Hlynur Stefansson

Discrete and continuous time representations and mathematical models for large production scheduling problems: A case study from the pharmaceutical industry

The underlying time framework used is one of the major differences in the basic structure of mathematical programming formulations used for production scheduling problems. The models are either based on continuous or discrete time representations. In the literature there is no general agreement on which is better or more suitable for different types of production or business environments. In this paper we study a large real-world scheduling problem from a pharmaceutical company. The problem is at least NP-hard and cannot be solved with standard solution methods. We therefore decompose the problem into two parts and compare discrete and continuous time representations for solving the individual parts. Our results show pros and cons of each model. The continuous formulation can be used to solve larger test cases and it is also more accurate for the problem under consideration.

Integrated Agent-based and System Dynamics Modelling for Simulation of Sustainable Mobility

In this article, a conceptual framework for a comprehensive evaluation of the diffusion process of alternative fuel vehicles is introduced. The framework takes into account the most influencing stakeholders, including car manufacturers, car dealers, consumers, energy supply system, fuel stations and government. The underlying mathematical models of different stakeholders are then integrated in one model of the whole energy and transport system. The hybrid modelling framework links the two powerful dynamic simulation approaches of system dynamics (SD) and agent-based (AB) modelling. Integrated modelling structure gives the potential of building more accurate and computationally efficient models for simulating the transition to sustainable mobility. We specify the integration process and the most important linking variables between various energy and transport components. Then the application of the integrated model is explained through a test case and, finally, the applicability of the hybrid AB and SD approach and its potential contribution to the models of transition to sustainable mobility will be concluded.

Decision Support Systems for the Food Industry

Applications of Decision Support Systems (DSSs) in the food industry, and in particular the seafood industry, are discussed. The amount of data recorded in the food industry has increased greatly in the last decade, parallel to descending cost of data recording through automatization and computer systems. The data can be used to fulfill the demands of consumers that want information on their food products, such as origin, impact on the environment and more. By using traceability this flow of data can be used for decision support. Many fields within food processing can gain from using DSS. Such fields include for example lowering environmental impact of food processing, safety management, processing management and stock management. Research and development projects that the authors have taken part in and the following implementations of software solutions are discussed and some examples given of practical usage of DSS in the food industry as a result of such work.

Procedure for reducing the risk of delayed deliveries in make-to-order production

Make-to-order production is generally operated in a very unpredictable and competitive environment, where the key factors to succeed are to provide high service levels and flexibility while at the same time offering inexpensive products. To receive customers, production companies must often promise short lead-times and the option of adjustable order quantities and delivery dates. Coping with uncertainty and variable demand is a challenging task. With the additional challenge of cutting down the production costs to be able to provide inexpensive products, proper planning and scheduling of the production becomes very difficult and crucial for success. It is therefore of crucial importance to develop systematic methods to address the problem of planning and scheduling under uncertainty in order to create efficient and reliable plans and thereby reduce the risk of delayed deliveries of customer orders. This study introduces the subject of creating robust production plans and schedules in the typical modern production environment characterised by several important sources of uncertainty. We introduce an efficient and practical modelling approach for creating robust production plans under uncertain and varying demand conditions. As an inspiration we have a large real-world problem originating from a complex pharmaceutical enterprise.

Cost-effectiveness and Potential of Greenhouse Gas Mitigation through the Support of Renewable Transport Fuels in Iceland

The system dynamics model of Iceland’s energy systems (UniSyD_IS) is used to explore the potential transition paths towards renewable transport fuels with implications for greenhouse gas (GHG) emissions and mitigation costs. The study focuses on Iceland’s potential fuel pathways including renewable electricity, hydrogen from electrolysis, biogas from municipal wastes, bioethanol from lignocellulosic biomass, and biodiesel from oil seeds and waste oils.

