Havbruksnæringen er en stor næring i Norge, og har opplevd store endringer i struktur og lovverk de siste årene. Sjøvanndelen av verdikjeden for oppdrettslaks har siden 2005 blitt regulert av et krav om maksimal tillatt biomasse på anleggs- og regionsbasis. I tillegg innføres det nå i store deler av landet geografiske soner hvor alle anlegg innenfor en sone har felles brakkleggingsperiode. Vi har laget en stokastisk optimeringsmodell som kan hjelpe aktører med taktisk planlegging av smoltutsett og utslakting. Modellen er rettet mot store aktører med en stor portefølje av anlegg, og har blitt testet for Marine Harvests anlegg i Midt-Norge. Vekst og dødelighet er usikre faktorer i modellen. Resultatene viser at usikker dødelighet er lettere å kompensere for i fremtidige utsett enn usikker vekst. Vi finner at det er gunstig å tillate, og planlegge for, utslakt på lavere vekter enn målvekten. Særlig gjelder dette for utslakt i månedene mai-juni. Et annet alternativ er å endre fra dagens målsatte slaktevekt på 6 kg til en målvekt på 5,5 kg. Ved å endre målvekten får vi med vår modell en økning i totalvolum av slaktet laks på over 10 %.

In this thesis I evaluate technical efficiency and productivity for use in performance evaluation in incentive systems. In light of agency theory and the organizational context, these techniques have several promising attributes for use in incentive schemes, despite their limited occurrence in the incentive literature. The use of data envelopment analysis (DEA) for estimating technical efficiency limit subjective evaluations and eliminate unwanted Nash-equilibrium under comparative evaluation. The Hicks-Moorsteen index prove to be the preferable index for measuring productivity change, as it cope with technologies exhibiting globally variable returns to scale. By coupling DEA and Hicks-Moorsteen we get four linear programs, which are easy to solve with developed software. However, infeasibility might occur when estimating the index and no remedies to this problem exist in the literature. Infeasibility will not occur for continuous time indices or when estimating technical efficiency with Stochastic Frontier Analysis (SFA). Although, SFA is poor on other aspects and software incorporating continuous time indexes are yet to be developed.The use of productivity as a method for performance evaluation might offset systematic bias for comparative evaluation in heterogeneous environments, and will in most cases give employees strong incentives to improve. Technical efficiency might induce efficient employees to only maintain their level of effort, but super efficiency models reduce this threat. When computing technical efficiency, environmental factors should be adjusted for through a stepwise regression procedure in order to reduce uncontrollable risk. Although the goal is to implement a model that minimize subjective evaluations that might lead to favoritism, a final expert judgment should verify or disprove the performance scores.

Value at risk (VaR) and Expected Shortfall (ES) are commonly used risk measures in the financial literature. They have however not been applied to a great extent on energy derivatives. This paper compares the performance of several VaR and ES models for energy commodity futures on some of the world’s largest commodity exchanges. In total 14 different VaR models and nine ES models are evaluated; GARCH and GJR-GARCH with normal, student t, GED and skewed student t distributions and EWQR are used to obtain both VaR and ES forecasts. In addition, five CAViaR models are used in the VaR analysis.EWQR is by far the best ES model. It has very good test results for all markets and quantiles considered. The VaR results vary greatly, and there does not appear to be any clear pattern in which some models are better suited for certain markets or commodities. The VaR models with best performance overall are however EWQR, the adaptive CAViaR and GARCH and GJR-GARCH models with student t and skewed student t distributions.

I find non-investment graded companies’ motives for issuing convertible bonds in the Norwegian market by evaluating logistic regression results from a two-step security choice model from samples of 28 convertible bond-, 102 bond- and 229 equity issuances from 2005 to 2011. The findings indicate that companies in the Norwegian market substitute convertibles for bonds if they have valuable investment opportunities at hand and are associated with risk and uncertainty. This paper argues that the issuers of convertible bonds substitute convertibles for bonds to mitigate the asset substitution problem and mitigate debt-related financing costs under the asymmetric information theory. I further deduce that convertibles are used as a debt-instrument in the Norwegian market, different from the US market and more similar to the Western European market. Finding the issuers’ motives for issuing convertibles in Norway extend current academic research, and can be a fundament for investors’ when evaluating different convertible bond investment opportunities.

