Self-scheduling and bidding strategies of thermal units with stochastic emission constraints

This paper is on the self-scheduling problem for a thermal power producer taking part in a pool-based electricity market as a price-taker, having bilateral contracts and emission-constrained. An approach based on stochastic mixed-integer linear programming approach is proposed for solving the self-scheduling problem. Uncertainty regarding electricity price is considered through a set of scenarios computed by simulation and scenario-reduction. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. A requirement on emission allowances to mitigate carbon footprint is modelled by a stochastic constraint. Supply functions for different emission allowance levels are accessed in order to establish the optimal bidding strategy. A case study is presented to illustrate the usefulness and the proficiency of the proposed approach in supporting biding strategies. (C) 2014 Elsevier Ltd. All rights reserved.

Importância da Programação Linear na Determinação de Informações de Gestão - Caso Moave, Sa

A Investigação Operacional vem demonstrando ser uma valiosa ferramenta de gestão nos dias de hoje em que se vive num mercado cada vez mais competitivo. Através da Programação Linear pode-se reproduzir matematicamente um problema de maximização dos resultados ou minimização dos custos de produção com o propósito de auxiliar os gestores na tomada de decisão. A Programação Linear é um método matemático em que a função objectivo e as restrições assumem características lineares, com diversas aplicações no controlo de gestão, envolvendo normalmente problemas de utilização dos recursos disponíveis sujeitos a limitações impostas pelo processo produtivo ou pelo mercado. O objectivo geral deste trabalho é o de propor um modelo de Programação Linear para a programação ou produção e alocação de recursos necessários. Optimizar uma quantidade física designada função objectivo, tendo em conta um conjunto de condicionalismos endógenas às actividades em gestão. O objectivo crucial é dispor um modelo de apoio à gestão contribuindo assim para afectação eficiente de recursos escassos à disposição da unidade económica. Com o trabalho desenvolvido ficou patente a importância da abordagem quantitativa como recurso imprescindível de apoio ao processo de decisão. The operational research has proven to be a valuable management tool today we live in an increasingly competitive market. Through Linear Programming can be mathematically reproduce a problem of maximizing performance or minimizing production costs in order to assist managers in decision making. The Linear Programming is a mathematical method in which the objective function and constraints are linear features, with several applications in the control of management, usually involving problems of resource use are available subject to limitations imposed by the production process or the market. The overall objective of this work is to propose a Linear Programming model for scheduling or production and allocation of necessary resources. Optimizing a physical quantity called the objective function, given a set of endogenous constraints on management thus contributing to efficient allocation of scarce resources available to the economic unit. With the work has demonstrated the importance of the quantitative approach as essential resource to support the decision process.

Production Flow Analysis - cases from manufacturing and service industry

Production flow analysis (PFA) is a well-established methodology used for transforming traditional functional layout into product-oriented layout. The method uses part routings to find natural clusters of workstations forming production cells able to complete parts and components swiftly with simplified material flow. Once implemented, the scheduling system is based on period batch control aiming to establish fixed planning, production and delivery cycles for the whole production unit. PFA is traditionally applied to job-shops with functional layouts, and after reorganization within groups lead times reduce, quality improves and motivation among personnel improves. Several papers have documented this, yet no research has studied its application to service operations management. This paper aims to show that PFA can well be applied not only to job-shop and assembly operations, but also to back-office and service processes with real cases. The cases clearly show that PFA reduces non-value adding operations, introduces flow by evening out bottlenecks and diminishes process variability, all of which contribute to efficient operations management.

