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Granular Computing
Published in: Granular Computing 6/2023

05-04-2023 | Original Paper

A priority-based heuristic approach for solving flexible flow-shop with parallel machine scheduling in a fuzzy environment

Authors: Syed Abou Iltaf Hussain, Ranbir Kalita, Uttam Kumar Mandal

Published in: Granular Computing | Issue 6/2023

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Abstract

The prime objective of the manufacturing industry is to meet the non-decreasing demand of customers with quality products. Evaluation of optimal job sequence can largely increase the productivity, thereby fulfilling the aim of an enterprise. Usually, determining the optimal job sequence is an arduous task due to the fact that unit increase in an input exponentially increases the problem size. Scheduling problems is a case of non-deterministic polynomial (NP) hard problem which implies it is impractical to compute optimal job sequence within feasible time. Therefore, in the present study, a novel two-phase heuristic algorithm is proposed for multi-stage scheduling problem. The first phase of the proposed model is to compute the job and machine priority. Job priority is the measure of the total work remaining and time taken for processing and completion of the job. On the other hand, machine priority determines the machine that shall take the job for processing. The job prioritization is computed by hybridizing the completion time (CT), processing time (PT) and total work remaining (TWR) for a job. Whereas, machines in each stage of the multi-stage scheduling problem is prioritized by a novel multi-criteria decision-making (MCDM) method which is based on the concept of risk minimization. In the proposed MCDM model, the risk is defined as the loss for choosing an unreliable machine to process a job. The second phase of the proposed method involves assigning of the jobs to the machines based on their priority. The potentiality of a proposed algorithm lies in the practicality and robustness of the model. Hence, it is applied in a flexible flow-shop scheduling (FFSS) problem of a medium-sized manufacturing industry. The performance of the model is statistically tested by Wilcoxon signed-rank test on the basis of make-span and execution. Finally, the proposed approach is validated by comparing the result with some benchmark problems from the literatures.
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Appendix
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Literature
Ahmad M, Cheng W (2022) A novel approach of fuzzy control chart with fuzzy process capability indices using alpha cut triangular fuzzy number. Mathematics 10(19):3572CrossRef
Ahmadi E, Zandieh M, Farrokh M, Emami SM (2016) A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms. Comput Oper Res 73:56–66MathSciNetMATHCrossRef
Alcan P, BaşLıGil H (2012) A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem. Adv Eng Softw 45(1):272–280CrossRef
Almeder C, Hartl RF (2013) A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer. Int J Prod Econ 145(1):88–95CrossRef
Amin GR, El-Bouri A (2018) A minimax linear programming model for dispatching rule selection. Comput Ind Eng 121:27–35CrossRef
Arashpour M, Wakefield R, Abbasi B, Lee EWM, Minas J (2016) Off-site construction optimization: sequencing multiple job classes with time constraints. Autom Constr 71:262–270CrossRef
Arık OA, Toksarı MD (2018) Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects. Int J Prod Res 56(7):2488–2505CrossRef
Asadzadeh L, Zamanifar K (2010) An agent-based parallel approach for the job shop scheduling problem with genetic algorithms. Math Comput Model 52(11–12):1957–1965MATHCrossRef
Aydilek H, Allahverdi A (2013) A polynomial time heuristic for the two-machine flowshop scheduling problem with setup times and random processing times. Appl Math Model 37(12–13):7164–7173MathSciNetMATHCrossRef
Bari P, Karande P (2022) Ranking of sequencing rules in a job shop scheduling problem with preference selection index approach. J Decis Anal Int Comput 2(1):12–25CrossRef
Bürgisser P (2013) Completeness and reduction in algebraic complexity theory, vol 7. Springer Science & Business Media, pp 5–6
