[Retracted] An Effective Hybrid Multiobjective Flexible Job Shop Scheduling Problem Based on Improved Genetic Algorithm

Table 1

Some of standard method methods in the literature of MO-FJSP.

Author (s)	Title	Algorithm	Purpose
Hurink and Knust [21]	Tabu search algorithms for job-shop problems with a single transport robot	Simulated annealing scheduling	To minimize both total completion time and total tardiness.
Zhang et al. [22]	Energy-efficient permutation flow shop scheduling problem using a hybrid multiobjective backtracking search algorithm	Hybrid multiobjective backtracking search	To minimize energy consumption and makespan
Dei et al. [2]	Solving flexible job shop scheduling problems with transportation time based on improved genetic algorithm	Transportation time based genetic algorithm	To minimize the maximum completion time and to optimally get the active scheduling policy.
Zhang et al. [23]	Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm	Energy-efficient mathematical model	To ensure energy-efficient flow shop scheduling via the use of mathematical model.
Giglio et al. [24]	Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops	Gene expression programming (eGEP) algorithm	To easily obtain optimization by generating energy-oriented heuristic rules
Rossi [25]	Integrated lot sizing and energy-efficient job shop scheduling problem in manufacturing/remanufacturing systems	Relax-and-fix heuristic	To decrease total weighted tardiness and energy consumption in a job shop scheduling problem
Karumi et al. [26]	Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships	Intelligent swarm approach based on the disjunctive graph model	To ensure effective scheduling with resource separable setup times