Productivity Improvement Using Heuristics Algorithm in Scheduling a Flow-Shop Manufacturing

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Manufacturing system consists and integrates entities such as machines, jobs with different operations to be processed in the corresponding machine, input materials, human operators, and all the things that facilitate the production system of a manufacturing industry so that enabling the firm to generate good wealth and to cope the dynamically changing market demand. The problem under study is a textile garment manufacturing industry of a flow shop-manufacturing. Even, giving high priority to the first arrival jobs and seems fair to customers and jobs, however, does not consider other customer and job characteristics such as production cost, idle time, make-span, and tardiness of jobs. In this flow shop type of scheduling problem, “n” jobs considered to process on “m” machines and preemption of jobs not allowed. In addition, assumed the machines could process only one job at a time. The study conducted with the main aim of productivity improvement by minimizing the idle time of machines to control criteria or parameters such as make-span, resource utilization, and production cost for the case company by finding the most optimal sequence of jobs under the study. To carry out the study and find the best and efficient sequence of jobs heuristics algorithms such as NEH, CDS, palmers and EDD rules in the flow shop-manufacturing used, and the NEH resulted in the best sequence of jobs. In the proposed sequence of jobs with a 3.6% utilization improvement, the productivity improved by 17.2% than the existing schedule. Flow shop Scheduling Heuristics algorithm Make-span Idle time Productivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The manufacturing sector is the heart and soul of developing and developed countries as detailed by [ 1 ]. In such manufacturing industries, meeting the dynamically changing need of customers and delivery times is the key to stay at the apex of the global or national competition. However, it is difficult to forecast future demand fluctuation and ever-increasing global competition. Then these pressures are the most determinant factors, which oblige the firms to think critically on how to improve the production process performance to produce the product within a minimum cost and deliver at a reasonable period of time as elaborated by [ 2 ]. On the other hand either to lead or keep on with the competition track, it is investigated that productivity maximization and reducing the production time play the major role as recent research findings show [ 3 ]. Also, this production time reduction leads to a reduction in the level of work in progress (WIP) inventory as discussed by [ 4 ]. While dealing with productivity maximization techniques enhancing the performance of machines is crucial and this approach makes them available for production [ 5 ]. However, challenged with productivity; the apparel industries for non-developed and developing countries are receiving high attention. Productivity, therefore, is the critical issue that must be taken into account for the success of these countries to achieve their goal in the sector as stated by [ 6 ]. Today the textile and garment industries in Ethiopia are increasing. Since its establishment in 1939 at Dire Dawa, the number of textile manufacturing companies, up to 1991, was less than 20, latter in 2012 the sector increased to 80, then in 2013 up to 110 whereas, in 2018 it reached about 130 as it is reported by [ 7 ]. Out of these 130 and the newly emerging textile and garment manufacturing companies, woven and knitted garment products are their major products for the sector respectively. Knitted garment products such as round neck & V-neck T-shirt, polo shirt, and pants are common and well-known major products for these garment industries. However, it is believed there is an intensive and unprecedented international market competition which poses another challenge for the newly emerging Ethiopian textile and garment industries as it has been discussed by [ 8 ]. Therefore, continuously looking and developing the new production system, production tools, and techniques are crucial to go forward with the rapidly changing needs of customers and market fluctuation. In addition, these garment industries must maximize the line efficiency and productivity; reduce lead times, ensure the required product quality to stay with this highly competitive marketplace as discussed by [ 9 ]. Investing in Ethiopia in the textile and garment sector is more attractive. Because of minimum power/energy cost, raw materials, and minimum manpower cost; still delivery time, efficiency of factories which is as slow as 40–45% in production of the garment assembly units, and cycle time are the most challenges that drastically pulling the sector back in general as reported by [ 10 ]. However, the sector given high attention by the government of Ethiopia, challenged with low productivity and long production time. Almeda Textile PLC is one of these in the sector facing the problem. As the annual actual production report of the company indicates for the last seven years, it was 74% in 2011/12 and 2012/13, 60% in 2014/15, 61% in 2015/16, and 72% in 2016/17 and 2017/18 years. However, in 2013/14 the company achieved 100% planned production. The maximum production achievement was only 74%, for the reset periods. Compared to actual capacity of the operators and machines their achievement 74% was not satisfactory, which characterized as low productivity. Among many reasons for the low productivity, machine idle time, doe to wrong scheduling rule used first come first served rule (FCFS), is the most prominent cause. Because this scheduling rule does not consider characteristics such as time required to complete the average number of jobs, machine idle time, and how to maximize facilities utilization. Therefore, due to these reasons this study aim to find a new way of production schedule to improve the productivity. 1.1 Background of the study Productivity is a broad concept both in its operational content and aim. It is the issue of common understanding that highest productivity implies a reduced cost of production, reduced the sales price of goods, increased demand for the item, and helps the goods to compete effectively in the global market. Actually, the strength of a country, the success of the economy, the living standard, and the wealth of the nation are highly dependent on production and productivity. With same input; increasing the output of goods and services or enhancing the productivity enables to reduce the cost of goods per each item so that to offer goods with a least selling price to the customer and generating good wealth [ 11 ] and [ 12 ]. The aim of this study is to improve the productivity reducing the idle time of machines, using heuristics scheduling algorithm. It is important to note that productivity improvement or the effective use of available resources is the only way for future development in the society. Productivity improvement results direct rises in the standard of living under conditions of distribution of productivity gains [ 13 ]. As proved by scholars, developing a methodology that facilitates the use of lean manufacturing tools is an option that improves productivity [ 14 ]. In addition, lean tools and techniques enhance productivity of manufacturing industries and scale up their long term competitive advantage [ 15 ]. 