Campbell HG, Dudek RA, Smith ML (1970) A heuristic algorithm for the n job, m machine sequencing problem. Manag Sci 16(10):B-630CrossRef
Choi BC, Leung JYT, Pinedo ML (2010) Complexity of a scheduling problem with controllable processing times. Oper Res Lett 38(2):123–126MathSciNetMATHCrossRef
Chua TJ, Yin X, Zhu J, Cai T (2011) U.S. Patent No. 7,904,192. Washington, DC: U.S. Patent and Trademark Office.
Dannenbring DG (1977) An evaluation of flow shop sequencing heuristics. Manag Sci 23(11):1174–1182MATHCrossRef
Davis RI, Tindell KW, Burns A (1993) Scheduling slack time in fixed priority pre-emptive systems. In: 1993 Proceedings Real-Time Systems Symposium, IEEE, pp. 222–231
Della Croce F, Scatamacchia R (2020) The longest processing time rule for identical parallel machines revisited. J Sched 23(2):163–176MathSciNetMATHCrossRef
Dubois D, Prade H (1982) The advantages of fuzzy approach in OR/MS demonstrated on two examples of resources allocation problems. Progress in cybernetics and systems research, vol VIII. Hemisphere, Washington, D.C, pp 491–497
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, pp. 39–43.
Erschler J, Roubellat F, Vernhes JP (1976) Finding some essential characteristics of the feasible solutions for a scheduling problem. Oper Res 24(4):774–783MathSciNetMATHCrossRef
Feng F, Zhang C, Akram M, Zhang J (2022) Multiple attribute decision making based on probabilistic generalized orthopair fuzzy sets. Granul Comput 28:1–29
Gao KZ, Suganthan PN, Pan QK, Chua TJ, Cai TX, Chong CS (2016) Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives. J Intell Manuf 27(2):363–374CrossRef
Gholami M, Zandieh M (2009) Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop. J Intell Manuf 20(4):481CrossRef
Gozali L, Kurniawan V, Nasution SR (2019) Design of job scheduling system and software for packaging process with SPT, EDD, LPT, CDS and NEH algorithm at PT. ACP. IOP Conf Ser: Mater Sci Eng 528(1):012045CrossRef
Hong TP, Wang TT (1999) A heuristic Palmer-based fuzzy flexible flow-shop scheduling algorithm. In: FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No. 99CH36315), IEEE, Vol. 3, pp. 1493–1497.
Hong TP, Wang CL, Wang SL (2000) A heuristic Gupta-based flexible flow-shop scheduling algorithm. In: Smc 2000 conference proceedings. 2000 IEEE international conference on systems, man and cybernetics. 'Cybernetics evolving to systems, humans, organizations, and their complex interactions'(cat. no. 0 (Vol. 1, pp. 319–322)). IEEE.
Hussain SAI, Mandal UK, Mondal SP (2018) Decision maker priority index and degree of vagueness coupled decision-making method: a synergistic approach. Int J Fuzzy Syst 20(5):1551–1566MathSciNetCrossRef
Hussain SAI, Baruah D, Dutta B, Mandal UK, Mondal SP, Nath T (2019) Evaluating the impact of service quality on the dynamics of customer satisfaction in the telecommunication industry of Jorhat, Assam. Telecommun Syst 71(1):31–53CrossRef
Jia Z, Yan J, Leung JY, Li K, Chen H (2019) Ant colony optimization algorithm for scheduling jobs with fuzzy processing time on parallel batch machines with different capacities. Appl Soft Comput 75:548–561CrossRef
Jungwattanakit J, Reodecha M, Chaovalitwongse P, Werner F (2008) Algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria. Int J Adv Manuf Technol 37(3–4):354–370MATHCrossRef
Kaban AK, Othman Z, Rohmah DS (2012) Comparison of dispatching rules in job-shop scheduling problem using simulation: a case study. Int J Simul Model 11(3):129–140CrossRef
Karger D, Stein C, Wein J (2010) Scheduling algorithms. Algorithms and theory of computation handbook: special topics and techniques. Springer, pp 20–20
Karim R, Karmaker CL (2016) Machine selection by AHP and TOPSIS methods. Am J Ind Eng 4(1):7–13
Kim W, Kim H, Hwang J (2020) Sustainable growth for the self-employed in the retail industry based on customer equity, customer satisfaction, and loyalty. J Retail Consum Serv 53:101963CrossRef
Kumar KK, Nagaraju D, Gayathri S, Narayanan S (2017) Evaluation and selection of best priority sequencing rule in job shop scheduling using hybrid MCDM technique. IOP Conf Ser Mater Sci Eng 197:1–10CrossRef
Li K, Chen J, Fu H, Jia Z, Fu W (2019) Uniform parallel machine scheduling with fuzzy processing times under resource consumption constraint. Appl Soft Comput 82:105585CrossRef
