Mathematical model for dynamic cell formation in fast fashion apparel manufacturing stage
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Abstract
This paper presents a mathematical programming model for dynamic cell formation to minimize changeoverrelated costs (i.e., machine relocation costs and machine setup cost) and intercell material handling cost to cope with the volatile production environments in apparel manufacturing industry. The model is formulated through findings of a comprehensive literature review. Developed model is validated based on data collected from three different factories in apparel industry, manufacturing fast fashion products. A program code is developed using Lingo 16.0 software package to generate optimal cells for developed model and to determine the possible costsaving percentage when the existing layouts used in three factories are replaced by generated optimal cells. The optimal cells generated by developed mathematical model result in significant cost saving when compared with existing product layouts used in production/assembly department of selected factories in apparel industry. The developed model can be considered as effective in minimizing the considered cost terms in dynamic production environment of fast fashion apparel manufacturing industry. Findings of this paper can be used for further researches on minimizing the changeoverrelated costs in fast fashion apparel production stage.
Keywords
Dynamic cell Laborintensive Apparel Product layout Changeover Cost savingIntroduction
Fast fashion apparels are highly fashionable products with affordable prices in the midtolow range, which demands for quick response and frequent assortment changes (Vecchi and Buckley 2016; Elavia 2014; Caro and MartínezdeAlbéniz 2015; Cachon and Swinney 2011). As mentioned by Bhardwaj and Fairhurst (2009), Jovanovic et al. (2014), Memic and Minhas (2011) and Cachon and Swinney (2011), frequent fluctuation of customer demand with smaller batch quantities and, short production and distribution leadtimes, are the key characteristics of fast fashion apparels. Because of the increasing consumer demand, fast fashion segment in apparel industry has shown a rapid growth internationally during past few years (Mo 2015; Jovanovic et al. 2014; Moretta Tartaglione and Antonucci 2013; Aus 2011). More importantly, Caro and MartínezdeAlbéniz (2015) stated it as a high growth potential area of international apparel business.
In order to remain competitive in dynamic market conditions of fast fashion apparel industry, the apparel manufacturers are under immense pressure to achieve high degree of manufacturing flexibility (Caro and MartínezdeAlbéniz 2015; Jovanovic et al. 2014). Low manufacturing cost is another important aspect that determines the competitiveness of manufacturing industries (Bayram and Sahin 2016; Khannan et al. 2016). Hence, it is essential to focus on improving manufacturing flexibility while ensuring low manufacturing cost to survive under volatile market conditions.
Several authors have emphasized the need of improving layout flexibility in order to increase the manufacturing flexibility (Neumann and Fogliatto 2013; Raman et al. 2009). Incorporating flexible layouts that can accommodate dynamic production environments while ensuring minimum manufacturing cost is vital to be competitive in volatile market conditions (De Carlo et al. 2013; Hamedi et al. 2012).
Niakan et al. (2016) and Nouri (2016) suggested Dynamic Cellular Manufacturing System (DCMS) as the most suitable approach in achieving high degree of flexibility and agility to manage changes in product mix.
High degree of manufacturing flexibility can be achieved by minimizing changeover time between different products (De Carlo et al. 2013; Neumann and Fogliatto 2013; Egilmez et al. 2012). Several authors have stated that dynamic cellular layouts show promising results in minimizing changeover times of industries with volatile demand conditions (Bayram and Sahin 2016; Dalfard 2013; Asgharpour and Javadian; 2004). Hence, minimization of changeoverrelated cost has become one of the primary objectives of dynamic cellular layout designs. Furthermore, as stated by Shafigh et al. (2017) about 20–50% of the manufacturing cost is related to material handling. Minimization of material handling cost is the most prominent cost function used in available studies on mathematical programming of DCMS designs (Sakhaii et al. 2016; Moradgholi et al. 2016). A welldesigned layout can minimize manufacturing cost through effective minimization of the material handling costs (Shafigh et al. 2017; Chang et al. 2013)
According to Bayram and Sahin (2016), and Kia et al. (2013) cell formation, group layout, group scheduling and resource allocation are four basic stages of designing Cellular Manufacturing System (CMS). As the first step of CMS design, Cell Formation (CF) seeks to assign parts to their respective families and grouping the corresponding machines to relevant machine cells. A part family comprises of part types having similar manufacturing characteristics, product design features, product demand, processing requirements, etc. (Mahdavi et al. 2013; Dalfard 2013). Construction of part families and machine cells, and assignment of part families to respective machine cells is done by optimizing a selected set of performance measures such as material handling cost, machine setup cost, grouping efficacy and exceptional elements (Deep and Singh 2015; Bagheri and Bashiri 2014; Rafiei and Ghodsi 2013).
This paper addresses the first stage of CMS design under dynamic environment (i.e., CF). This paper presents a mathematical programming model developed for the Dynamic Cell Formation (DCF) that aims to generate optimal cells that can minimize the costs of machine relocation, machine setup and intercell material handling of production environment with machine reliability issues in a laborintensive apparel manufacturing industry under volatile demand conditions of fast fashion apparels. Performance of the developed model is validated based on data collected from actual production environments of three apparel manufacturing factories that are currently manufacturing fast fashion products. These factories use product layout in their production environments. Numerical results of developed model show that dynamic cellular layouts lead to significant cost saving when it is applied to volatile production environments that are currently using product layout.
Literature review
CMS design approaches
Group Technology (GT) is one of the most widely used approaches in handling shorter product life cycles and high variety of products with minimum manufacturing costs (Nunkaew and Phruksaphanrat 2013; Rafiei and Ghodsi 2013). GT is a manufacturing philosophy that exploits the similarities within a manufacturing system. Under GT, products with similar design and manufacturing characteristics are grouped into product families (Rajput 2007) and relevant machines that are required to process the product families are grouped into GT cells (Giri and Moulick 2016).
CMS is the corresponding feature of GT to the layout of manufacturing industries. Reduced setup time and cost required to perform setups, simplified material flows and reduced material handling, reduced workinprogress inventory, reduced throughput time and improved sequencing and scheduling on the shop floor are some of the most outstanding benefits of CMS (Nunkaew and Phruksaphanrat 2013; Modrák 2011; Hachicha et al. 2006). The main purpose of CMS is to retain benefits of high productivity in product layout and flexibility of processoriented layouts (Rajput 2007; Case and Newman 2004).
As mentioned by Bayram and Sahin (2016), Kia et al. (2013) and Mahdavi et al. (2013), designing of a CMS comprise of four stages as CF, group layout, group scheduling and resource allocation.
Cell Formation Problem (CFP) involves grouping machines and products into families based on their similarities (Rajput 2007). Routing similarities and/or processing similarities are used to generate product families. These two types of similarities are likely to occur more or less independent to each other. In other words, products that require same operation do not necessarily share similar routings. Best approach to address the CFP is combining both routing and processing similarities such that resultant product family has a set of products with similar operations and similar routes (Kumar and Moulick 2016).
Three main approaches are used to address the CFP (Kahraman 2012; Modrák 2011; Curry and Feldman 2010).
 1.
Product family identification (PFI),
 2.
Machine group identification (MGI),
 3.
Product families/machine grouping (PF/MG).
In the first approach, initially the product families are identified by using an appropriate technique. Thereafter, the machines are allocated to the respective product families. Machine group identification (MGI) approach groups the machines into cells based on routing similarities followed by assignment of product families to the formed cells. In the third approach, product family formation and machine grouping are done simultaneously. Out of these, the third approach is highlighted as optimum CF method (Kahraman 2012; Mungwattana 2000).
According to Kia et al. (2013), group layout of CMS design deals with two aspects as intercell layout and intracell layout. Intercell layout determines the location of cells with respect to each other whereas intracell layout considers machine arrangement with each cell (Mahdhavi et al. 2013). Scheduling of part families is done in third stage of CMS design (Kia et al. 2013). Resource allocation stage consists of assignment of required resources to the cells (i.e., man, material and other required tools.).
Based on the production requirements and desired design attributes, CMS can be broadly categorized into two segments as Static Cellular Manufacturing System (SCMS) and Dynamic Cellular Manufacturing System (DCMS) (Niakan et al. 2016; Khannan et al. 2016).
Designing of a SCMS is done by assuming deterministic product demand and product mix for the considered planning horizon (Hachicha et al. 2006). In other words, it is assumed that the product demand and product mix are known with certainty for the periods in considered planning horizon. In SCMS, cells that optimize the selected performance measures for all the product demand and product mix are used for the entire planning horizon considered in SCMS design (Hachicha et al. 2006).
As stated by Houshyar et al. (2014), presently the low volumehigh variety products with volatile demand and shorter leadtimes are popular in most of the industries. Optimal cells of a particular period may not be optimal for other periods due to possible variations of production requirements of different product mixes (Niakan et al. 2016; Deep and Singh 2016). According to Niakan et al. (2016), static cells are beneficial if the same product mix is manufactured for entire planning horizon or the new products are perfectly matched with existing product families being manufactured in static cells. Pillai et al. (2011), Modrák (2011) and, Marsh et al. (1997) argued that the static cells are inflexible for introduction of completely new product mix. Introduction of new products to the static cells result in deteriorate of the cell performance and eventually cause a major rearrangement of machines (Balakrishnan and Hung Cheng 2005; Chowdary et al. 2005). Furthermore, Marsh et al. (1997) argued that static cells are associated with low routing flexibility. It will directly deteriorate the layout flexibility (Neumann and Fogliatto 2013).
DCMS is introduced to overcome the drawbacks of CMS. As mentioned by Niakan et al. (2016), Deep and Singh (2016) and Mahdavi et al. (2010), DCF is done by dividing considered planning horizon into multiple planning periods. Instead of using stable product mix and demand for entire planning horizon, the dynamic cellular layouts are formed by considering possible variations in multiple periods (Niakan 2015). These variations require reconfiguration of the dynamic cells (Niakan 2015; Houshyar et al. 2014). Cell reconfigurations are minimized by considering all the possible demands in corresponding planning horizon and optimizing the selected performance measures for considered planning horizon and defined planning periods (Niakan et al. 2016; Süer et al. 2010). As stated by Niakan (2015) and Houshyar et al. (2014), layout reconfiguration of dynamic cells is done by switching of existing machines between cells, adding new machines to the cells and removing existing machines from cells.