1.2 Research gap Prior research findings only focus on one parameter such as make-span, tardiness, earliness minimization. However, the proposed study integrates make-span to idle time, increased machine efficiency with same machine and SMV relation with output improvement. Previous studies did not show the effect of optimizing one parameter over the other. Therefore, the proposed study focused on improving productivity by minimizing the idle time of machines, production cost, and combined in addition to the make-span criterion. 1.3 Statement of the problem Recent research findings indicate that for any garment industry good productivity is a must to sustain and to be profitable in the global marketplace [ 16 ]. However, this is the reality, especially in Ethiopia most garment industries are challenged with low productivity and long production lead times [ 17 ]. Hence, to overcome this challenge line balancing and work-study techniques employed. In addition, simulation, methods used to identify and enhance the performance of the garment industry. Nevertheless, no research had done in this area to improve the productivity of the garment industry by scheduling the jobs using heuristics algorithms. Almeda Textile private limited company is one of the oldest textile manufacturers, though not able to achieve its targeted production. In addition, the in-time delivery of products to the customers is not possible for the case company as well for others in Ethiopia. Long lead-time, machine idle time, and customer dissatisfaction are important issues that the company has to resolve to stay with the competitive marketplace. In addition, the company has to focus on new manufacturing technologies such as production scheduling since the existing scheduling rule didn’t contribute to minimizing the total completion time for jobs, reducing the idle time of the machine, and reducing the production cost by increasing the output. At Almeda the planned production and actual output has large variation. Fabric shortage, machine breakdown, and scheduling are among the causes for this performance inefficiency of the line or low productivity of the knitted garment section. This all are because the scheduling rule is “First Come First Served (FCFS)’’ rule which is a dispatching priority rule. This rule used to process the jobs according to their order of arrival. On the other hand, the main attention of this scheduling rule only considers minimizing, job completion time and customer waiting time. In addition, FCFS rule do not consider other characteristics such as; total cost, time to complete average number of jobs, work- in process (WIP) inventory, resource utilization, and machine idle time[ 19 ]. This makes FCFS rule is unreliable and unaccountable in justifying productivity since it does not concede any other customer or job characteristics. 2. Research methodology This section of the methodology designed to accomplish the objective of the study. However, reaching the best scheduling algorithm is a bit challenging task it can bring a revolutionary change in productivity [ 18 ]. Previous researches proved heuristics algorithms used to minimize the make-span or total completion time of the job on the last machine. This methodology part of the proposed study is designed to reduce the idle time of machines to increase their availability, reduce the production cost, reduce the work in process (WIP) inventory, improve resource utilization, and increase the line efficiency as well as the productivity of the case company. Since the main objective of the research is to increase the productivity by minimizing the make-span, idle time, production cost, and increasing the resource utilization; different heuristics algorithms such as, NEH, Palmers, CDS, and EDD were used to carry out the comparative analysis and select the one that minimizes the above parameters most importantly. As it has been proved by scholars, NEH is the best heuristics algorithm for NP-hard m- machines and n- jobs sequencing problems to minimize the make-span as discussed by [ 20 ] and [ 21 ]. There are optimal solution approaches for flow shop scheduling problems, however, distinguished that these approaches require longer manipulation time and memory to keep track of the calculations, which is much expensive even for small-sized problems. Hence, the proposed tools for this study can bring a revolutionary change in productivity as noted before in the literature review part of this work so far. Again, as discussed so far, the selected tools used for this study are the least biased and most effective solution approaches as well can provide near to optimal solutions. Therefore, because of these reasons the proposed study employed heuristics algorithms that can offer near to optimal solutions for flow shop scheduling problems. 2.1 Nawaz Enscore Ham (NEH) Algorithm This insertion algorithm used to establish the final sequence by inserting an additional job in each partial sequence. The principle of this algorithm states that higher priority should give to a job that has maximum total processing time in all the machines than the job with minimum total processing time. Steps for this algorithm followed represented by the following flow chart. 2.2 Campbell Dudek Smith (CDS) Algorithm This is the second algorithm that the researcher employed. According to the CDS (Campbell Dudek and Smith) algorithm in order to obtain the most optimal sequence of job extension of Johnson’s algorithm used as clearly described below. For “n” jobs with “m” operations m = M1, M2, M3… ( M n ) machines are required for each of the operations. The chart below illustrates how the CDS algorithm works 2.3 Palmer’s Algorithm In this type of algorithm, the scheduler is required to offer weight to each machine and finds out a weighted sum for each job. Here below the flow chart shows how the algorithm optimization works. 2.4 Earliest Due Date (EDD) Rule The aim of EDD rule to reduce tardiness. EDD rule gives priority to the most imperative job or a job that requires quick decisions based on its delivery time or deadline. This is the last rule the thesis employed. According to this rule, jobs arranged in order of increasing their due dates. The main objective of this rule is to minimize the maximum job tardiness and maximum job lateness. The flow chart below illustrates how the scheduling rule can have achieved systematically. Using the above systematically, optimization approaches the researcher intended how the data analysis carried out using each optimization algorithm. For the data analysis the first assumption, i.e. all jobs have an equal chance for scheduling be analyzed using the first three heuristics Nawaz Enscore Ham (NEH), Campbell Dudek and Smith (CDS), and Palmer’s algorithms. 