Models and Implementation of Integrated Process Planning and Scheduling

Abstract

Integrated process planning and scheduling (IPPS) is a good way to achieve a global improvement for the performance of a manufacturing system, it has been extensively researched over the past years and it continues to attract the interest of both academic researchers and practitioners. This paper first summarizes the critical problems of IPPS and then a survey of the integrated model and optimal implementation method for IPPS is presented. The integrated model is categorized into interface-oriented integration and function-oriented integration two types based on the integration object and are discussed in detail respectively. The deficiencies of the current integrated models and the suggestion for further improvement are also given. The integration of process planning and scheduling has been implemented by variety methods and agent-based approach is discussed in more detail. Finally, future research directions and conclusions in IPPS research are discussed.

Modeling Supply Chain Facility Location Problem and Its Solution Using a Genetic Algorithm

“…In light of the common point, Goldberg puts forward the basic genetic algorithm. Genetic algorithm has been widely used in the fields of combinatorial optimization [14,15], machine learning [16][17][18], aided design [19], adaptive control [20,21], partner selection in virtual enterprise [22], robotic assembly line balancing [23], electronics component placement design [24] , flexible manufacturing [25][26][27], resource scheduling problem [28][29][30] and so on. The operation process of basic genetic algorithm is easy to understand, which is the initial model and the basis of genetic algorithm.…”

Modeling Production scheduling problem and its solution by genetic algorithm

“…It uses the probability optimization method to automatically access and guide optimal search space, which adaptively adjust the search direction and do not need to determine the rules. Genetic algorithm has been widely used in the fields of combinatorial optimization [11][12][13], machine learning [14][15][16], aided design [17], adaptive control [18,19], partner selection in virtual enterprise [20,21], robotic assembly line balancing [22], electronics component placement design [23], flexible manufacturing [24][25][26] and resource scheduling problem [27][28][29]. It is the key technology of modern intelligent calculation.…”