As stated by Mungwattana (2000), four basic types of production requirement are considered in GTbased cellular layout designs. They are static, dynamic, stochastic and deterministic. Production requirement in any industry can be represented by using one or more of these types. Static production requirement assumes a constant product mix and demand for entire planning horizon. There can be either staticdeterministic production requirement or staticstochastic production requirement. In first case, the product mix and demand for entire period is exactly known at the cell formation stage. For the second one, possible product mix and demand for the period is known with certain probabilities. Similarly, dynamic cells incorporate the possible production requirements in either stochastic or deterministic nature (Balakrishnan and Hung Cheng 2005; Mungwattana 2000). In both types of product demand, dynamic cells form physical grouping of cells based on GT principles while rearranging the cells when necessary. This allows the dynamic cellular layouts to retain the flexibility through cell reconfiguration on a planned basis and to gain advantages of static cells. Excessive rearrangement of cells may significantly increase cost of machine movement and lost of production time (Kia et al. 2013). Conversely, increasing robustness for multiple demand scenarios deteriorates the cell performance due to increased material handling. Furthermore, using an inappropriate cell layout for a particular period may lead to increased reconfiguration costs in subsequent periods (Niakan 2015). Designing process of DCMS aims to obtain optimal cells by balancing these two conflicting scenarios.
Machineintensive and laborintensive manufacturing cells
As mentioned by Egilmez et al. (2012), manufacturing cells can be either machineintensive or laborintensive. Limited operator involvement in operations is the key characteristic of machineintensive cells. Operators load the raw material or halfassembled product to the machine, control quality and unload the output from machine.
In laborintensive cells, complete operator involvement in operations is essential and the output and performance of operation significantly fluctuate based on operatorrelated factors (Zhao and Yang 2011). As mentioned by Süer and Dagli (2005), laborintensive cells consist of lightweight small machines and equipments that are easy to relocate. Utilization of the existing machines is encouraged in laborintensive manufacturing cells (Süer and Dagli 2005).
Production/assembly department of apparel industry is known as highly laborintensive (Islam et al. 2015; Guo et al. 2015). According to Zhao and Yang (2011) and Mittlehauser (1997), machines used in production department of apparel manufacturing factories can be categorized into two types based on the level of operator intervention to complete an operation.
Fully automatic machines Operators load the raw material or halfassembled product to the machine, monitor the quality and unload output from machine. Machines can be preprogrammed to operate automatically with little intervention of the operator. Examples for such machines are buttonhole, bartack and pocket sewer machines.
Semiautomatic machines Operator should continually attend to control the machine to process a particular operation.
DCMS design with laborrelated issues
Nonlinear integer programming model for dynamic cell formation is developed by Mahdavi et al. (2010) to address the problem of operator assignment to cells. Improving operator assignment flexibility concurrently with dynamic cell formation is the main feature of the developed model. Multiple attributes are considered in their model as; multiperiod production planning, machine duplication, dynamic system reconfiguration, machine capacity, available time of operators and operator assignment. The objective function seeks to minimize eight cost functions namely; holding cost, backorder cost, intercell material handling cost, maintenance and overhead cost of machines, machine relocation cost, salary cost, hiring cost and firing cost. Mahdavi et al. (2010) emphasized the need of considering operatorrelated factors in cell design to achieve expected benefits of cellular layouts. Niakan et al. (2016) introduced a biobjective mathematical model for dynamic cell formation by considering both machine and operator skill levels. Niakan et al. (2016) formulated and validated their model by using theoretical data sets. Sakhaii et al. (2016) developed a robust optimization approach for a mixedinteger linear programming model to obtain solutions for a DCMS with unreliable machines and a production planning problem in a simultaneous manner. Main considerations of their study are DCFP, intercell layout, operator assignment problem, unreliable machines, alternative process routes and production planning decisions. Objective function of the mathematical model developed by Sakhaii et al. (2016) sought to minimize the costs of inter and intracell material handling, operator training and hiring, machine relocation, machine breakdowns, inventory holding and backorder. The biobjective stochastic model developed by Zohrevand et al. (2016) addresses humanrelated problems in DCFP by considering labor utilization, worker overtime cost, worker hiring/layingoff, and worker cell assignment. Their model seeks to minimize the total costs of machine procurement, machine relocation, intercell moves, overtime utilization, worker hiring/layingoff, and worker moves between cells while maximizing the labor utilization. The model proposed by TavakkoliMoghaddam et al. (2011) is one of the noticeable studies on incorporating humanrelated factors in cell formation. Their model consists of cell formation problem with two conflicting objectives as; optimizing the labor allocation while maximizing the cell utilization. The developed model is solved using multiobjective particle swarm optimization.
Impact of machine breakdowns on DCMS design
 1.
Failure to maintain machines, i.e., cleaning and minor repairs,
 2.
Failure to maintain operating conditions such as temperature, speed,
 3.
Insufficient operator skills such as improper and erroneous machine handling,
 4.
Deterioration of machine parts,
 5.
Poor design of machine parts due to wrong materials and sizes.
In the presence of chronic breakdowns, operators are able to perform required operations but with reduced speed. These breakdowns are continual and may result in minor stoppages that can be repaired within a short time (Ireland and Dale 2001; Leflar 2001). Neglecting the chronic breakdowns leads to sporadic breakdowns, which are suddenly exposed and unexpected. Badiger and Laxman (2013) discussed that sporadic breakdowns can cease entire operation and it typically requires major troubleshooting to restore the machine to working condition or to replace with new machine.
Machine breakdowns restrain the machine availability when designing a DCMS (Esmailnezhad et al. 2015; Houshyar et al. 2014). Majority of the previous CMS design considered 100% machine reliability, which is practically hard to achieve (Nouri et al. 2014; Saxena and Jain 2011; Chung et al. 2011). As stated by Kannan (2011), the severity of machine reliability issues is high in CMS. Reason for that is failure of any machine/tools assigned for a particular cell will halt entire production of the manufacturing cell (Kannan 2011). According to Houshyar et al. (2014), machine breakdowns have direct influence on due dates and optimal cost of the system. Seifoddini and Djassemi (2001) stated that machine breakdowns have greater effect on productivity of entire manufacturing operations. Machine breakdowns are a crucial factor that must be incorporated in designing of CMS (Houshyar et al. 2014; Chung et al. 2011). One of the studies that incorporate variable failure rates of the machines is the model proposed by Yadollahi et al. (2014). The objective functions of their model are minimizing the purchase cost of machines, intracellular movements and the intercellular movement costs of materials while minimizing the total repair time for failed machines.
Model formulation
Mathematical model is formulated to generate optimal dynamic cells that can ensure minimum costs of machine relocation, machine setup and intercell material handling in laborintensive production environments subjected to machine breakdowns.
 i)
Moving machines between different cells,
 ii)
Moving machines between cells and setup area.
In the mathematical model development, summation of costs for above two relocations is referred as total machine relocation cost.
 (i)
Machines required for particular operations in current period are available in dynamic cells of previous period and it is needed to perform different machine settings to use them in current period.
 (ii)
Machines required for particular operations in current period are not available in dynamic cells of previous period and required machine setup activities are performed in setup area.
Intercell material handling cost occurs when a particular part requires a machine that is located outside of the cell assigned for that part type.
It is assumed that the material handling between operations is done manually without using an automated system. Laborintensive cells consist of lightweight small machines and equipments that are easy to relocate (Süer and Dagli 2005). Hence, an assumption is made as the machine movements between two locations are done by using manual trolleys. According to Heizer (2016) and Chary (1988) Method Time Measurement (MTM) system provides standard times for elements of fixed standard categories of work motions such as reach, move, turn, grasp. MTM data are widely used when determining the standard times of manual operations (Aft 2000). MTM values are used when calculating the time taken for respective material and machine movements of developed model.
Development of mathematical programming model
Assumptions
 1.
Each part type has a set of operations that must be processed based on the given operation sequence.
 2.
Product mix and respective demand for each part type are known in advance.
 3.
Each machine has a limited capacity in each period and it is expressed in minutes.
 4.
Each machine is capable of processing more than one operation.
 5.
Standard processing times for each operation and setup times for each machine setup activity are known.
 6.
All the operators and mechanics are multiskilled. Hence, no additional training is required during product changeovers.
 7.
Multiskilled operator pool is available to mitigate the effects of absenteeism.
 8.
There are no delays due to raw material supply, management failures or power failures.
 9.
Production requirement is dynamicdeterministic.
 10.
Cell reconfiguration (if any) involves machine setup activities and machine relocations between and/or within the cells.
 11.
Physical partitioning of the cells is prohibited. Furthermore, cell reconfiguration does not require modifications to the buildings. Therefore, other than the machine relocation costs, any physical reconfiguration costs (i.e., changes in lighting and ventilation systems) are not allowed.
 12.
Adequate lighting and environmental conditions required for the operations are provided.
 13.
Multiple duplicate machines of each type are available. Existing machines at the beginning of each period are utilized when developing dynamic cells. Therefore, machine procurement is excluded.
 14.
Machine availability is limited due to machine breakdowns.
 15.
Preventive maintenance activities are done outside the plant floor and it do not affect the numbers of machines available within the floor.
 16.
Intercell material handling cost does not depend on the product type.
 17.
Handling of materials in cells and machine movements are done manually.
 18.
Cost per unit time for each period is known.
Indices

