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Please log in to your account The power of a rapid modeling approach that assists manufacturing system designers and planners to evaluate their factory production systems is illustrated here. The study demonstrates how various members of an interdiciplinary design and analysis team can evaluate the process capabilities before introducing new methods and machines. MANUPLAN II User's Manual. Simstarter Users Manual.Sign in Full Access Get this Publication Request Permissions View Digital Edition Figures Other Share this Publication link Copy Link Share on Social Media. Our overarching goal is to find the most cost-effective assignment of service requests to cross-trained agents in a large-scale network. We present a novel heuristic algorithm that assigns an analytically described allocation index to each service request that has arrived. It incorporates factors such as variability in agents' capabilities, uncertainty in request inter-arrival times and complex service level agreements (SLA).https://congviendisan.vn/vi/3mz-fe-engine-manual We investigate the effectiveness of our proposed assignment algorithm using real world data from an IT service environment on a small problem instance. Download full-text PDF Our overarching goal i s to find the most cost - effective assignment of service requests to cross - trained agents in a large - scale network. We present a novel heuristic alg orithm that assigns an analyt ically described allocation index to each service request tha t has arrive d. It incorporates factors such as variab ility in agents’ capa- bilities, uncertai nty in reques t inter - arrival times and complex service level agreements (SLA). We investigate t he effectiv e- ness of our proposed assig nment algorithm using real wo rld data from an IT service environme nt on a small prob lem i n- stance. W e discuss ho w t he results o f this simula tion can help improve the terms o f service level contract s a s well as agent training pro grams. 1 INTRODUCTION Recently, the ability to deli ver IT services from multip le locations has given ri s e to an ent irely new IT service delivery mo d- el. In this model, organizations outsource components of their IT infrastructure operations to one or more service providers, who in turn use a combinati on of onsite and offsite res ources to manage the compone nts on behalf of their clients. To provide these capabilities, service providers have pioneered a new ons ite - offsite delivery model called the Glo bal Delivery Model, in which a service provider uses a number of delivery centers aroun d the globe to provide services to its clients. The agents at these delivery centers perform a variety of tasks including remote monitoring and management of hardware and software on a 24x 7 basis, develop new applications, test configurati ons, apply security patches, etc.https://duluthtaxiservice.com/images/bose-wsc-1-manual.pdf While call centers are often the primary interface between clients and service providers, the delivery centers perform a variety of tasks at different levels of servi ce co mplexity behind the scenes. Using this model, the clients of the global delivery model can scale their core business oper a- tions to match demand wit hout worryi ng about reso urces and s kills requir ed to manage their IT infr astructure. At the sam e time, b y le v eraging local skills, co st structure and process standardization, service provid ers can ensur e high qualit y of the services performed from different loc ations. The critical factor for a service provider to achieve a hig h service qua lity in the global del ivery model is efficient u tiliz a- tion of the agents and resources available at its deliv ery centers. The process by which a service provider assigns a service re- quest to an agent at a service delivery center is known as “dispatching”. The nature of the IT s ervice centers pre sents a nu m- ber of challenges to efficient dispatching, pri marily due to variability in agent skil ls and complexity of service requests. It is difficult to incorporate agent skill variabilit y an d service request complexity in the dispatchi ng procedure for several reasons. First, there have been ver y few studie s on quantif ying agent skill variability in performing IT infrastructure services. The e x- perience level and ski ll sets of an agent strongly affec t diagnosis and resolution of IT ser vic e requests, and therefore it is cr u- cial that service requests are dispatched to the most suitable agen ts. Second, IT service requests exhibit a wide range of co m- plexity and therefore require varying levels o f effort and coordinatio n by the agents. Althou g h the well - studied call center staffing problem has some similarity with the service - dispatchi ng problem, there are many important