Analyzing conformance to clinical protocols involvingadvanced synchronizationsCitation for published version (APA):Yan, H., Van Gorp, P. M. E., Kaymak, U., Lu, X., Vdovják, R., Korsten, H., & Duan, H. (2013). Analyzingconformance to clinical protocols involving advanced synchronizations. (BETA publicatie : working papers; Vol.435). Technische Universiteit Eindhoven.

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Analyzing Conformance to Clinical Protocols Involving Advanced Synchronizations

Hui Yan, Pieter Van Gorp, Uzay Kaymak, Xudong Lu,

Richard Vdovjak, Hendriks H.M. Korsten, Huilong Duan

Beta Working Paper series 435

BETA publicatie WP 435 (working paper)

ISBN ISSN NUR

982

Eindhoven October 2013

Analyzing Conformance to Clinical ProtocolsInvolving Advanced Synchronizations

Hui Yan∗, Pieter Van Gorp∗, Uzay Kaymak∗, Xudong Lu†, Richard Vdovjak‡,Hendriks H.M. Korsten§ and Huilong Duan†

∗School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands{h.yan,p.m.e.v.gorp,u.kaymak}@tue.nl

†Department of Biomedical Engineering, Zhejiang University, Hangzhou, P.R. China{lvxd,duanhl}@zju.edu.cn

‡Philips Research, Eindhoven, The Netherlandsrichard.vdovjak@philips.com

§Aneasthiaology and Intensive Care in Catharina Ziekenhuis, Eindhoven, The Netherlandserik.korsten@cze.nl

Abstract—Clinical protocols are a popular instrument to doc-ument how clinicians are expected to behave under specificconditions. Protocols are typically based on internationally peerreviewed clinical guidelines as well as on hospital-local agree-ments. Existing techniques for monitoring protocol adherenceonly support protocol descriptions involving simple sequencesand local decision rules. As care and cure processes are becomingincreasingly complex, the need for more advanced techniquesnaturally emerges. In this paper we present a novel approachto defining and monitoring complex clinical protocols. By usingBPMN to document protocols we enable the concise specificationof protocols that involve multiple stakeholders that operate inparallel and under uncertainty. Uncertainty relates to the factthat protocols may involve complex loops and choices. Whilethis specification style was becoming increasingly popular in theliterature and practice of hospital management and operationsmanagement in general, corresponding conformance analysistechniques were still lacking. This paper contributes the firstsuch technique and evaluate it on a complex compliance patternfrom the cardiology domain.

I. INTRODUCTION

Clinical Guidelines (CGs) are, “work consisting of a setof directions or principles to assist the health care practition-ers with patient care decisions about appropriate diagnostic,therapeutic, or other clinical procedures for specific clinicalcircumstances” [1]. Although clinical guidelines are regardedas best practices for clinicians [2], clinician activities arenot always compliant with guideline recommendations [3].To check whether real clinical behaviour is compliant to thedirections or principles is of great importance in the contextof patient safety and quality control in general.

The medical literature has shown that there have only beenlimited efforts to evaluate the use and impact of guidelinesin clinical practice [4]. And there is a consensus that (1)paper-based guidelines are often cumbersome to read, (2)younger clinicians are usually not familiar with them [5],and (3) overall guideline conformance levels are not veryhigh. Computer-based decision support systems (e.g., remindersystems) have emerged to improve clinician performance byincreasing guideline compliance. The idea is to deliver timely

advice which is relevant in a specific context [6]. Still, thereis an unanswered need to analyze guideline conformance con-tinuously and also to provide feedback where appropriate [7].

The New England Healthcare Institute (NEHI) concludesfrom a literature review, a nationwide physician interviewand an expert panel that IT systems “should allow physi-cians to generate reports on their practice, enabling themto monitor their own adherence relative to that of similarlysituated physicians” [8]. This paper provides novel support foranalyzing where a specific patient case deviates from an agreedclinical principles. The NEHI study also acknowledges resultsfrom other studies that emphasize the need for IT systems toallow physicians to override IT system recommendations. Inthis paper we do not argue against ??? that but emphasizethat a computerized analysis of systematic practice deviationscan help to improve recommendation systems or even theguidelines upon which they are based.

