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PurposeThis paper aims to examine the current state of reverse supply chain management (RSCM) initiatives in several Turkish industries.Design/methodology/approachThis study is based on an exploratory research regarding RSCM activities of Turkish automotive, white goods, electric/electronics, and furniture industries. The sample consists of all the companies included in the Top‐500 Industrial Enterprises List of The Assembly of the Istanbul Chamber of Industry (ISO).FindingsThe research findings show that the RSCM initiatives in the considered industries are still in a very early stage. Companies' involvement in product returns is mostly due to the legislative liabilities, and system inadequacies are emphasized as the most important reason for not being able to implement an efficient RSCM.Research implications/limitationsThis paper investigates the reverse supply chain practices of selected industries in Turkey and aims to enable researchers to use this study as a building block in understanding these practices and related problems. The limitation of this study is to solely include the medium and large‐sized companies in the industries.Practical implicationsReverse supply chain operations contribute to the economic sustainability by reducing waste and saving energy and material. In this research, an empirical study in the electronics, white goods, automotive and furniture industries is conducted, and potential research opportunities are discussed to streamline reverse supply chain activities in the industries. Hence, this study can be viewed as an attempt to increase the level of awareness on reverse supply chain issues.Originality/valueNo field study has been conducted to analyze reverse supply chain activities of the industries in Turkey. This research is a pioneering study and will provide a benchmark for the various research activities on related topics.

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This paper introduces some advanced genetic algorithms for a complex hybrid flexible flow line problem with a makespan objective that was recently formulated. General precedence constraints among jobs are taken into account, as are machine release dates, time lags and sequence dependent set-up times; both anticipatory and non-anticipatory. This combination of constraints captures many real world industrial problems; among those is the ceramic tile production that served as our inspiration. The introduced algorithms employ solution representation schemes with different degrees of directness. Several new machine assignment rules are introduced and implemented in some proposed genetic algorithms. The different genetic algorithms are compared among each other and to some heuristics as well. The results indicate that simple solution representation schemes result in the best performance, even for complex scheduling problems and that the genetic algorithms lead to a better solution quality than all tested heuristics. and he is currently in the thesis process of his PhD at the Genetic algorithms with different representation schemes 31 Polytechnical University of Valencia, Spain. Within the Applied Optimisation Systems Group, he is occupied with the development of software solutions for both forecasting and machine scheduling problems. Furthermore, he cooperates in technology transfer projects with industrial companies.Rubén Ruiz is an Associate Professor in the Applied Statistics and Operations Research and Quality Department at the Polytechnic University of Valencia. He received his BS, MSc and PhD from the same university. His MSc dissertation received the Bancaja prize and his PhD thesis received the UPV best dissertation award. He has given more than 100 presentations at national as well as international meetings and workshops and has published over 25