Local optima networks of hard combinatorial landscapes

Combinatorial optimization involves finding an optimal solution in a finite set of options; many everyday life problems are of this kind. However, the number of options grows exponentially with the size of the problem, such that an exhaustive search for the best solution is practically infeasible beyond a certain problem size. When efficient algorithms are not available, a practical approach to obtain an approximate solution to the problem at hand, is to start with an educated guess and gradually refine it until we have a good-enough solution. Roughly speaking, this is how local search heuristics work. These stochastic algorithms navigate the problem search space by iteratively turning the current solution into new candidate solutions, guiding the search towards better solutions. The search performance, therefore, depends on structural aspects of the search space, which in turn depend on the move operator being used to modify solutions. A common way to characterize the search space of a problem is through the study of its fitness landscape, a mathematical object comprising the space of all possible solutions, their value with respect to the optimization objective, and a relationship of neighborhood defined by the move operator. The landscape metaphor is used to explain the search dynamics as a sort of potential function. The concept is indeed similar to that of potential energy surfaces in physical chemistry. Borrowing ideas from that field, we propose to extend to combinatorial landscapes the notion of the inherent network formed by energy minima in energy landscapes. In our case, energy minima are the local optima of the combinatorial problem, and we explore several definitions for the network edges. At first, we perform an exhaustive sampling of local optima basins of attraction, and define weighted transitions between basins by accounting for all the possible ways of crossing the basins frontier via one random move. Then, we reduce the computational burden by only counting the chances of escaping a given basin via random kick moves that start at the local optimum. Finally, we approximate network edges from the search trajectory of simple search heuristics, mining the frequency and inter-arrival time with which the heuristic visits local optima. Through these methodologies, we build a weighted directed graph that provides a synthetic view of the whole landscape, and that we can characterize using the tools of complex networks science. We argue that the network characterization can advance our understanding of the structural and dynamical properties of hard combinatorial landscapes. We apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and predict problem hardness as measured from the performances of trajectory-based local search heuristics.

Ohutlevyn taivuttaminen pienerätuotannossa

Tutkimuksessa on selvitetty ohutlevyn taivuttamismenetelmien tärkeimmät kustannustekijät ja menetelmien taloudelliset käyttöalueet. Vertailtavina menetelminä on käsinsärmäys, robotisoitu särmäys, taivutusautomaatti ja taivutuskone. Tulosta on sovellettu Hackman Metos Oy:n keittiölaitteiden tuotantoon. Tutkimusmenetelminä oli haastattelututkimus, kirjallisuustutkimus, työntutkimustulosten käyttö, ryhmäteknologian soveltaminen ja kokeellinen tutkimus. Särmäysrobotin tärkein kustannustekijä on ohjelmointiaika, mikä vaikuttaa ratkaisevasti sen soveltuvuuteen pienerätuotantoon. Nykyisten särmäyssolujen taloudellinen käyttöalue on tuhansien kappaleiden vuosivolyymi satojen kappaleiden eräkoolla. taivutusautomaatin ohjelmointi- ja asetusajat ovat erittäin lyhyet ja sen tärkein kustannustekijä on käyttöaste. Mikäli käyttöaste on korkea, taivutusautomaatti on kannattava pienerätuotannossa pienille vuosivolyymeille. Taivutusautomaatin käyttöönotossa tuotteiden suunnittelu on tärkeä tekijä, sillä särmättäväksi suunnitellut osat eivät välttämättä sovellu taivutusautomaatilla taivutettavaksi. Taivutuskoneen investointikustannus on alhaisempi kuin särmäyspuristimen, mutta sillä on paljon tuotteen valmistettavuuden liittyviä rajoituksia. Taivutuskone on kannattava investointi, mikäli tuotannossa on paljon levyjä, joiden taivutukset ovat samaan suuntaan ja ne vaativat kaksi särmääjää. Tutkimuksen perusteella Hackman Metso Oy:ssä teknis-taloudellisin taivutusmenetelmä on käsinsärmäys. Tuotannon kasvaessa taivutusautomaatti tulee olemaan särmäysrobottia edullisempi. Taivutuskoneella on niin paljon valmistettavuusrajoituksia, että se ei sovellu yrityksen tuotantoon.