4. Result and discussion After the analysis has been conducted the researcher carried out comparison between the existing scheduling approach FCFS rule of the case company and the newly proposed scheduling algorithms of NEH, Palmer’s, CDS, EDD, and finally the GA which is used to validate the performance of the proposed heuristics algorithms in the rescheduling process of the selected jobs in the study. Summarized result of the comparison given for the make-span and the idle time one by one. 4.1 Make-span comparison Out of the alternatives obtained in the iterative process of the data analysis; only alternatives, which provide the minimum make-span value, have taken for the comparison in each scheduling algorithms. As the analysis result revealed the minimum make-span obtained by using NEH and CDS scheduling algorithms with the same sequence of jobs. In addition, the make-span value obtained using EDD and Palmer’s scheduling approaches was the same since both of the scheduling algorithms reached the same sequence of jobs. In short, the results for these scheduling algorithms and that of the GA values given in Fig. 4.6 below. 4.2 Idle time comparison Likewise, the make-span comparison of the existing schedule and newly scheduled jobs has done. Idle time, which is the main parameter in the proposed study, carried out for the same fashion and this result of the idle time illustrated in Fig. 4.7 below. From the very beginning of the methodology and data analysis process, the researcher deployed four different scheduling heuristics algorithms. Nawaz Enscore Ham's heuristics scheduling algorithm, which resulted in a minimum idle time, was the best scheduling algorithm out of the four and even the existing scheduling algorithm of the case company and the proposed study that can be minimized the idle time of machines better than the other scheduling algorithms. This research mainly focused on reducing the idle time of machines and improving the productivity of the case company knitting section of the garment department taking in to account the polo shirt as the product studied. The performance of these scheduling heuristics algorithms has been verified by the GA and it is observed that both the result of make-span and idle time obtained was almost the same. Particularly, the comparative results of the above Fig. 4.17 indicated that the Genetic Algorithm obtained the most preferable reduced idle time with an idle time of 37.74 minutes. This idle time was a bit less than the idle time obtained using NEH, which is 38.27 minutes. Therefore, here we can deduce that heuristics algorithms have good capacity in idle time reduction as the above analysis result revealed. 3.3 Productivity analysis So far, in the literature review, partly discussed what productivity means. Simply it is the ratio of total outputs to inputs. In addition, productivity measured in terms of partial and multi-factor productivity. The partial productivity is productivity measured by dividing the output to the single input/ single resource consumed. In addition, the multi-factor productivity measured by dividing the total output to all the input factors consumed during the production process. Therefore, in this study, the productivity measured for the existing schedule and the new proposed sequence or schedule of jobs. At the same time, both partial and multi-factor productivity are taken into account. Using the formula given below the productivity calculated in terms of working hours both for the existing and proposed sequence of jobs. $$\:Productivity=\frac{Output\:}{\:Input}$$ i. Existing productivity measure The partial and multi-factor productivity for the existing and proposed schedule carried out. $$\:productivity=\frac{Total\:garments\:producced\:in\:existing\:schedule\:}{working\:hours\:per\:day}$$ $$\:Existing\:schedule\:productivity=\frac{1855.92\:}{16}=116\:\varvec{p}\varvec{c}\varvec{s}\:$$ This productivity result of the existing schedule shows it is possible to produce around 116 pcs of polo shirt an hour by using the 37 operators, and 37 machines. Now let’s measure the productivity of the proposed schedule; $$\:Proposed\:schedule\:Productivity=\frac{2175.4\:}{16}=136\:\varvec{p}\varvec{c}\varvec{s}\:$$ In the proposed schedule with the same machines and operators, the productivity changed from 116 to 136 pcs per hr. hence, with the proposed schedule it is possible to produce an additional 20 units of polo shirt every hour without the addition of any resource. Therefore, if the output of a manufacturing company increased without additional usage of any resource then one can deduce the productivity of that company is increased. To indicate the percentage productivity change or improvement of the above calculation the following formula employed. $$\:\%\:Productivity\:change=\frac{136-116\:}{116}*100=17.2\varvec{\%}$$ As the percentage change, the result depicted the proposed schedule of the jobs brought about 17.2% productivity improvement than the existing schedule. Therefore, it is very important to note that manufacturers have to think critically about how to improve the productivity of their company by rescheduling jobs. In general, this study addressed how the scheduling of jobs brought good productivity improvement; as well the effect of each scheduling algorithm is observed in reducing the idle time of the machines and minimizing the total completion time of the last job on the last machine comparatively. Finally, the result of the best scheduling algorithm is tested or validated with the genetic algorithm by integrating it with visual studio 2017 software. This was to check whether the proposed algorithms can solve the problems, even the problem size increased and arrived with a solution except for its longer time requirement for manipulation. The proposed algorithms can solve whatever type of flow shop scheduling problem might face researchers. 