Hexaly

🚀 We are pleased to announce the release of Hexaly 13.0, which comes with many new features and performance improvements. • Dial-a-Ride Problem (DARP): 81 new records over the 96 instances of the realistic Chassaing dataset with up to 128 points obtained within 1 minute, with an average improvement of 2.7% of the best known solutions in the research; average gap of 3.6% in 1 minute for the classical Cordeau dataset with up to 144 points. • Team Orienteering Problem (TOP): average gap of 0.8% within 1 minute for instances with up to 401 points. • Heterogeneous Vehicle Routing Problem (HVRP): average gap of 0.9% within 1 minute for instances with up to 144 points. • Capacitated Vehicle Routing Problem (CVRP): Optimality gap below 0.1% after 1 minute for all instances with up to 100 points. • Job Shop Scheduling Problem: average gap of 1.7% on classical instances from the literature with up to 2,000 tasks and 0.8% on very large-scale instances with up to 1,000,000 tasks within 1 minute of running time. • Open Shop Problem: optimal solution found for all instances of the Taillard benchmark with up to 400 tasks within 1 minute. • Flexible Job Shop Problem: average gap of 0.5% on instances with up to 500 tasks within 1 minute. • Resource-Constrained Project Scheduling Problem: average gap of 2.0% on instances with up to 300 tasks within 1 minute. The “gaps” mentioned are the relative gaps in % between the solutions computed by Hexaly within 1 minute on a standard server (AMD Ryzen 7 7700 processor, 8 cores, 3.8 GHz, 32MB cache, 32GB RAM) and the best known solutions available by the research. 🔗 Want to learn more? Check out the link below: https://lnkd.in/ehybZugg #optimization #operationsresearch

Hexaly is delighted to be a Gold Sponsor of the new International Conference on Operations Research edition, #OR2024. 📅 The conference will be held from September 3 to 6, 2024, at the Technical University of Munich. Visit our booth to discover the latest features and applications of Hexaly 13.0, meet our team, and explore our job opportunities. Feel free to attend the presentations given by our team to learn more about Hexaly: 🔸Disjunctive scheduling using interval decision variables with Hexaly Optimizer by Léa Blaise 🔸Modeling and Solving Routing Problems with Hexaly Studio by Léa Blaise & Julien Darlay 🔸Modeling large optimization problems with Hexaly by Julien Darlay 🔸Optimization of Workforce Planning: Satisfying Company Requirements and Employee Wishes by Emeline Tenaud 🔸Solving the Time-Dependent Traveling Salesman Problem (TDTSP) with Hexaly by Théo Bordillon 🔗 For more details: https://lnkd.in/dx98kbnb

Hexaly against Gurobi, OR-Tools, jsprit (GraphHopper), and OptaPlanner on the Capacitated Vehicle Routing Problem (CVRP): ➡️ Hexaly finds solutions close to the state of the art in 1 minute, even for instances with 1,000 customers. ➡️ Despite specializing in solving Vehicle Routing problems, OR-Tools delivers poor-quality solutions (> 5% gap), even for medium-sized instances; OptaPlanner and jsprit perform worse (>10% gap). ➡️ Gurobi fails to find decent solutions in 1 minute, even for small-sized instances; additional experiments show the results remain the same with 1 hour of computation. The “gaps” mentioned are the relative gaps in % between the solutions computed by Hexaly within 1 minute on a standard server (AMD Ryzen 7 7700 processor, 8 cores, 3.8 GHz, 32MB cache, 32GB RAM) and the best known solutions available by the research. 🔗 Check the benchmark here: https://lnkd.in/etzyrd-8 #optimization #operationsresearch

Hexaly is proud to sponsor the 7th YinzOR Conference at the Tepper School of Business at Carnegie Mellon University on August 23rd and 24th! 🗓️ For all the students attending, don't miss the presentation by Fred Gardi, Founder & CEO at Hexaly, on Saturday, August 24th at 1:30 PM. 👉 Find all the conference details here: https://lnkd.in/ddmQJNA8 See you at YinzOR 2024! #YinzOR2024 #OperationsResearch

