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MultimachineFlexibleManufacturingCellAnalysisUsingaMarkovChain-BasedApproach

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DOI:10.1109/TCPMT.2015.2394232

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IEEE TRANSACTIONS ON COMPONENTS, PACKAGING AND MANUFACTURING TECHNOLOGY, VOL. 5, NO. 3, MARCH 2015 439

Multimachine Flexible Manufacturing Cell AnalysisUsing a Markov Chain-Based Approach

Mohammad M. Hamasha, Azmi Alazzam, Sa’d Hamasha, Faisal Aqlan,Osama Almeanazel, and Mohammad T. Khasawneh

Abstract— In this paper, a stochastic model is developed toanalyze the performance of a flexible manufacturing cell (FMC).The FMC considered in this paper consists of a single conveyor, asingle robot, and one or more machine(s). The conveyor belt deliv-ers the working part to the robot, which loads it onto the machine.A Markov chain model is constructed for one-machine and two-machine FMCs, after which the model is generalized to anFMC with n machines. Most importantly, the model providesan estimate of the overall machine utilization and productionrate for the FMC under consideration and also illustrates theeffect of different operational factors on machine utilization andproduction rate. The results indicated that the overall machineutilization increases with conveyor belt and robot delivery ratesand decreases with machine rate, as expected. However, thisdecrease or the increase in the overall machine utilization issharp at low levels of each parameter (e.g., conveyor belt deliveryand robot loading), but it gradually stabilizes at higher levels ofthe parameters. Finally, the production rate increases sharply atlow levels of each parameter and gradually stabilizes at higherlevels.

Index Terms— Flexible manufacturing cell (FMC), Markovchain, multiple machine, single machine.

I. INTRODUCTION

AFLEXIBLE manufacturing system (FMS) can be definedas a collection of different numerically controlled flexible

machines, with each having the ability to perform multiplefunctions and produce a variety of products [1]. Furthermore,an FMS consists of an automated handling system to man-age product flow and a centralized computer that controlsthe entire functions of the FMS. The environment of anFMS is dynamic in nature and it changes with respect totime [2]. Moreover, FMSs are very useful when there is a

Manuscript received August 28, 2014; revised December 22, 2014; acceptedJanuary 11, 2015. Date of publication February 11, 2015; date of currentversion March 5, 2015. Recommended for publication by Associate EditorI. Fidan upon evaluation of reviewers’ comments.

M. M. Hamasha is with the University of Business and Technology,Jeddah 21589, Saudi Arabia (e-mail: mhamash1@binghamton.edu).

A. Alazzam is with Universal Solution, Conklin, NY 13748 USA (e-mail:aalazam1@binghamton.edu).

S. Hamasha and M. T. Khasawneh are with the Department ofSystems Science and Industrial Engineering, Binghamton University—State University of New York, Binghamton, NY 13902 USA (e-mail:shamash1@binghamton.edu; mkhasawn@binghamton.edu).

F. Aqlan is with the Penn State Erie—The Behrend College, Erie, PA 16563USA (e-mail: faqlan1@binghamton.edu).

O. Almeanazel is with Hashemite University, Zarqa 13115, Jordan (e-mail:oalmean1@binghamton.edu).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TCPMT.2015.2394232

wide variety in product demand. In addition, using a flexiblesystem also reduces the cost of production and increasesquality. Many researchers have addressed the advantages ofusing FMSs, including Dimitrov [3], where he discussedthe importance of inventory reduction among the variousadvantages of FMS implementation. Other authors wrote aboutFMS from different aspects such as the following examples.Novas and Henning [4] studied the integrated scheduling ofresource-constrained FMSs using constraint programming.Huang et al. [5] developed a search strategy for schedulingFMSs simultaneously using admissible heuristic functionsand nonadmissible heuristic functions. Lee et al. [6] studiedthe applications of intelligent data management in resourceallocation for effective operation of manufacturing systems.Huanga et al. [7] studied the time matrix controller design ofFMSs.