5. Conclusion In this paper the primary focus is on comparing four well-established heuristic algorithms in the literature to improve the productivity of the case company Almeda Textile PLC With the help of job scheduling. In the course of actions, the researchers employed different scheduling heuristics algorithms such as NEH, Palmer’s, CDS, EDD, and finally, the performance of these heuristics algorithms has validated with the Genetic Algorithm integrated with visual studio 2017. Prior to start the proposed heuristics scheduling algorithms the existing scheduling rule of the case company was calculated and obtained 48.92 minute and 51.1-minute make-span and idle time respectively. After the analysis conducted by the proposed heuristics algorithms, the most optimal sequence ( J3-J2-J4-J1) of jobs obtained by Nawaz Enscore Ham (NEH) heuristic scheduling algorithm with 42.43 and 38.27-minute make-span and idle times respectively. This result revealed an idle time of about 13.36 minutes wasted because of poor scheduling of jobs for the case company. In this research we attempted different scheduling algorithms and their effect in minimizing the make-span and reducing idle time of machines by that the most scheduling algorithm with good effect in reducing the idle time was taken as the best with a near to optimal sequence of jobs. As the validity test of this research show, whatever problem size might happen heuristics algorithms such as NEH and CDS are good in providing a good result of job schedule with minimized makes-span and reduced idle time machines. NEH’s result revealed that on the last machine there was about 23.3% idle time reduction than the existing schedule. Resource utilization was another focus of this research. As the analysis indicated the proposed scheduling algorithm, provide about 3.6% resource utilization improvement than the existing scheduling algorithm of the case company. In general, by reducing the idle time of machines and increasing the resource utilization so that to improve the productivity of the case company with the considered jobs and machines has addressed and shown about 17.2% of productivity change with the proposed sequence or schedule of jobs. In the near future the application of Genetic algorithm to test the performance of the proposed heuristics algorithms for more than 4 jobs *5 machines will be considered to show their computational efficiency for scheduling problems. Declarations Conflicts of interest The authors declare that they have no competing financial and nonfinancial interests. Funding No financial support was received from any organization for the work presented in this manuscript. Author Contribution Author ContributionsTibebu Alene Asresa: write the first draft of the manuscripts, data collection, curation, analysis, conceptualization, and editing.Dr. Bereket Haile Woldegiorgis: , proofreading and revising the final manuscript. All authors have read and agreed to the published version of the manuscript. References Rao KR, Tesfahunegn SZ (2015) Performance measurement of manufacturing industries in ethiopia-an analytical study. Journal of Poverty, Investment and Development, 7 (2422) Guo ZX, Wong W, Keung L, Yung-Sun S, Fan JT, Chan SF (2006) Mathematical model and genetic optimization for the job shop scheduling problem in a mixed-and multi-product assembly environment: A case study based on the apparel industry. Comput Ind Eng 50(3):202–219 Ren T, Guo, Meiting, Lin L, Miao Y (2015) A local search algorithm for the flow shop scheduling problem with release dates. 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An improved neh heuristic to minimize makespan in permutation flow shops. computers Oper Res, 3001–3008 leisten, framinan & (2003) Different initial sequences for the heuristic of nawaz, enscore and ham to minimize makespan, idletime or flowtime in the static permutation flowshop sequencing problem. Int J Prod Res, 121–148 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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the EDD rule works\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6749907/v1/7d6bacd29c14940316bc597d.png"},{"id":83808300,"identity":"823e0462-3b29-4ae6-884d-b49e34107dc9","added_by":"auto","created_at":"2025-06-03 06:03:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16433,"visible":true,"origin":"","legend":"\u003cp\u003eResult of make-span values using different scheduling rules\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6749907/v1/f7c63436ae0d39a325692224.png"},{"id":83808302,"identity":"7ca919db-189a-4a41-ad17-2c4839c1298d","added_by":"auto","created_at":"2025-06-03 06:03:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16902,"visible":true,"origin":"","legend":"\u003cp\u003eResult of idle time with different scheduling algorithms\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6749907/v1/96ba82a88b393f1e6880bd59.png"},{"id":98775790,"identity":"84a8f325-6e98-4072-9cb4-9cce8420287d","added_by":"auto","created_at":"2025-12-22 12:21:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":616895,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6749907/v1/adbb4781-fbf7-4b55-b03d-0473bdcdb935.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eProductivity Improvement Using Heuristics Algorithm in Scheduling a Flow-Shop Manufacturing\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe manufacturing sector is the heart and soul of developing and developed countries as detailed by [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In such manufacturing industries, meeting the dynamically changing need of customers and delivery times is the key to stay at the apex of the global or national competition. However, it is difficult to forecast future demand fluctuation and ever-increasing global competition. Then these pressures are the most determinant factors, which oblige the firms to think critically on how to improve the production process performance to produce the product within a minimum cost and deliver at a reasonable period of time as elaborated by [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand either to lead or keep on with the competition track, it is investigated that productivity maximization and reducing the production time play the major role as recent research findings show [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Also, this production time reduction leads to a reduction in the level of work in progress (WIP) inventory as discussed by [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While dealing with productivity maximization techniques enhancing the performance of machines is crucial and this approach makes them available for production [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, challenged with productivity; the apparel industries for non-developed and developing countries are receiving high attention. Productivity, therefore, is the critical issue that must be taken into account for the success of these countries to achieve their goal in the sector as stated by [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eToday the textile and garment industries in Ethiopia are increasing. Since its establishment in 1939 at Dire Dawa, the number of textile manufacturing companies, up to 1991, was less than 20, latter in 2012 the sector increased to 80, then in 2013 up to 110 whereas, in 2018 it reached about 130 as it is reported by [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Out of these 130 and the newly emerging textile and garment manufacturing companies, woven and knitted garment products are their major products for the sector respectively. Knitted garment products such as round neck \u0026amp; V-neck T-shirt, polo shirt, and pants are common and well-known major products for these garment industries.