\(h:{\text{Index}}\,{\text{for}}\,{\text{period}}; h = 1,2, \ldots , H\)

\(t:{\text{Index}}\,{\text{for}}\,{\text{part}}\,{\text{type}}; t = 1,2, \ldots ,T\)

\(n:{\text{Index}}\,{\text{for}}\,{\text{number}}\,{\text{of}}\,{\text{operations}}; n = 1,2, \ldots ,N\)

\(O_{t,n} :{\text{Index}}\,{\text{for}}\,{\text{operation}}\,n \,{\text{of}}\,{\text{part}}\,{\text{type}}\,t\)

\(i:{\text{Index}}\,{\text{for}}\,{\text{machine}}\,{\text{types}}; i,i^{\prime} = 1,2, \ldots , I\)

\(j:{\text{Index}}\,{\text{for}}\,{\text{machine}}\,{\text{number}}\,{\text{in}}\,{\text{each}}\,{\text{machine}}\,{\text{type}}; j,j' = 1,2, \ldots , J\)

\(m_{i,j} :{\text{Index}}\,{\text{for}}\,j{\text{th}}\, {\text{machine}}\, {\text{of}}\,{\text{machine}}\, {\text{type}}\, i\)

\(l:{\text{Index}}\,{\text{for}}\,{\text{machine}}\,{\text{setting}}; l = 1,2, \ldots ,L\)

\(k:{\text{Index }}\,{\text{for}}\,{\text{cells}}; k,k^{\prime} = 1,2, \ldots \ldots , K\)
Input parameters

\(T:{\text{Number of part types in planning horizon}}\)

\(N:{\text{Number of operations in each part type}}\)

\(I:{\text{Number}}\,{\text{of}}\,{\text{available}}\,{\text{machine}}\,{\text{types}}\)

\(J:{\text{Number}}\,{\text{of}}\,{\text{machines}}\,{\text{available}}\,{\text{from}}\,{\text{each}}\,{\text{machine}}\,{\text{type}}\)

