An Effective Hybrid Genetic Algorithm and Variable Neighborhood Search for Integrated Process Planning and Scheduling in a Packaging Machine Workshop

Li, Xinyu; Gao, Liang; Pan, Quan-Ke; Wan, Liang; Chao, Kuo-Ming

doi:10.1109/tsmc.2018.2881686

Cited by 107 publications

(41 citation statements)

References 51 publications

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“…Step 4: According to the two indexes of dominance rank and population fitness value, the individual is selected as the parent from the population by using the tournament method. A new population is generated by adopt crossover operation and mutation operation proposed by Li et al 8 Step 5: Individual selection: merge population P 3 and population P 3new into population P 3merge , and assign dominant ranks for population P 3merge Calculate the individual's crowding degree, and then calculate the individual's comprehensive score according to the individual's comprehensive evaluation method. Update the archive 3 with the non-dominated solution in the population P 3merge .…”

Section: Methods For Job Shop Scheduling Layermentioning

confidence: 99%

“…Encoding and decoding scheme. Operation-based encoding method 8 is used in this paper. As seen in Figure 7, the length of the scheduling string is the number of all the operations for jobs.…”

Section: Methods For Job Shop Scheduling Layermentioning

confidence: 99%

“…The element 2 in the first position indicates the first operation of job 2 and the number 2 in the 3rd position denotes the second operation of job 2. The greedy decoding method is adopted to decode the coding scheme into active scheduling, 8 which can get the start time, end time of each job.…”

Section: Methods For Job Shop Scheduling Layermentioning

confidence: 99%

“…6 Many results of researches show that IPPS is of great significance to resolution resource conflicts, improvement of equipment utilization ratio and production efficiency. 7,8 The research of IPPS has become an effective way to solve the problems of workshop production management. IPPS is an expansion of shop scheduling problem, so the problem solution space is larger and more complicated.…”

Section: Introductionmentioning

confidence: 99%

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

Wen

Lian

Wang

et al. 2020

Measurement and Control

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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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Section: Methods For Job Shop Scheduling Layermentioning

confidence: 99%

Section: Methods For Job Shop Scheduling Layermentioning

confidence: 99%

Section: Methods For Job Shop Scheduling Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wen

Lian

Wang

et al. 2020

Measurement and Control

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show abstract

“…6 (2) Randomly insert operator: A gene position is randomly selected and shifted it into a previous position. 43 (3) Reordering operator: We randomly select two different gene positions on the current chromosome and reorder the genes between the two selected positions.…”

Section: Mutationmentioning

confidence: 99%

Improved artificial immune algorithm for the flexible job shop problem with transportation time

Chen

Han

et al. 2020

Measurement and Control

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The flexible job shop problem (FJSP), as one branch of the job shop scheduling, has been studied during recent years. However, several realistic constraints including the transportation time between machines and energy consumptions are generally ignored. To fill this gap, this study investigated a FJSP considering energy consumption and transportation time constraints. A sequence-based mixed integer linear programming (MILP) model based on the problem is established, and the weighted sum of maximum completion time and energy consumption is optimized. Then, we present a combinational meta-heuristic algorithm based on a simulated annealing (SA) algorithm and an artificial immune algorithm (AIA) for this problem. In the proposed algorithm, the AIA with an information entropy strategy is utilized for global optimization. In addition, the SA algorithm is embedded to enhance the local search abilities. Eventually, the Taguchi method is used to evaluate various parameters. Computational comparison with the other meta-heuristic algorithms shows that the improved artificial immune algorithm (IAIA) is more efficient for solving FJSP with different problem scales.

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IPPS Considering Machine State Effect Using Hybrid GA-based Algorithm

Abd-Elaziz

Awad

Tolba

2023

Lecture Notes in Networks and Systems

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An Effective Hybrid Genetic Algorithm and Variable Neighborhood Search for Integrated Process Planning and Scheduling in a Packaging Machine Workshop

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 107 publications

References 51 publications

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

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

Improved artificial immune algorithm for the flexible job shop problem with transportation time

IPPS Considering Machine State Effect Using Hybrid GA-based Algorithm

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