Sucher et al. explicitly separate the level of (evidence-based) CGs from local adaptations/refinements thereof [9]. Therefined rules are called Clinical Protocols (CPs). We adoptthe same terminology to emphasize that particularly thosehospitals that are moving towards computerized decision sup-port have also defined rules that constrain physician behavioureven more than a clinical guideline does. The technique fromthis paper can be used for CGs as well as CPs but excelsin constraining situations that involve multiple concurrentactivities and decision points. For conciseness, we just usethe terminology Clinical Protocols (CPs) in this paper.

Conformance checking is typically studied in the field ofBusiness Process Management (BPM). The academic literatureon BPM describes two basic types: “(1) forward compliancechecking aims to design and implement processes whereconformant behavior is enforced and (2) backward compli-ance checking aims to detect and localize non-conformantbehavior” [10]. Backward checks are designed to compareprescribed behavior (e.g., a process model or a set of rules)with observed behavior (e.g., audit trails, workflow logs,transaction logs, message logs, and databases). Checking

conformance between CPs and observed behaviour classifiesas backward compliance checking, and so it defines morespecifically the research area of this paper. Within that area,research has long focused on efficiently deciding whetheror not a specific case conforms to a compliance rule. Forexample, [11] demonstrate how Linear Temporal Logic (LTL)can be used for this purpose. Recent work has advanced thestate-of-the-art by giving diagnostic information in the case ofnon-conformance [10]. That work is based on classic Petri-Nets, which are known not to support the natural specificationof advanced synchronization patterns such as the GeneralSynchronizing Merge [12]. In this paper, we use the BusinessProcess Model and Notation (BPMN) rather than classic Petri-Nets. BPMN 2.0 does support patterns such as the GeneralSynchronizing Merge. For long, BPMN was not suited forconformance analysis due to its informal semantics but wehave overcome that issue by formalizing BPMN 2.0 in recentprevious work [13].

BPMN was designed to be comprehensible by both ITspecialists and professionals [14]. Unlike GLIF and otherhealthcare specific languages, it is industry domain agnostic.Various other authors treat BPMN as a cost efficient, rational,standardized, intuitive and flexible instrument for modelinghealthcare processes [15]–[17]. Therefore, the popularity ofthe language and its lack for conformance analysis support al-ready motivates our work from the practical side. Additionally,we consider the increased expressiveness of BPMN comparedto classic Petri-Nets as a nice opportunity to advance thestate-of-the-art on conformance analysis from the theoreticalperspective.

The remainder of this paper is organized as follows: Sec-tion II explains background knowledge about clinical guide-lines conformance monitoring and BPMN 2.0 semantics. Insection III we propose A* based log replay technique can beused in BPMN for conformance check. Section IV illustratesexamples of checking conformance between event logs andCPs patterns in advanced BPMN synchronizations. Finally,section V discusses and concludes the paper.

II. BACKGROUND

In industries such as avionics, banking and manufacturing,organizations continuously check whether business processesare executed within the boundaries set by managers, gov-ernments, and other stakeholders. When only using perfor-mance indicators to analyze clinical practice (e.g., analyzingthe access time to a radiology image), one gets a view ofprocess performance that is too limited to individual tasks orindividual resources [7]. Recent work is based on compliancerules that involve multiple activities that can involve multiplestakeholders that operate in parallel, under uncertainty [10].Such work is based on five key activities: (1) compliance ruleelicitation, (2) compliance rule formalization, (3) compliancerule implementation, (4) compliance rule checking and (5)compliance improvement. In our work, rules are elicited fromCPs. By formalizing compliance rules in BPMN 2.0 we canexpress compliance patterns that were not yet supported by

the state-of-the-art. The rule execution engine is based onour previous work [13]. Also, A* based algorithms havealready proven to be useful in the context of Petri-Net basedconformance checking [10], [18]. It is the combination of bothprevious works that enables the analysis of new compliancepatterns. This provides a novel basis for achieving practicalimprovements in a clinical setting.