Since FMSs are usually expensive to implement, some ofthe small manufacturing companies cannot afford to buy acomplete FMS; instead, they can implement one or moreflexible manufacturing cells (FMCs). The FMC consists ofone or more flexible machines, a robot, and an automatedhandling system. Thus, FMC implementation provides flexiblemanufacturing in the area of this cell, without significantinvestments to change the whole system into flexiblemanufacturing. In the last few decades, many researchershave developed different algorithms to optimize these FMCsand increase their efficiency. Most research studies focused onthe scheduling and routing of products to various machines.In those efforts, they have also modeled the performanceof FMCs using different approaches, such as artificialintelligence, simulation, fuzzy logic, and so on. Reeb et al. [8],for example, discussed the use of discrete event simulationto develop and select part families for cell manufacturing.Lee and Kim [9], Sawik [10], and Gultekin et al. [11] arethe examples of researchers who have designed mathematicalalgorithms for FMC scheduling. Pezzellaa et al. [12] have useda genetic algorithm for flexible job flow scheduling, and theirresults were quite comparable with those obtained by the bestknown algorithm, based on tabu search. Neto and Filho [13]proposed a multiobjective optimization approach to solvethe manufacturing cell formation problem. Their approachis powerful to generate a set of alternative configurations ofmanufacturing cell while optimizing multiple performancemeasures. A number of simulation studies have been devotedto compare the cellular layout with the functional layout,such as [14]–[16].

2156-3950 © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

440 IEEE TRANSACTIONS ON COMPONENTS, PACKAGING AND MANUFACTURING TECHNOLOGY, VOL. 5, NO. 3, MARCH 2015

Fig. 1. Schematic of an FMC with n machines.

Some research studies presented stochastic analysis todetermine the performance of an FMC, such as [17]–[24].

In this paper, a stochastic model will be developed todetermine the performance of an FMC. First, a model foran FMC with a single machine is developed. The model isthen extended to two machines and, finally, generalized toan FMC with n machines. Finally, numerical examples arepresented and used to study the impact of various systemparameters on the overall performance of the proposed FMCs.The layout of this FMC is not addressed by any previousresearch. Further, the stochastic analysis conducted on theoverall machine utilization and production rate has not beenaddressed in previous research efforts.

II. SYSTEM DESCRIPTION AND NOTATION

The FMC considered in this paper consists of one (or more)machine(s), a robot, and a conveyor belt. An FMC withn machines is shown in Fig. 1. The conveyor belt delivers theworking parts to the robot, which picks the part up and loadsit onto the machine. Once the part is loaded onto the machine,the processing of the part starts, and when the part processingis completed, it is pushed to the next manufacturing/handlingprocess.

To analyze the performance of the system, three parametersare considered: 1) rate of conveyor belt in terms of the numberof parts per unit time it can deliver; 2) loading rate of therobot in parts per unit time; and 3) processing rate of themachine in parts per unit time. It is assumed that the deliverytime of the conveyor, the loading time of the robot, and theprocessing time of the machine all follow the exponentialdistribution. If the system parameters are deterministic, theproblem could be handled by a man–machine assignmentchart for nonidentical machines, as outlined by Francis et al.in 1992.

The following notations are used throughout this paper.i Part availability to be picked up by the robot

(e.g., i =1 if the part is available and i =0 otherwise).j Number of parts being processed by the machines

(e.g., j = 1, 2, 3, …, m).n Number of machines in the cell.Si j State of the FMC in the steady state in terms of

parameters i and j .Pij Steady-state probability that the system is in

state Si j .

l Loading rate of the robot (parts per unit time).v Machine processing rate (parts per unit time).b Conveyor belt delivery rate (parts per unit time).

III. PROPOSED MODEL

In this section, a Markovian chain-based model of the FMC,with one, two, and n machines, is introduced. The followingassumptions are used: 1) the machine can process only onepart at a time; 2) the robot can transfer one part at a time;3) no in-process inventory is allowed; 4) working parts areidentical; 5) machines are identical; and 6) robot loading speedis the same for each machine. The state for the system isdescribed by two variables: 1) the availability of the part tobe picked up by robot (i ) and 2) the number of parts beingprocessed by machine ( j ).

Considering an FMC with one machine, the system can bein one of the following four states.

1) No part is available to be picked up and no part is beingprocessed (S00).

2) One part is available to be picked up and no part is beingprocessed (S10).

3) No part is available to be picked up and one part is beingprocessed (S01).

4) One part is available to be picked up and one part isbeing processed (S11).

