Achieving sustainability through improvement in the textile wet process using particle swarm optimization and the tabu search algorithm

Abstract

As far as cloth production is concerned, the dyeing process in textiles forms a bottleneck procedure, as this process consumes a great deal of time and high volumes of water per unit of fabric for processing, which causes depletion of groundwater levels at a high rate. Besides, textile effluents are discharged into rivers or wetlands without proper treatment in many cases. Untreated textile effluent can contaminate groundwater and water bodies, reduce dissolved oxygen in the water, and affect aquatic ecosystems, which may indirectly cause climate change. The waste generated in dyeing is mainly due to the cleaning process. Thus, the dyeing process needs to be improved and optimized to solve the problem and reduce delay. To take effective measures for future improvement, it is essential to develop a nature-inspired tracking system. The amount of emission and the performance can be improved by utilizing scheduling as a tool. In this view, the dyeing process is formulated as a bi-objective optimization model to reduce the tardiness cost and minimize the emission of wastewater during the cleaning process of the dyeing vat. The current problem is of a difficult nature; thus, multi-objective particle swarm optimization in collaboration with the tabu search algorithm has been used to attain good and nondominant results. The tabu search algorithm is used along with ejection chains to focus on the objectives of emission reduction and increase the number of desired solutions. It was found that the total processing time was reduced by 4–5 hours and water was reduced by 500 liters for dyeing 200 kg of yarn daily.

Keywords

Textile sector waste particle swarm optimization tabu search

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References

Piai

Blokland

van der Wal

, et al. Biodegradation and adsorption of micropollutants by biological activated carbon from a drinking water production plant. J Hazard Mater 2020; 388: 122028.

Zhu

Pei

Wang

An efficient dye-fixation technique with reactive dye for a wash-free process of silk fabrics without water discharge. Text Res J. Epub ahead of print May 2, 2022. DOI: 10.1177/00405175211069876.

Gendreau

Laporte

Guimarães

EM.

A divide and merge heuristic for the multiprocessor scheduling problem with sequence-dependent setup times. Eur J Oper Res 2001; 133: 183–189.

Gomes

Pinheiro

Saraiva

RD.

Dye schedule optimization: a case study in a textile industry. Appl Sci 2021; 11: 6467.

Narayanaswamy

Scott

JA.

Lessons from cleaner production experiences in Indian hosiery clusters. J Cleaner Prod 2001; 9: 325–340.

De Oliveira Neto

Ferreira Correia

Silva

, et al. Cleaner production in the textile industry and its relationship to sustainable development goals. J Cleaner Prod. Epub ahead of print 10 August 2019. DOI: 10.1016/j.jclepro.2019.04.334.

Schramm

Possibilities and limitations of a comparative assessment of process technologies from a cleaner production point of view. J Cleaner Prod 1998; 6: 227–235.

Ghazinoory

Cleaner production in Iran: necessities and priorities. J Cleaner Prod 2005; 13: 755–762.

Tong

Shao

Zhang

, et al. An AHP-based water conservation and waste-reduction indicator system for CP of textile-printing industry in China and technique integration. Clean Technol Environ Pol 2012; 14: 857–868.

10.

Chen

Wang

, et al. A process-level water conservation and pollution control performance evaluation tool of cleaner production technology in textile industry. J Cleaner Prod 2017; 143: 1137–1143.

11.

Zhang

Chang

P-C

Song

, et al. Local search enhanced multiobjective PSO algorithm for scheduling textile production processes with environmental considerations. Appl Soft Comput J. Epub ahead of print December 2017. DOI: 10.1016/j.asoc.2017.08.013.

12.

Potts

Kovalyov

MY.

Scheduling with batching: a review, Eur J Oper Res 2000; 120: 228–249.

13.

Kashan

Karimi

Scheduling a single batch-processing machine with arbitrary job sizes and incompatible job families: an ant colony framework. J Oper Res Soc 2008; 59: 1269–1280.

14.

Méndez

Cerdá

Grossmann

, et al. State-of-the-art review of optimization methods for short-term scheduling of batch processes. Comput Chem Eng 2006; 30: 913–946.