In order to make this paper self-contained, we first providea basic concepts for conformance analysis. Then, we clarifythe previous works on BPMN and A* that we have used forbuilding our technical contribution.

A. Event Logs and Process Models

In this paper, we use a process model to represent acompliance rule. A process can be defined as a set of actions oractivities that happen over time, but which are related to eachother by a common goal [19]. More and more organizationsuse Information Technology (IT) systems (including HospitalInformation Systems) to support their business processes insome form, these IT systems leave their “footprints”, recordingwhat happened when. These footprints are called event logsand they are usually stored in data bases or in log files.

An event log contains many cases. Each case is describedby a trace. Each trace describes the life-cycle of a particularcase (i.e., a process instance) as a sequence of events. Anevent often refers to the activity executed. From a formalpoint of view AL denotes the set of activities that maybe recorded in the log. A∗L denotes the multi-set over AL.σ ∈ A∗L is a trace, i.e., a sequence of activities. L ∈ B (A∗L)is an event log, i.e., a multi-set of traces. For example,L = [acdeh, acdeh, abdeg] is an event log with three cases,two of which follow the same trace. However, event logs maystore additional information about events [18]. For example,many process mining techniques use extra information suchas the resource (i.e., person or device) executing or initiatingthe activity, the timestamps of the event, or data elementsrecorded with the event (e.g., the size of an order). Taking anexample (preparedecision; start; John; gold; 50euro) ∈ σLmay refer to an event describing the “start” of activity “preparedecision” by “John” for a “gold” customer claiming “50 euro”.Most organizations document their processes in some form,for example, to comply with regulations or for certificationpurposes.

A process model M = (S, SI , SF , AM , T ) is a transitionsystem over a set of activities AM with states S, initial statesSI ⊂ S, final states SF ⊂ S, and transitions T ⊂ S ×AM ×S [20]. The transition system starts in a state in SI and movesfrom one state to another according to the transition relationT , e.g., (s1, a, s2) ∈ T means that in state s1 the transitionsystem can move to state s2 while producing an event labeleda. Eventually, a final state in SF should be reached in orderto complete.

B. Modeling and Analyzing Processes via BPMN 2.0

The aforementioned approach for conformance analysisbased on Petri-Nets is using the so-called classic variant of

Fig. 1. BPMN model

Petri-Nets. This implies that the process models that are usedto encode the compliance patterns can only leverage one typeof activity modeling construct (referred to as places) and onetype of event modeling construct (referred to as transitions).

The advantage of this simplicity is that it is very easy toformalize the Petri-Net semantics. The disadvantage is that theusability of the formalism is limited. Conversely, BPMN 2.0provides a rich pallet of modeling constructs. Therefore, thelanguage is considered very usable by practitioners. At thesame time, the more advanced modeling constructs have notbeen formalized for long (such that conformance analysis wasnot possible in the first place).

Fig.1 shows an example process model in BPMN 2.0. Forthis example, M = ({s1, s2, s3, s4}, {s1},{s4},{a, b, c, d},{(s1, a, s2), (s2, b, s3), (s3, c, s4), (s3, d, s4)}). s1,s2,s3 and s4mean the states: “before execution of task a”, “after task a butbefore execution of task b”, “after task b but before executionof task c and d” and “after execution of task c or d”. AM ={a, b, c, d} is the set of activities of model M . A∗M is the multi-set over AM . β(M) ⊂ A∗M is the set of all full executionsequences, i.e., possible traces starting in a state in SI = {s1}and ending in a state in SF = {s4}. In this case, β(M) ={abc, abd}.