When the system is in state S00, the next transition will beto state S10; when a part arrives to the robot and becomesready to be picked up. The transition rate will be the samewith conveyor belt delivery rate (b). Afterward, state S10will be transferred to S01 when the part is loaded onto themachine by the robot with a loading rate of l. At state S01,there is a chance of moving to state S11 when new partsarrive to the robot with a rate of b or returning to stateS00 with a rate of v, if the machine finishes processing thepart that is being processed with a rate of v and delivers itfor further processing. At state S11, the only choice for thenext transition is to move to state S10 when the machinefinishes processing the part. Fig. 2(a) shows the transitionflow between the different possible states for an FMC witha single machine. It can be observed that the system is totallyrecurrent.

The net flow rate at each state is equal to zero (i.e., therates of flow-in and flow-out are equal). Therefore, keeping inmind that the sum of these state probabilities equals to one, thefollowing equations can be constructed for the FMC in Fig. 1to calculate the steady-state probabilities:

vP01 − bP00 = 0

bP00 + vP11 − l P10 = 0

l P10 − (v + b)P01 = 0

bP01 − v P11 = 0

P00 + P10 + P01 + P11 = 1. (1)

This represents a set of five equations with four unknowns,which can be then solved to estimate the probability of eachstate, which corresponds to the proportion of time that thesystem can be in each state. System performance measures,

HAMASHA et al.: MULTIMACHINE FMC ANALYSIS 441

Fig. 2. State transition flow diagram for the flexible cell operation with(a) one machine, (b) two machines, and (c) n machines.

such as machine’s utilization and production rate, are thenestimated using the following equations:

Machine utilization = (P01 + P11) × 100% (2)

Production rate = (P01 + P11) × v (3)

where (P01 + P11) represents the percentage of time thatthe machine is busy. Similarly, the FMC with two machines(n = 2) can be expressed with the following possible states.

1) No part is available to be picked up or beingprocessed (S00).

2) One part is available to be picked up and no part is beingprocessed (S10).

3) No part is available to be picked up and one part is beingprocessed (S01).

4) One part is available to be picked up and one part isbeing processed (S11).

5) No part is available to be picked up and two parts arebeing processed (S02).

6) One part is available to be picked up and two parts arebeing processed (S12).

The transitions from and to S00 and S10 are the same as thosefor the FMC with a single machine. However, the other states’transitions are different and can be illustrated as follows.

1) State S01 can transfer to state S11 at a rate of b or stateS00 at a rate of v.

2) State S02 can transfer to S12 at a rate of l or state S01at a rate of 2v.

The machine processing rate is multiplied by two in thistransition because two parts are being processed, that is, theprocessing rate of two parts is equal to the processing rate ofone part multiplied by two. Finally, state S12 can transfer onlyto state S11 at a rate of 2v. Fig. 2(b) shows the transition flowbetween the different possible states for this system. As can beobserved, the system is recurrent in the case of two machinesas well

vP01 − bP00 = 0

bP00 + vP11 − l P10 = 0

l P10 + 2vP02 − (v + b)P01 = 0

bP01 + 2vP12 − (v + l)P11 = 0

l P11 − (2v + b)P02 = 0

bP02 − 2vP12 = 0

P00 + P10 + P01 + P11 + P02 + P12 = 1. (4)

The previous system is solvable, and further system mea-surement analysis can be performed. The overall machineutilization is equivalent to the percentage of time that bothmachines are busy plus half of the percentage of time thatonly one machine is busy (because only half of the system isutilized)

Overall machine utilization

=[

1

2(P01 + P11) + (P02 + P12)

]× 100%. (5)

The production rate is equivalent to the overall machineutilization as a fraction instead of percentage (e.g., 0.55 insteadof 55%) multiplied by the production rate at 100% systemutilization (2v). Equation (5) and the following represent theoverall machine utilization and production rate:

Production rate

=[

1

2(P01 + P11) + (P02 + P12)

]× 2 × v. (6)