Carlos Armando Zetina, Ph.D. Thank you so much for this post. Focusing on the optimality gap is the most significant bias and threat for OR practitioners, particularly for the youngest who are starting their first projects. I always take time to explain this carefully to students in OR classes; it is one of the major differences between Research and Practice in OR and Mathematical Optimization. Key arguments: 1) Data are approximate, models are approximate. So, the math optimum may be quite far from the business optimum. 2) Businesses, processes, rules, and goals (aka, KPIs) change over time, and in the modern, fast-paced economy, they tend to change frequently. More important to quickly adapt the model to the business reality than to focus on math optimality (point 1 above, again). 3) In business and industry, the goals/KPIs to optimize are multiple. It is not rare to have at least 3-5 objectives to optimize, many of them competing together. What does the "optimality gap" mean in this case? Difficult to provide meaningful insight to the business users here. The "optimality gap" may be useful to OR scientists. Talking about, or even worse, showing the "optimality gap" to business users is useless, if not dangerous.

How does Hexaly perform against CP Optimizer, OR-Tools, Gurobi, and Cplex on the Job Shop Scheduling Problem (JSSP)? In this benchmark, we focus on large-scale instances of the Job Shop Scheduling Problem (JSSP): 1,000 jobs and 1,000 machines for 1,000,000 activities. Hexaly offers an innovative modeling approach based on interval and list variables, making the Job Shop Scheduling Problem (JSSP) modeling compact and straightforward. With this model, Hexaly Optimizer greatly outperforms traditional general-purpose optimization solvers and improves the research’s best known solution by an average of 7.4% within 60 seconds. 👇 You will find below the link to the entire Job Shop Scheduling Problem (JSSP) benchmark. #optimization #operationsresearch #datascience

We are excited to participate in the 25th International Symposium on Mathematical Programming, ISMP 2024, until July 26th! Don't hesitate to visit us at our stand and attend the presentations our team gave. We are looking forward to seeing you there! 🔗 For more details: https://lnkd.in/dqBNABdp #OperationsResearch #Optimization

We're thrilled to announce that Hexaly will host a stand at the 25th International Symposium on Mathematical Programming, #ISMP2024, in Montréal from July 21st through July 26th, 2024. Visit our booth to discover the latest features and applications of Hexaly 13.0, meet our team, and explore our job opportunities. Feel free to attend the presentations given by our team to learn more about Hexaly: 🔸Automatic model decomposition in Hexaly Optimizer by Bienvenu Bambi 🔸Hexaly, a new kind of global optimization solver by Frédéric Gardi 🔸Hybridizing combinatorial heuristics and continuous optimization methods for Mixed-Integer Programming by Julien Darlay 🔗 For more details: https://lnkd.in/dqBNABdp

Hexaly against Gurobi and OR-Tools on the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW): ➡️ Hexaly finds solutions close to best known solutions for instances with up to 1,000 customers. ➡️ Gurobi struggles to find feasible solutions for instances with more than 400 customers. ➡️ OR-Tools finds solutions with a gap greater than 10% for instances with more than 600 customers. The “gaps” mentioned are the relative gaps in % between the solutions computed by Hexaly within 1 minute on a standard server (AMD Ryzen 7 7700 processor, 8 cores, 3.8 GHz, 32MB cache, 32GB RAM) and the best known solutions available by the research. 🔗 Check the benchmark here: https://lnkd.in/geHK_KpM #optimization #operationsresearch

We are excited to participate in the EURO 2024 conference until July 3rd! Don't hesitate to visit us at our stand and attend the presentations our team gave. We are looking forward to seeing you there! 🔗 For more details: https://lnkd.in/efHjXuKX #OperationsResearch #Optimization