\(H:{\text{Number }}\,{\text{of}}\,{\text{periods}}\,{\text{in}}\,{\text{planning}}\,{\text{horizon}}\)

\(L:{\text{Number}}\,{\text{of}}\,{\text{available}}\,{\text{machine }}\,{\text{settings}}\)

\(D_{t,h} : {\text{Demand}}\,{\text{quantity}}\,{\text{for}}\,{\text{part}}\,{\text{type}}\, t\, {\text{during}}\,{\text{period}}\,h\)

\(\eta_{{O_{t,n} ,m_{i,j} }} :{\text{Standard}}\,{\text{processing}}\,{\text{time}}\,{\text{for}}\,{\text{operation}}\,n\,{\text{of}}\,{\text{part}}\,{\text{type}}\, t\,{\text{on}}\,{\text{machine}}\,m_{i,j}\)

\(U_{{m_{i,j} }} :{\text{Time}}\,{\text{taken}}\,{\text{to}}\,{\text{load}}\,{\text{and}}\,{\text{unload}}\,{\text{machine}}\,m_{i,j} \, {\text{to/from }}\,{\text{the}}\, {\text{trolley}}\)

\(\gamma_{h} :{\text{Cost}}\,{\text{per}}\,{\text{minute}}\,{\text{value}}\,{\text{during}}\,{\text{period}}\,h\)

\(\varphi_{{l,m_{i,j} }} :{\text{Time}}\,{\text{to}}\,{\text{perform }}\,{\text{machine }}\,{\text{setting}}\, l\, {\text{on}}\, {\text{machine}}, m_{i,j}\)

\(\vartheta_{{h,m_{i,j} }} :{\text{Total}}\,{\text{number}}\,{\text{of}}\,{\text{machines}}\,{\text{in}}\,{\text{plant}}\,{\text{floor}}\,{\text{during}}\,{\text{period}}\, h\)

\(\lambda : {\text{Number}}\,{\text{of}}\,{\text{turning}}\,{\text{motions}}\,{\text{when}}\,{\text{moving}}\,{\text{materials}}\,{\text{between}}\,{\text{cells}}\)

\(\tau_{{m_{i,j} }} : {\text{Non  negative}}\,{\text{random }}\,{\text{number }}\,{\text{for}}\, {\text{corrective}}\,{\text{repair}}\,{\text{time }}\,{\text{of}}\,{\text{machine}}\,m_{ij}\)

\(dF_{{t,m_{i,j} }} \left( h \right): {\text{Breakdown }}\,{\text{rate}}\,{\text{of}}\,{\text{machine}}\, m_{i,j} \,{\text{when}}\,{\text{processing}}\,{\text{part}}\,{\text{type}}\, t\,{\text{during}}\,{\text{period}}\, h\)

\(\varOmega_{{h,m_{i,j} }} : {\text{Capacity}}\,{\text{of}}\,{\text{machine}}\, m_{i,j} \, {\text{during}}\, {\text{period}}\, h \,\left( {{\text{given}}\,{\text{in}}\,{\text{minutes}}} \right)\)

\(\omega_{{m_{i,j} ,l,O_{t,n} }} = \left\{ {\begin{array}{*{20}l} 1 \hfill & {{\text{if}}\,{\text{operation}}\, n\, {\text{of}}\, {\text{part}}\, {\text{type}}\, t\, {\text{requires}}\, {\text{machine}}\, m_{i,j} \,{\text{with}}\, {\text{setting}}\, l} \hfill \\ 0 \hfill & {\text{otherwise}} \hfill \\ \end{array} } \right.\)
Decision variables
Integer variables
\(f_{{t,m_{i,j} ,m_{{i^{',} j^{\prime}}} }} : {\text{Number }}\,{\text{of}}\, {\text{times}}\,{\text{that}}\,{\text{an}}\,{\text{operation}}\,{\text{at}}\,{\text{machine}}\,m_{i,j} \, {\text{immediately}}\,{\text{follows}}\,{\text{an}}\,{\text{operation}}\,{\text{at}}\,{\text{machine}}\,m_{{i^{\prime},j^{'} }} \,{\text{or}}\,{\text{vice}}\,{\text{versa}}\)
Binary variables
 $$\delta_{{h,m_{i,j} }} = \left\{ {\begin{array}{*{20}l} 1 \hfill & { {\text{if}}\, {\text{machine}}\,m_{i,j} \, {\text{is}}\,{\text{at}}\,{\text{machine}}\,{\text{set}}{}{\text{up}}\,{\text{area}}\,{\text{during}}\,{\text{period}}\, h} \hfill \\ 0 \hfill & {\text{otherwise}} \hfill \\ \end{array} } \right.$$$$b_{{k,h,m_{i,j} }} = \left\{ {\begin{array}{*{20}l} 1 \hfill & {{\text{if}}\,{\text{machine}}\, m_{i,j} \,{\text{is}}\,{\text{in}}\,{\text{cell}}\, k\, {\text{during}}\,{\text{period}}\, h} \hfill \\ 0 \hfill & {\text{otherwise}} \hfill \\ \end{array} } \right.$$$$e_{{l,h,m_{i,j} }} = \left\{ {\begin{array}{*{20}l} 1 \hfill & {{\text{if}}\, {\text{machine}}\, m_{i,j} \,{\text{is}}\,{\text{with}}\,{\text{setting}}\, l\, {\text{during}}\,{\text{period}}\,h} \hfill \\ 0 \hfill & {\text{otherwise}} \hfill \\ \end{array} } \right.$$$$\mu_{{m_{i,j} ,O_{t,n} }} = \left\{ {\begin{array}{*{20}l} 1 \hfill & {{\text{if}}\,{\text{machine}}\,m_{i,j} \,{\text{is}}\,{\text{required }}\,{\text{for}}\,{\text{operation}}\,n\,{\text{of}}\,{\text{part}}\,{\text{type}}\,t} \hfill \\ 0 \hfill & {\text{otherwise}} \hfill \\ \end{array} } \right.$$$$\theta_{t,k,h} = \left\{ {\begin{array}{*{20}l} 1 \hfill & {{\text{if}}\,{\text{part}}\,{\text{type}}\,t\,{\text{is}}\,{\text{assigned}}\,{\text{to}}\,{\text{cell}}\,k\,{\text{during}}\,{\text{period}}\,h} \hfill \\ 0 \hfill & {\text{otherwise}} \hfill \\ \end{array} } \right.$$
Mathematical model