Based on the state transitions in both the single- and two-machine systems, two observations can be highlighted asfollows. First, the total number of possible states is expressedas 2(n + 1) (e.g., six in the two-machine systems). Second,the states can be arranged due to the number of parts in thesystem and part flow in the following series, where it startswith S00 and ends with S1n: S00, S10, S01, S00, . . . , S1n . Thestate transition can be depicted in a systematic manner, asshown in Fig. 2(c) [i.e., it can transfer to the next state witha rate of b if no part is available to be picked up and l ifotherwise]. In addition, the state can transfer two states backwith a rate that equals to the processing machine number(e.g., n or n − 1) multiplied by v. Therefore, the system with

442 IEEE TRANSACTIONS ON COMPONENTS, PACKAGING AND MANUFACTURING TECHNOLOGY, VOL. 5, NO. 3, MARCH 2015

n machines can be expressed as shown in Fig. 2(c)

vP01 − bP00 = 0

bP00 + vP11 − l P10 = 0

l P10 + 2vP02 − (v + b)P01 = 0

bP01 + 2vP12 − (v + l)P11 = 0

l P11 + 3vP03 − (2v + b)P02 = 0

bP02 + 3vP13 − (2v + l)P12 = 0...

......

l P1n−1 + nvP0n − ((n − 1)v + b)P0n−1 = 0

bP0n−1 + nv P1n − ((n − 1)v + l)P1n−1 = 0

l P1n−1 − (nv + b)P0n = 0

bP0n − nv P1n = 0

P00 + P10 + P01 + P11

+P02 + P12 + · · · + P0n + P1n = 1. (7)

Therefore, the overall machine utilization and productionrate can be calculated as follows:Overall machine utilization

=[

1

n(P01 + P11) + 2

n(P02 + P12)

+ · · · + n

n(P0n + P1n)

]× 100%

Production rate

=[

1

n(P01 + P11) + 2

n(P02 + P12)

+ · · · + n

n(P0n + P1n)

]× n × v. (8)

IV. NUMERICAL EXAMPLE

In this section, two numerical cases will be presentedand discussed to illustrate the applicability of the pre-viously developed models. The first case deals with asingle-machine FMC, and the second case deals with anFMC with three machines as an example of an n-machinesystem. The effects of each of the process parameters(i.e., robot loading rate, machine processing rate, and conveyorbelt delivery rate) on the overall machine utilization andproduction rate are investigated and explained as well.

A. Single-Machine FMC Example

Consider a single-machine FMC with a robot loading rateof 5 parts/min, a machine processing rate of 2 parts/min,and a conveyor belt delivery rate of 2 parts/min. Solvingthe resulting system of equations (1) leads to P00 = 0.123,P10 = 0.146, P01 = 0.244, and P11 = 0.488. The systemperformance can be measured by 1) machine utilization and2) production rate. The machine utilization is calculated using(2) and found to be 73.2%. Moreover, the production rate (thenumber of processed parts that the cell can perform per unittime) is calculated using (3) and found to be 1.464 parts/min.

The numerical example is then used to draw therelationships between each system parameter and machineutilization and production rate for a single-machine FMC by

Fig. 3. Effect of robot loading rate on machine utilization for a single-machine FMC.

Fig. 4. Effect of machine processing rate on machine utilization for asingle-machine FMC.

varying one parameter at a time (while keeping the othersconstant). The effect of the robot’s loading rate on machineutilization at four different combinations of conveyor beltdelivery rate and machine processing rate is shown in Fig. 3.The general trend shown indicates that increasing the robot’sloading rate increases machine utilization. However, the rate atwhich machine utilization increases decreases with the robot’sloading rate and approaches zero (theoretically) at very highloading rates. This is primarily due to the fact that the fractionof time that the part spends in the robot arm is negligiblecompared with the machine processing time at a high robotloading rate. For example, if the machine takes 10 min tofinish a part, the machine utilization will vary significantly ifthe robot loading rate drops from 10 to 5 parts/min, mainlybecause the difference between the idle times of the twocases is 5 min, which is equivalent to 50% of the machineprocessing time. However, if the robot loading rate drops from1 to 0.1 parts/min, the difference between the two rates is 0.5(1% of the machining time), which is negligible comparedwith machining time.

The effect of machine processing rate on machine utilizationat four different combinations of robot loading rate and con-veyor belt delivery rate is shown in Fig. 4. At all combinations,machine utilization decreases with the machine processingrate. As discussed earlier, the idle time for a slow machineis low because the next part becomes ready for processing,

HAMASHA et al.: MULTIMACHINE FMC ANALYSIS 443

Fig. 5. Effect of conveyor built delivery rate on machine utilization for asingle-machine FMC.