differences.http://protech.com.ng/wp-content/plugins/formcraft/file-upload/server/content/files/1626c71fd6584d---carrier-fc4-installation-manual.pdf In a service request fulfillment system there is no abandonment whereas in a call center env i- ronment, requests may leave the system even before their service begins. Furthermore, IT service centers typically serve re- quests with different levels of prior ity (or severity) whereas call centers have a more homogenous demand structure.As demonstrated by Bartholdi III ( 1981) even in the deterministic case where the demand and supply are fixed, finding the nu mber of age nts r e- quired for each time period can be a com plex task. I n rea l world scenarios, on e face s even more complexities d ue to the st o- chastic nature of the demand. A nother dimensi on of comple xity is taking into account an agent ’ s skill le vel, where scheduling of different skills is necessary to serve an in coming r equest. Van O yen et al. ( 2001) ad dressed, how cross training can be aligned with organizational strategies. T heir model includes improving per formance measures of t he system but does not consider fixed deadline s for jobs in the system. Brusco et al. ( 1998) used integer p rogramming to show that cross training can be a very useful appr oach when agent skill s can be combined in a sequential setting. Perhaps the most relevant research framework to our problem can be f ound in the call center liter ature. Similar to call center s, IT service centers require agents with a variety of skills to ha ndle the arr iving requests. However, it is almost impos s- ible to expect that every agent i s ful ly c ross - trained for every task. T herefore it is important to rout e problems to the best agent s considering their skills and skill levels. Investigatio n of different algorithms and m ethodologies for routi ng traffic has been done with the purpose of system i m- provement. Ma ny researchers considered this concept to impro ve the Q uality of Ser vice ( QoS ).chrishuzzard.com/userfiles/files/conia-air-conditioner-manual.pdf Ma and Steenkiste ( 1998) proposed a model in order to de crease delay time of calls for a call center as a measure of QoS by f inding a feasible path. Feld man et al. ( 2008) and Whitt ( 2006) studied the problem of call center st affing in a c omplex str ucture. The y invest i- gated the ef fect of tim e - varying d emand on the p erformance measures o f the system. T he y also studied variability of the se r- vice time. Finding the mos t appropr iate agent tra ining policy is another important factor which can affect both the stability and the performance of the system. It is important to note that training all a gents for all skills is typicall y too expensi ve, or rathe r i m- possible. A suitable al gorithm ca n approximate the number of agents with the required set of skills. Wallace a nd Whitt ( 2005) introduced an algorithm to route problem s based o n the skill level of the agent. O ther algo rithm s were developed by Sisselman a nd Whitt ( 2007). They int roduced the concept s of “ value - b ased routing” and “pre fe rence - based routing” to the existing skill -b ased r outing al gorithm. All of these algorithms are based on simulation and heuristic approaches but none a d- dressed havi ng different pr iority of requests and strict (or hard) deadline s, both of whi ch are incorp orated in our proposed ap- proach. 3 SYSTEM DESCRIP TION The dispatc hing system consists of several age nts who are assigned to different service request resolut ion gr oups. Each res o- lution group is capable of handlin g several different ty pes of service requests. Each service request can be characterized by a request type and a priority level. We assume that each service request with a specified type and priority level has a lump sum penalty cost associated with violating its deadline according to a service level agreement (SLA) contracted with the customer.http://terapie-psi.ro/wp-content/plugins/formcraft/file-upload/server/content/files/1626c720449342---carrier-fc4dnf060-manual.pdf The inter - arrival ti me distribution s are independ ent (but n ot necessaril y identical) for each request type and priority level. The time required to resolve a request type can vary by a gent. T he goal of an efficient dis patching policy is to minimize the total penalty cost of violating de adlines. The differences between a traditional call center problem and the IT service center problem motivate us to stud y this problem. H ere we me n- tion some of these differenc es. First, in a typical call center, the SLA is s olely a functio n of the waiting time for the customer before the service starts ( i.e. waiting t ime in the queue ), w hereas in an IT envir onment, the SLA is concerned with the total time a reque st spend s in the syste m until the request is resolved. This, in tur n, adds anot