In our previous work we have formalized the semanticsof the majority of the BPMN 2.0 modeling constructs [13].Amongst others, the formalization enables the computation ofa so-called statespace from a process model. A statespace is aformal encoding of all possible execution traces of the model.More specifically, a statespace is a directed graph where eachnode represents a valid execution state and where an arcdenotes that the model allows moving from the source stateto the target state. Each path from the source node Nodesto a leaf node Nodel therefore represents a fully conformingprocess execution (i.e., a valid sequence of execution states).Nodes is the node in the statespace that does not haveincoming arcs. A leaf node Nodel is a node without outgoingarcs. Fig.2 shows the statespace which is generated afterthe execution of the simple model from Fig.1. The examplestatespace shows that for any execution of M , events a andsubsequently b should occur first. Then, either c or d can occurbut they should not have happened together throughout oneexecution.

Statespaces can be generated automatically for complex

Fig. 2. Generated Simple Statespace

BPMN 2.0 models, involving (even unstructured) combina-tions of advanced constructs such as the BPMN 2.0 inclusiveOR join (which realizes the aforementioned General Synchro-nizing Merge pattern), subprocess nodes, compensation activi-ties, termination events, etc [13]. Such advanced constructs arerelevant for describing compliance requirements that involvecomplex synchronization rules for clinical events. The BPMNmodels are allowed to be unstructured in the sense that (1)the number of branches per split or join construct is arbitrary(as opposed to approaches that support for example only ORjoins with two branches) and that (2) the formalization doesnot require the strict pairing of split and join constructs (asopposed to approaches that support for example only the strictclosing of an OR split by exactly one OR join). In the sectionIV, we present an example that demonstrates these strengths.

C. Aligning Event Logs and Process Model

In this section, we explain the notion of alignment to relateobserved and modeled behavior. Such an alignment shows howthe event log can be replayed on the process model [20].It is assumed that the relationship between the activities inthe model and events in the log is known, i.e., the names ofactivities in the model can be matched to the names of theevents in the log. In the following, we summarize the alreadyknown theory in a way applicable to multiple process modelinglanguages (Petri-Nets, BPMN and others).

To establish an alignment between modeled behavior(namely statespace in this paper) and event log we need torelate “moves” in the log to “moves” in the model. Essentially,a “move” in the log means traversing to the next event. Forconvenience, we introduce the set A⊥L = AL ∪ {⊥} wherex ∈ AL refers to “move x in log” and ⊥ refers to “no movein log”. A “move” in the model means one step going furtheron one of the path of the statespace. Similarly, we use theset A⊥M = AM ∪ {⊥} where y ∈ AM refers to “move y inmodel” and ⊥ refers to “no move in model”. AM is the setof the names of the activities in the model. For the examplegiven in Fig.1, A⊥M = {a, b, c, d,⊥}. The move in the log canbe mimicked by the model if the name of the move in the logis the same as the name of the move in the model. It may bethe case that some of the moves in the log cannot be mimickedby the model and vice versa.

One step in an alignment is represented by a pair (x, y) ∈A⊥L ×A⊥M and• (x, y) is a move in log if x ∈ AL and y =⊥,

• (x, y) is a move in model if and x =⊥ and y ∈ AM ,• (x, y) is a move in both if x ∈ AL and y ∈ AM ,• (x, y) is an illegal move if x =⊥ and y =⊥.

ALM = {(x, y) ∈ A⊥L ×A⊥M |x ∈ AL ∨ y ∈ AM} is the set ofall legal moves.

Let σL ∈ L be a trace in event log L and let σM ∈ β(M)be a full execution sequence of model M . An alignment of σLand σM is a sequence γ ∈ A∗LM (A∗LM is the multi-set over

ALM ). Three examples of alignments are: γ1 =a b ca b c

, γ2 =a e ⊥ ca ⊥ b c

and γ3 =a e ca b c

. Note that

we represent the moves vertically, e.g. the first move of γ1is (a, a) indicating that both the log and the model make amove.

To qualify the quality of an alignment we introduce adistance function δS . For each element (x, y) in the alignmentsequence, δ(x, y) is the cost of this element. For x ∈ AL

and y ∈ AM : δS(x,⊥) = 1 , δS(x, y) = 0 if x = y, andδS(x, y) = ∞ if x 6= y. Using the distance function δS ,δS(γ1) = δS(a, a) + δS(b, b) + δS(c, c) = 0. δS(γ2) = 2and δS(γ3) = ∞. γ1 is the best alignment. So the alignmentrelating observed and modeled behavior gives a basis to doconformance analysis.

