Modeling and mitigation of supply-demand mismatches

In this thesis, I develop analytical models to price the value of supply chain investments under demand uncer¬tainty. This thesis includes three self-contained papers. In the first paper, we investigate the value of lead-time reduction under the risk of sudden and abnormal changes in demand forecasts. We first consider the risk of a complete and permanent loss of demand. We then provide a more general jump-diffusion model, where we add a compound Poisson process to a constant-volatility demand process to explore the impact of sudden changes in demand forecasts on the value of lead-time reduction. We use an Edgeworth series expansion to divide the lead-time cost into that arising from constant instantaneous volatility, and that arising from the risk of jumps. We show that the value of lead-time reduction increases substantially in the intensity and/or the magnitude of jumps. In the second paper, we analyze the value of quantity flexibility in the presence of supply-chain dis- intermediation problems. We use the multiplicative martingale model and the "contracts as reference points" theory to capture both positive and negative effects of quantity flexibility for the downstream level in a supply chain. We show that lead-time reduction reduces both supply-chain disintermediation problems and supply- demand mismatches. We furthermore analyze the impact of the supplier's cost structure on the profitability of quantity-flexibility contracts. When the supplier's initial investment cost is relatively low, supply-chain disin¬termediation risk becomes less important, and hence the contract becomes more profitable for the retailer. We also find that the supply-chain efficiency increases substantially with the supplier's ability to disintermediate the chain when the initial investment cost is relatively high. In the third paper, we investigate the value of dual sourcing for the products with heavy-tailed demand distributions. We apply extreme-value theory and analyze the effects of tail heaviness of demand distribution on the optimal dual-sourcing strategy. We find that the effects of tail heaviness depend on the characteristics of demand and profit parameters. When both the profit margin of the product and the cost differential between the suppliers are relatively high, it is optimal to buffer the mismatch risk by increasing both the inventory level and the responsive capacity as demand uncertainty increases. In that case, however, both the optimal inventory level and the optimal responsive capacity decrease as the tail of demand becomes heavier. When the profit margin of the product is relatively high, and the cost differential between the suppliers is relatively low, it is optimal to buffer the mismatch risk by increasing the responsive capacity and reducing the inventory level as the demand uncertainty increases. In that case, how¬ever, it is optimal to buffer with more inventory and less capacity as the tail of demand becomes heavier. We also show that the optimal responsive capacity is higher for the products with heavier tails when the fill rate is extremely high.

Implicit methods for qualitative modeling of gene regulatory networks.

Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.

Modeling information in software languages

Ohjelmiston kehitystyökalut käyttävät infromaatiota kehittäjän tuottamasta lähdekoodista. Informaatiota hyödynnetään ohjelmistoprojektin eri vaiheissa ja eri tarkoituksissa. Moderneissa ohjelmistoprojekteissa käytetyn informaation määrä voi kasvaa erittäin suureksi. Ohjelmistotyökaluilla on omat informaatiomallinsa ja käyttömekanisminsa. Informaation määrä sekä erilliset työkaluinformaatiomallit tekevät erittäin hankalaksi rakentaa joustavaa työkaluympäristöä, erityisesti ongelma-aluekohtaiseen ohjelmiston kehitysprosessiin. Tässä työssä on analysoitu perusinformaatiometamalleja Unified Modeling language kielestä, Python ohjelmointikielestä ja C++ ohjelmointikielestä. Metainformaation taso on rajoitettu rakenteelliselle tasolle. Ajettavat rakenteet on jätetty pois. ModelBase metamalli on yhdistetty olemassa olevista analysoiduista metamalleista. Tätä metamallia voidaan käyttää tulevaisuudessa ohjelmistotyökalujen kehitykseen.

