Integration of process planning and scheduling using a hybrid GA/PSO algorithm

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

“…The majority of the hybrid methods are the combinations of different metaheuristics, such as the combination of Genetic Algorithm and Tabu Search procedures (as discussed by Zhang et al [258], Meeran and Morshed [259], Li and Gao [260], Yu et al [261], and Noori and Ghannadpour (2012)) and the integration of Genetic Algorithm and Simulated Annealing (as discussed by Safari and Sadjadi [262], Rafiei et al [263], and Bettemir and Sonmez [264]). In recent years, the combination of Genetic Algorithm and Particle Swarm Optimization (PSO) had been widely applied in both scheduling and vehicle routing problems (as discussed by Du et al [265], Yu et al [266], Liu et al [267], and Kumar and Vidyarthi [268]). Some other Genetic Algorithm-based hybrid methods were also observed in the literature, such as the hybrid Genetic-Monkey algorithm [269], hybrid Genetic Algorithm combined with the LP-relaxation of the targeted model (as discussed by Mohammad and Ghasem [270]), and the combination of Genetic Algorithm and Local Search procedure with Fuzzy Logic Control, where Fuzzy Logic Control is used to enhance the search ability of the Genetic Algorithm (as discussed by Chamnanlor et al [271]); some Pareto-based hybrid Genetic Algorithms were also developed for dealing with multiobjective problems (as discussed by Zhang et al [272] and Tao et al [273]).…”

An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling

“…In order to further enhance the exploration capability, some other methods are also combined with GA to construct hybrid algorithms. For instance, Li et al [9] combined GA with a local search strategy, and Yu et al [10] used an additional particle swarm optimization to select the appropriate machines in the scheduling part. These combinations prevented GA from falling into local optima and have achieved better outcomes in the experiments than GA-only strategies.…”

“…Meanwhile, this substitution method is also used to represent FCT in Figures 8,9,11,and 12. In order to determine the proportion of population that the local enhancement strategy will apply on, we first build a randomly simulated case which adopts the configuration of MC, job, plan, and machine from the six-job case and randomly sets the number of optional machines and the corresponding operating and transportation time for each job. To be specific, the most possible value of each̃, , , , or̃, is randomly generated in [10,40], while the minimum value and the maximum value are in [5, p) and (p, 45]. Then, the algorithm will run five times in each proportion (0, 10%, 20%, 30%, 40%, and 50%) and output the best FCT and the average FCT.…”

“…For example, in the case with two machines, each operation can randomly choose two optional machines from all machines (there are five machines in each MC of the established case). Meanwhile, the most possible value of each̃, , , , or̃, is also randomly generated in [10,40], while the minimum value and the maximum value are in [5, p) and (p, 45]. Figure 12 and Table 6 show the best FCT results of EGA and GA used in IPPS in dealing with random-generated cases.…”

Integrated optimization of process planning and scheduling for reducing carbon emissions

“…Li et al [19] used the weight sum method to solve multi-objectives including energy consumption, makespan and the balanced machine utilization in a sustainable process planning and scheduling problem. Yu et al [42] used the weight sum method to optimize machining cost and makespan in integrated process planning and scheduling. The weight sum method is easy for decision-makers to understand, convenient for developers to implement and available to change the weight of different objectives for satisfying the requirement of decision-makers [32], but the weights need to be predetermined and the optimal result is influenced by weight allocation.…”