caa a22 4500 606160248 CHVBK 20210128100629.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00521-014-1778-7 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1778-7 Mokhtari Hadi Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran aut Designing an efficient bi-criteria iterated greedy heuristic for simultaneous order scheduling and resource allocation: a balance between cost and lateness measures [Elektronische Daten] [Hadi Mokhtari] In this paper, an iterated greedy (IG) heuristic as an intelligent decision-making algorithm is designed for solving a general variant of order scheduling problem with resource allocation. It is assumed that the processing times can be controlled by allocation of a non-renewable common resource, as it is the case in many real-world processes. In order to jointly minimize the number of late orders and the amount of resources consumed, the global criterion as a multiple criteria decision-making method is adapted. Furthermore, a two-layered IG heuristic is devised as solution method. IG heuristic employs a simple but efficient principle and is easy to implement with high capability of evolutionary performance. In suggested IG, a set of solutions is produced by iterating over a greedy construction heuristic by using both destruction and construction functions and then an improving local search is implemented to further enhance the quality of solutions. The simulation experiments show that the proposed IG heuristic is an effective tool in producing high-qualified solutions with respect to the traditional algorithms. The Natural Computing Applications Forum, 2014 Iterated greedy heuristic nationallicence Multiple criteria decision making nationallicence Resource allocation nationallicence Order scheduling nationallicence Neural Computing and Applications Springer London 26/5(2015-07-01), 1085-1101 0941-0643 26:5<1085 2015 26 521 https://doi.org/10.1007/s00521-014-1778-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1778-7 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Mokhtari Hadi Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/5(2015-07-01), 1085-1101 0941-0643 26:5<1085 2015 26 521