Two meta-heuristic algorithms for solving multi-objective flexible job-shop scheduling with parallel machine and maintenance constraints

Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning

“…Rustogi and Strusevich [21] presented polynomial-time algorithms for single machine problems with generalized positional deterioration effects and imperfect machine maintenance to minimize the makespan. Dalfard and Mohammadi [22] discussed a multi-objective flexible job shop scheduling problem (FJSP) with maintenance, in which three objectives are equally treated and weighted into one. Two meta-heuristic algorithms, a genetic algorithm (GA) and a simulated annealing (SA) are proposed.…”

Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate

“…Constraint set (18) designates the set of machines can process O jh , and restricts the variable Y ijk to be zero when machine M i cannot process operation O jk ðm ijk ¼ 0Þ. Restriction (19) implies that total availability of system should be greater than the minimum threshold α. Constraint (20) ensures than just one operation can be assigned to a unique position of a machine. Constraint (21) implies that each operation can process by one machine.…”

“…In another work, Shahsavari-Pour and Ghasemishabankareh [51] suggested a new hybrid genetic algorithm and simulated annealing entitled NHGASA for solving FJSP. Moreover, Dalfard and Mohammadi [19] analyzed a FJSP with parallel machines and maintenance operations, and then developed an optimization model and two meta-heuristic algorithms, a genetic algorithm and a simulated annealing algorithm for solving their problem. In another study presented by Raich et al [47], a SA based approach was designed for solving job-shop scheduling problem to obtain an optimal or a near optimal solution and compared the SA results with heuristic techniques like SPT and LPT.…”

Enhanced Simulated Annealing for Solving Aggregate Production Planning

“…Conversely, the main disadvantage of the method is its extremely slow convergence in big problems. In SA, first a primary solution is randomly created, and a neighbour is found and this is accepted with a probability of min (1, (− / ) ), where is the control parameter corresponding to the temperature of the physical analogy and is called temperature, and is the cost difference [23]. On gradual decrease of temperature, the algorithm congregates to the global minimum.…”