The Hydrophobic interaction: modeling hydrophobic interactions and aggregation of non-polar particles in aqueous solutions

Malgré son importance dans notre vie de tous les jours, certaines propriétés de l?eau restent inexpliquées. L'étude des interactions entre l'eau et les particules organiques occupe des groupes de recherche dans le monde entier et est loin d'être finie. Dans mon travail j'ai essayé de comprendre, au niveau moléculaire, ces interactions importantes pour la vie. J'ai utilisé pour cela un modèle simple de l'eau pour décrire des solutions aqueuses de différentes particules. Récemment, l?eau liquide a été décrite comme une structure formée d?un réseau aléatoire de liaisons hydrogènes. En introduisant une particule hydrophobe dans cette structure à basse température, certaines liaisons hydrogènes sont détruites ce qui est énergétiquement défavorable. Les molécules d?eau s?arrangent alors autour de cette particule en formant une cage qui permet de récupérer des liaisons hydrogènes (entre molécules d?eau) encore plus fortes : les particules sont alors solubles dans l?eau. A des températures plus élevées, l?agitation thermique des molécules devient importante et brise les liaisons hydrogènes. Maintenant, la dissolution des particules devient énergétiquement défavorable, et les particules se séparent de l?eau en formant des agrégats qui minimisent leur surface exposée à l?eau. Pourtant, à très haute température, les effets entropiques deviennent tellement forts que les particules se mélangent de nouveau avec les molécules d?eau. En utilisant un modèle basé sur ces changements de structure formée par des liaisons hydrogènes j?ai pu reproduire les phénomènes principaux liés à l?hydrophobicité. J?ai trouvé une région de coexistence de deux phases entre les températures critiques inférieure et supérieure de solubilité, dans laquelle les particules hydrophobes s?agrègent. En dehors de cette région, les particules sont dissoutes dans l?eau. J?ai démontré que l?interaction hydrophobe est décrite par un modèle qui prend uniquement en compte les changements de structure de l?eau liquide en présence d?une particule hydrophobe, plutôt que les interactions directes entre les particules. Encouragée par ces résultats prometteurs, j?ai étudié des solutions aqueuses de particules hydrophobes en présence de co-solvants cosmotropiques et chaotropiques. Ce sont des substances qui stabilisent ou déstabilisent les agrégats de particules hydrophobes. La présence de ces substances peut être incluse dans le modèle en décrivant leur effet sur la structure de l?eau. J?ai pu reproduire la concentration élevée de co-solvants chaotropiques dans le voisinage immédiat de la particule, et l?effet inverse dans le cas de co-solvants cosmotropiques. Ce changement de concentration du co-solvant à proximité de particules hydrophobes est la cause principale de son effet sur la solubilité des particules hydrophobes. J?ai démontré que le modèle adapté prédit correctement les effets implicites des co-solvants sur les interactions de plusieurs corps entre les particules hydrophobes. En outre, j?ai étendu le modèle à la description de particules amphiphiles comme des lipides. J?ai trouvé la formation de différents types de micelles en fonction de la distribution des regions hydrophobes à la surface des particules. L?hydrophobicité reste également un sujet controversé en science des protéines. J?ai défini une nouvelle échelle d?hydrophobicité pour les acides aminés qui forment des protéines, basée sur leurs surfaces exposées à l?eau dans des protéines natives. Cette échelle permet une comparaison meilleure entre les expériences et les résultats théoriques. Ainsi, le modèle développé dans mon travail contribue à mieux comprendre les solutions aqueuses de particules hydrophobes. Je pense que les résultats analytiques et numériques obtenus éclaircissent en partie les processus physiques qui sont à la base de l?interaction hydrophobe.<br/><br/>Despite the importance of water in our daily lives, some of its properties remain unexplained. Indeed, the interactions of water with organic particles are investigated in research groups all over the world, but controversy still surrounds many aspects of their description. In my work I have tried to understand these interactions on a molecular level using both analytical and numerical methods. Recent investigations describe liquid water as random network formed by hydrogen bonds. The insertion of a hydrophobic particle at low temperature breaks some of the hydrogen bonds, which is energetically unfavorable. The water molecules, however, rearrange in a cage-like structure around the solute particle. Even stronger hydrogen bonds are formed between water molecules, and thus the solute particles are soluble. At higher temperatures, this strict ordering is disrupted by thermal movements, and the solution of particles becomes unfavorable. They minimize their exposed surface to water by aggregating. At even higher temperatures, entropy effects become dominant and water and solute particles mix again. Using a model based on these changes in water structure I have reproduced the essential phenomena connected to hydrophobicity. These include an upper and a lower critical solution temperature, which define temperature and density ranges in which aggregation occurs. Outside of this region the solute particles are soluble in water. Because I was able to demonstrate that the simple mixture model contains implicitly many-body interactions between the solute molecules, I feel that the study contributes to an important advance in the qualitative understanding of the