Genetic Algorithms

Unlike all other known algorithms, which are artificial, the genetic algorithms were "borrowed" from nature and not invented. They ensure the diversity of life forms on Earth, the evolution of species and their adaptation to a changing environment. Why couldn't they help to build robust and adaptable organizations or more effective and efficient businesses? The genetic algorithms (GAs) were discovered by Charles Darwin in 1859 and then applied to artificial systems by John Holland in 1975.

From the '80s, the applications of GAs emerged in mathematics, image recognition, and structural optimization. The high computational effort didn’t allow for the generalization of GA’s use in more fields, but the new high power computing clusters has allowed the exponential extension of the applications and the impact of GAs; the new fields that now use GAs are: acoustics (Sato et al, 2002); astronomy; astrophysics; aerospace engineering (Kean&Brown 1996; Williams, Crossley and Lang 2001); chemistry (Gillet 2002); electrical engineering (Altshuler&Linden 1997); financial markets (Mahfoud and Mani 1996; Andreou, Georgopoulos and Likothanassis 2002); game theory (Giro, Cyrillo and Galvăo 1999); military sciences (Kewley and Embrechts 2002); molecular biology (Koza et al. 1999); image recognition and data mining (Au, Chan and Yao 2003; Rizi, Zmuda and Tamburino 2002); robotics (Andre and Teller 1999); routing, programming and system engineering (Benini and Toffolo 2002). We may notice that the majority of these applications are recent.

In economic sciences (organizational and operational management), a number of problems there have been addressed through GAs, such as optimal routing (transport optimization) and scheduling optimization:

• Burke and Newall experimented successfully in 1999 a software based on Gas for programming exams and courses.

• He and Mort published in 2000 the optimal routing theory using GAs on the web.

• Jensen 2003 and Chryssolouris and Subramaniam 2001 applied genetic algorithms to the task of generating schedules for job shops. This is an NP-hard optimization problem with multiple criteria: factors such as cost, tardiness, and throughput must all be taken into account, and job schedules may have to be rearranged on the fly due to machine breakdowns, employee absences, delays in delivery of parts, and other complications, making robustness in a schedule an important consideration. Both papers concluded that GAs are significantly superior to commonly used dispatching rules, producing efficient schedules that can more easily handle delays and breakdowns.

• Naik published in 1996 how GAs where used for programming the Paralympic Games of 1992.

• Petzinger published in 1995 the GA based scheduling implemented at John Deere & Co factories.

• Rao showed in 1998 how Volvo used an evolutionist program, named OptiFlex, for programming a factory in Dublin, Virginia, with hundreds of restrictions and millions of permutations for each vehicle.

• As reported in Lemley 2001, United Distillers and Vintners, a Scottish company that is the largest and most profitable spirits distributor in the world and accounts for over one-third of global grain whiskey production, uses a genetic algorithm to manage its inventory and supply. This is a daunting task, requiring the efficient storage and distribution of over 7 million barrels containing 60 distinct recipes among a vast system of warehouses and distilleries, depending on a multitude of factors such as age, malt number, wood type and market conditions. Previously, coordinating this complex flow of supply and demand required five full-time employees. Today, a few keystrokes on a computer instruct a genetic algorithm to generate a new schedule each week, and warehouse efficiency has nearly doubled.

• Beasly, Sonander and Havelock use Gas in 2001 for scheduling landings on London Heathrow airport. This is a multi-objective complex problem that searches to minimize delays, and the number of operations, keeping at the same time the separation minima between aircraft. Result: a decrease of the mean waiting time by 2-5%, i.e. one to three additional aircraft took-off or landed per hour.

• Wired published in 2002 an implementation study of similar solutions on several airports (Heathrow, Toronto, Sydney, Las Vegas, San Francisco), and also for programming flight operations for companies such as America West Airlines, AeroMexico and Delta Airlines. The software is used by Ascent Technology's SmartAirport Operations Center software ( The reports show 30% increases on every airport where the software was used.

In Romania, the use of GAs started in 2004 with the publishing of several aerospace engineering optimizations articles by C. E. Constantinescu, O. T. Pleter and I. B. Stefanescu. The applications were extended to organizational and operations management in 2007 by G. Moldoveanu, I. Rosca and O. T. Pleter.