her source o f uncertaint y to the di s- patching problem. Second, in a call center, a customer may leave the system before the service starts. However, in a typical IT application, a customer would never leave the system before the request is resolved. T hird, the nature of o perations i n IT environment s require a more sophisticated set of skills for agents, whereas agents in a call center generally have a more l i- mited set of skills. In fact, reque st s that cannot be resolved at the call center are often passed on to the IT service centers. Fourth, IT service requests may be han dled by multiple agents simultaneously depen ding on their com plexity, where as in a call center, each agent typically handles a single call in its entirety or possibly hands of the call in a sequential fashion. In this research, we stud y agent - level variabilit y and complexit y of the service request to build a dispatching model for IT infrastructure service reques ts in a single - stage service delivery center. Figure 1 pres ents an example of dispatching sy s- tems commonly found in many service delivery centers.http://cageart.ca/wp-content/plugins/formcraft/file-upload/server/content/files/1626c7213c0877---carrier-fe4a-installation-manual.pdf We discuss factors critical to developing an efficient dispatchi ng po l icy. These factors include an agent ’s s kill lev el, service time variability, and t he penalty cost a ssociated with violating the deadline. 3182 This variability stems from experience level (e.g. number of years), certifications, subject - matter expertise, specia- liz ed training, and other factors. As a result, some agents are able to diagnose the root causes of a problem faster and more accurately than others, resulting in a shorter mean service restoration tim e and a smaller standard d eviation. T here is also a tempor al variation in performance of an agent in performing the same task when mo nitored over time. This ca n be attributed as an inconsistent performance level and is generally complex to model. In recent years, manufacturing processes have been automated to the point that o nly random sources of variatio n are allowed resulting in mostl y normal distribution s of the process outcomes. However, many IT service management processes such as maintaining servers, patch management, and installation have a hi gh degre e of manual i nvolvement. This result s in a high agent - i nduced vari ability in the pr ocess. Variabilit y has many sour ces. Howeve r, our input a nalysis sho ws that the two most imp ortant facto rs that cont ribute to variability in service resolutio n time are the compl exit y level of service req uests and t he ir priority level. The comple xity level of service requests is illustrated in Figure 2. Figure 2: Inco rporating tas k complexity i n defining a re quest t y pe can decrease variability Figure 2(a) shows a histogram of service time associated with a sampled agent, ser ving requests categorized as “software problems”. Based on this high - level categorization, the service time has a standard deviation of 146.AUTOMOVILESMONTES.COM/userfiles/files/conia-air-conditioner-manual-ca12001.pdfT his way, the “software pr oblem” is broken into two ca tegories: “low - complexity softw are problems (LC)” and “high - complexity software problems (HC)”. Examples of such problems are restar t- ing an application (LC) versus ins talling a new applicat ion middleware on a server (HC ). As shown in Figures 2(b) and 2(c ), adding a dimension of complexity dramatically reduces the standard deviation of service time. As a result of this analysis, w e redefine the request categorization across all request types in the system to account for the complexity level of service re- quests. This way, for each request type, at each complexity level, and for ea ch agent we fit a probability distribution to the service time. We note that mapping an incoming service request to its complexity level can be done either manually (by a human dispatcher) or automatically by a classification agent that analyzes the text description of the request and other struc- tured attributes befor e assigning a complexity class to the request. We describe the key aspects of our simulation model next. 5 SIMULATION MO DEL 5.1 Priority -B ased Allocation Inde x In order to heuristically address the dispatching model described above, we develop a policy to assig n each service request to the appropriate agent, based on an allocation index. Whenever an a gent finishes serving a re quest ( i.e. the system is non - preemptive), we update all the indices for all the r e- quests and the r equest with the highest inde x will be assigned to the idle agent. In order to develop this index, we calculate the av erage service rate at which agent j can sol ve the type associated with the service request i ( ? ?? ) from historical