III. AN A*-BASED ALGORITHM FOR CHECKING BPMNPATTERN CONFORMANCE

Now that we have explained the concepts for understandingconformance analysis between event logs and processes. Wepresent the A*-based log replay technique for conformancecheck between BPMN models and event logs. We use smallexamples to illustrate the technique.

A* algorithm is for finding the shortest path between twonodes in a directed graph. It has been published many timesand readers are referred to [21] for a description of the basicalgorithm. Various approaches based on the A* algorithmto find the optimal alignment are presented in [18]. In thispaper, we use the A* algorithm to find the best alignmentbetween a statespace generated from execution of the BPMNmodel and a trace, which is also called log replay technique.In the A* problem domain, the shortest path between twonodes is obtained through searching the search space graph.Typically, the search space graph is known in advance, but inour technique, it is constructed during replay. Essentially, weare finding the shortest path between an initial node withoutany alignments and a final node where the alignment betweenthe trace and a given statespace cost the least. The search spacegraph is constructed during replay. Nodes in the search spacegraph represent steps of the alignment of the given statespacematching the subsequence of the trace or itself. Here we denotethe subsequence of the trace or itself as prefix for concision.The path which is found is the optimal result alignment. Givena trace σL = 〈a, e, c〉 to be replayed on a statespace in Fig.2,we start by constructing the search space graph consisting ofonly an empty step in an alignment (i.e. as indicated by thestart node in Fig.3). Based on the start step in an alignment, we

Fig. 3. Optimization of alignment

construct other steps in an alignment (i.e. the nodes in Fig.3) assuccessors in the graph. We still use a pair (x, y) ∈ A⊥L ×A⊥Mindicating one step in an alignment. A successor is again analignment step such that:• (x, y) is a move in both, i.e., the statespace has one more

marking than its predecessor and the prefix is extendedwith a corresponding event, or

• (x, y) is a move in log, i.e., the statespace stays the same,but the prefix contains one more event, or

• (x, y) is a move in model, i.e., the statespace has onemore marking but the prefix remains the same.

Starting from the start node, there are three branches to go,(left) move in both (a, a) and cost is zero, (middle) move in log(a,⊥) with cost 1 and (right) move (⊥, a) in model with cost1. For each node, if there is a move in both, namely cost thelowest, a new node is constructed with cost 0 as a successor.Otherwise, for both move in log and move in model, thetechnique separately construct two nodes as successors. Takethe third layer for instance, the leftmost node is move in both(a, b) which cost infinitely as their names are not matched. Insuch case, the technique expand both the middle node (e,⊥)and rightmost node (⊥, b) which both cost 1. In Fig.3 nodemarked yellow does not indicate real marking in statespace,but it is to show dummy markings added corresponding to thetrace, i.e., ⊥. Similarly, the “X” in prefix means an added log

Algorithm 1 A* based log replay technique1: procedure REPLAYTECHNIQUE(σ,M, TailMatchSet) .

a trace σ, a statespace M and a set of nodesTailMatchSet with one artificial node

2: while TailMatchSet is not empty do3: Let Tail ← TailMatchSet[1]4: Delete Tail from TailMatchSet5: Let e ← Tail.Entry’s next entry in σ6: Let MarkingSet ← Tail.MarkingSet’s next

generation in M7: if e is the end of the σ & MarkingSet is the end

of the M then8: Add Tail to LeafMatches9: end if

10: if e.Name ∈MarkingSet.Name then11: Creat a new match mNode12: mNode.Type← BothCorrect13: mNode.Cost← Tail.Cost14: Add mNode to the TailMatchSet15: else16: Creat two new matches mNode0 and mNode117: mNode0.T ype← FtaskCentry18: mNode0.Cost← Tail.Cost+ 119: mNode1.T ype← CtaskFentry20: mNode1.Cost← Tail.Cost+ 121: Add mNode0 and mNode1 to the

TailMatchSet22: end if23: end while24: end procedure

entry corresponding to the statespace which can also be treatedas “skipped” activity from the model view. The condition ofstop is both the statespace and the log reach the end. The costof final node is the sum of branches from the start node to thefinal node itself. Then the leaf node with the lowest cost isselected as the path with the shortest path. On the path fromthe start node to final node, each node on the path representsan alignment step which are a move in both, a move in the logand a move in the model. Note that at some markings, there aremore than one branches to go in the statespace graph. Hencethere may be more than one successor in the graph with amove in the model.