Loading machine to the manually operated trolley

Transporting machine to required locations

Unloading machine from manually operated trolley
Mital et al. (2017) and Karger and Bayha (1987) stated that walking time per foot value when transporting machines using manually operated trolleys in obstructed paths is 17 TMU (Time Measurement Unit) or 0.0102 min as per MTM systems. Total machine relocation cost for considered planning periods is calculated by Eq. (1.1). Equation (1.2) calculates the machine relocation cost between cells, whereas machine relocation cost between cells and setup area is calculated by Eq. (1.3).
Specific setup activities must be performed when two operations can be processed at same machine but with different machine settings in consecutive periods. Total machine setup time when converting from one machine setting to another is used to calculate machine setup cost for each machine. If the machine requires same setting for two consecutive periods, no setup activity for such machines is performed. Machine setup cost for considered planning periods is calculated as given in Eq. (1.4).
Operators’ walking between cells will possibly restrict due to movement of other operators and machines. As stated by Mital et al. (2017) and Karger and Bayha (1987), walking time per foot is 17.0 TMU (0.0102 min) for obstructed paths and 37.2 TMU (0.02232 min) per turn. Intercell material handling cost is calculated as given in Eq. (1.5).
Equation (2) determines the number of times that an operation at machine m_{i,j} immediately follows an operation at machine \(m_{{i^{\prime},j^{\prime}}}\).
Equation (3) guarantees that the total number of machines in plant floor should be greater than or equal to summation of number of machines in cells and setup area for a particular period. It prevents additional machine procurement when generating dynamic cells.
In a dynamic production environment, it is possible to have fluctuations of product demand during different periods. Workload for the production environment must be balanced among the cells to prevent possible complications arise due to unbalanced workload. One of the possible issues due to unbalanced workload is operators assigned to different cells may get different workloads and thereby have different incentive ceilings. It may result in operator frustration due to feel of unfairness. Developed model considers three main approaches of cell workload balancing based on possible demand fluctuations. If the product demand for particular period is significantly lower than other periods of considered planning horizon, two approaches are considered to balance the cell workload. First approach is reducing number of operating cells by disintegrating the existing cells from previous period and forming minimum number of cells in current period. Since the developed model assumes existing machines are used to generate dynamic cells, first approach will lead to machine idling in the particular period. Second approach is to operate with reduced workload that is equally distributed among available machines of the period. This will lead to underutilization of cells. If the workload for particular period is not less than other periods, third approach is used to balance the workload among optimum the number of cells, while ensuring maximum resource utilization.
Workload balancing constraint, Eq. (4) is formulated to address those three approaches. By using Eq. (4), it is possible to customize the workload balancing among cells as per the desired approach. The factor q ∊ [0, 1] where, {q = 0 ≤ q ≤ 1} is used to determine the extent of workload balance between the dynamic cells. Setting q ≈ 0 while simultaneously reducing the number of operating cells (K), corresponds to the first approach used when total workload leads to underutilized resources. Second approach can be satisfied by setting q ≈ 0 with unchanged number of cells. Third approach considers q ≈ 1 with equally distributed workload while operating optimal number of cells with maximum machine utilization.
The part processing on machines is limited by available machine capacity for a particular period. In ideal situation, the total machine capacity, i.e., shift operating time can be utilized for part processing. Practically, machine capacities are limited due to possible machine breakdowns and setup activities. Using shift time as the available machine capacity is erroneous in this situation. Constraint given in Eq. (5) limits the part processing capability of all machine types based on total machine capacity available for individual machine types.
In case of laborintensive manufacturing industries, simultaneous processing of multiple different part types within a single cell will lead to forgetting effect, complicated supervision and increased machine stoppages due to variable machine settings. Hence, the maximum number of part types assigned to a single cell is limited to one at a time by using Eq. (6).
Equations (7) and (8) are used to define integer and binary variables.
Linearization of the proposed model
The developed model is nonlinear due to the terms (1.2), (1.3), (1.4) and (1.5) of the objective function, the constraints Eq. (2), and Eq. (5).
Solution approach
The cell formation problem is considered as NPhard (nondeterministic polynomial hard) combinatorial optimization problem due to solution complexity (Bayram and Sahin 2016; Rodriguez Leon et al. 2013). Due to solution complexity, manual computation to obtain solutions may produce erroneous results.
LINGO, CPLEX and GAMS are the most commonly used software packages to obtain optimal solutions for mathematical programming models (Agrawal et al. 2015; Esmailnezhad et al. 2015; Anbumalar and Raja Chandra Sekar 2015; Pinheiro et al. 2016; Azadeh et al. 2015; Kasimbeyli et al. 2010).
The developed model is solved by generating a program code using Lingo 16.0 software package.
Model evaluation and validation
Detailed description of the factories selected for the evaluation and validation of the developed model
Case studies on three different apparel manufacturing factories were selected for the evaluation and validation of the developed model. The program code generated on Lingo 16.0 software package is used to identify the optimal dynamic cells for the collected data from factories. These factories are referred as Factory 1, 2 and 3. Data collection was done in production/assembly department.
Semiautomatic sewing machines are used in majority of the apparel manufacturing plants for past few decades (Zhao and Yang 2011). Therefore, operator should be continually attended to control the machine to process a particular operation. According to the analysis done by Zhao and Yang (2011) and Moll et al. (2009), over 90% of the operations in production department of apparel industry is done by using semiautomatic machines. Similar situation is observed in the selected factories for case studies. Percentage of semiautomatic machines used in production department of factory 1, 2 and 3 are 95.7, 92.0 and 98.1, respectively. All the selected factories are currently using product layouts with machine sharing in their production environments.
Model validation procedure
Number of part types and machine types used for model validation
Factory  Number of part types  Number of machine types 

1  21  13 
2  11  15 
3  18  12 
Initially, the developed model was evaluated by using data collected from Factory 1. According to the initial evaluation based on Factory 1, the developed model resulted in 31.12% of total costs saving for the considered three cost terms. After the initial evaluation the model was validated by using data collected from Factory 2 and 3.
Numerical example
Outputs of the developed system are presented by using a numerical example by considering data collected from factory 2 for 11 part types with 15 machine types. Input data used for the numerical example are given in “Appendix A”.
Resultant part families and corresponding part types
Part family  Part type (t) 

1  2, 5, 4 
2  6, 10, 1, 3 
3  8, 7, 9 
Part types and their respective dynamic cells with number of machines of each machine type
t  k  h  Number of machines of type i  

1  2  3  4  5  6  7  8  9  10  11  12  13  
2  1  1  1  3  1  4  1  1  2  0  0  0  0  0  0 
5  2  2  1  3  1  4  2  0  2  0  0  0  0  0  0 
4  3  1  1  3  1  0  3  3  2  0  0  0  0  0  0 
6  4  1  2  1  0  3  2  1  2  3  2  0  0  0  0 
10  5  1  1  2  0  2  1  1  2  2  2  0  0  0  1 
1  6  2  1  1  1  2  3  1  2  2  2  0  0  0  0 
3  7  2  2  3  1  3  0  2  3  1  1  0  0  0  0 
8  8  1  1  0  1  1  2  1  2  0  1  1  2  2  1 
7  9  2  0  1  0  0  3  4  2  1  1  0  1  1  1 
9  10  2  2  1  1  0  1  2  1  0  0  1  1  2  0 
Assigned machines of each machine type to the respective dynamic cell
k  Assigned machines of each machine type (m_{i,j})  