Fig. 6. Effect of the robot loading rate on the production rate for a single-machine FMC.

thereby making the machine status very busy. It is importantto note that the rate at which machine utilization decreasesis slower at higher processing rates, thereby making machineutilization approach zero at very high processing rates. This isdue to the fact that when the processing rate is very high, theprocessing time (i.e., inverse of the processing rate) is close tozero. The best utilization rate can be achieved at the combi-nation of higher robot loading rate and conveyor belt deliveryrate (l = 4 and b = 10), among the combinations considered inthis specific example. Although increasing machine utilizationcan be achieved by slowing the machine down, this leads toa reduction in production rate, thereby requiring a tradeoffbetween the two performance measures. The improvement inmachine utilization can be accomplished by reducing the timelost at the optimum machine speed.

The effect of conveyor belt delivery rate on machine utiliza-tion is shown in Fig. 5. Four different combinations of robotloading rate and machine processing rate were considered.The machine utilization increases with the conveyor beltdelivery rate due to the enhancement in the part loading rate,thereby increasing machine utilization. The rate at which themachine utilization rate increases is low at higher conveyorbelt delivery rates. In other words, the extra increase in theconveyor belt delivery rate cannot provide significant reductionin the machine’s idle time. The results also show that machineutilization is higher with higher loading rates and lowerprocessing rates, as discussed earlier.

Figs. 6–8 show the effect of various system parame-ters on the production rate of a single-machine FMC.

Fig. 7. Effect of the machine processing rate on the production rate for asingle-machine FMC.

Fig. 8. Effect of the conveyor belt delivery rate on the production rate fora single-machine FMC.

First, Fig. 6 shows the relationship between the robot’s loadingrate and the production rate at four different combinationsof conveyor belt delivery rate and machine processing rate.The production rate increases with loading rate, but startsleveling off at high loading rates (i.e., production rate increasesrapidly at low robot loading rates and slowly at high loadingrate). As expected, studying the various combinations revealsthat the production rate increases with both the conveyor beltdelivery rate and machine processing rate.

Fig. 7 shows the effect of the machine processing rate onproduction rate at four different combinations of robot loadingrate and conveyor belt delivery rate. In general, the productionrate does increase with the machine processing rate. However,the increase in production rate is high at low processing ratesand low at high processing rates.

Fig. 8 shows the effect of the conveyor belt delivery rateon the production rate at four different combinations of robotloading rate and machine processing rate. In general, theproduction rate does increase with the conveyor belt deliveryrate; however, the increase in production rate is high at lowconveyor belt delivery rates and low at high conveyor beltdelivery rates.

B. Multimachine FMC Example

Consider a multimachine FMC with three machines, arobot loading rate of 10 parts/min, a machine processing rate

444 IEEE TRANSACTIONS ON COMPONENTS, PACKAGING AND MANUFACTURING TECHNOLOGY, VOL. 5, NO. 3, MARCH 2015

Fig. 9. Effect of robot loading rate on machine utilization rate for athree-machine FMC.

Fig. 10. Effect of machine processing rate on machine utilization rate for athree-machine FMC.

of 2 parts/min (for each machine), and a conveyor belt deliveryrate of 6 parts/min. Solving the system of equations for thisFMC (7) leads to P00 = 0.007, P10 = 0.014, P01 = 0.02,P11 = 0.023, P02 = 0.6, P12 = 0.055, P03 = 0.069,and P13 = 0.209. As in the single-machine FMC, systemperformance is measured using the overall machine utilizationand the overall production rate. The overall machine utilizationfor three-machine systems is estimated by calculating thepercentage of time that all machines are busy plus two-thirdsof the portion of time that two machines are busy (i.e., 2/3 ofthe system is utilized), plus one-third of the percentage of timethat one machine is busy (i.e., 1/3 of the system is utilized).For example, if one machine is processing parts while therest are just idle (i.e., one machine out of three is utilized),then the overall machine utilization is 33.3%; if this machineis processing just half of the time while the others are idle(i.e., one machine out of three is utilized only for half of itsavailable time), then the overall machine utilization is 16.6%.Therefore, the overall machine utilization in this numericalexample is 73.1% according to (8), and the production rate is4.38 parts/min according to (9).