We developed the algorithm show in Algorithm 1. Givena trace σ and a statespace M , we compare the name of theentries in the log with the corresponding marking set in oneof the generation of statespace. If the names are matched, wecreate a new match node in the search space graph mNodewith cost the same as its parent. If not, we create two newmatch nodes with increment 1 based on the parent cost foreach entry and marking pair. The output is a set of final nodesLeafMatches where each node has no successors and a costassociated as the sum of all nodes from start node which isempty to the final node itself.

Here we choose “1” as increment since we treat all the

activities as equally important. The increment represent theweights of activity and we can distinguish different activitiesby assigning them different values to carry on more intelligentanalysis.

There are three kinds of alignment steps in the alignment.Applied in practice, they have three meanings correspondingly:matched activity in both log and model, added activity in log

and skipped activity in log. Take γ2 =a e ⊥ ca ⊥ b c

for

example,aa

indicates both log and model have an activity

named a.e⊥ indicates activity e appears in the log while

according to the model, it is not supposed to be there. So

activity e is an added activity. Similarly,⊥b

indicates activity

b is supposed to be in the log, according to the model, however,it is not.

IV. ILLUSTRATIVE EXAMPLES

Given a CG/CP pattern, expressing it in BPMN languageand then with the statespace generated in [13], using A*-based log replay technique, the results of compliance betweenpattern and a trace shows not only whether it is compliant ornot but also shows where deviation happens. CPs representclinical algorithms that unfold over time by specifying theordering of tasks and activities [22]. The ordering of tasks ina process model is also referred to in the literature as control-flow, known as “workflow patterns” as well. A collectionof workflow patterns has been developed to analyze theexpressive power of languages. We use the workflow patternsfrom [23] as a frame of reference to express the CPs patternshere.

There are six classifications of work-flow patterns which arebasic control-flow, advanced branching and synchronization,structural patterns, multiple instances, state-based, cancella-tion and iteration patterns. Taking into account the clini-cal situation, we focus on basic control-flow and advancedbranching and synchronization. Additionally, we didn’t includethe patterns involving complex join gateway and conditionalevents which are not implemented in [13]. Totally, thereare 5 basic control-flow patterns and 4 advanced branchingand synchronization patterns. The descriptions are shown inTABLE I. Note that, multi-choice and General SynchronizingMerge are hard or cannot be represented by classic Petrinets. In Fig.5, we specify control-flow examples in BPMNfor clinical protocols in the cardiology domain.

We filter of the traces of an event log before conformancecheck. Let Ar and Ai be sets of activity names. Ar has thenames of activities which are involved in the compliance rule.Ai includes the names which are not in the specification ofcompliance rule but are the interest of users. Through filtering,names of the events in the traces from Ar and Ai are retained.Irrelevant events are omitted. Let σ1 =< a, p, q, e, c > be theoriginal trace and let compliance rule involve activity a andc. e though is not in the rule but it is in the scope of user

TABLE ICONTROL-FLOW PATTERNS

Pattern Name Pattern Description BPMN Syntax

Sequence An activity in a workflow process is enabled after the completion of a preceding activity in thesame process. [23]

Parallel Split The divergence of a branch into two or more parallel branches each of which execute concurrently.[23]

Synchronization The convergence of two or more branches into a single subsequent branch such that the thread ofcontrol is passed to the subsequent branch when all input branches have been enabled. [23]