\u003c/p\u003e \u003cp\u003eHowever, it is believed there is an intensive and unprecedented international market competition which poses another challenge for the newly emerging Ethiopian textile and garment industries as it has been discussed by [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, continuously looking and developing the new production system, production tools, and techniques are crucial to go forward with the rapidly changing needs of customers and market fluctuation. In addition, these garment industries must maximize the line efficiency and productivity; reduce lead times, ensure the required product quality to stay with this highly competitive marketplace as discussed by [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInvesting in Ethiopia in the textile and garment sector is more attractive. Because of minimum power/energy cost, raw materials, and minimum manpower cost; still delivery time, efficiency of factories which is as slow as 40\u0026ndash;45% in production of the garment assembly units, and cycle time are the most challenges that drastically pulling the sector back in general as reported by [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the sector given high attention by the government of Ethiopia, challenged with low productivity and long production time. Almeda Textile PLC is one of these in the sector facing the problem.\u003c/p\u003e \u003cp\u003eAs the annual actual production report of the company indicates for the last seven years, it was 74% in 2011/12 and 2012/13, 60% in 2014/15, 61% in 2015/16, and 72% in 2016/17 and 2017/18 years. However, in 2013/14 the company achieved 100% planned production. The maximum production achievement was only 74%, for the reset periods. Compared to actual capacity of the operators and machines their achievement 74% was not satisfactory, which characterized as low productivity.\u003c/p\u003e \u003cp\u003eAmong many reasons for the low productivity, machine idle time, doe to wrong scheduling rule used first come first served rule (FCFS), is the most prominent cause. Because this scheduling rule does not consider characteristics such as time required to complete the average number of jobs, machine idle time, and how to maximize facilities utilization. Therefore, due to these reasons this study aim to find a new way of production schedule to improve the productivity.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Background of the study\u003c/h2\u003e \u003cp\u003eProductivity is a broad concept both in its operational content and aim. It is the issue of common understanding that highest productivity implies a reduced cost of production, reduced the sales price of goods, increased demand for the item, and helps the goods to compete effectively in the global market. Actually, the strength of a country, the success of the economy, the living standard, and the wealth of the nation are highly dependent on production and productivity. With same input; increasing the output of goods and services or enhancing the productivity enables to reduce the cost of goods per each item so that to offer goods with a least selling price to the customer and generating good wealth [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this study is to improve the productivity reducing the idle time of machines, using heuristics scheduling algorithm. It is important to note that productivity improvement or the effective use of available resources is the only way for future development in the society. Productivity improvement results direct rises in the standard of living under conditions of distribution of productivity gains [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As proved by scholars, developing a methodology that facilitates the use of lean manufacturing tools is an option that improves productivity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, lean tools and techniques enhance productivity of manufacturing industries and scale up their long term competitive advantage [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Research gap\u003c/h2\u003e \u003cp\u003ePrior research findings only focus on one parameter such as make-span, tardiness, earliness minimization. However, the proposed study integrates make-span to idle time, increased machine efficiency with same machine and SMV relation with output improvement. Previous studies did not show the effect of optimizing one parameter over the other. Therefore, the proposed study focused on improving productivity by minimizing the idle time of machines, production cost, and combined in addition to the make-span criterion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Statement of the problem\u003c/h2\u003e \u003cp\u003eRecent research findings indicate that for any garment industry good productivity is a must to sustain and to be profitable in the global marketplace [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, this is the reality, especially in Ethiopia most garment industries are challenged with low productivity and long production lead times [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Hence, to overcome this challenge line balancing and work-study techniques employed. In addition, simulation, methods used to identify and enhance the performance of the garment industry. Nevertheless, no research had done in this area to improve the productivity of the garment industry by scheduling the jobs using heuristics algorithms.\u003c/p\u003e \u003cp\u003eAlmeda Textile private limited company is one of the oldest textile manufacturers, though not able to achieve its targeted production. In addition, the in-time delivery of products to the customers is not possible for the case company as well for others in Ethiopia. Long lead-time, machine idle time, and customer dissatisfaction are important issues that the company has to resolve to stay with the competitive marketplace. In addition, the company has to focus on new manufacturing technologies such as production scheduling since the existing scheduling rule didn\u0026rsquo;t contribute to minimizing the total completion time for jobs, reducing the idle time of the machine, and reducing the production cost by increasing the output.\u003c/p\u003e \u003cp\u003eAt Almeda the planned production and actual output has large variation. Fabric shortage, machine breakdown, and scheduling are among the causes for this performance inefficiency of the line or low productivity of the knitted garment section. This all are because the scheduling rule is \u0026ldquo;First Come First Served (FCFS)\u0026rsquo;\u0026rsquo; rule which is a dispatching priority rule. This rule used to process the jobs according to their order of arrival. On the other hand, the main attention of this scheduling rule only considers minimizing, job completion time and customer waiting time. In addition, FCFS rule do not consider other characteristics such as; total cost, time to complete average number of jobs, work- in process (WIP) inventory, resource utilization, and machine idle time[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This makes FCFS rule is unreliable and unaccountable in justifying productivity since it does not concede any other customer or job characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Research methodology","content":"\u003cp\u003eThis section of the methodology designed to accomplish the objective of the study. However, reaching the best scheduling algorithm is a bit challenging task it can bring a revolutionary change in productivity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Previous researches proved heuristics algorithms used to minimize the make-span or total completion time of the job on the last machine.