Tuotannonsuunnittelu- ja -ohjausprosessin kehittäminen konepajayrityksessä

Tehdyssä kirjallisuus- ja teoriakatsauksessa vuosien 2006 - 2010 välisenä aikana, Keski-Suomessa toimivan konepajateollisuuden järjestelmätoimittajayrityksen toimeksiannosta, pyrittiin muodostamaan kokonaiskuva laajasta tuotannonsuunnittelun ja -ohjauksen aihealueesta. Perustutkimuskysymykset liittyivät ns. MPC-systeemiin, jolla tarkoitetaan sitä, että tuotannonsuunnittelu- ja ohjauskysymyksissä on huomioitava aina henkilöiden, organisaation, teknologioiden ja prosessien muodostama kokonaisuus. Operatiivisen johtamisen tehtävänä on yrityksen tuotteita koskevan kysynnän ja tarjonnan tasapainottaminen niin, että resursseja käytettäisiin ja tarvittaisiin mahdollisimman vähän vastattaessa kysyntään asiakasvaatimukset huomioiden. Tuotantostrategian pohjalta on voitava rakentaa MPC-systeemi, jonka avulla ja jota kehittäen tuotanto saavuttaisi sille asetetut suorituskykytavoitteet mm. kustannusten, laadun, nopeuden, luotettavuuden sekä tuottavuuskehityksen osalta. Työssä tarkasteltiin yleisen kolmitasoisen viitekehyksen kautta ”perinteisistä MPC-systeemien perusratkaisuista” hierarkkisia, suunnittelu- ja laskentaintensiiviä, MRP-pohjaisia sekä yksinkertaistamiseen ja nopeuteen perustuvia JIT/Lean -menetelmiä. Tämä viitekehys käsittää: 1) kysynnän- ja resurssien hallinnan, 2) yksityiskohtaisemman kapasiteetin ja materiaalien hallinnan sekä 3) tarkemman tuotannon ja hankintojen ohjauksen sekä tuotannon lattiatason osa-alueet. Johtamisen ja MPC-systeemien kehittämisen ”uusina aaltoina ja näkökulmina” raportissa käsiteltiin myös johtamisen eri koulukuntia sekä em. viitekehyksen pohjalta tarvittavia tietojärjestelmiä. Olennaisimpana johtopäätöksenä todettiin, että MRP-pohjaisten ratkaisujen lisäksi, etenkin monimutkaisia tuotteita tilausohjautuvasti valmistavien kappaletavarateollisuuden yritysten, on mahdollisesti hyödynnettävä myös kehittyneempiä suunnittelu- ja ohjausjärjestelmiä. Lisäksi huomattiin, että ”perinteisten strategioiden” rinnalle yritysten on nostettava myös tieto- ja viestintäteknologiastrategiat. On tärkeää ymmärtää, että täydellistä MPC-systeemiä ei ole vielä keksitty: jokaisen yrityksen tehtäväksi ja vastuulle jää ”oman totuutensa” muodostaminen ja systeeminsä rakentaminen sen pohjalta.

Tuotannonohjausjärjestelmän kehittäminen PK-yrityksessä

Työn tarkoitus on kehittää pk-konepajayrityksen tuotannonohjausta ja toteuttaa tuotannonohjausjärjestelmän hallittu käyttöönotto. Tavoitteena on parantaa yrityksen kapasiteetin hallintaa ja toimitusaikapitävyyttä sekä kehittää tuotannon päivittäisohjausta ja koordinointia. Työssä on sovellettu prosessijohtamisen kuvausmenetelmiä ja prosessin kehittämistyökaluja tuotannonohjausprosessin kehittämiseen ja tuotannonohjausjärjestelmän käyttöönottoon. Lisäksi työssä on lähdekirjallisuuden avulla tutkittu eri tuotannonohjausperiaatteiden soveltuvuutta asiakasohjautuvaan joustavaan konepajatuotantoon. Työ on toteutettu kvalitatiivisena toimintatutkimuksena. Tuotannonohjausjärjestelmän käyttöönoton avulla on mahdollista kehittää tuotannon kapasiteetin ohjausta ja tuotannonkoordinaatiota. Tämä kuitenkin edellyttää tuotannonohjausprosessin kuvaamista sekä ohjaukseen osallistuvien henkilöiden roolien ja vastuiden selkeää määrittelyä. Erityisen kriittistä on saada koko organisaatio suhtautumaan muutokseen positiivisesti.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Otimização de escalas de serviço de tripulações estudo de caso em uma empresa de transporte urbano na região metropolitana de Natal/RN

