An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling

TL;DR: This paper investigates a bi-criteria energy-efficient two-stage hybrid flow shop scheduling problem, in which parallel machines with eligibility are at stage 1 and a batch machine is at stage 2, and the results show effectiveness of the proposed methods, in particular the bi-objective tabu search.

TL;DR: The mixed integer programming model is presented and the proposed HMOEA/D is more effective than other optimization algorithms in solving the FJSPTOUEP to minimize both makespan and total electricity cost (TEC) simultaneously.

TL;DR: A systematic literature review of the recent literature that attempts to reflect environmental aspects and increased energy-awareness into production management finds substantial progress with regard to various aspects such as pushing system boundaries, coming up with advanced conceptual approaches, and striving for more practical relevance.

TL;DR: The efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions, and the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and mean tardiness are evaluated.

TL;DR: This review paper summarizes the current state-of-the-art IoT in industries systematically and identifies research trends and challenges.

TL;DR: This book is not only a theoretical document, but also provides good coverage of practical issues and can be recommended to a broad audience, ranging from those in academic institutions to practitioners, as well as those who are interested in finding information on multiple criteria approaches, methods and techniques.

TL;DR: A rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs) and the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM.

TL;DR: This research shows that a single criterion genetic algorithm can be expected to outperform other methods in efficiency, accuracy, and speed on problems of moderate to high complexity.