\u003c/p\u003e \u003cp\u003eThis methodology part of the proposed study is designed to reduce the idle time of machines to increase their availability, reduce the production cost, reduce the work in process (WIP) inventory, improve resource utilization, and increase the line efficiency as well as the productivity of the case company.\u003c/p\u003e \u003cp\u003eSince the main objective of the research is to increase the productivity by minimizing the make-span, idle time, production cost, and increasing the resource utilization; different heuristics algorithms such as, NEH, Palmers, CDS, and EDD were used to carry out the comparative analysis and select the one that minimizes the above parameters most importantly. As it has been proved by scholars, NEH is the best heuristics algorithm for NP-hard m- machines and n- jobs sequencing problems to minimize the make-span as discussed by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are optimal solution approaches for flow shop scheduling problems, however, distinguished that these approaches require longer manipulation time and memory to keep track of the calculations, which is much expensive even for small-sized problems. Hence, the proposed tools for this study can bring a revolutionary change in productivity as noted before in the literature review part of this work so far. Again, as discussed so far, the selected tools used for this study are the least biased and most effective solution approaches as well can provide near to optimal solutions. Therefore, because of these reasons the proposed study employed heuristics algorithms that can offer near to optimal solutions for flow shop scheduling problems.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Nawaz Enscore Ham (NEH) Algorithm\u003c/h2\u003e \u003cp\u003eThis insertion algorithm used to establish the final sequence by inserting an additional job in each partial sequence. The principle of this algorithm states that higher priority should give to a job that has maximum total processing time in all the machines than the job with minimum total processing time.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSteps for this algorithm followed represented by the following flow chart.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Campbell Dudek Smith (CDS) Algorithm\u003c/h2\u003e \u003cp\u003eThis is the second algorithm that the researcher employed. According to the CDS (Campbell Dudek and Smith) algorithm in order to obtain the most optimal sequence of job extension of Johnson\u0026rsquo;s algorithm used as clearly described below.\u003c/p\u003e \u003cp\u003eFor \u0026ldquo;n\u0026rdquo; jobs with \u0026ldquo;m\u0026rdquo; operations m\u0026thinsp;=\u0026thinsp;M1, M2, M3\u0026hellip; ( M\u003csub\u003en\u003c/sub\u003e ) machines are required for each of the operations.\u003c/p\u003e \u003cp\u003eThe chart below illustrates how the CDS algorithm works\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Palmer\u0026rsquo;s Algorithm\u003c/h2\u003e \u003cp\u003eIn this type of algorithm, the scheduler is required to offer weight to each machine and finds out a weighted sum for each job. Here below the flow chart shows how the algorithm optimization works.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Earliest Due Date (EDD) Rule\u003c/h2\u003e \u003cp\u003eThe aim of EDD rule to reduce tardiness. EDD rule gives priority to the most imperative job or a job that requires quick decisions based on its delivery time or deadline. This is the last rule the thesis employed. According to this rule, jobs arranged in order of increasing their due dates. The main objective of this rule is to minimize the maximum job tardiness and maximum job lateness. The flow chart below illustrates how the scheduling rule can have achieved systematically.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing the above systematically, optimization approaches the researcher intended how the data analysis carried out using each optimization algorithm. For the data analysis the first assumption, i.e. all jobs have an equal chance for scheduling be analyzed using the first three heuristics Nawaz Enscore Ham (NEH), Campbell Dudek and Smith (CDS), and Palmer\u0026rsquo;s algorithms.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Result and discussion","content":"\u003cp\u003eAfter the analysis has been conducted the researcher carried out comparison between the existing scheduling approach FCFS rule of the case company and the newly proposed scheduling algorithms of NEH, Palmer\u0026rsquo;s, CDS, EDD, and finally the GA which is used to validate the performance of the proposed heuristics algorithms in the rescheduling process of the selected jobs in the study. Summarized result of the comparison given for the make-span and the idle time one by one.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Make-span comparison\u003c/h2\u003e \u003cp\u003eOut of the alternatives obtained in the iterative process of the data analysis; only alternatives, which provide the minimum make-span value, have taken for the comparison in each scheduling algorithms. As the analysis result revealed the minimum make-span obtained by using NEH and CDS scheduling algorithms with the same sequence of jobs. In addition, the make-span value obtained using EDD and Palmer\u0026rsquo;s scheduling approaches was the same since both of the scheduling algorithms reached the same sequence of jobs. In short, the results for these scheduling algorithms and that of the GA values given in Fig.\u0026nbsp;4.6 below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Idle time comparison\u003c/h2\u003e \u003cp\u003eLikewise, the make-span comparison of the existing schedule and newly scheduled jobs has done. Idle time, which is the main parameter in the proposed study, carried out for the same fashion and this result of the idle time illustrated in Fig.