1  2  3  4  5  6  7  8  9  10  11  12  13  
1  1  9  11  1, 5, 6, 11  1  15  3, 4  0  0  0  0  0  0 
2  12  26, 10, 16  12  2, 17, 28, 14  16, 14  0  2, 5  0  0  0  0  0  0 
3  18, 13  13, 8, 6  7  0  10, 11, 13  2, 3, 5  1, 8  0  0  0  0  0  0 
4  2  1  0  12, 15, 18  2, 3  6  16, 6  13, 10, 1  2, 6  0  0  0  0 
5  10  21, 22  0  16, 20  9  8  12, 7  18, 21  16, 10  0  0  0  4 
6  11, 20  11  5  25, 19  7, 8, 12  7  14, 18  11, 8  4, 9  0  0  0  0 
7  23  14, 12, 23  18  3, 7, 10  0  12, 16  11, 15, 16  2  1  0  0  0  0 
8  16  0  2  9  26, 4  9  13, 22  0  10  2  12, 5  2, 5  1 
9  0  2  0  0  5, 6  10, 15, 17, 18  20, 10  14  8  0  1  3  2 
10  21, 17  4  13  0  18  2  17  0  0  18  14  22, 1  0 
Results and discussion
Costsaving percentages of selected factories
Factory  1  2  3 