Figs. 9–11 show the overall machine utilization as a functionof robot loading rate, processing rate, and conveyor beltdelivery rate, respectively. As can be observed, machine uti-lization increases as the loading rate of the robot increases.However, the rate at which machine utilization increasesdecreases as the loading rate increases. As shown in Fig. 9,

Fig. 11. Effect of conveyor belt delivery rate on machine utilization rate fora three-machine FMC.

Fig. 12. Effect of robot loading rate on production rate for a three-machineFMC.

machine utilization is higher at higher conveyor belt deliv-ery rates and lower machine processing time. The effect ofmachine processing rate on machine utilization at variouscombinations of robot loading rate and conveyor belt deliveryrate is shown in Fig. 10. At all combinations, the overallmachine utilization decreases with the machine processingrate, with the best utilization rate achieved at higher robotloading rates and conveyor belt delivery rates. Furthermore,the effect of conveyor belt delivery rate on machine utilizationis shown in Fig. 11. Four combinations of robot loading rateand machine processing rate are considered. Machine utiliza-tion increases as the conveyor belt delivery rate increases.In general, the explanations of these effects are very similarto the single-machine FMC (Section IV-A).

In Figs. 12–14, the effect of the various system parame-ters on the production rate for a three-machine FMC willbe illustrated. Fig. 12 shows the relationship between theproduction rate and the robot’s loading rate at four differentcombinations of conveyor belt delivery rate and machineprocessing rate. Fig. 13 shows the relationship between theproduction rate and the machine processing rate at fourdifferent combinations of robot loading rate and conveyorbelt delivery rate. Fig. 14 shows the relationship betweenthe production rate and conveyor belt delivery rate at fourdifferent combinations of robot loading rate and machineprocessing rate. In general, the results show that productionrate increases with all system parameters: robot loading rate,

HAMASHA et al.: MULTIMACHINE FMC ANALYSIS 445

Fig. 13. Effect of machine processing rate on production rate for athree-machine FMC.

Fig. 14. Effect of conveyor belt delivery rate on production rate for athree-machine FMC.

machine processing rate, and conveyor belt delivery rate.In general, the explanations of these effects are very similarto the single-machine FMC (Section IV-A).

V. CONCLUSION AND FUTURE WORK

Given the stochastic nature of FMCs, such systems canbe effectively modeled and analyzed using a Markov chain-based approach. Therefore, this paper develops a stochasticmodel for an FMC that consists of a single conveyor belt,a single robot, and one or more machine(s) to estimatethe effect of robot loading rate, machining rate, and con-veyor belt delivery rate on machine utilization and produc-tion rate. Numerical examples were conducted to simulatethe constructed models. It can be concluded that machineutilization for a single-machine FMC increases with robotloading rate and conveyor belt delivery rate and decreases withmachine processing rate. The machine utilization–conveyorbelt delivery rate and machine utilization–robot loading ratecurves are concave, indicating that the increase in both curvesis sharp at low levels and gradually stabilizes at higherlevels. However, the machine utilization–machine processingrate relationship is convex, indicating that the decrease issharp at low levels and gradually stabilizes at higher lev-els. The production rate increases with robot loading rate,machine processing rate, and conveyor belt delivery rate.The curves of the production rate against robot loading rate,

machine processing rate, or conveyor belt delivery rate areconcave, indicating that the increase is sharp at low levelsand becomes slower at higher levels. Comparing the single-machine and three-machine FMCs considered in this paper,the models behave in a similar manner with more advancedfeatures associated with the three-machine FMC. Thoseadvanced features are mainly jumps in the concave curves anddrops in the convex curves.

The FMC model developed in this paper can handle asystem with multiple machines with one robot. Therefore,this research can provide opportunities for using the proposedmodel for building more advanced systems. For example, asystem with two or three robots can be considered, as this ismore common in some industrial applications. Furthermore,possible delays due to machine and robot breakdowns canbe considered and characterized. Moreover, future models canallow for inventory by adding a buffer with a certain capacitybetween various stages in the system.