\u0026nbsp;4.7 below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom the very beginning of the methodology and data analysis process, the researcher deployed four different scheduling heuristics algorithms. Nawaz Enscore Ham's heuristics scheduling algorithm, which resulted in a minimum idle time, was the best scheduling algorithm out of the four and even the existing scheduling algorithm of the case company and the proposed study that can be minimized the idle time of machines better than the other scheduling algorithms. This research mainly focused on reducing the idle time of machines and improving the productivity of the case company knitting section of the garment department taking in to account the polo shirt as the product studied. The performance of these scheduling heuristics algorithms has been verified by the GA and it is observed that both the result of make-span and idle time obtained was almost the same. Particularly, the comparative results of the above Fig.\u0026nbsp;4.17 indicated that the Genetic Algorithm obtained the most preferable reduced idle time with an idle time of 37.74 minutes. This idle time was a bit less than the idle time obtained using NEH, which is 38.27 minutes. Therefore, here we can deduce that heuristics algorithms have good capacity in idle time reduction as the above analysis result revealed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Productivity analysis\u003c/h2\u003e \u003cp\u003eSo far, in the literature review, partly discussed what productivity means. Simply it is the ratio of total outputs to inputs. In addition, productivity measured in terms of partial and multi-factor productivity. The partial productivity is productivity measured by dividing the output to the single input/ single resource consumed. In addition, the multi-factor productivity measured by dividing the total output to all the input factors consumed during the production process. Therefore, in this study, the productivity measured for the existing schedule and the new proposed sequence or schedule of jobs. At the same time, both partial and multi-factor productivity are taken into account.\u003c/p\u003e \u003cp\u003eUsing the formula given below the productivity calculated in terms of working hours both for the existing and proposed sequence of jobs.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Productivity=\\frac{Output\\:}{\\:Input}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ei. \u003cb\u003eExisting productivity measure\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe partial and multi-factor productivity for the existing and proposed schedule carried out.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:productivity=\\frac{Total\\:garments\\:producced\\:in\\:existing\\:schedule\\:}{working\\:hours\\:per\\:day}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Existing\\:schedule\\:productivity=\\frac{1855.92\\:}{16}=116\\:\\varvec{p}\\varvec{c}\\varvec{s}\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis productivity result of the existing schedule shows it is possible to produce around 116 pcs of polo shirt an hour by using the 37 operators, and 37 machines.\u003c/p\u003e \u003cp\u003eNow let\u0026rsquo;s measure the productivity of the proposed schedule;\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:Proposed\\:schedule\\:Productivity=\\frac{2175.4\\:}{16}=136\\:\\varvec{p}\\varvec{c}\\varvec{s}\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the proposed schedule with the same machines and operators, the productivity changed from 116 to 136 pcs per hr. hence, with the proposed schedule it is possible to produce an additional 20 units of polo shirt every hour without the addition of any resource. Therefore, if the output of a manufacturing company increased without additional usage of any resource then one can deduce the productivity of that company is increased. To indicate the percentage productivity change or improvement of the above calculation the following formula employed.\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\%\\:Productivity\\:change=\\frac{136-116\\:}{116}*100=17.2\\varvec{\\%}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAs the percentage change, the result depicted the proposed schedule of the jobs brought about 17.2% productivity improvement than the existing schedule. Therefore, it is very important to note that manufacturers have to think critically about how to improve the productivity of their company by rescheduling jobs. In general, this study addressed how the scheduling of jobs brought good productivity improvement; as well the effect of each scheduling algorithm is observed in reducing the idle time of the machines and minimizing the total completion time of the last job on the last machine comparatively. Finally, the result of the best scheduling algorithm is tested or validated with the genetic algorithm by integrating it with visual studio 2017 software. This was to check whether the proposed algorithms can solve the problems, even the problem size increased and arrived with a solution except for its longer time requirement for manipulation. The proposed algorithms can solve whatever type of flow shop scheduling problem might face researchers.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this paper the primary focus is on comparing four well-established heuristic algorithms in the literature to improve the productivity of the case company Almeda Textile PLC With the help of job scheduling. In the course of actions, the researchers employed different scheduling heuristics algorithms such as NEH, Palmer\u0026rsquo;s, CDS, EDD, and finally, the performance of these heuristics algorithms has validated with the Genetic Algorithm integrated with visual studio 2017. Prior to start the proposed heuristics scheduling algorithms the existing scheduling rule of the case company was calculated and obtained 48.92 minute and 51.1-minute make-span and idle time respectively. After the analysis conducted by the proposed heuristics algorithms, the most optimal sequence (\u003cb\u003eJ3-J2-J4-J1)\u003c/b\u003e of jobs obtained by Nawaz Enscore Ham (NEH) heuristic scheduling algorithm with 42.43 and 38.27-minute make-span and idle times respectively. This result revealed an idle time of about 13.36 minutes wasted because of poor scheduling of jobs for the case company. In this research we attempted different scheduling algorithms and their effect in minimizing the make-span and reducing idle time of machines by that the most scheduling algorithm with good effect in reducing the idle time was taken as the best with a near to optimal sequence of jobs.\u003c/p\u003e \u003cp\u003eAs the validity test of this research show, whatever problem size might happen heuristics algorithms such as NEH and CDS are good in providing a good result of job schedule with minimized makes-span and reduced idle time machines. NEH\u0026rsquo;s result revealed that on the last machine there was about 23.3% idle time reduction than the existing schedule.\u003c/p\u003e \u003cp\u003eResource utilization was another focus of this research. As the analysis indicated the proposed scheduling algorithm, provide about 3.6% resource utilization improvement than the existing scheduling algorithm of the case company.