hydrophobic effect. I have also studied the aggregation of hydrophobic particles in aqueous solutions in the presence of cosolvents. Here I have demonstrated that the important features of the destabilizing effect of chaotropic cosolvents on hydrophobic aggregates may be described within the same two-state model, with adaptations to focus on the ability of such substances to alter the structure of water. The relevant phenomena include a significant enhancement of the solubility of non-polar solute particles and preferential binding of chaotropic substances to solute molecules. In a similar fashion, I have analyzed the stabilizing effect of kosmotropic cosolvents in these solutions. Including the ability of kosmotropic substances to enhance the structure of liquid water, leads to reduced solubility, larger aggregation regime and the preferential exclusion of the cosolvent from the hydration shell of hydrophobic solute particles. I have further adapted the MLG model to include the solvation of amphiphilic solute particles in water, by allowing different distributions of hydrophobic regions at the molecular surface, I have found aggregation of the amphiphiles, and formation of various types of micelle as a function of the hydrophobicity pattern. I have demonstrated that certain features of micelle formation may be reproduced by the adapted model to describe alterations of water structure near different surface regions of the dissolved amphiphiles. Hydrophobicity remains a controversial quantity also in protein science. Based on the surface exposure of the 20 amino-acids in native proteins I have defined the a new hydrophobicity scale, which may lead to an improvement in the comparison of experimental data with the results from theoretical HP models. Overall, I have shown that the primary features of the hydrophobic interaction in aqueous solutions may be captured within a model which focuses on alterations in water structure around non-polar solute particles. The results obtained within this model may illuminate the processes underlying the hydrophobic interaction.<br/><br/>La vie sur notre planète a commencé dans l'eau et ne pourrait pas exister en son absence : les cellules des animaux et des plantes contiennent jusqu'à 95% d'eau. Malgré son importance dans notre vie de tous les jours, certaines propriétés de l?eau restent inexpliquées. En particulier, l'étude des interactions entre l'eau et les particules organiques occupe des groupes de recherche dans le monde entier et est loin d'être finie. Dans mon travail j'ai essayé de comprendre, au niveau moléculaire, ces interactions importantes pour la vie. J'ai utilisé pour cela un modèle simple de l'eau pour décrire des solutions aqueuses de différentes particules. Bien que l?eau soit généralement un bon solvant, un grand groupe de molécules, appelées molécules hydrophobes (du grecque "hydro"="eau" et "phobia"="peur"), n'est pas facilement soluble dans l'eau. Ces particules hydrophobes essayent d'éviter le contact avec l'eau, et forment donc un agrégat pour minimiser leur surface exposée à l'eau. Cette force entre les particules est appelée interaction hydrophobe, et les mécanismes physiques qui conduisent à ces interactions ne sont pas bien compris à l'heure actuelle. Dans mon étude j'ai décrit l'effet des particules hydrophobes sur l'eau liquide. L'objectif était d'éclaircir le mécanisme de l'interaction hydrophobe qui est fondamentale pour la formation des membranes et le fonctionnement des processus biologiques dans notre corps. Récemment, l'eau liquide a été décrite comme un réseau aléatoire formé par des liaisons hydrogènes. En introduisant une particule hydrophobe dans cette structure, certaines liaisons hydrogènes sont détruites tandis que les molécules d'eau s'arrangent autour de cette particule en formant une cage qui permet de récupérer des liaisons hydrogènes (entre molécules d?eau) encore plus fortes : les particules sont alors solubles dans l'eau. A des températures plus élevées, l?agitation thermique des molécules devient importante et brise la structure de cage autour des particules hydrophobes. Maintenant, la dissolution des particules devient défavorable, et les particules se séparent de l'eau en formant deux phases. A très haute température, les mouvements thermiques dans le système deviennent tellement forts que les particules se mélangent de nouveau avec les molécules d'eau. A l'aide d'un modèle qui décrit le système en termes de restructuration dans l'eau liquide, j'ai réussi à reproduire les phénomènes physiques liés à l?hydrophobicité. J'ai démontré que les interactions hydrophobes entre plusieurs particules peuvent être exprimées dans un modèle qui prend uniquement en compte les liaisons hydrogènes entre les molécules d'eau. Encouragée par ces résultats prometteurs, j'ai inclus dans mon modèle des substances fréquemment utilisées pour stabiliser ou déstabiliser des solutions aqueuses de particules hydrophobes. J'ai réussi à reproduire les effets dûs à la présence de ces substances. De plus, j'ai pu décrire la formation de micelles par des particules amphiphiles comme des lipides dont la surface est partiellement hydrophobe et partiellement hydrophile ("hydro-phile"="aime l'eau"), ainsi que le repliement des protéines dû à l'hydrophobicité, qui garantit le fonctionnement correct des processus biologiques de notre corps. Dans mes études futures je poursuivrai l'étude des solutions aqueuses de différentes particules en utilisant les techniques acquises pendant mon travail de thèse, et en essayant de comprendre les propriétés physiques du liquide le plus important pour notre vie : l'eau.