data. Howe ver, i t is important to n ote that the average service times do not capture the in here nt variability of the service time. In th e case of IT app lications, this is even more important due to non - normal distributions tha t are a consequence of manual ly - performed steps, as e xplained b y Pyzd e ( 1995). T herefore, we need to include another measure that better represents the se r- vice time variability. An appropriate measure of variability can be defined based on the confidence level at which an agent can serve a request before its deadline. This function take s the value of one when the time to dead line is relatively large. As a request get s clos e to its dead line, this function ta kes the value of M, where M is a sufficiently large number. This gi ves a high prior ity to the servi ce requests that are critically close to their deadline.We note that there may be service requests for which the penalty fun ction may be duration - based rather than a deadline - based, e.g. how long a web server remains down after an outage. This type of penalty may be associated with the most critical or revenue - gene rating components o f IT infrastruc ture (e.g., a web server that processes payments). The present simulation model, however, does not address thes e types of penalty cost func tions. 5.2 Simulation Results In our ini tial testing, we consider a system cons isting of four agents, four problem (service request) type s as a way to inco r- porate their complexit y, and t hree levels of prior ity. We ass ume that age nts have di fferent skill level s. Tab le 1 illustrates the mean service time of each agent for each service request type. T he penalty of missing the deadline for priorities 1, 2, and 3 are 100, 80 and 30 respectively. T he deadlines for priorities 1, 2 and 3 are 40, 60 and 85 minutes re sp ectively. Our perfor- mance criterion is to maximize the long run average SLA violation penalty per unit time.Infinity represents that the agent is not skilled in hand ling the type of request. Service Request Type ( i ) Agent (j) 1 2 3 4 1 10 10 ? ? 2 ? ? ? 3 3 10 ? 15 ? 4 ? ? 5 8 In order to demonstrate the performance of th e allocation indices, we simulate this system under two setti ngs. In the first setting, service requests are assigned to agents based on a first - come - first - serve (FCFS) policy. In the alternative setting, we use the prior ity - based allo cation index defined in (1). W e use commo n random numbers for both systems in order to reduce the variance o f the performance difference. We then analyz e the warm up period using W elch’s m et hod, presented by Law and Kelton ( 2000). After e xamining the output we con clude that 100 service requests pr ovide s a sufficient warm - up period. After deleting the data from the warm - up period we calculate the average difference between the cost of our proposed alg o- rithm and that based on FCFS policy and construct 9 0 confidence interval s. Our initial computations show that the proposed dispatching algori thm dramatically reduce s the SLA violation penalty co st compared to a FCFS policy. However, in spite of reducing th e penalty cost, the propos ed algorithm increases the average queue length and average d elay in the que ue. This is primarily due to the fact that the s ervice requests passed the ir de adline receive the lowest priority and therefore have to wait longer. We summarize the results ( SLA violation penalty) of 40 replic a- tions in Table 2. Table 2: Comparing t he long run aver age SLA violation penalt y per unit time of the proposed dispatching policy with FCFS. To investigate the sensitivity of the proposed algorithm to any possible cha nge in arrival rate, we analyze th e system where the aggregate arrival rate is increased fr om 0. 5 to 4 arrivals per minute (see Figure 3). W e observe that the penalty 3185 In other words, prioritizing t he service requests is more critical as the system becomes more crowded. Figure 3: SLA violatio n penalty as a function of arrival rate The second impor tant analysis here is to show how the SLA violatio n cost changes as the agent ’s skill level s improve marginally. The results (see Figure 4 ) show that the proposed algorithm takes advantage of this im provement; however, th e i m- provement under FCFS is not si gnificant. T his result sug gest s that the overall performance of the system in terms of cost is highly sensitive to the efficiency of the dispatching system. Moreover, improving the skill levels of age nts does not necessar i- ly decrease the penalty cost associated with deadline violation, because the policy employed may not b e effective. Finally, we examine h ow our proposed algorithm respon ds to modifications of th e terms of the service cont r act, partic u- larly, shortening the deadlines. In order to test this scenario, we reduce the deadline for