Abstract
Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to “learn” from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.
Highlights
Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry
This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make
A novel Genetic Algorithm (GA) has been developed to deal with multidimensional optimization for fully integration of production planning and scheduling associated with precedence constraints
Summary
Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. One of the important constraints in the integration of production planning and scheduling is a so called precedence constraint. This constraint has two classes, namely hard precedence constraint and soft precedence constraint. The production planning and scheduling problems, associated with both hard and soft precedence constraints, are considered. It should be noted that NP is a technical term in computational complexity theory in computer science and mathematics, which stands for Non-deterministic Polynomial-time. NP problems are the set of decision problems that can be solved by non-deterministic polynomial-time bounded Turing machines (Cadoli et al 2000). There is no exact method that can find the global optimal solutions to NP-hard problems in polynomial time, and fast approximate heuristics and metaheuristics are the popular approaches to search for highquality/practical solutions (He et al 2012)
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