Schedule

Presentations are still being contributed, so a precise schedule is not yet known. However, we won't start earlier or finish later than at the following times. We currently have 300 minutes worth of presentations - not all (yet) with abstracts!

Thursday 26 June

11:00-12:15 Registration, coffee/tea and networking

12:15-13:50 Lunch (DIY)

13:50-15:30 Presentations

15:30-16:00 Coffee/tea

16:00-17:10 Presentations

18:30-21:00 Workshop dinner at Scotts Kitchen: Aperitif (18:30); Dinner (19:15).

Numbers at Scotts are limited to 30, so dinner for students will be at a venue to be arranged by local PhD students attending the workshop. 

Due to late registrations and the number of true guest bookings being uncertain, we may not be able to offer places at the dinner to anyone registering after 20 June.

An after-dinner venue for all will be arranged.

Friday 27 June

09:30-11:00 Presentations

11:00-11:30 Coffee/tea

11:30-12:15 Presentations

12:15-13:50 Lunch (DIY)

13:50-15:30 Presentations

15:30 The Pear Tree

Oscar Dowson 

JuMP + HiGHS 

JuMP is an algebraic modeling language for mathematical optimization written in the Julia programming language. We have been an enthusiastic adopter of HiGHS since early 2021. In this talk we will discuss how we adopted HiGHS and who is using it in the Julia ecosystem.

Julian Hall 

HiGHS: Introductions, review and outlook 

The HiGHS team has grown considerably over the past 12 months, so this presentation will introduce the new members of the team. Developments in HiGHS over the past year and future plans will be summarised.

Julian Hall 

PDLP: Navigating the hype 

GPU accelerated solution of LPs using Chambolle and Pock's primal-dual hybrid gradient technique (PFLP) is undoubtedly exciting, but it's important not to consider it as a silver bullet. It is less robust and can be very much slower and less accurate than traditional techniques. That said, some HiGHS users have seen big performance gains! This presentation will explore the pros and cons of the technique.

Tom Lauwers 

highsv, A graphical front end to the HiGHS library 

As part of my course on linear optimizations I got introduced to Lingo, but I found it frustrating due to its proprietary limitations. In response I began working on a free and open-source  graphical tool oriented towards educational settings that makes solving linear problems more accessible.

Harley Mackenzie 

Back to Basics, Forward to HiGHS: juLinear.jl in the Age of Open-Source and AI 

This presentation introduces juLinear.jl, an open-source linear programming solver developed in Julia with the support of HARD software. The motivation for juLinear.jl arises from the need for a solver that not only functions, but also approachable and transparent, making it ideal for students, educators, and researchers who want to understand the inner workings of linear programming solvers from the ground up. 

juLinear.jl’s clear and mathematically expressive Julia codebase allows new learners to engage directly with core solver concepts, experiment with algorithmic techniques, and build confidence before contributing to major projects like HiGHS. By demystifying solver internals, juLinear.jl helps foster the next generation of open-source solver contributors and provides a platform for rapid experimentation and teaching.

The talk will also explore how AI tools can assist with solver development, highlighting both their potential benefits, such as code generation and bug detection, and their limitations, including the need for expert oversight and mathematical rigor. By combining a student-friendly open-source codebase with new development tools, juLinear.jl aims to accelerate learning, innovation, and collaboration in the global optimization community.

Luke Marshall 

Accelerating Column Generation via Template Pricing 

Column Generation can be an effective technique for dealing with large-scale integer programming problems – however, it is known to suffer from convergence issues.  There is much research on stabilization to avoid this issue, with various levels of success and implementation difficulty.

Using a simple example, I’ll introduce a new approach “Template Pricing” that converges orders of magnitude faster than the “standard”.  It is easy to implement and, surprisingly, yields high-quality integer solutions as a side-effect.  In the talk, I’ll give insights on why it works so well – and how it might be generalized to other problem

Andrew McGee 

Optimizing Pet Food production with HiGHS 

The manufacture of pet food has many steps and processes leading to a nonlinear model. This paper outlines the how Datacor has solved these programs using the HiGHS solver.

Md Shahrukh Anjum 

A cardinality-based extended formulation for the unsplittable multicommodity network design problem 

Given an underlying directed graph, the multicommodity capacitated fixed charge network

design problem (MCND) is concerned with the selection of optimal paths and flows for all

commodities in the network at a minimal cost, while respecting flow balance constraints at each

node and capacity constraints along each arc. In this research, we consider the unsplittable

multicommodity capacitated fixed charge network design problem (UMCND), a variant of the

MCND, wherein the flow of a commodity from its origin to its destination is restricted along a

single path in the network. Practical instances of the UMCND can be found in

telecommunication networks, single sourcing production-distribution problems, express

package delivery, etc. In this research, we develop a new cardinality-based extended

formulation for the UMCND, wherein we associate new variables based on the cardinality of

each arc, where arc-cardinality is defined as the maximum number of possible commodities

that can flow along each arc in the network. This extended formulation, which consists of

𝑂(𝑛𝑘!) variables and constraints where 𝑛 and 𝑘 denote the number of nodes and commodities,

respectively, is proven to be stronger than its original formulation, and furthermore, the

extended formulation can also be gainfully exploited to develop specialized valid inequalities,

which generalize the well-known Cover, (1, 𝑝)-Configuration inequalities, Flow Forcing

inequality and the Missed Commodity Cutset inequality. Computational results on benchmark

instances demonstrate substantial savings in the number of subproblems taken in a branch-and-bound

process taken to determine the optimal solution.

Sebastian Van Thienen 

Airline Crew Scheduling

Crew scheduling in aviation involves assigning pilots and cabin crew to sequences of flights while respecting a wide range of legal, contractual, and operational constraints. Solving these large-scale optimization problems efficiently is essential for airline operations and cost control. In this talk, we explain how we model the movement and activities of pilots on a time-space graph, and how this gives rise to a mixed-integer linear programming problem. To solve the relaxation of this problem, we can leverage the Dantzig-Wolfe decomposition structure and apply the column generation algorithm. In this algorithm, we solve a sequence of linear programs, that are growing in size. We show how we can apply HiGHS to solve these master problems.

Renzo Wijngaarden 

Optimising Airport Operations with HiGHS: Current Use and Future Potential 

At Edinburgh Airport, HiGHS is currently used to support our security operation by forecasting the number of security lanes required at 5-minute intervals throughout the day. This model helps minimise passenger queue times while optimising for operational efficiency. In addition to sharing our experience with this application, I will also discuss a future opportunity: applying HiGHS to the stand allocation problem. This complex task involves minimising coaching operations and thus optimising pier service level, while accounting for constraints such as destination-based stand restrictions, aircraft size compatibility, and jet bridge preferences amongst many other things.

Filippo Zanetti 

Introducing the new HiGHS interior point solver 

In this talk, we introduce the new HiGHS interior point solver. Its main features and the choices taken during its development are discussed, highlighting the differences with the existing solver. Some current limitations and plans to improve the code are mentioned, as well as issues related to parallelisation, accuracy and extension to quadratic programming. We present some results coming from energy modelling problems which highlight the large performance gain that the new solver can achieve compared to the current one.