Process planning and machine sequence
Computers and Industrial Engineering
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Genetic algorithms in process planning
Computers in Industry - Special issue on IMS'91—Learning in IMS
A generic Petri net model for dynamic process planning and sequence optimization
Advances in Engineering Software - Special issue: computer-aided process planning
New directions in AI planning
Computers and Industrial Engineering
Petri net techniques for process planning cost estimation
Advances in Engineering Software
Consensus-based intelligent group decision-making model for the selection of advanced technology
Decision Support Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
A consensus model for multiperson decision making with different preference structures
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A review of clonal selection algorithm and its applications
Artificial Intelligence Review
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Pertaining to the intricacies involved in the formulation of an optimal process planning system, operation sequencing has been recognized as a complex and crucial task to be accomplished. The operation sequencing problem determines the preferred order to perform a set of selected operations that satisfies the precedence constraints along with the satisfaction of the optimization goals. In general, the problem is characterized by its combinatorial nature and complex precedence relations that make it computationally complex. A psycho-clonal-algorithm-based approach has been proposed in this paper to solve optimally the operation sequencing problem. The objective function has been made more comprehensive for the parts types of varying complexities. This approach is an extension of the artificial immune system (AIS) approach and inherits its characteristics from the Maslow's need hierarchy theory related to psychology. The various need levels present in the algorithm help in maintaining the viability of solution, whereas the path towards optima is revealed by the trait of affinity maturation. Effectiveness of the algorithm is authenticated by solving the problems of varying complexities cited in the literature and comparing its performance with other established metaheuristic approaches.