Bi-Gender genetic algorithm to solve production and transportation scheduling in multi-site manufacturing system

Document Type : Scientific - Research

Authors

1 Industrial engineering department, Semnan University, Semnan, Iran

2 master of science in business administration

3 M.Sc. in industrial engineering, Semnan university

Abstract

Today, the use of multi-site manufacturing systems has attracted the attention of many factories due to its benefits, such as reduced transportation costs, concentration of population, pollution, facilities and traffic in one area, and improved service to customers. This paper addresses the problem of scheduling a distributed flexible job-shop scheduling problem with two objective functions of minimizing the total delivery times of orders and the total production and transportation costs. In this case, it is assumed that there are several manufacturing units in different geographic regions, each of which has a flexible job-shop environment. The purpose of this paper is to determine how to allocate orders to manufacturing units, assign operations to machines of the related manufacturing unit, and determine sequence of processing of the assigned operations to a machine for minimizing the total delivery times of orders and the total production and transportation costs. Since the problem has NP-Hard complexity, meta-heuristic algorithms should be used solve it. In this paper, a genetic algorithm is proposed to solve the problem called a Bi-Gender genetic algorithm with two sets of chromosomes. The first group of chromosomes is male and the second group of chromosomes is female. In order to perform a crossover operator, one parent should be selected from the first category and the second one from the second category. Comparing the results of this algorithm with the development of a genetic algorithm in the subject literature indicates the high efficiency of the proposed algorithm.

Keywords

References

- بهشتی نیا، محمد علی، فیض, داود و سدادی, فاطیما (1397) "یکپارچگی مساله مسیریابی وسایل نقلیه با زمانبندی حمل‌ونقل و تولید در زنجیره‌تأمین"، فصلنامه علمی - پژوهشی مهندسی حمل و نقل، دور 9، شماره 4، ص. 549-570.
 
- سلم آبادی، نرجس و بهشتی نیا، محمد علی (1398) "مدل ریاضی چند هدفه برای مساله تولید-موجودی-مسیریابی دو -
- مرحله‌ای محصولات دارویی"، فصلنامه علمی - پژوهشی مهندسی حمل و نقل، پذیرفته شده.
 
- Azab, A., & Naderi, B. (2014) "Greedy Heuristics for Distributed Job Shop Problems", Procedia CIRP, Vol. 20, pp. 7-12.
 
- Bargaoui, H., Belkahla Driss, O., & Ghédira, K. (2017) "A novel chemical reaction optimization for the distributed permutation flowshop scheduling problem with makespan criterion", Computers & Industrial Engineering, Vol. 111, pp. 239-250.
 
- Beheshtinia, M., & Ghazivakili, N. (2018) "Reference group genetic algorithm for flexible job shop scheduling problem with multiple objective functions", Journal of Industrial and Systems Engineering, Vol. 11, pp. 153-169.
 
- Beheshtinia, M. A., Ghasemi, A., & Farokhnia, M. (2018) "Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company)", Journal of Modelling in Management, Vol. 13, pp. 27-49.
- Borumand, A., & Beheshtinia, M. A. (2018) "A developed genetic algorithm for solving the multi-objective supply chain scheduling problem", Kybernetes, Vol. 47, pp. 1401-1419.
 
- Chang, H.-C., & Liu, T.-K. (2015) "Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms", Journal of Intelligent Manufacturing, Vol. pp.
 
- De Giovanni, L., & Pezzella, F. (2010) "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem", European Journal of Operational Research, Vol. 200, pp. 395-408.
 
- Deng, J., & Wang, L. (2017) "A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem", Swarm and Evolutionary Computation, Vol. 32, pp. 121-131.
 
- Hosseini-Motlagh, S.-M., Ahadpour, P., & Haeri, A. (2015) "Proposing an approach to calculate headway intervals to improve bus fleet scheduling using a data mining algorithm", Journal of Industrial and Systems Engineering, Vol. 8, pp. 72-86.
 
- Hsu, C.-Y., Kao, B.-R., Ho, V. L., & Lai, K. R. (2016) "Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling", Engineering Applications of Artificial Intelligence, Vol. 53, pp. 140-154.
 
- Issabakhsh, M., Hosseini-Motlagh, S.-M., Pishvaee, M.-S., & Saghafi Nia, M. (2018) "A Vehicle Routing Problem for Modeling Home Healthcare: a Case Study", International Journal of Transportation Engineering, Vol. 5, pp. 211-228.
 
- Lin, J., Wang, Z.-J., & Li, X. (2017) "A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem", Swarm and Evolutionary Computation, Vol. pp.
 
- Lin, J., & Zhang, S. (2016) "An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem", Computers & Industrial Engineering, Vol. 97, pp. 128-136.
 
- Liu, T. K., Chen, Y. P., & Chou, J. H. (2014) "Solving Distributed and Flexible Job-Shop Scheduling Problems for a Real-World Fastener Manufacturer", IEEE Access, Vol. 2, pp. 1598-1606.
 
- Naderi, B., & Azab, A. (2014) "Modeling and heuristics for scheduling of distributed job shops", Expert Systems with Applications, Vol. 41, pp. 7754-7763.
 
- Rifai, A. P., Nguyen, H.-T., & Dawal, S. Z. M. (2016) "Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling", Applied Soft Computing, Vol. 40, pp. 42-57.
 
- Taheri, S. M. R., & Beheshtinia, M. A. (2019) "A Genetic Algorithm Developed for a Supply Chain Scheduling Problem", Iranian Journal of Management Studies, Vol. 12, pp. 281-306.
 
- Ullrich, C. A. (2013) "Integrated machine scheduling and vehicle routing with time windows", European Journal of Operational Research, Vol. 227, pp. 152-165.
 
- Ziaee, M. (2014) "A heuristic algorithm for the distributed and flexible job-shop scheduling problem", The Journal of Supercomputing, Vol. 67, pp. 69-83.

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History

  • Receive Date: 20 May 2019
  • Revise Date: 16 September 2019
  • Accept Date: 17 September 2019

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