Liu GS, Zhou Y, Yang HD (2017) Minimizing energy consumption and tardiness penalty for fuzzy flow shop scheduling with state-dependent setup time. J Clean Prod 147:470–484CrossRef
Mokhtari H, Hasani A (2017) An energy-efficient multi-objective optimization for flexible job-shop scheduling problem. Comput Chem Eng 104:339–352CrossRef
Naderi B, Gohari S, Yazdani M (2014) Hybrid flexible flowshop problems: models and solution methods. Appl Math Model 38(24):5767–5780MathSciNetMATHCrossRef
Nawaz M, Enscore EE Jr, Ham I (1983) A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11(1):91–95CrossRef
Nguyen S, Mei Y, Zhang M (2017a) Genetic programming for production scheduling: a survey with a unified framework. Complex Intell Syst 3(1):41–66CrossRef
Nguyen S, Zhang M, Tan KC (2017b) Surrogate-assisted genetic programming with simplified models for automated design of dispatching rules. IEEE Trans Cybern 47(9):2951–2965CrossRef
Novshek W, Thoman L (2006) Demand for customized products, production flexibility, and price competition. J Econ Manag Strateg 15(4):969–998CrossRef
Palmer DS (1965) Sequencing jobs through a multi-stage process in the minimum total time—a quick method of obtaining a near optimum. J Oper Res Soc 16(1):101–107CrossRef
Petroni A, Rizzi A (2002) A fuzzy logic based methodology to rank shop floor dispatching rules. Int J Prod Econ 76(1):99–108CrossRef
Prade H (1979) Using fuzzy set theory in a scheduling problem: a case study. Fuzzy Sets Syst 2(2):153–165MATHCrossRef
Qin X, Jiang H (2005) A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters. J Parallel Distrib Comput 65(8):885–900MATHCrossRef
Rapine C, Brauner N (2013) A polynomial time algorithm for makespan minimization on one machine with forbidden start and completion times. Discret Optim 10(4):241–250MathSciNetMATHCrossRef
Sakamoto S (2010) Beyond world-class productivity: Industrial engineering practice and theory. Springer Science & Business MediaCrossRef
Salvador MS (1973) A solution to a special class of flow shop scheduling problems. Symposium on the theory of scheduling and its applications. Springer, Berlin, Heidelberg, pp 83–91CrossRef
Schultz CR (1989) An expediting heuristic for the shortest processing time dispatching rule. Int J Prod Res 27(1):31–41CrossRef
Schwiegelshohn U, Yahyapour R (1998) Analysis of first-come-first-serve parallel job scheduling. SODA 98:629–638MathSciNetMATH
Singh G, Singh PP, Lubana PPS, Singh KG (2006) Formulation and validation of a mathematical model of the microclimate of a greenhouse. Renew Energy 31(10):1541–1560CrossRef
Sly D, Helwig M, Hu G (2017) Improving the efficiency of large manufacturing assembly plants. Proc Manuf 11:1818–1825
Subramaniam V, Lee GK, Ramesh T, Hong GS, Wong YS (2000) Machine selection rules in a dynamic job shop. Int J Adv Manuf Technol 16(12):902–908CrossRef
Sumichrast RT, Russell RS, Taylor BW (1992) A comparative analysis of sequencing procedures for mixed-model assembly lines in a just-in-time production system. Int J Prod Res 30(1):199–214MATHCrossRef
Tran TT, Araujo A, Beck JC (2016) Decomposition methods for the parallel machine-scheduling problem with setups. Informs J Comput 28(1):83–95MathSciNetMATHCrossRef
Wang L, Li D (2002) A scheduling algorithm for flexible flow shop problem. Proceedings of the 4th world congress on intelligent control and automation (Cat. No. 02EX527), vol 4. IEEE, pp 3106–3108CrossRef
Woolson RF (2007) Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials, pp 1–3
Zhou W, Chen F, Ji X, Li H, Zhou J (2022) A Pareto-based discrete particle swarm optimization for parallel casting workshop scheduling problem with fuzzy processing time. Knowl-Based Syst 256:109872CrossRef
Metadata
Title
A priority-based heuristic approach for solving flexible flow-shop with parallel machine scheduling in a fuzzy environment
Authors
Syed Abou Iltaf Hussain
Ranbir Kalita
Uttam Kumar Mandal
Publication date
05-04-2023
Publisher
Springer International Publishing
Published in
Granular Computing / Issue 6/2023
Print ISSN: 2364-4966
Electronic ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-023-00377-y

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