\u003c/p\u003e \u003cp\u003eIn general, by reducing the idle time of machines and increasing the resource utilization so that to improve the productivity of the case company with the considered jobs and machines has addressed and shown about 17.2% of productivity change with the proposed sequence or schedule of jobs.\u003c/p\u003e \u003cp\u003eIn the near future the application of Genetic algorithm to test the performance of the proposed heuristics algorithms for more than 4 jobs *5 machines will be considered to show their computational efficiency for scheduling problems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing financial and nonfinancial interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo financial support was received from any organization for the work presented in this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor ContributionsTibebu Alene Asresa: write the first draft of the manuscripts, data collection, curation, analysis, conceptualization, and editing.Dr. Bereket Haile Woldegiorgis: , proofreading and revising the final manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRao KR, Tesfahunegn SZ (2015) Performance measurement of manufacturing industries in ethiopia-an analytical study. \u003cem\u003eJournal of Poverty, Investment and Development, 7\u003c/em\u003e(2422)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo ZX, Wong W, Keung L, Yung-Sun S, Fan JT, Chan SF (2006) Mathematical model and genetic optimization for the job shop scheduling problem in a mixed-and multi-product assembly environment: A case study based on the apparel industry. Comput Ind Eng 50(3):202\u0026ndash;219\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen T, Guo, Meiting, Lin L, Miao Y (2015) A local search algorithm for the flow shop scheduling problem with release dates. \u003cem\u003eDiscrete Dynamics in Nature and Society, 2015\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmstr\u0026ouml;m P, Sundkvist R (2011) \u003cem\u003eProfitability analysis based on production improvements in the electronics manufacturing industry.\u003c/em\u003e Paper presented at the Proceedings of the 4th Swedish Production Symposium, SPS11, Lund, May 3\u0026ndash;5, 2011\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SH, Lee BH, Park T-H (2000) A hierarchical method to improve the productivity of multi\u0026ndash;head surface mounting machines. Intell Autom Soft Comput 6(4):291\u0026ndash;301\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdhuno T, Berhane and (2017) Improving the productivity of the sewing section through line balancing techniques: A case study of almeda garment factory. Int J Sciences: Basic Appl Res (IJSBAR) 318\u0026ndash;328\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhurana (2018) An overview of textile and apparel business advances in ethiopia. Res J Text Appar, 212\u0026ndash;223\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitaw D, Matebu A (2010) Competitiveness for ethiopian textile and garment industries: A way forward\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNabi F, Mahmud R, Islam MM (2015) Improving sewing section efficiency through utilization of worker capacity by time study technique. 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Zede J 27:69\u0026ndash;80\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakma and Rayhan (2015) \u003cem\u003eA comparative analysis of heuristics for optimizing the makespan in flow shop scheduling.\u003c/em\u003e Paper presented at the International Conference on Mechanical, Industrial and Materials Engineering (ICMIME), Rajshahi, Bangladesh\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzila-Nadiah D, Nazif NA, Hafizuddin MM (2012) \u003cem\u003eAssessing the priority rules of scheduling application in job shop manufacturing company.\u003c/em\u003e Paper presented at the Proceedings of the 2012 international conference on industrial engineering and operations management\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamburowski, Kalczynski \u0026amp; (2008). An improved neh heuristic to minimize makespan in permutation flow shops. computers Oper Res, 3001\u0026ndash;3008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eleisten, framinan \u0026amp; (2003) Different initial sequences for the heuristic of nawaz, enscore and ham to minimize makespan, idletime or flowtime in the static permutation flowshop sequencing problem. Int J Prod Res, 121\u0026ndash;148\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Flow shop Scheduling, Heuristics algorithm, Make-span, Idle time, Productivity","lastPublishedDoi":"10.21203/rs.3.rs-6749907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6749907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn manufacturing industries, meeting the dynamically changing need of customers and delivery times is the key to stay at the apex of global or national competitions. Manufacturing system consists and integrates entities such as machines, jobs with different operations to be processed in the corresponding machine, input materials, human operators, and all the things that facilitate the production system of a manufacturing industry so that enabling the firm to generate good wealth and to cope the dynamically changing market demand. The problem under study is a textile garment manufacturing industry of a flow shop-manufacturing. Even, giving high priority to the first arrival jobs and seems fair to customers and jobs, however, does not consider other customer and job characteristics such as production cost, idle time, make-span, and tardiness of jobs. In this flow shop type of scheduling problem, \u0026ldquo;n\u0026rdquo; jobs considered to process on \u0026ldquo;m\u0026rdquo; machines and preemption of jobs not allowed. In addition, assumed the machines could process only one job at a time. The study conducted with the main aim of productivity improvement by minimizing the idle time of machines to control criteria or parameters such as make-span, resource utilization, and production cost for the case company by finding the most optimal sequence of jobs under the study. To carry out the study and find the best and efficient sequence of jobs heuristics algorithms such as NEH, CDS, palmers and EDD rules in the flow shop-manufacturing used, and the NEH resulted in the best sequence of jobs. In the proposed sequence of jobs with a 3.6% utilization improvement, the productivity improved by 17.2% than the existing schedule.\u003c/p\u003e","manuscriptTitle":"Productivity Improvement Using Heuristics Algorithm in Scheduling a Flow-Shop Manufacturing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 06:03:35","doi":"10.21203/rs.3.rs-6749907/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fa869d6-a3f8-4782-8749-9e064017e0f3","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-20T09:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 06:03:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6749907","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6749907","identity":"rs-6749907","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}