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Mohammad M. Hamasha received the B.S. degreein biosystems engineering and the M.S. degree inindustrial engineering from the Jordan Universityof Science and Technology, Irbid, Jordan, in 2005and 2008, respectively, and the Ph.D. degreein industrial and systems engineering from theBinghamton University—State University ofNew York, Binghamton, NY, USA, in 2011.

He is currently an Assistant Professor with theDepartment of Industrial Engineering, Universityof Business and Technology, Jeddah, Saudi Arabia.

His current research interests include operational research, reliability engi-neering, design and analysis of production systems, stochastic and simulationoptimization, and lean six sigma.

Azmi Alazzam received the bachelor’s degree inelectrical engineering from the Jordan University ofScience and Technology, Irbid, Jordan, in 1999, themaster’s degree in computer engineering from theUniversity of Houston—Clear Lake, Houston, TX,USA, in 2007, and the Ph.D. degree in industrialand systems engineering from the BinghamtonUniversity—State University of New York,Binghamton, NY, USA, in 2013.

He is currently an Optimization Engineer withUniversal Solution, Conklin, NY, USA, a company

that is a global leader in the design and manufacture of advanced automationand assembly equipment solutions for the electronics manufacturing industry.His current research interests include different fields of industrial engineering,including electronic packaging reliability and modeling, quality control,metrology, simulation, and optimization.

Sa’d Hamasha received the B.S. degree inmechanical engineering and the M.S. degree inindustrial engineering from the Jordan Universityof Science and Technology, Irbid, Jordan. He iscurrently pursuing the Ph.D. degree with the Depart-ment of Systems Science and Industrial Engineer-ing, Hashemite University, Jordan, Binghamton, NY,USA.

He currently serves as a Graduate ResearchAssociate on a funding by the AREA Consortiumthrough Universal Solution, Conklin, NY, USA.

His current research interests include microelectronics manufacturing, statis-tical reliability modeling of electronic packages, and electronic interconnects.

Faisal Aqlan received the B.Sc. and M.Sc. degreesin industrial engineering from the Jordan Universityof Science and Technology, Irbid, Jordan, in 2007and 2010, respectively, and the Ph.D. degreein industrial and systems engineering from theBinghamton University—State University ofNew York, Binghamton, NY, USA, in 2013,

He was a Faculty Member with the Department ofMechanical, Civil and Environmental Engineering,University of New Haven, West Haven, CT,USA. He is currently an Assistant Professor with

the Department of Industrial Engineering, Pennsylvania State University,University Park, PA, USA, and the Penn State Erie—The Behrend College,Erie, PA, USA. His current research interests include the design and analysisof production systems, supply chain management, analytics, lean six sigma,human factors, and digital human modeling.

Dr. Aqlan is a member of the Institute of Industrial Engineers (IEE).He serves as the Board of Director of the IIE Logistics and Supply ChainDivision.

Osama Almeanazel received the B.S. degreein industrial engineering from The University ofJordan, Amman, Jordan, in 2006, the M.S. degreein engineering management from SunderlandUniversity, Sunderland, U.K., in 2008, and thePh.D. degree in industrial and system engineeringfrom the Binghamton University—State Universityof New York, Binghamton, NY, USA, in 2013.

He is currently an Assistant Professor with theDepartment of Industrial Engineering, HashemiteUniversity, Zarqa, Jordan. He serves and directs

research with the Human Factors and Ergonomics Research Laboratory,Binghamton University—State University of New York.

Dr. Al Meanazel is a member of the Institute of Industrial Engineers andthe Human Factors and Ergonomics.

Mohammad T. Khasawneh received the B.S.and M.S. degrees in mechanical engineering fromthe Jordan University of Science and Technology,Irbid, Jordan, in 1998 and 2000, respectively, andthe Ph.D. degree in industrial engineering fromClemson University, Clemson, SC, USA, in 2003.

He is currently a Professor with the Departmentof Systems Science and Industrial Engineering,Binghamton University—State University ofNew York, Binghamton, NY, USA, where he servesas an Assistant Director of the Watson Institute

of Systems Excellence, an institute for advanced studies, and also directsresearch in the Healthcare Systems Engineering Center.

Dr. Khasawneh is a member of the Institute of Industrial Engineers.

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