Lars Relund Nielsen
Department of Economics and Business Economics
Fuglesangs Allé 4
DK-8210 Aarhus V
Office 2621-114
Phone: +45 871 65145/+45 61 300 299 (mobile)
E-mail: lars@relund.dk
ORCID: 0000-0002-4802-3071

Cand.scient.oecon. (M.Sc.), Ph.D. 2004 in Mathematics and Economics at the University of Aarhus. Professor at Department of Economics and Business Economics, Business and Social Sciences, Aarhus University.

## INFORMS 2018

Conference/meeting 7. November 2018

From November 4-7, 2018 we have been participating the annual INFORMS conference, Phoenix, AZ, US. Talks have been interesting and for the first time CORAL joined the job fair to promote open Assistant and Associate Professor positions. The interest was overwhelming and some good candidates were found. Læs mere »

## New publication

Research 15. August 2017

The research paper “An improved cut-and-solve algorithm for the single-source capacitated facility location problem” have been published in EURO Journal on Computational Optimization.

Abstract: In this paper, we present an improved cut-and-solve algorithm for the single-source capacitated facility location problem. The algorithm consists of three phases. The first phase strengthens the integer program by a cutting plane algorithm to obtain a tight lower bound. The second phase uses a two-level local branching heuristic to find an upper bound, and if optimality has not yet been established, the third phase uses the cut-and-solve framework to close the optimality gap. Extensive computational results are reported, showing that the proposed algorithm runs 10–80 times faster on average compared to state-of-the-art problem-specific algorithms.

## Multi-Objective Optimization Repository (MOrepo)

7. July 2017

The past weeks we have created Multi-Objective Optimization Repository (MOrepo) which is a response to the needs of researchers from the MCDM society to access multi-objective (MO) optimization instances. The repository contains instances, results, generators etc. for different MO problems and is continuously updated. The repository can be used as a test set for testing new algorithms, validating existing results and for reproducibility. All researchers within MO optimization are welcome to contribute. For more information see https://github.com/MCDMSociety/MOrepo.

## Using LocalSolver to solve VRP problems

17. May 2017

The last days I have been playing a bit with LocalSolver and tried to model various vehicle routing problems (VRP). LocalSolver is a heuristic solver based on a heuristic search approach combining different optimization techniques. It includes a math modeling language (LSP) and there is a package so that it can be used from R. You need to download and install LocalSolver (I used v7.0) and get an academic license from their webpage. Læs mere »

## Plotting IP and MO-IP models in R

20. February 2017

I recent released a small package gMOIP which can make 2D plots of linear and integer programming models (LP/IP). With the package you can make plots of the feasible region (or solution space) of an LP, visualize the integer points inside the region and the iso profit curve. Moreover, can also make a plot of a bi-objective criterion space and the non-dominated (Pareto) set. Figures are prepared for LaTeX and can automatically be transformed to TikZ using tikzDevice. Læs mere »

## New publications

Research 23. December 2016

The research paper “A bi-objective approach to discrete cost-bottleneck location problems” have been published in Annals of Operations Research.

Abstract: This paper considers a family of bi-objective discrete facility location problems with a cost objective and a bottleneck objective. A special case is, for instance, a bi-objective version of the (vertex) p-centdian problem. We show that bi-objective facility location problems of this type can be solved efficiently by means of an epsilon-constraint method that solves at most (n-1)m minisum problems, where n is the number of customer points and m the number of potential facility sites. Additionally, we compare the approach to a lexicographic epsilon-constrained method that only returns efficient solutions and to a two-phase method relying on the perpendicular search method. We report extensive computational results obtained from several classes of facility location problems. The proposed algorithm compares very favorably to both the lexicographic epsilon-constrained method and to the two phase method.of pig weights and feeding and is updated using a Bayesian approach. Numerical examples are given to illustrate the features of the proposed optimization model.

Moreover a short communication “Culling pigs under price fluctuations” have been published in OrBit about some Reza’s and my’s recent work (currently submitted).

## PhD thesis: Discrete Location Problems – Theory, Algorithms, and Extensions to Multiple Objectives

Research 2. August 2016

Sune one of my PhD students has just finished his defense of his thesis titled “Discrete Location Problems – Theory, Algorithms, and Extensions to Multiple Objectives“. I would like to congratulate Sune with his PhD title and for a good presentation at the defense. I have included the thesis summary below.

Summary

This PhD–dissertation proposes a number of solution procedures for discrete facility location problems. In the literature of operations research, location problems are mathematical models describing optimization problems where one or more facilities need to be placed in relation to a given set of customers or demand points. An example is the location of hospitals which needs to be performed in such a way as to take into account capacity limits and the sizes of nearby towns and cities. Læs mere »

## PhD thesis: Optimization Methods in a Stochastic Production Environment

Research 14. June 2016

Reza one of my PhD students has just finished his defense of his thesis titled “Optimization Methods in a Stochastic Production Environment“. I would like to congratulate Reza with his PhD title and for a good presentation at the defense. I have included the thesis summary below.

Summary

This dissertation with an interdisciplinary approach applies techniques from Operations Research and Statistics in order to develop models that support decisions regarding feeding and marketing of growing/finishing pigs. Stochastic dynamic programming is used as the main optimization tool to model decisions, and state space models are used as the primary statistical technique to describe the stochastic nature of the system. Based on data streams from online farm sensors and market prices, the state space model transforms data into information which is embedded into the decision models using Bayesian updating. Læs mere »

## New publication

Research 19. January 2016

The research paper “A hierarchical Markov decision process modeling feeding and marketing decisions of growing pigs” have been published in European Journal of Operational Research.

Abstract: Feeding is the most important cost in the production of growing pigs and has a direct impact on the marketing decisions, growth and the final quality of the meat. In this paper, we address the sequential decision problem of when to change the feed-mix within a finisher pig pen and when to pick pigs for marketing. We formulate a hierarchical Markov decision process with three levels representing the decision process. The model considers decisions related to feeding and marketing and finds the optimal decision given the current state of the pen. The state of the system is based on information from on-line repeated measurements of pig weights and feeding and is updated using a Bayesian approach. Numerical examples are given to illustrate the features of the proposed optimization model.

## New publication

Research 19. January 2016

The research paper “Markov decision processes to model livestock systems” have been published as a chapter in Handbook of Operations Research in Agriculture and the Agri-Food Industry.

The paper gives a review of the increasing amount of papers using MDPs to model livestock farming systems and provide an overview over the recent advances within this branch of research. Moreover, theory and algorithms for solving both ordinary and hierarchical MDPs are given and possible software for solving MDPs are considered.