Collecting Transaction Data in a Fourth Generation Mobile Network

Tässä diplomityössä on oletettu että neljännen sukupolven mobiiliverkko on saumaton yhdistelmä olemassa olevia toisen ja kolmannen sukupolven langattomia verkkoja sekä lyhyen kantaman WLAN- ja Bluetooth-radiotekniikoita. Näiden tekniikoiden on myös oletettu olevan niin yhteensopivia ettei käyttäjä havaitse saanti verkon muuttumista. Työ esittelee neljännen sukupolven mobiiliverkkoihin liittyvien tärkeimpien langattomien tekniikoiden arkkitehtuurin ja perustoiminta-periaatteet. Työ kuvaa eri tekniikoita ja käytäntöjä tiedon mittaamiseen ja keräämiseen. Saatuja transaktiomittauksia voidaan käyttää tarjottaessa erilaistettuja palvelutasoja sekä verkko- ja palvelukapasiteetin optimoimisessa. Lisäksi työssä esitellään Internet Business Information Manager joka on ohjelmistokehys hajautetun tiedon keräämiseen. Sen keräämää mittaustietoa voidaan käyttää palvelun tason seurannassa j a raportoinnissa sekä laskutuksessa. Työn käytännön osuudessa piti kehittää langattoman verkon liikennettä seuraava agentti joka tarkkailisi palvelun laatua. Agentti sijaitsisi matkapuhelimessa mitaten verkon liikennettä. Agenttia ei kuitenkaan voitu toteuttaa koska ohjelmistoympäristö todettiin vajaaksi. Joka tapauksessa työ osoitti että käyttäjän näkökulmasta tietoa kerääville agenteille on todellinen tarve.

