A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility

Gong, Xuran; Deng, Qianwang; Gong, Guiliang; Liu, Wei; Ren, Quan

doi:10.1080/00207543.2017.1388933

Cited by 82 publications

(32 citation statements)

References 26 publications

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“…DRC scheduling problem can be formally described as follows (Gong et al, 2018). A shop contains m machines (M1, M2, …, Mm) and w workers (W1, W2, …, Ww).…”

Section: Dual Resource Constrained (Drc) Systemmentioning

confidence: 99%

Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey

Hashemi-Petroodi

Thevenin

Kovalev

et al. 2020

Annual Reviews in Control

103

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A manufacturing system able to perform a high variety of tasks requires different types of resources. Fully automated systems using robots possess high speed, accuracy, tirelessness, and force, but they are expensive. On the other hand, human workers are intelligent, creative, flexible, and able to work with different tools in different situations. A combination of these resources forms a human-machine/robot (hybrid) system, where humans and robots perform a variety of tasks (manual, automated, and hybrid tasks) in a shared workspace. Contrarily to the existing surveys, this study is dedicated to operations management problems (focusing on the applications and features) for human and machine/robot collaborative systems in manufacturing. This research is divided into two types of interactions between human and automated components in manufacturing and assembly systems: dual resource constrained (DRC) and human-robot collaboration (HRC) optimization problems. Moreover, different characteristics of the workforce and machines/robots such as heterogeneity, homogeneity, and ergonomics are introduced. Finally, this paper identifies the optimization challenges and problems for hybrid systems. The existing literature on HRC focuses mainly on the robotic point of view and not on the operations management and optimization aspects. Therefore, the future research directions include the design of models and methods to optimize HRC systems in terms of ergonomics, safety, and throughput. In addition, studying flexibility and reconfigurability in hybrid systems is one of the main research avenues for future research.

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“…DRC scheduling problem can be formally described as follows (Gong et al, 2018). A shop contains m machines (M1, M2, …, Mm) and w workers (W1, W2, …, Ww).…”

Section: Dual Resource Constrained (Drc) Systemmentioning

confidence: 99%

Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey

Hashemi-Petroodi

Thevenin

Kovalev

et al. 2020

Annual Reviews in Control

103

View full text Add to dashboard Cite

show abstract

“…Sánchez and Torres [9] studied a hand-intensive production flow-shop scheduling problem with variable worker productivity. Gong et al [10] introduced a flexible job shop scheduling problem with worker flexibility considerations and designed a new memetic algorithm to solve the corresponding problem. Furthermore, some review literatures concerning the workforce scheduling in product-oriented industries have been conducted.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (9) means the set of riders in each period is comprised of the beginner riders and skilled riders. Constraint (10) calculates the number of skilled riders at the beginning of each period, which is the sum of the remaining skilled riders from last period and the new recruited riders in last period. Constraint (11) represents the role transformation from beginners to skilled riders after training.…”

mentioning

confidence: 99%

Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations

2020

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The order assignment in the food delivery industry is of high complexity due to the uneven distribution of order requirements and the large-scale optimization of workforce resources. The delivery performance of employees varies in different conditions, which further exacerbates the difficulty of order assignment optimization. In this research, a non-linear multi-objective optimization model is proposed with human factor considerations in terms of both deteriorating effect and learning effect, in order to acquire the optimal solutions in practice. The objectives comprised the minimization of the operational cost in multiple periods and the workload balancing among multiple employees. The proposed model is further transformed to a standardized mixed-integer linear model by the exploitation of linearization procedures and normalization operations. Numerical experiments show that the proposed model can be easily solved using commercial optimization softwares. The results indicate that the variance of employee performance can affect the entire delivery performance, and significant improvement of workload balancing can be achieved at the price of slight increase of the operational cost. The proposed model can facilitate the decision-making process of order assignment and workforce scheduling in the food delivery industry. Moreover, it can provide managerial insights for other labor-intensive service-oriented industries.

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“…Job 1 2 3 2 3 3 5 4 3 8 Job 2 2 4 3 2 2 3 3 5 7 Job 3 4 3 3 4 1 3 2 4 7 Job 4 4 5 2 3 1 2 1 2 5 Job 5 3 2 2 1 3 1 3 3 6 The decoding process is to select the right machine for each job and the right worker for each machine, determine the sequence of all operations and their starting and ending times, and finally obtain the objective functions value. This paper refers to the decoding method of literature [32], [36]- [37], the decoding process is detailed as follows:…”

Section: A Encoding and Decodingmentioning

confidence: 99%

Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm

Geng

Liu

2020

IEEE Access

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The classical hybrid flow shop scheduling problem (HFSP) only treats machines as the only resource constraint, ignoring the dominant role of workers in production and manufacturing. Considering the dual flexibility of machine and worker, this paper studies the multi-objective hybrid flow shop scheduling problem with dual resource constraints (DHFSP). Firstly, according to the characteristics of DHFSP and various constraints, the model is built to minimize the maximum completion time (makespan), total tardiness time and workload balance of worker. Then, an improved multi-objective memetic algorithm (IMOMA) is proposed to solve the DHFSP, which mainly includes the improvement of initial population, crossover, mutation and local search. In addition, Taguchi method is used to set parameters. Finally, through numerical experiments, IMOMA is compared with NSGA-II, MODE and MOMVO algorithms. The experimental results show that IMOMA can solve the multi-objective hybrid flow shop scheduling problem with dual resource constraints effectively. In terms of convergence, diversity and dominance of non-dominated solutions, IMOMA is significantly superior to other algorithms, but the distribution uniformity of non-dominated solutions of the four algorithms are not significantly different.

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A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility

Cited by 82 publications

References 26 publications

Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey

Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey

Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations

Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm

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