In order to determine the structure of the optimal CEO contract, we create a principal agent model and implement it on a sample of Norwegian firms. The model takes account of executives’ loss‐ and risk‐aversion and the fact that undiversified and risk‐averse executives do not put the same value on stock options as shareholders do. We employ the certainty equivalent approach in determining the perceived value of the CEO’s stock options and conduct a comprehensive numerical computer simulation to determine the optimal CEO contract.Our analysis delivers four important findings. First, our results show that the option’s exercise price of long term option grants does not affect executive performance in the optimal contract. When awarding the executive premium or at‐the‐money options, we find similar CEO performance and contracts cost for long term options grants. Premium options do however have a lower incentive effect. At‐the‐money options must therefore be substituted with larger amounts of premium options to induce the same level of effort.Second, the CEO’s ability to affect the firm outcome has a large impact on the optimal contract. As the executive’s action has an increasing impact on the firm performance, the marginal incentive effect from equity awards increase. This results in higher levels of effort and cheaper contracts.Third, our results indicate that Norwegian firms suffer large agency costs as they fail to present executives with optimal contracts. Norwegian executives should receive more stocks and options and a lower base salary. This tradeoff would involve a transfer of risk from the shareholders to the executive, resulting in a higher total compensation due to the CEO’s need for a risk premium. The increase in executive compensation is however more than outweighed by the increase in CEO effort and shareholder return. We also find it to be optimal from the shareholders view that executives invest more of their personal wealth in the company.Finally, we show that our model is a good predictor of CEO performance. When we run our model for the actual CEO contract of the sample firms, we predict a performance close to the observed level. This suggests that executives indeed are risk‐ and loss‐averse. We propose some societal constraints on the magnitude of the executive compensation in Norway in an attempt to explain why firms fail to present executives with optimal performance incentives.

To deal with the threat of climate change, many technologies should be investigated, and power generation through an IRCC with CO2 capture is one alternative. However, capturing CO2 has a negative effect on the efficiency of the process as it requires a lot of energy. In this work, we try to reduce the energy consumption of an IRCC process with CO2 capture by developing a tool for finding the optimal process design with extensive heat integration.The design of an IRCC process involves many parameters which interfere in complex relationships. In this report, an MINLP model is established for optimizing important parameters simultaneously. The model relies on metamodeling based on process simulations in Aspen HYSYS to approximate difficult correlations, combined with a more direct approach for modeling computationally easier parts of the process.A general model for heat recovery targeting is developed for the heat integration optimization, and implemented as a part of the full IRCC optimization model.The global solver BARON is used for solving the problem, together with a relaxation procedure based on pinch analysis insights, and optimal solutions are usually found within several hours.The optimized IRCC process reaches a net electric efficiency of 49.97 %, assuming maximum heat integration, with only 1 % of the cooling and heating demands to be covered by utilities. The accuracy of the model is relatively good when compared to process simulations, but a less idealistic version of the IRCC should be designed based on the results to confirm the capability of the model.

The amount of power consumed by data centres world wide is increasing, and due to growing electricity bills, service providers aim more attention on energy-efficient management of their data centres. In order to achieve this goal, a service provider need to make smart decisions regarding the deployment of his services. At the same time, in order to satisfy his end-users, a service provider needs to focus on delivery of services complying with the quality of service (QoS) requirements. Consequently, he needs to make decisions related to replication level of his services, as well.In this thesis, I propose two interrelated mixed integer linear programming (MILP) models aiming at supporting service providers in their decision making. The first MILP concerns energy-efficient deployment of a service provider's services in his own virtualized data center, where the objective is to minimized the cost of energy usage, while satisfying the response time and availability requirements of the end-users. The second MILP introduces the flexibility of Cloud computing by letting the service provider have the opportunity to deploy services in a public cloud, and hence the objective is to minimize the total cost of deployment, while still, ensuring satisfactory QoS levels. The proposed MILP models are tested on test instances of varying size with the intention to discuss scalability issues and commenting on modelling choices. The results show that the second model is the hardest to solve, in terms of closing the optimality gap, but nevertheless, it is depicted that good solutions are found early in the branch and bound search. Furthermore, different modelling choices illustrate the trade-off between energy-efficient management of data center resources and service performance.