Business Process Modeling in Software Requirements Engineering for Small and Medium Software Projects

Vaatimusmäärittelyn tavoitteena on luoda halutun järjestelmän kokonaisen, yhtenäisen vaatimusluettelon vaatimusten määrittämiseksi käsitteellisellä tasolla. Liiketoimintaprosessien mallintaminen on varsin hyödyllinen vaatimusmäärittelyn varhaisissa vaiheissa. Tämä työ tutkii liiketoimintaprosessien mallintamista tietojärjestelmien kehittämistä varten. Nykyään on olemassa erilaisia liiketoimintaprosessien mallintamiseen tarkoitettuja tekniikoita. Tämä työ tarkastaa liiketoimintaprosessien mallintamisen periaatteet ja näkökohdat sekä eri mallinnustekniikoita. Uusi menetelmä, joka on suunniteltu erityisesti pienille ja keskisuurille ohjelmistoprojekteille, on kehitetty prosessinäkökohtien ja UML-kaavioiden perusteella.

Financial Modeling to Support Organizational Control

Tutkimus tarkastelee taloudellisia mallintamismahdollisuuksia metsäteollisuuden liiketoimintayksikössä. Tavoitteena on suunnitella ja luoda taloudellinen malli liiketoimintayksikölle, jonka avulla sen tuloksen analysoiminen ja ennustaminen on mahdollista. Tutkimusta tarkastellaan konstruktiivisen tutkimusmenetelmän avulla. Teoreettinen viitekehys tarkastelee olemassa olevan informaation muotoilemista keskittyen tiedon jalostamisen tarpeisiin, päätöksenteon asettamiin vaatimuksiin sekä mallintamiseen. Toiseksi, teoria esittää informaatiolle asetettavia vaatimuksia organisatorisen ohjauksen näkökulmasta.Empiirinen tieto kerätään osallistuvan havainnoinnin avulla hyödyntäen epävirallisia keskusteluja, tietojärjestelmiä ja laskentatoimen dokumentteja. Tulokset osoittavat, että liikevoiton ennustaminen mallin avulla on vaikeaa, koska taustalla vaikuttavien muuttujien määrä on suuri. Tästä johtuen malli täytyykin rakentaa niin, että se tarkastelee liikevoittoa niin yksityiskohtaisella tasolla kuin mahdollista. Testauksessa mallin tarkkuus osoittautui sitä paremmaksi, mitä tarkemmalla tasolla ennustaminen tapahtui. Lisäksi testaus osoitti, että malli on käyttökelpoinen liiketoiminnan ohjauksessa lyhyellä aikavälillä. Näin se luo myös pohjan pitkän aikavälin ennustamiselle.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling.

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model's prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.

A System-Level Simulation Model for a Protocol Processor

As the development of integrated circuit technology continues to follow Moore’s law the complexity of circuits increases exponentially. Traditional hardware description languages such as VHDL and Verilog are no longer powerful enough to cope with this level of complexity and do not provide facilities for hardware/software codesign. Languages such as SystemC are intended to solve these problems by combining the powerful expression of high level programming languages and hardware oriented facilities of hardware description languages. To fully replace older languages in the desing flow of digital systems SystemC should also be synthesizable. The devices required by modern high speed networks often share the same tight constraints for e.g. size, power consumption and price with embedded systems but have also very demanding real time and quality of service requirements that are difficult to satisfy with general purpose processors. Dedicated hardware blocks of an application specific instruction set processor are one way to combine fast processing speed, energy efficiency, flexibility and relatively low time-to-market. Common features can be identified in the network processing domain making it possible to develop specialized but configurable processor architectures. One such architecture is the TACO which is based on transport triggered architecture. The architecture offers a high degree of parallelism and modularity and greatly simplified instruction decoding. For this M.Sc.(Tech) thesis, a simulation environment for the TACO architecture was developed with SystemC 2.2 using an old version written with SystemC 1.0 as a starting point. The environment enables rapid design space exploration by providing facilities for hw/sw codesign and simulation and an extendable library of automatically configured reusable hardware blocks. Other topics that are covered are the differences between SystemC 1.0 and 2.2 from the viewpoint of hardware modeling, and compilation of a SystemC model into synthesizable VHDL with Celoxica Agility SystemC Compiler. A simulation model for a processor for TCP/IP packet validation was designed and tested as a test case for the environment.

