GAMS MINLP Solver comparisons and some improvements to the AlphaECP algorithm

I doktorsavhandlingen undersöks förmågan att lösa hos ett antal lösare för optimeringsproblem och ett antal svårigheter med att göra en rättvis lösarjämförelse avslöjas. Dessutom framläggs några förbättringar som utförts på en av lösarna som heter GAMS/AlphaECP. Optimering innebär, i det här sammanhanget, att finna den bästa möjliga lösningen på ett problem. Den undersökta klassen av problem kan karaktäriseras som svårlöst och förekommer inom ett flertal industriområden. Målet har varit att undersöka om det finns en lösare som är universellt snabbare och hittar lösningar med högre kvalitet än någon av de andra lösarna. Det kommersiella optimeringssystemet GAMS (General Algebraic Modeling System) och omfattande problembibliotek har använts för att jämföra lösare. Förbättringarna som presenterats har utförts på GAMS/AlphaECP lösaren som baserar sig på skärplansmetoden Extended Cutting Plane (ECP). ECP-metoden har utvecklats främst av professor Tapio Westerlund på Anläggnings- och systemteknik vid Åbo Akademi.Väitöskirjassa tutkitaan optimointiongelma-ratkaisijoiden ratkaisukykyä ja valaistaan joitakin vaikeuksia suorittaessa vertailua ratkaisijoiden ratkaisukyvystä. Lisäksi esitetään joitakin parannuksia yhteen ratkaisijoista joka on nimeltään GAMS/AlphaECP. Optimoinnilla tarkoitetaan tässä yhteydessä parhaan ratkaisun etsimistä. Luokka johon tutkitut ongelmat kuuluvat voidaan kuvata vaikeasti ratkaistaviksi ja niitä esiintyy monella teollisuuden alalla. Tavoitteena on ollut tutkia jos jokin ratkaisija yleisesti pystyy löytämään nopeammin ja koreampaa tasoa olevan ratkaisun kuin mikään muu ratkaisija. Kaupallista optimointijärjestelmää GAMS (General Algebraic Modeling System) ja kattavia ongelmakirjastoja on hyödynnetty kun ratkaisijoita on vertailtu. Parannukset tehtiin GAMS/AlphaECP ratkaisijaan, joka pohjautuu leikkaavien tasojen menetelmään nimeltä Extended Cutting Plane (ECP). ECP menetelmän on ensisijaisesti kehittänyt Laite- ja systeemitekniikan professori Tapio Westerlund Åbo Akademista

Economic model-predictive control of building heating systems using Backbone energy system modelling framework

Preprint for a journal paper demonstrating the viability of the Backbone energy system modelling framework for capturing the essence of building-level flexibility for economic model-predictive control

Economic model-predictive control of building heating systems using Backbone energy system modelling framework

Accessing the demand-side management potential of the residential heating sector requires sophisticated control capable of predicting buildings’ response to changes in heating and cooling power, e.g., model-predictive control. However, while studies exploring its impacts both for individual buildings as well as energy markets exist, building-level control in large-scale energy system models has not been properly examined. In this work, we demonstrate the feasibility of the open-source energy system modelling framework Backbone for simplified model-predictive control of buildings, helping address the above-mentioned research gap. Hourly rolling horizon optimisations were performed to minimise the costs of flexible heating and cooling electricity consumption for a modern Finnish detached house and an apartment block with ground-to-water heat pump systems for the years 2015–2022. Compared to a baseline using a constant electricity price signal, optimisation with hourly spot electricity market prices resulted in 3.1–17.5% yearly cost savings depending on the simulated year, agreeing with comparable literature. Furthermore, the length of the optimisationhorizon was not found to have a significant impact on the results beyond 36 h. Overall, the simplified model-predictive control was observed to behave rationally, lending credence to the integration of simplified building models within large-scale energy system modelling frameworks

Comparison of Some High-Performance MINLP Solvers

In this paper some high-performance mixed integer non-linear programming (MINLP) solvers in GAMS (General Algebraic Modeling System) are evaluated. AlphaECP, Baron, Dicopt and SBB are the MINLP (mixed integer non-linear programming) solvers in focus. A large test set with 250 problems is used and special attention is given to a new GAMS solver, GAMS/AlphaECP, which has recently been included in GAMS. The results show that each solver improves the overall performance of the GAMS system. Furthermore the results reveal that the combined performance of some pairs of solvers is significantly better than other pairs. 1

Alternative Approach for Improvement Sustainable Supply Chain Management in the Large European Container Ports

The main objectives of the EU transport policy relate to the limitation of the negative environmental impact of ports. Similarly, companies are adopting sustainable supply chain management practices to respond to policymakers’ and consumers’ demands for sustainable operations. This paper aims to discover how the largest European container ports communicate about their efforts to improve the sustainability of their operations to find out how the ports themselves see their position as a part of the transition towards more sustainable supply chain operations. Based on the study, different large European container ports consider environmental issues differently. The risk is that some ports may get competitive advantages by slipping on environmental questions. Alternatively, if the port does not take sustainability questions seriously and it gets a bad reputation, the risk is that the customers and consumers do not accept the port's behavior and shipping companies start to avoid that port. Doi: 10.28991/HIJ-2021-02-02-06 Full Text: PD

CounterSwarm:Turning Collective Intelligence against Hostile Drone Swarms

The CounterSwarm project aims at testing the hypothesis that the best way to defend efficiently and affordably against a drone swarm is to play “tit for tat” and use another drone swarm. In nature, the species that most successfully resist an attack by social insects (the original inspiration for autonomous drone swarms) are other social insects. The reason is simple: both sides have the same characteristics in terms of their intrinsic ability to self-organise in response to highly dynamic circumstances, based on incomplete information and without any centralised control structure (and the vulnerability associated with it). The end goal of this project is to identify methods for adapting the collective intelligence paradigm to swarm tactics, and to develop conceptual, analytical, and numerical tools to evaluate performance in defence applications