Market Penetration of Alternative Fuel Vehicles in Iceland: A Hybrid Modeling Approach

In this paper, an integrated agent-based (AB) and system dynamics (SD) model is developed to study the market share evolution of light duty vehicles in Iceland. The model takes into account the conventional internal combustion engine vehicles that are currently dominant in the market and the alternative fuel vehicles including various types of electric vehicles, bio-fuels and fuel cell vehicles. SD approach is used to simulate the development of continuous, homogenous and aggregate variables of the energy supply system over time. The AB approach is employed to study the consumer behavior and market share evolution of passenger vehicles. Different vehicles compete for market penetration through a vehicle choice algorithm that accounts for social influences and consumers’ attractiveness for vehicle attributes. The linking variables between AB and SD components are identified and then these two bottom-up and top-down approaches are integrated to provide a comprehensive framework. The main results provided by the application of this modeling approach for the case study of Iceland are market share evolution of various vehicle types and consumers’ fuel demands during the period 2013–2050.

Integrating the housing market into an agent-based economic model

In this paper, we develop an agent-based model of the housing market and integrate it into a larger agent-based artificial economy. The model is characterized by four types of agents: households, firms, banks and a central bank, which interact through different types of markets: a consumption goods market, a labor market, a housing market and a credit market. We model a wealth effect of housing wealth into households consumption budget as the main link between the housing market and the real economy. Banks will extend mortgages to households only if the expenditure on housing, as a proportion of total income, is lower than a given threshold (β). Different simulations are preformed to see how changing βeffects the housing market and the real economy. We find that by lowering the constraint on bank lending, i.e. increasing β, housing prices boom, positively affecting the real economy

Multi-scale planning and scheduling in the pharmaceutical industry

Abstract Most sophisticated planning and scheduling approaches for the process industry consider a fixed time horizon and assume that all data is given at the time of application. In this contribution we propose a planning and scheduling approach for a continuous and dynamic decision process where decisions have to be made before all data are available. As an inspiration we have a real world problem originating from a complex pharmaceutical production plant. The approach we propose is based on a hierarchically structured moving horizon framework. At each level we propose optimisation models to provide support for the relevant decisions. The levels are diverse regarding time scope, aggregation, update rate and availability of data at the time applied. The framework receives input data piece by piece and has to make decisions with only a partial knowledge of the required input. Solution procedures have been developed and the optimisation models have been tested with data from the real world problem. The solution procedures were able to obtain solutions of good quality within acceptable computational times.

Framework for Threat Based Failure Rates in Transmission System Operation

Reliability of electrical transmission systems is presently managed by applying the deterministic N-1 criterion, or some variant thereof. This means that transmission systems are designed with at least one level of redundancy, regardless of the cost of doing so, or the severity of the risks they mitigate. In an operational context, the N-1 criterion provides a reliability target but it fails to accurately capture the dynamic nature of shortterm threats to transmission systems. Ongoing research aims to overcome this shortcoming by proposing new probabilistic reliability criteria. Such new criteria are anticipated to rely heavily on component failure rate calculations. This paper provides a threat modelling framework, using the Icelandic transmission system as an example, highlighting the need for improved data collection and failure rate modelling. The feasibility of using threat credibility indicators to achieve spatio-temporal failure rates, given minimal data, is explored in a case study of the Icelandic transmission system. The paper closes with a discussion on the assumptions and simplifications that are implicitly made in the formulation, and the additional work required for such an approach to be included in existing practices. Specifically, this paper is concerned only with short term and real-time management of electrical transmission systems.

Economic impact of adaptation to climate change in Iceland's energy supply sector

The economic value of adaptation to climate impact on renewable resources is examined using the UniSyD_IS model of Icelands energy system. The model captures the entire energy system including interactions among supply sectors, energy markets, infrastructure and fuel demand. In this framework, climate adaptation options are evaluated in the context of a rational decision making process. Comparison of two scenarios with and without climate impact on hydro and biomass resources reflects the direct and indirect economic effects of adaptation strategies and provides insights for generation and energy supply investment planning. The simulation results show that the adaptation to climate change in Iceland leads to higher installed capacities for hydro, biodiesel, and bioethanol plants with lower supply costs. The findings indicate that the adaptation of energy supply to climate change could provide net benefits from an energy supply perspective.