This work aims to "build" rostering urban bus crews to minimize the cost of overtime. For this purpose a mathematical model was developed based on case study in an urban transport company in the metropolitan region of Natal. This problem is usually known in the literature as the Crew Scheduling Problem (CSP) and classified as NP-hard. The mathematical programming takes into account constraints such as: completion of all trips, daily and maximum allowable range of home and / or food. We used the Xpress-MP software to implement and validate the proposed model. For the tested instances the application of the model allowed a reduction in overtime from 38% to 84%

Mathematical Optimization Applied to Thermal and Electrical Energy Systems

This Thesis aims at building and discussing mathematical models applications focused on Energy problems, both on the thermal and electrical side. The objective is to show how mathematical programming techniques developed within Operational Research can give useful answers in the Energy Sector, how they can provide tools to support decision making processes of Companies operating in the Energy production and distribution and how they can be successfully used to make simulations and sensitivity analyses to better understand the state of the art and convenience of a particular technology by comparing it with the available alternatives. The first part discusses the fundamental mathematical background followed by a comprehensive literature review about mathematical modelling in the Energy Sector. The second part presents mathematical models for the District Heating strategic network design and incremental network design. The objective is the selection of an optimal set of new users to be connected to an existing thermal network, maximizing revenues, minimizing infrastructure and operational costs and taking into account the main technical requirements of the real world application. Results on real and randomly generated benchmark networks are discussed with particular attention to instances characterized by big networks dimensions. The third part is devoted to the development of linear programming models for optimal battery operation in off-grid solar power schemes, with consideration of battery degradation. The key contribution of this work is the inclusion of battery degradation costs in the optimisation models. As available data on relating degradation costs to the nature of charge/discharge cycles are limited, we concentrate on investigating the sensitivity of operational patterns to the degradation cost structure. The objective is to investigate the combination of battery costs and performance at which such systems become economic. We also investigate how the system design should change when battery degradation is taken into account.

Robust linear semi-infinite programming duality under uncertainty

In this paper, we propose a duality theory for semi-infinite linear programming problems under uncertainty in the constraint functions, the objective function, or both, within the framework of robust optimization. We present robust duality by establishing strong duality between the robust counterpart of an uncertain semi-infinite linear program and the optimistic counterpart of its uncertain Lagrangian dual. We show that robust duality holds whenever a robust moment cone is closed and convex. We then establish that the closed-convex robust moment cone condition in the case of constraint-wise uncertainty is in fact necessary and sufficient for robust duality. In other words, the robust moment cone is closed and convex if and only if robust duality holds for every linear objective function of the program. In the case of uncertain problems with affinely parameterized data uncertainty, we establish that robust duality is easily satisfied under a Slater type constraint qualification. Consequently, we derive robust forms of the Farkas lemma for systems of uncertain semi-infinite linear inequalities.

Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06

Optimization of transit transfer times: A system-wide approach

This dissertation presents a system-wide approach, based on genetic algorithms, for the optimization of transfer times for an entire bus transit system. Optimization of transfer times in a transit system is a complicated problem because of the large set of binary and discrete values involved. The combinatorial nature of the problem imposes a computational burden and makes it difficult to solve by classical mathematical programming methods. ^ The genetic algorithm proposed in this research attempts to find an optimal solution for the transfer time optimization problem by searching for a combination of adjustments to the timetable for all the routes in the system. It makes use of existing scheduled timetables, ridership demand at all transfer locations, and takes into consideration the randomness of bus arrivals. ^ Data from Broward County Transit are used to compute total transfer times. The proposed genetic algorithm-based approach proves to be capable of producing substantial time savings compared to the existing transfer times in a reasonable amount of time. ^ The dissertation also addresses the issues related to spatial and temporal modeling, variability in bus arrival and departure times, walking time, as well as the integration of scheduling and ridership data. ^

Branch and price solution approach for order acceptance and capacity planning in make -to -order operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. ^ For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver.^ The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. ^ The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.^

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.