Multi-layer collaborative optimization method for solving fuzzy multi-objective integrated process planning and scheduling

Research on integrated process planning and scheduling (IPPS) is of great significance to the improvement of the overall quality of machinery manufacturing system. In the actual manufacturing process, the manufacturing system is often accompanied by some unpredictable uncertain disturbance factors, for instance uncertain processing time of jobs and changes of due date, etc. These uncertain disturbance events will ultimately affect production efficiency and customer satisfaction. Consequently, this paper considers the multi-objective IPPS problem with uncertain processing time and uncertain due date. A multi-layer collaborative optimization (MLCO) method is designed for the fuzzy multi-objective IPPS (FMOIPPS) problem, including three layers. For the process planning layer, the basic genetic algorithm is used to provide various near optimal process plans for the process selection system. For the process selection layer, a multi-objective genetic algorithm (MOGA) is designed to optimize the process selection population. A sharing function method is introduced to maintain population diversity. An individual comprehensive evaluation method is introduced to evaluate non-dominated solutions. The crowded distance, fast non-dominated sorting and elite strategy based on NSGAII is adopted in the proposed MOGA. The external archive method is employed to preserve the non-dominated solutions generated during population evolution. For the scheduling layer, a MOGA with a boundary search strategy is proposed. The boundary search strategy is designed to improve the search ability of boundary solutions. Three optimization objectives are minimizing the spread of fuzzy makespan, minimizing fuzzy makespan and maximizing average customer satisfaction simultaneously. The target of scheduling layer is to make scheduling arrangements for the process information obtained by process selection layer. Through mutual cooperation among each layer, guide the overall optimization process, and finally get satisfactory solutions. Different problem examples of various scales are employed to verify feasibility and effectiveness of the MLCO method. The experimental results indicate that the MLCO method can effectively address FMOIPPS problem.

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Modified honey bees mating optimization algorithm for multi-objective uncertain integrated process planning and scheduling problem

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420925236 ◽

2020 ◽

Vol 17 (3) ◽

pp. 172988142092523 ◽

Cited By ~ 1

Author(s):

Xiaoyu Wen ◽

Xinyu Li ◽

Liang Gao ◽

Kanghong Wang ◽

Hao Li

Keyword(s):

Process Planning ◽

Optimization Algorithm ◽

Honey Bees ◽

Processing Time ◽

Intelligent Manufacturing ◽

Scheduling Problem ◽

Integrated Process ◽

Due Date ◽

Planning And Scheduling ◽

Multi Objective

The new technology of intelligent manufacturing makes the data of process planning and shop scheduling easier to interconnect, and the integration optimization of different manufacturing processes is an important technology to ensure the implementation of intelligent manufacturing. Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. At present, the research on integrated process planning and scheduling is insufficient to consider the multi-objective and uncertain characteristics widely existing in real manufacturing environment. Therefore, multi-objective integrated process planning and scheduling problem with uncertain processing time and due date is addressed in this article. The mathematical model of multi-objective uncertain integrated process planning and scheduling problem with uncertain processing time and fuzzy due date is established based on fuzzy set, in which the calculation method of uncertainty measurement objective is designed. An effective modified honey bees mating optimization algorithm has been designed to solve the proposed model. Queens set is constructed to maintain the non-dominated solutions found in the optimization process. The calculation method of mating probability between drone and queen bee based on Euclidean distance is designed. Fuzzy operators were utilized to evaluate fitness, judge the non-dominated relationship, and decode the scheduling solution. Different instances were designed and carried out to test the performance of the proposed method. The results show that the proposed method is very effective for solving multi-objective uncertain integrated process planning and scheduling.

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Multi-objective genetic algorithm for integrated process planning and scheduling with fuzzy processing time

2013 Sixth International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci.2013.6748519 ◽

2013 ◽

Cited By ~ 1

Author(s):

Xiaoyu Wen ◽

Xinyu Li ◽

Liang Gao ◽

Liang Wan ◽

Wenwen Wang

Keyword(s):

Genetic Algorithm ◽

Process Planning ◽

Processing Time ◽

Integrated Process ◽

Planning And Scheduling ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Fuzzy Processing Time

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A New Process Quality-based Multi-objective Multi-part Approach for the Integrated Process Planning and Scheduling (IPPS) Problem in Reconfigurable Manufacturing Environment

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2857 ◽

2020 ◽

Vol 53 (2) ◽

pp. 10755-10760

Author(s):

Luca Morganti ◽

Hichem Haddou Benderbal ◽

Lyes Benyoucef ◽

Marco Bortolini ◽

Francesco Gabriele Galizia

Keyword(s):

Process Planning ◽

Process Quality ◽

Reconfigurable Manufacturing ◽

Integrated Process ◽

Manufacturing Environment ◽

Planning And Scheduling ◽

Multi Objective ◽

New Process

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Integrated process planning, WSPT scheduling and WSLK due-date assignment using genetic algorithms and evolutionary strategies

An International Journal of Optimization and Control Theories & Applications (IJOCTA) ◽

10.11121/ijocta.01.2018.00412 ◽

2017 ◽

Vol 8 (1) ◽

pp. 73-83

Author(s):

Halil Ibrahim Demir ◽

Onur Canpolat

Keyword(s):

Genetic Algorithms ◽

Process Planning ◽

Random Search ◽

Evolutionary Strategies ◽

Integrated Process ◽

Due Dates ◽

Due Date ◽

Planning And Scheduling ◽

Due Date Assignment ◽

Genetic Search

Process planning, scheduling and due-date assignment are three important manufacturing functions in our life. They all try to get local optima and there can be enormous loss in overall performance value if they are handled separately. That is why they should be handled concurrently. Although integrated process planning and scheduling with due date assignment problem is not addressed much in the literature, there are numerous works on integrated process planning and scheduling and many works on scheduling with due date assignment. Most of the works in the literature assign common due date for the jobs waiting and due dates are determined without taking into account of the weights of the customers. Here process planning function is integrated with weighted shortest processing times (WSPT) scheduling and weighted slack (WSLK) due date assignment. In this study unique due dates are given to each customer and important customers gets closer due dates. Integration of these three functions is tested for different levels of integration with genetic algorithms, evolutionary strategies, hybrid genetic algorithms, hybrid evolutionary strategies and random search techniques. Best combinations are found as full integration with genetic search and hybrid genetic search. Integration of these three functions provided substantial improvements in global performance.

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