Computers & Operations Research 34 (2007) 2191 – 2214 www.elsevier.com/locate/cor Short communication A niched genetic algorithm to solve a pollutant emission reduction problem in the manufacturing industry: A case study L. Grandinetti ∗ , F. Guerriero, G. Lepera, M. Mancini Dipartimento di Elettronica, Informatica e Sistemistica Università della Calabria, Rende, Italy Available online 29 March 2006 Abstract A multiobjective optimization approach to deal with a pollutant emission reduction problem in the manufacturing industry, through implementation of the best available technical options, is presented in this paper. More specifically, attention is focused on the industrial painting of wood and the problem under investigation is formulated as a bicri- teria combinatorial optimization problem. A niched Pareto genetic algorithm based approach is used to determine sets of methods, tools and technologies, applicable both in the design and in the production phase, allowing to simultaneously minimize the total cost and maximize the total pollutant emission reduction.  2005 Elsevier Ltd. All rights reserved. Keywords: Genetic algorithms; Fitness sharing; Best available options; Multiple criteria analysis 1. Introduction In the last few decades, environmental problems have received much attention from society and govern- ments. In this context, the European Union has introduced the concept of Best Available Technologies (BAT, for short) as a new integrated approach to deal with environmental problems caused by pollution produced by manufacturing/production industries. This approach highlights the requirement that the development of new manufacturing/production pro- cesses must be balanced by the implementation of best available technical options, that eliminate or at least reduce the amount of pollutants released into the environment. The BATs are all the methods, tools and technologies that can be applied in the design and production phase, in order to achieve a good reduction of emission level with an acceptable additional cost. ∗ Corresponding author. (L. Grandinetti). 0305-0548/$ - see front matter  2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.cor.2005.08.005