organizational system performance measures, although much attention has been paid to productivity, is still one of the most confusing concepts of the management area. This thesis covers a systematic approach for productivity measurement, evaluation and improvement; analyzing profitability as a function of productivity and price recovery. Selected methodology for productivity measurement is tested on an existing company in the Glass Industry. An interactive package is designed as a Decision Support System for managers who are not accustomed to use computers, and they are· allowed to make scenerio analysis for future applications.

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