The development of the Aluminum and Paper industry is assessed by A.T. Kearney’s Merger Endgame framework. The implications for Norsk Hydro and Norske Skog are also addressed along with value creation opportunities for the corresponding stockholders. The Merger Endgame framework reveals that up/midstream in the Aluminum industry is entering the Balance and Alliance Stage while downstream is deconsolidating. The implications for Norsk Hydro are that the ability to cooperate and build joint ventures will be increasingly important in up/midstream while the rivalry is likely to increase downstream. Norsk Hydro has invested heavily in up/midstream and is well positioned for such a development in this business area. However, the company’s expansion in up/midstream and the increasing rivalry downstream imply that the management does not have the capacity to build top performers within these two business areas which rely on different skills. The greatest value creation opportunity is therefore to further demerge downstream operations in order to secure operational mobility and responsiveness to shifting market conditions. The Merger Endgame framework reveals that the Paper industry is entering the Focus Stage. The implication for Norske Skog is that the industry will continue to consolidate but at a slower speed. Financial distress and unsustainable prices in Europe are limiting Norske Skog’s ability to invest in value creation opportunities or assess acquisition targets on its own. Further consolidation in Europe might limit the current overcapacity problem but the Merger Endgame framework reveals that the consolidation speed is decreasing. From the perspective of the stockholders the greatest value creation opportunity is therefore to sell to liquidate. However, Norske Skog is not considered an attractive acquisition target and potential acquirers are limited. If neither a partner is found nor a consolidation within the newsprint and magazine segment occurs, there is a severe risk of bankruptcy in the future for Norske Skog. The Merger Endgame framework’s main strength is its potential to make Porter’s five forces analysis more dynamic. The main weakness is the uncertainty regarding the correctness and precision of the framework. Further empirical research is needed to evaluate the framework as there are several weaknesses in its empirical foundation.

Energy markets are complex networks of producers, exporters, traders and consumerscharacterized by different market structures in each sector. The infrastructuralnetwork connecting the markets plays an important role in determining thevolume of the trade flows and the location of the final consumption.The market players’ behavior in an energy market can be described using a gametheoretic approach where each player’s decision depends on the other market playersdecisions. Over the last decades these ideas have evolved and there have beenproduced some material where markets for a single commodity are modeled, usingideas from game theory to describe the players’ influence on each other’s decisions.However, little work has been done analyzing multi-commodity markets with thesame set of tools. Based on existing literature that is written on single-commoditymodeling, we have applied equilibrium programming with complementarity structureto describe the markets for electricity and natural gas in Northern Europethrough a strategic market model. The complexity level is potentially high, so wedecided to limit ourselves to a deterministic and myopic model without investmentpossibilities.The problem is formulated through a strategic MCP model where each marketparticipant solves an optimization problem connected through the market clearingconditions. Besides showing that the model is an MCP we implemented the modelin GAMS and solved it for the gas and electricity market in Northern Europe.Our results indicates that a Cournot model gives an adequate description of theelectricity and gas market in Northern Europe, and that considerable changes inproduction, consumption, traded volumes and prices in one market can lead toprice, quantity and welfare effects in markets far away from the initial cause. Wecan also register close links between the markets for electricity and natural gas,suggesting that agents’ behavior in one commodity market might affect the othercommodity market and vice versa.

Salmon farmers face an uncertain production environment and considerable price volatility, making planning a vital success criteria. This thesis describes the sources of uncertainty that are most important when planning sales, and demonstrates how this uncertainty can be taken into consideration by the use of stochastic programming.The basis for this thesis is the tactical planning problem of deciding when to harvest salmon and how to allocate sales between available contracts and sales in the spot market. Uncertainty relevant to the planning problem is described, and a multistage stochastic model that maximizes profit is proposed. The goal of the model is to provide salmon producers with a tool that can aid them in making profitable decisions regarding harvesting and future sales, by taking into account the uncertainty associated with biomass and price development.The model is implemented in three versions; a deterministic model, a two-stage stochastic model, and a multistage stochastic model. The implemented models are somewhat simplified, the most important simplification being that price is assumed deterministic in the stochastic models. This is done in order to make the stochastic models computationally tractable for a personal computer. All three models are written in Mosel, implemented in Xpress-IVE, and solved by the Xpress Optimizer.The implemented models are applied to Marine Harvest Region Mid, illustrating how the models can be used to solve a realistic salmon sales planning problem. In addition, a quantitative assessment of the gains from implementing a stochastic solution is demonstrated. The results obtained show that using the two-stage stochastic model provides almost no additional value over the deterministic model. For the multistage stochastic model, this value is substantially higher, though still marginal, largely due to the simplifications made in the implementation. Based on the simplifications made in the implemented models, possible extensions to the thesis are suggested.