Total machine relocation (%)  30.29  39.87  56.05 
Machine setup (%)  34.10  23.49  29.61 
Intercell material handling (%)  28.41  47.66  37.48 
Total cost of the objective function (%)  31.12  34.60  47.14 
Minimized costs of manufacturing including changeovers and shorter manufacturing leadtimes are essential to remain competitive in fast fashion apparel industry. Several researchers have addressed the issues related with supply chain management and retailing decisions of fast fashion apparel products in order to achieve the demanded shorter leadtimes (Sabet et al. 2017; Orcao and Pérez 2014; Shen 2014; Zhelyazkov 2011; Zhenxiang and Lijie 2011; Mihm 2010). As the fast fashion apparel segment is introduced recently, there exists a significant gap in the available literature on production layout systems applicable for fast fashion orders (Kentli et al. 2013).
Lago et al. (2013) and Johnson (2003) stated that manufacturing leadtime can be drastically reduced by decreasing changeover time between different products. Positive values of costsaving percentage (Table 5) imply that the dynamic cells generated from developed model are capable of improving the current layout system with respect to the considered cost terms. It is possible to conclude the validity of developed model in minimizing the considered cost terms for fast fashion apparel products manufactured in dynamic production environment of laborintensive apparel industry. Hence, the developed model can be used to address the prevailing gap of literature on a layout system appropriate for fast fashion products.
As stated by Malakooti (2014), product layout or assembly line layout is suitable for products with high volume and low product variety. Kumar and Suresh (2006) and, Nunkaew and Phruksaphanrat (2013) mentioned that two of the key problems of product layout are high cost of layout reconfiguration and lack of flexibility. According to the results given in Table 5, the optimal dynamic cells generated from developed model can surpass the product layouts in minimizing the considered cost terms. Therefore, it is encouraged to use the developed model to mitigate the drawbacks of product layout in dynamic production environment.
Future research directions
According to Bayram and Sahin (2016), Kia et al. (2013) and Mahdavi et al. (2013), cell formation, group layout, group scheduling and resource allocation are four basic stages of designing CMS. This paper is focused on first stage of CMS design and the developed model can be extended to remaining three stages.
The developed model is tested for three selected factories in apparel industry. It is expected to validate the developed model for other laborintensive manufacturing industries in future.
In the developed model, it is assumed that all the operators and mechanics in production environment are multiskilled and processing time of each operation is a predefined standard value. As stated by Badiru (2013) and, Mir and Rezaeian (2016), the distribution of skill levels, degree of workforce crosstraining, impact of individual operators’ learning and forgetting characteristics, motivational issues and attitudes, absenteeism rates, operator turnover rates, frequency of product revisions, and workforce assignment patterns are some of the important factors that determine the performance of the system. The present research can be extended by considering such operatorrelated issues.
Forghani et al. (2013) and Suresh and Kay (2012) emphasized that the maximum benefits of cell layout are only achievable by incorporating production control, process planning, wage payment, accounting, purchasing, material handling systems and determining staff level. As stated by Duncan (2011), significant reduction of changeover time can be achieved by scheduling of similar products to cells that are processing same product families. As a future research direction, it is possible to consider these factors when designing dynamic cells for volatile product demand and product mix.
References
 Aft LS (2000) Work measurement and methods improvement, vol 9. Wiley, HobokenGoogle Scholar
 Agrawal AK, Bhardwaj P, Kumar R, Sharma S (2015) Particle swarm optimization for natural grouping in context of group technology application. In: 2015 international conference on IEEE industrial engineering and operations management (IEOM), pp 1–8Google Scholar
 Anbumalar V, Raja Chandra Sekar M (2015) Methods for solving cell formation, static layout and dynamic layout cellular manufacturing system problems: a review. Asian J Sci Technol 6(12):2107–2112Google Scholar
 Asgharpour MJ, Javadian N (2004) Solving a stochastic cellular manufacturing model by using genetic algorithms. Int J Eng Trans A 17:145–156zbMATHGoogle Scholar
 Aus R (2011) Trash to trend – using upcycling in fashion design. Retrieved from: http://vana.artun.ee/popFile.php?id=2269
 Azadeh A, Moghaddam M, NazariDoust B, Jalalvand F (2015) Fuzzy and stochastic mathematical programming for optimisation of cell formation problems in random and uncertain states. Int J Oper Res 22(2):129–147MathSciNetzbMATHGoogle Scholar
 Badiger S, Laxman R (2013) Total quality management and organisation development. Int J Bus Manag Invent 2(7):34–37Google Scholar
 Badiru AB (2013) Handbook of industrial and systems engineering. CRC Press, Boca RatonGoogle Scholar
 Bagheri M, Bashiri M (2014) A new mathematical model towards the integration of cell formation with operator assignment and intercell layout problems in a dynamic environment. Appl Math Model 38(4):1237–1254MathSciNetzbMATHGoogle Scholar
 Balakrishnan J, Hung Cheng C (2005) Dynamic cellular manufacturing under multiperiod planning horizons. J Manuf Technol Manag 16(5):516–530Google Scholar
 Bayram H, Şahin R (2016) A comprehensive mathematical model for dynamic cellular manufacturing system design and Linear Programming embedded hybrid solution techniques. Comput Ind Eng 91:10–29Google Scholar
 Bhardwaj V, Fairhurst A (2009) Fast fashion: response to changes in the fashion industry. Int Rev Retail Distrib Consum Res 20(1):165–173Google Scholar
 Cachon GP, Swinney R (2011) The value of fast fashion: quick response, enhanced design, and strategic consumer behavior. Manag Sci 57(4):778–795zbMATHGoogle Scholar
 Caro F, MartínezdeAlbéniz V (2015) Fast fashion: business model overview and research opportunities. In: Agrawal N, Smith S (eds) Retail supply chain management. Springer, Boston, pp 237–264Google Scholar
 Case K, Newman ST (eds) (2004) Advances in manufacturing technology VIII: proceedings of the 10th national conference on manufacturing research. CRC Press, Boca RatonGoogle Scholar
 Chang CC, Wu TH, Wu CW (2013) An efficient approach to determine cell formation, cell layout and intracellular machine sequence in cellular manufacturing systems. Comput Ind Eng 66(2):438–450Google Scholar
 Chary SN (1988) Production and operations management. Tata McGrawHill, New YorkGoogle Scholar
 Cheng TC, Podolsky S (1996) Justintime manufacturing: an introduction. Springer, BerlinGoogle Scholar
 Chowdary BV, Slomp J, Suresh NC (2005) A new concept of virtual cellular manufacturing. West Indian J Eng 28(1):45–60Google Scholar
 Chung SH, Wu TH, Chang CC (2011) An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations. Comput Ind Eng 60(1):7–15Google Scholar
 Curry GL, Feldman RM (2010) Manufacturing systems modeling and analysis. Springer, Berlin, pp 117–128Google Scholar
 Dalfard VM (2013) New mathematical model for problem of dynamic cell formation based on number and average length of intra and intercellular movements. Appl Math Model 37(4):1884–1896MathSciNetzbMATHGoogle Scholar
 De Carlo F, Arleo MA, Borgia O, Tucci M (2013) Layout design for a low capacity manufacturing line: a case study. Int J Eng Bus Manag 5:35Google Scholar
 Deep K, Singh PK (2015) Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm. J Manuf Syst 35:155–163Google Scholar
 Deep K, Singh PK (2016) Dynamic cellular manufacturing system design considering alternative routing and part operation tradeoff using simulated annealing based genetic algorithm. Sādhanā 41(9):1063–1079MathSciNetzbMATHGoogle Scholar
 Duncan WP (2011) Methods for reducing changeover times through scheduling. University of Rhode Island, KingstonGoogle Scholar
 Egilmez G, Süer GA, Huang J (2012) Stochastic cellular manufacturing system design subject to maximum acceptable risk level. Comput Ind Eng 63(4):842–854Google Scholar
 Elavia S (2014) How the lack of copyright protections for fashion designs affects innovation in the fashion Industry. Senior Theses, Trinity College, HartfordGoogle Scholar
 Esmailnezhad B, Fattahi P, Kheirkhah AS (2015) A stochastic model for the cell formation problem considering machine reliability. J Ind Eng Int 11(3):375–389Google Scholar
 Forghani K, Mohammadi M, Ghezavati V (2013) Designing robust layout in cellular manufacturing systems with uncertain demands. Int J Ind Eng Comput 4(2):215–226Google Scholar
 Giri PK, Moulick SK (2016) Comparison of cell formation techniques in cellular manufacturing using three cell formation algorithms. Int J Eng Res Appl 6(1):98–101Google Scholar
 Guo ZX, Ngai EWT, Yang C, Liang X (2015) An RFIDbased intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment. Int J Prod Econ 159:16–28Google Scholar
 Hachicha W, Masmoudi F, Haddar M (2006) Principal component analysis model for machinepart cell formation problem in group technology. In: The international conference on advances in mechanical engineering and mechanics (ICAMEM 2006)Google Scholar
 Hamedi M, Esmaeilian GR, Ismail N, Ariffin MKA (2012) Capabilitybased virtual cellular manufacturing systems formation in dualresource constrained settings using Tabu Search. Comput Ind Eng 62(4):953–971Google Scholar
 Heizer J (2016) Operations management, 11/e. Pearson Education India, NoidaGoogle Scholar
 Houshyar AN, Leman Z, Moghadam HP, Ariffin MKAM, Ismail N, Iranmanesh H (2014) Literature review on dynamic cellular manufacturing system. In: IOP conference series: materials science and engineering, vol 58, No. 1, p. 012016. IOP PublishingGoogle Scholar
 Ireland F, Dale BG (2001) A study of total productive maintenance implementation. J Qual Maint Eng 7(3):183–192Google Scholar
 Islam I, Rahman MF, LeHew ML (2015) Predicting total assembling time for different apparel products utilizing learning curve and time study approaches: a comparative case study. In: International Textile and Apparel Association (ITAA) Annual Conference Proceedings 110Google Scholar