15.

Allahverdi

Cheng

TCE

, et al. A survey of scheduling problems with setup times or costs. Eur J Oper Res 2008; 187: 985–1032.

16.

Petrovic

Akoz

A fuzzy goal programming approach to integrated loading and scheduling of a batch processing machine. J Oper Res Soc 2008; 59: 1211–1219.

17.

Gupta

Smith

JF.

Algorithms for single machine total tardiness scheduling with sequence-dependent setups. Eur J Oper Res 2006; 175: 722–739.

18.

Mathirajan

Sivakumar

Minimizing total weighted tardiness on heterogeneous batch processing machines with incompatible job families. Int J Adv Manuf Technol 2006; 28: 1038–1047.

19.

Damodaran

Hirani

Velez-Gallego

MC.

Scheduling identical parallel batch processing machines to minimize makespan using genetic algorithms. Eur J Ind Eng 2009; 3: 187–206.

20.

Zhou L, Kun Xu, Xun Cheng, et al. Study on optimizing production scheduling for water-saving in the textile dyeing industry. Journal of Cleaner Production. 10 January 2017, doi: 10.1016/j.jclepro.2016.09.047.

21.

Jiang

Pei

Liu

, et al. Uniform parallel batch machines scheduling considering transportation using a hybrid DPSO-GA algorithm. Int J Adv Manuf Technol 2017; 89: 1887–1900.

22.

Jia

Z-H

Wang

Leung

JY-T.

An ACO algorithm for makespan minimization in parallel batch machines with non-identical job sizes and incompatible job families. Appl Soft Comput 2016; 38: 395–404.

23.

Bean

JC.

Scheduling parallel-machine batch operations to maximize on-time delivery performance. J Schedul 2016; 19: 583–600.

24.

Mitchell

An introduction to genetic algorithms. Cambridge, MA: MIT Press, 1996, pp.1–5.

25.

Hsu

Hsiung

Chen

, et al. A GA methodology for the scheduling of yarn-dyed textile production. Exp Syst Appl. 2009; 36: 12095–12103.

26.

Zeng

Hong

Man

, et al. Multi-object optimization of flexible flow shop scheduling with batch process — consideration total electricity consumption and material wastage. J Cleaner Prod. Epub ahead of print 10 May 2018. DOI: 10.1016/j.jclepro.2018.02.224.

27.

Shahvari

Logendran

An enhanced tabu search algorithm to minimize a bi-criteria objective in batching and scheduling problems on unrelated-parallel machines with desired lower bounds on batch sizes. Comput Oper Res 2017; 77: 154–176.

28.

Alharkan

Saleh

Ghaleb

, et al. Tabu search and particle swarm optimization algorithms for two identical parallel machines scheduling problem with a single server. J King Saud Univ Eng Sci. Epub ahead of print July 2019. DOI: 10.1016/j.jksues.2019.03.006.

29.

Huynh

Chien

A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study. Comput Ind Eng. Epub ahead of print November 2018. DOI: 10.1016/j.cie.2018.01.005.

30.

Kennedy J and Eberhart R. Particle Swam Optimisation. In: Proceedings of ICNN’ 95 – International Conference on Neural Networks, Perth, WA, Australia, 27 November 1995 – 01 December 1995, 1942–1948. IEEE.

31.

Glover

Ejection chains, reference structures and alternating path methods for traveling salesman problems. Discrete Appl Math 1996; 65: 223–253.

32.

Ismail

ZB.

Mathematical formulation of tabu search in combinatorial optimization. Skudai Johor: Department of Mathematic, University Technology Malaysia, 2009.

33.

Lim

Zhang

A two-stage heuristic with ejection pools and generalized ejection chains for the vehicle routing problem with time windows. INFORMS J Comput 2007; 19: 443–457.

34.

Audet C, Bigeon J, Cartier D, et al. Performance indicators in multiobjective optimization. Technical Report. Les Cahiers du GERAD G–2018–90, GERAD, HEC Montréal, Canada, October 2018.