Discrete distributions when modeling the disability severity score of motor victims

Many European states apply score systems to evaluate the disability severity of non-fatal motor victims under the law of third-party liability. The score is a non-negative integer with an upper bound at 100 that increases with severity. It may be automatically converted into financial terms and thus also reflects the compensation cost for disability. In this paper, discrete regression models are applied to analyze the factors that influence the disability severity score of victims. Standard and zero-altered regression models are compared from two perspectives: an interpretation of the data generating process and the level of statistical fit. The results have implications for traffic safety policy decisions aimed at reducing accident severity. An application using data from Spain is provided.

Knowledge modeling for innovative companies: case of business incubator

The paper presents a study which is aimed at building a knowledge model for a case company – business incubator “Ingria” (St. Petersburg, Russia). The business incubator is one of its kind organization in St. Petersburg, and one of the few in Russia, providing services for innovative entrepreneurial companies at an international level. Business incubation impact is deeply researched from the point of view of knowledge engineering. The paper also provides a broad analysis of various knowledge engineering tools used for visualization of knowledge, as well as knowledge modeling techniques.

Modeling and Analysis of Noise and Interconnects for On-Chip Communication Link Design

This thesis considers modeling and analysis of noise and interconnects in onchip communication. Besides transistor count and speed, the capabilities of a modern design are often limited by on-chip communication links. These links typically consist of multiple interconnects that run parallel to each other for long distances between functional or memory blocks. Due to the scaling of technology, the interconnects have considerable electrical parasitics that affect their performance, power dissipation and signal integrity. Furthermore, because of electromagnetic coupling, the interconnects in the link need to be considered as an interacting group instead of as isolated signal paths. There is a need for accurate and computationally effective models in the early stages of the chip design process to assess or optimize issues affecting these interconnects. For this purpose, a set of analytical models is developed for on-chip data links in this thesis. First, a model is proposed for modeling crosstalk and intersymbol interference. The model takes into account the effects of inductance, initial states and bit sequences. Intersymbol interference is shown to affect crosstalk voltage and propagation delay depending on bus throughput and the amount of inductance. Next, a model is proposed for the switching current of a coupled bus. The model is combined with an existing model to evaluate power supply noise. The model is then applied to reduce both functional crosstalk and power supply noise caused by a bus as a trade-off with time. The proposed reduction method is shown to be effective in reducing long-range crosstalk noise. The effects of process variation on encoded signaling are then modeled. In encoded signaling, the input signals to a bus are encoded using additional signaling circuitry. The proposed model includes variation in both the signaling circuitry and in the wires to calculate the total delay variation of a bus. The model is applied to study level-encoded dual-rail and 1-of-4 signaling. In addition to regular voltage-mode and encoded voltage-mode signaling, current-mode signaling is a promising technique for global communication. A model for energy dissipation in RLC current-mode signaling is proposed in the thesis. The energy is derived separately for the driver, wire and receiver termination.

Computational modeling for irrigated agriculture planning. Part I: general description and linear programming

Linear programming models are effective tools to support initial or periodic planning of agricultural enterprises, requiring, however, technical coefficients that can be determined using computer simulation models. This paper, presented in two parts, deals with the development, application and tests of a methodology and of a computational modeling tool to support planning of irrigated agriculture activities. Part I aimed at the development and application, including sensitivity analysis, of a multiyear linear programming model to optimize the financial return and water use, at farm level for Jaíba irrigation scheme, Minas Gerais State, Brazil, using data on crop irrigation requirement and yield, obtained from previous simulation with MCID model. The linear programming model outputted a crop pattern to which a maximum total net present value of R$ 372,723.00 for the four years period, was obtained. Constraints on monthly water availability, labor, land and production were critical in the optimal solution. In relation to the water use optimization, it was verified that an expressive reductions on the irrigation requirements may be achieved by small reductions on the maximum total net present value.

