Research And Application Of Intelligent Approaches To Production Scheduling For A Complex Manufacturing System

Customer needs are increasingly personified and diversified and require quick response, which cause fierce competition in the global market. How are customer needs satisfied to the largest extent with limited amount of resource and time? How is production optimization scheduling with multi-variety and small batch realized? How is an intelligent scheduling problem for a complex manufacturing system with large-scale, multi-objective and multi-resource constraints delivered?This study conducts relevant research regarding the above-mentioned issues, and the main contributions of this study include as below,(1) Development of a decomposition-optimization-integration intelligent scheduling strategyOn the basis of analysis of a large number of existing scheduling methodologies, this study proposes a three-fold (Decomposition-Optimization-Integration, DOI) approach for solving large-scale job shop scheduling problems and designs optimization item in three different stages. Firstly, in terms of classification and decomposition rules, manufacturing scheduling information is classified in order that scheduling units are achieved in a reasonable manner. Secondly, according to scheduling objectives and manufacturing information of scheduling units, schedules are made by the use of intelligent algorithm. Lastly, the final schedule for complex large-scale manufacturing system is optimized through "integration" approach according to overall scheduling objectives and resource constraints. Thus, the proposed methodology can solve NP-hard problems characterized by enormous solution space in an effective way.(2) Design of decomposition rules and related calculation modelsBased on analysis of features of a complex manufacturing system, such as scheduling type, scheduling objective, resource constraints and their contradictory relations, this study designs decomposition rules and relevant calculation models according to the requirements of optimization units, namely, due dates, and process similarity, etc. in order to support the integration of different scheduling modules.(3) Research on biological intelligence based scheduling optimizationThe study introduces the mechanisms of biological immune system and genetic evolution, analyzes the mechanisms of biological immune system and genetic evolution to solve complex scheduling problems, and discusses the applications of intelligent approaches combined with other strategies on complex scheduling problems.(4) Research on weighted self-adaptive intelligent algorithm for multi-objective scheduling problemIn the process of manufacturing on the floor shop, scheduling objectives and their relations are extremely complicated due to different goals in different enterprises over scheduling horizons. The study models multi-objective scheduling problem. Afterwards, based on the features of biological intelligent algorithm, the study proposes a weighted self-adaptive intelligent algorithm (WSAIA) for a multi-objective scheduling problem. By the use of evolution of intelligent algorithm and reproduction coefficient, it can overcome the limitations of conventional weighted-sum in which the importance of each objective are manually set in advance, furthermore ensure the diversity of population and balance the exploration and exploitation so that it can increase the effectiveness of search for optimal solution considering overall objectives.(5) Development of chaos-based intelligent scheduling algorithmThis study models resource constrained project scheduling problem which features multi resource types. The objective is to minimize makespan with satisfying precedence and resource constraints. The study devises a chaotic generator by using Logistic function, Tent function and Sinusoidal functions. Analyzing the features of artificial immune system, the study introduces chaotic operator and parallel mutation operator. Therefore, the study proposes Chaos-based Improved Immune Algorithm (CBIIA). In the initialization phase, chaotic generator is utilized instead of conventional random number generator. In the mutation phase, parallel mutation is deployed rather than point mutation. Parallel mutation comprises of two mutation strategies viz. Gaussian and Cauchy. Gaussian strategy is applied for small step mutation and Cauchy strategy is applied for large step mutation. The objective of parallel mutation mechanism is deployed to balance exploitation and exploration in search space.(6) Development of an intelligent scheduling software systemThis study develops an intelligent scheduling software system. Large-scale simulated instances based on test bed of job shop scheduling problem, a multi-objective job shop scheduling problem in the literature, and benchmark problems for resource-constrained project scheduling problems are tested. Test results are analyzed and compared with existing methodologies in literature, and it is proven that the proposed methodologies are effective in converging towards the optimal solution.