 Johnson DJ (2003) A framework for reducing manufacturing throughput time. J Manuf Syst 22(4):283MathSciNetGoogle Scholar
 Jovanovic VM, Mann M, Katsioloudis PJ, Dickerson DL (2014) Enabling multidisciplinary perspective in student design project: fast fashion and sustainable manufacturing systems. Paper presented at 2014 ASEE annual conference & exposition, Indianapolis, Indiana. https://peer.asee.org/20370
 Kahraman C (2012) Computational intelligence systems in industrial engineering: with recent theory and applications. Springer, Berlin, pp 505–508Google Scholar
 Kannan B (2011) Reliability/availability of manufacturing cells and transfer lines (Doctoral dissertation, Auburn University)Google Scholar
 Karger DW, Bayha FH (1987) Engineered work measurement: the principles, techniques, and data of methodstime measurement background and foundations of work measurement and methodstime measurement, plus other related material. Industrial Press Inc., New YorkGoogle Scholar
 Kasimbeyli R, Dincer C, Ozpeynirci S (2010) Subgradient based solution approach for cell formation problem with alternative routes. In: International conference 24th mini EURO conference “continuous optimization and informationbased technologies in the financial sector” (MEC EurOPT 2010), June 23–26, 2010, Izmir, TurkeyGoogle Scholar
 Kentli A, Dal V, Alkaya AF (2013) Minimizing machine changeover time in product line in an apparel industry. J Text Appar/Tekst Konfeks 23(2):159–167Google Scholar
 Khannan MSA, Maruf A, Wangsaputra R, Sutrisno S, Wibawa T, (2016) Cellular manufacturing system with dynamic lot size material handling. In: IOP conference series: materials science and engineering, vol 114, No 1, p 012144. IOP PublishingGoogle Scholar
 Kia R, Shirazi H, Javadian N, TavakkoliMoghaddam R (2013) A multiobjective model for designing a group layout of a dynamic cellular manufacturing system. J Ind Eng Int 9(1):8Google Scholar
 Kumar SA, Suresh N (2006) Production and operations management. New Age International, New DelhiGoogle Scholar
 Kumar PG, Moulick SK (2016) Comparison of cell formation techniques in cellular manufacturing using three cell formation algorithms. Int J Eng Res Appl 6(1):98–101 (Part  5) Google Scholar
 Lago A, MartinezdeAlbeniz V, Moscoso P, Vall A (2013) The role of quick response in accelerating sales of fashion goods. Retrieved from: http://webprofesores.iese.edu/valbeniz/RoleQRFashion_web.pdf. Accessed 27 Feb 2014
 Leflar J (2001) Practical TPM. Productivity Press, PortlandGoogle Scholar
 Mahdavi I, Aalaei A, Paydar MM, Solimanpur M (2010) Designing a mathematical model for dynamic cellular manufacturing systems considering production planning and worker assignment. Comput Math Appl 60(4):1014–1025MathSciNetzbMATHGoogle Scholar
 Mahdavi I, Teymourian E, Baher NT, Kayvanfar V (2013) An integrated model for solving cell formation and cell layout problem simultaneously considering new situations. J Manuf Syst 32(4):655–663Google Scholar
 Malakooti B (2014) Operations and production systems with multiple objectives. Wiley, HobokenGoogle Scholar
 Marsh RF, Meredith JR, McCutcheon DM (1997) The life cycle of manufacturing cells. Int J Oper Prod Manag 17(12):1167–1182Google Scholar
 Memic M, Minhas FN (2011) The fast fashion phenomenon: luxury fashion brands responding to fast fashion. The Swedish School of Textiles AprilGoogle Scholar
 Mihm B (2010) Fast fashion in a flat world: global sourcing strategies. The International Business & Economics Research Journal 9(6):55Google Scholar
 Mir MSS, Rezaeian J (2016) A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines. Appl Soft Comput 41:488–504Google Scholar
 Mital A, Desai A, Mital A (2017) Fundamentals of work measurement: what every engineer should know. CRC Press, Boca RatonGoogle Scholar
 Mittlehauser M (1997) Employment trends in textiles and apparel, 1973–2005. Monthly Labor Review, August, 1997, pp 24–35Google Scholar
 Mo Z (2015) Internationalization process of fast fashion retailers: evidence of H&M and Zara. Int J Bus Manag 10(3):217Google Scholar
 Modrák V (2011) Operations management research and cellular manufacturing systems: innovative methods and approaches: innovative methods and approaches. IGI Global, HersheyGoogle Scholar
 Moll P, Schütte U, Zöll K, Molfino R, Carca E, Zoppi M, Montorsi R (2009) Automated garment assembly and manufacturing simulation. In: Walter L, Kartsounis GA, Carosio S (eds) Transforming clothing production into a demanddriven, knowledgebased, hightech industry. Springer, London, pp 9–59Google Scholar
 Moradgholi M, Paydar MM, Mahdavi I, Jouzdani J (2016) A genetic algorithm for a biobjective mathematical model for dynamic virtual cell formation problem. J Ind Eng Int 12(3):343–359Google Scholar
 Moretta Tartaglione A, Antonucci E (2013) Value creation process in the fast fashion industry: towards a networking approach. In: The 2013 Naples Forum on Service. Service Dominant Logic, Networks & Systems Theory and Service Science: Integrating Three Perspectives for a New Service Agenda, p 91Google Scholar
 Mungwattana A (2000) Design of cellular manufacturing systems for dynamic and uncertain production requirements with presence of routing flexibility (Doctoral dissertation)Google Scholar
 Neumann CSR, Fogliatto FS (2013) Systematic approach to evaluating and improving the flexibility of layout in dynamic environments. Manag Prod 20(2):235–254Google Scholar
 Niakan F (2015) Design and configuration of sustainable dynamic cellular manufacturing systems (Doctoral dissertation, Lyon, INSA)Google Scholar
 Niakan F, Baboli A, Moyaux T, BottaGenoulaz V (2016) A biobjective model in sustainable dynamic cell formation problem with skillbased worker assignment. J Manuf Syst 38:46–62Google Scholar
 Nouri H (2016) Development of a comprehensive model and BFO algorithm for a dynamic cellular manufacturing system. Appl Math Model 40(2):1514–1531MathSciNetGoogle Scholar
 Nouri HA, Leman Z, Moghadam HP, Sulaiman R (2014) Literature review on machine reliability in cellular manufacturing system. Am J Appl Sci 11(12):1964–1968Google Scholar
 Nunkaew W, Phruksaphanrat B (2013) Effective fuzzy multiobjective model based on perfect grouping for manufacturing cell formation with setup cost constrained of machine duplication. Songklanakarin J Sci Technol 35(6):715–726Google Scholar
 Orcao AIE, Pérez DR (2014) Global production chains in the fast fashion sector, transports and logistics: the case of the Spanish retailer Inditex. Investigaciones Geográficas, Boletín del Instituto de Geografía 2014(85):113–127Google Scholar
 Pillai VM, Hunagund IB, Krishnan KK (2011) Design of robust layout for dynamic plant layout problems. Comput Ind Eng 61(3):813–823Google Scholar
 Pinheiro RG, Martins IC, Protti F, Ochi LS, Simonetti LG, Subramanian A (2016) On solving manufacturing cell formation via Bicluster Editing. Eur J Oper Res 254(3):769–779MathSciNetzbMATHGoogle Scholar
 Rafiei H, Ghodsi R (2013) A biobjective mathematical model toward dynamic cell formation considering labor utilization. Appl Math Model 37(4):2308–2316zbMATHGoogle Scholar
 Rajput RK (2007) A textbook of manufacturing technology: manufacturing processes. Firewall Media, New DelhiGoogle Scholar
 Rodriguez Leon J, Quiroga Méndez JE, Ortiz Pimiento NR (2013) Performance comparison between a classic particle swarm optimization and a genetic algorithm in manufacturing cell design. Dyna 80(178):29–36Google Scholar
 Sabet E, Sabet E, Yazdani N, Yazdani N, De Leeuw S, De Leeuw S (2017) Supply chain integration strategies in fast evolving industries. Int J Logist Manag 28(1):29–46Google Scholar
 Sakhaii M, TavakkoliMoghaddam R, Bagheri M, Vatani B (2016) A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Appl Math Model 40(1):169–191MathSciNetGoogle Scholar
 Saxena L, Jain P (2011) Dynamic cellular manufacturing systems design—a comprehensive model. Int J Adv Manuf Technol 53:11–34. https://doi.org/10.1007/s0017001028429 Google Scholar
 Seifoddini S, Djassemi M (2001) The effect of reliability consideration on the application of quality index. Comput Ind Eng 40:65–77. https://doi.org/10.1016/S03608352(00)000723 Google Scholar
 Shafigh F, Defersha FM, Moussa SE (2017) A linear programming embedded simulated annealing in the design of distributed layout with production planning and systems reconfiguration. Int J Adv Manuf Technol 88(1–4):1119–1140Google Scholar
 Shen B (2014) Sustainable fashion supply chain: lessons from H&M. Sustainability 6(9):6236–6249Google Scholar
 Süer GA, Dagli C (2005) Intracell manpower transfers and cell loading in laborintensive manufacturing cells. Comput Ind Eng 48(3):643–655Google Scholar
 Süer GA, Huang J, Maddisetty S (2010) Design of dedicated, shared and remainder cells in a probabilistic demand environment. Int J Prod Res 48(19):5613–5646Google Scholar
 Suresh NC, Kay JM (eds) (2012) Group technology and cellular manufacturing: a stateoftheart synthesis of research and practice. Springer, BerlinGoogle Scholar
 TavakkoliMoghaddam R et al (2011) Solving a new biobjective model for a cell formation problem considering labor allocation by multiobjective particle swarm optimization. Int J Eng Trans A Basics 24(3):249Google Scholar
 Vecchi A, Buckley C (2016) Handbook of research on global fashion management and merchandising. IGI GlobalGoogle Scholar
 Yadollahi MS et al (2014) Design a biobjective mathematical model for cellular manufacturing systems considering variable failure rate of machines. Int J Prod Res 52(24):7401–7415Google Scholar
 Zhao, L. and Yang, J. (2011) Clothing workshop production line analysis and improvement research based on IE. In: Advanced material research, vol 291–294, pp 3147–3151. Trans Tech Publications, SwitzerlandGoogle Scholar
 Zhelyazkov G (2011) Agile supply chain: Zara’s case study analysis. Design, manufacture & engineering management. Strathclyde University Glasgow, Velika Britanija, pp 2–11Google Scholar
 Zhenxiang W, Lijie Z (2011) Case study of online retailing fast fashion industry. Int J eEduc eBus eManag eLearn 1(3):195Google Scholar
 Zohrevand AM, Rafiei Hamed, Zohrevand AH (2016) Multiobjective dynamic cell formation problem: a stochastic programming approach. Comput Ind Eng 98:323–332Google Scholar
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