Fuzzy rule-based system for evaluation of uncertainty transaction in cassava chain

A fuzzy ruled-based system was developed in this study and resulted in an index indicating the level of uncertainty related to commercial transactions between cassava growers and their dealers. The fuzzy system was developed based on Transaction Cost Economics approach. The fuzzy system was developed from input variables regarding information sharing between grower and dealer on “Demand/purchase Forecasting”, “Production Forecasting” and “Production Innovation”. The output variable is the level of uncertainty regarding the transaction between seller and buyer agent, which may serve as a system for detecting inefficiencies. Evidences from 27 cassava growers registered in the Regional Development Offices of Tupa and Assis, São Paulo, Brazil, and 48 of their dealers supported the development of the system. The mathematical model indicated that 55% of the growers present a Very High level of uncertainty, 33% present Medium or High. The others present Low or Very Low level of uncertainty. From the model, simulations of external interferences can be implemented in order to improve the degree of uncertainty and, thus, lower transaction costs.

Software Development Process Modeling. Developers Perspective to Contemporary Modeling Techniques

Formal software development processes and well-defined development methodologies are nowadays seen as the definite way to produce high-quality software within time-limits and budgets. The variety of such high-level methodologies is huge ranging from rigorous process frameworks like CMMI and RUP to more lightweight agile methodologies. The need for managing this variety and the fact that practically every software development organization has its own unique set of development processes and methods have created a profession of software process engineers. Different kinds of informal and formal software process modeling languages are essential tools for process engineers. These are used to define processes in a way which allows easy management of processes, for example process dissemination, process tailoring and process enactment. The process modeling languages are usually used as a tool for process engineering where the main focus is on the processes themselves. This dissertation has a different emphasis. The dissertation analyses modern software development process modeling from the software developers’ point of view. The goal of the dissertation is to investigate whether the software process modeling and the software process models aid software developers in their day-to-day work and what are the main mechanisms for this. The focus of the work is on the Software Process Engineering Metamodel (SPEM) framework which is currently one of the most influential process modeling notations in software engineering. The research theme is elaborated through six scientific articles which represent the dissertation research done with process modeling during an approximately five year period. The research follows the classical engineering research discipline where the current situation is analyzed, a potentially better solution is developed and finally its implications are analyzed. The research applies a variety of different research techniques ranging from literature surveys to qualitative studies done amongst software practitioners. The key finding of the dissertation is that software process modeling notations and techniques are usually developed in process engineering terms. As a consequence the connection between the process models and actual development work is loose. In addition, the modeling standards like SPEM are partially incomplete when it comes to pragmatic process modeling needs, like light-weight modeling and combining pre-defined process components. This leads to a situation, where the full potential of process modeling techniques for aiding the daily development activities can not be achieved. Despite these difficulties the dissertation shows that it is possible to use modeling standards like SPEM to aid software developers in their work. The dissertation presents a light-weight modeling technique, which software development teams can use to quickly analyze their work practices in a more objective manner. The dissertation also shows how process modeling can be used to more easily compare different software development situations and to analyze their differences in a systematic way. Models also help to share this knowledge with others. A qualitative study done amongst Finnish software practitioners verifies the conclusions of other studies in the dissertation. Although processes and development methodologies are seen as an essential part of software development, the process modeling techniques are rarely used during the daily development work. However, the potential of these techniques intrigues the practitioners. As a conclusion the dissertation shows that process modeling techniques, most commonly used as tools for process engineers, can also be used as tools for organizing the daily software development work. This work presents theoretical solutions for bringing the process modeling closer to the ground-level software development activities. These theories are proven feasible by presenting several case studies where the modeling techniques are used e.g. to find differences in the work methods of the members of a software team and to share the process knowledge to a wider audience.