service requests of priority le vel 1, from 40 to 20 minute s. Our results (see F igure 4) show that both the proposed and FCFS policies are very sensi tive to this r e- duction. In both settings the SLA violation penalty is increased drastically. However th e increase in the total penalty cost is more pronounced u nder the FCFS policy. This result is consistent with ou r hypothesis that a s ound dispatching poli cy can better dampen the negative impacts of environmental factors, in this case, the terms of the contract. Figure 4: Effec ts of impr o ving agents’ skill levels and decreasing the deadlines on the overall cost of the system 6 CONCLUSION AND FUTURE CHALLENGE S In this research, w e propose a new priority - bas ed dispatching policy tha t incorporates the inher ent complexity of the IT se r- vice delivery environment. In the proposed approach, we first introduce a m ore accurate service complexity categorization by refining the granula rity level of our input mo del. We also develop a ne w allocation index that c aptures unc ertainty i n agents’ serv ice time, which is a signi ficant sourc e of variabili ty commonly o bserved in suc h environ ments. This ind ex consider s i m- 0 10 20 30 40 50 60 0.5 1 1.5 2 2.5 3 3.5 4 SLA Violation Penalty Aggregate Ar rival Rate FCFS Proposed 0 1 2 3 4 5 6 7 8 9 10 FCFS Proposed FCFS Proposed FCFS Pro posed Base Case Improve skill level Decrease Deadline SLA Violation Penalty 3186 It also incorporates the nature of pr obability distrib utions to add ress a more accurate measure of variability. Our proposed dispatch ing algorithm assigns a priority - based allocation index to e ach service request in t he queue. T his index is dyna mically upd ated upon e ach service terminatio n in the syste m (i.e., we assume non - preemptive service). Our in i- tial results show tha t the proposed dispatching poli cy can have a significant impa ct on reducing the penalty cost of viol atin g deadlines. Se nsitivity a nalysis sho ws that the p roposed d ispatching poli cy is even mo re signific a nt in reducing penalty cost when the aggre gate arrival rate increase s or deadlines are shortened (both cases represent a more congested system in some sense). Further benchmarking is warranted. In this research, we restrict ed attention to only no n - idling p olicies. However, there is no guaranty that our proposed pol i- cy can always perform well in other setting s such as the case where idling is allowed. In this particular problem setting where agents are cross - trained, one may argue that keeping some skilled a gents idle for short periods of time may produce better r e- sults. Consider this simplified example: Agent 1 is very efficient at resolving service requests of type 1. At a given time, Agent 1 becomes idle but there is no request of type 1 in the queue. Here we have two choices: we can either assign another service request (e.g. type 2 at which Agent 1 is not so sk illed) or wait for a certain period of time expecting that another se r- vice request of type 1 arrives. In the current f ramework, we only restrict ou r approach to the first option. Future investig a- tions are require d to address this type of policies. ACKNOWLEDGMENTS The authors would like to thank Kyle Cr esci for his he lp in develop ing the si mulation code. They also tha nk Shervin AhmadBey gi for his f eedback on the original idea of this res earch and providing references. The work of Pa rvin and Van Oyen was sup ported in p art by NSF grant DM I - 0542063. The work of Parvin was al so partially suppor ted through STIET program funded by NSF IGERT grant N o. 011 4368 and 0654014. REFERENCES Bartholdi III, J. J. 1981. A g uearanteed -a ccuracy round -o ff a lgorithm for c yclic s ched uling and s et c overing. O perations Research 29 (3): 501 - 510. Brusco, M. J., Johns, T. R., and Reed, J. H. 1998. Cross - utilization of a t wo - skilled w orkforce. International Journal of Operations and Product ion Management 18 (6): 555 - 564. Feldman, Z., Mandelbaum, A., Massey, W. A., and Whitt, W. 2008. Staffing of time - varying q ueues to a c hieve time -s tab le p erformance. Quality Engineering 7 (4): 769 - 777. Sisselman, M. E., and Whitt, W. 2007. Value -b ased r outing and p reference - b ased r outing in c ustome r c ontact c enters. Production and Operatio ns Management 16 (3): 277 - 291. Van Oyen, M. P., Gel, E. G., and Hopp, W. J. 2001. Performance o pportunity for w orkforce a gility in c ollab orative and n oncollaborative w ork s yst ems. IIE Transactions 33 (9): 761 - 777. Wallace, R. B., and Whitt, W. 2005. A s taffing a l gorithm for c all c enters wit h s kill -b ased r outing. Manufacturing and Service Operations Management 7 (4): 246 - 294. Whitt, W. 2006. Staffin g a c all c enter with u ncertain a rrival r ate and a bsenteeism. Production and Operations Manageme nt 15 (1): 88 - 102.