scholarly journals A Neutrosophic Number-Based Memetic Algorithm for the Integrated Process Planning and Scheduling Problem With Uncertain Processing Times

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96628-96648
Author(s):  
Liangliang Jin ◽  
Chaoyong Zhang ◽  
Xiaoyu Wen ◽  
George Gershom Christopher
Keyword(s):  
Process Planning ◽  
Memetic Algorithm ◽  
Scheduling Problem ◽  
Integrated Process ◽  
Planning And Scheduling ◽  
Processing Times ◽  
Neutrosophic Number

scholarly journals Realizing Energy Savings in Integrated Process Planning and Scheduling

Processes ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 120 ◽  
Author(s):  
Liangliang Jin ◽  
Chaoyong Zhang ◽  
Xinjiang Fei
Keyword(s):  
Power Consumption ◽  
Process Planning ◽  
Machine Tools ◽  
Energy Savings ◽  
Memetic Algorithm ◽  
Total Power ◽  
Integrated Process ◽  
Planning And Scheduling ◽  
Total Power Consumption ◽  
Makespan Criterion

The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programming (MILP) model based on the network graph is formulated with both makespan as well as total power consumption criteria. Due to the complexity of the problem, a multi-objective memetic algorithm with variable neighborhood search (VNS) technique is then developed for this problem. The Kim’s benchmark instances are employed to test the proposed algorithm. Moreover, the TOPSIS decision method is used to determine the most satisfactory non-dominated solution. Several scenarios are considered to simulate different machine automation levels and different machine workload levels. Computational results show that the proposed algorithm can strike a balance between the makespan criterion and the total power consumption criterion, and the total power consumption can be affected by machine tools with different automation levels and different workloads. More importantly, results also show that energy saving can be realized by completing machining as early as possible on a machine tool and taking advantage of machine flexibility.

scholarly journals Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Shuai Zhang ◽  
Yangbing Xu ◽  
Zhinan Yu ◽  
Wenyu Zhang ◽  
Dejian Yu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Process Planning ◽  
Imperialist Competitive Algorithm ◽  
Scheduling Problem ◽  
Integrated Process ◽  
Planning And Scheduling ◽  
Machine Breakdown ◽  
Competitive Algorithm ◽  
Effectiveness And Efficiency

Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

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

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092523 ◽  
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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