Exclusive ChoiceThe divergence of a branch into two or more branches. When the incoming branch is enabled,the thread of control is immediately passed to precisely one of the outgoing branches based onthe outcome of a logical expression associated with a branch. [23]

Simple Merge The convergence of two or more branches into a single subsequent branch. Each enablement ofan incoming branch results in the thread of control being passed to the subsequent branch. [23]

Multi-choiceThe divergence of a branch into two or more branches such that when the incoming branch isenabled, the thread of control is immediately passed to one or more of the outgoing branchesbased on the outcome of distinct logical expressions associated with each of the branches. [23]

Structured SynchronizingMerge

The convergence of two or more branches (which diverged earlier in the process at a uniquelyidentifiable point) into a single subsequent branch such that the thread of control is passed to thesubsequent branch when each active incoming branch has been enabled. [23]

Multi-merge The convergence of two or more branches into a single subsequent branch. Each enablement ofan incoming branch results in the thread of control being passed to the subsequent branch. [23]

General SynchronizingMerge

The convergence of two or more branches which diverged earlier in the process into a singlesubsequent branch such that the thread of control is passed to the subsequent branch when either(1) each active incoming branch has been enabled or (2) it is not possible that any branch thathas not yet been enabled will be enabled at any future time. [23]

Combination ofother gatewaysand Inclusive JoinGateway

interest. Therefore, after filtering, we obtain σ1=< a, e, c >.In the examples below, we show the examples of analysis withfiltered traces.

A. Example of Advanced Synchronization

General Synchronizing Merge cannot be representedby classic Petri nets [12] but it is useful in the clinicaldomain. The following example in Fig.4 illustrates GeneralSynchronizing Merge. After a patient is admitted to cardiologydepartment, both Take Blood Test (by nurses) andGive Anticoagulation are performed, which is based onthe CRCL value. Based on the result of Blood Test,where INR is within range or not, it is decided whetherStop the medicine anticoagulation is skipped. Then theDetermination of medicine for the next day is made bythe cardiologists. Note that Advice Stop Anticoagulationcan be either executed before the Determine the medicineor not executed at all. For the example given, before theInclusive Join, the active incoming branches are either “GiveFondaparimux” or “Give Enoxaparin” with or without “No”branch, depending on whether “INR in range” (Condition forExclusive gateway) is true or not. Fig.6 shows the generatedstatespace of General Synchronizing Merge Example whichrecords the execution paths of activities in model Fig.4.Suppose σ1 = < Give FON,Blood Test, Advice Stop,Deter Medic >, then the result of compliance check is: γ1 =

Give FON Blood Test Advice Stop Deter MedicGive FON Blood Test Advice Stop Deter Medic

The upper row shows the event trace and the lower row shows

Fig. 4. Clinical Scenario

one of the paths from the generated statespace in Fig.6, i.e., <Give FON,Blood Test, Advice Stop,Deter Medic >.

and σ2 = < Give FON,Blood Test,Deter Medic >and the result is γ2 =:

Give FON Blood Test Deter MedicGive FON Blood Test Deter Medic

It shows given the filtered traces σ1 and σ2 is compliant tothe clinical protocol given.σ3 =< Give FON,Blood Test,Deter Medic,

Advice Stop > and the result is γ3 =:

Give FON Blood Test Deter Medic Advice StopGive FON Blood Test Deter Medic ⊥

Fig. 5. Control-flow Pattern Examples in BPMN

Fig. 6. Generated Statespace of General Synchronizing Merge Example

It shows that the specified rule given is violated as theactivity Advice Stop is present in the log while it is assumednot according to the model.

B. Evaluation

For validation, we also test other cases as listed in TABLEII and we manually check the correctness of these results. Weobserved that this method is reliable and that the number ofbranches per split or join construct is arbitrary and that the for-malization does not require the strict pairing of split and joinconstructs. This demonstrates the advantages of using BPMNconformance checking over other pattern formalizations, suchas classic Petri Nets.

V. CONCLUSION

While clinical protocols are becoming increasingly popularto hospital management and clinicians are expected to behave

under clinical protocols, corresponding conformance analy-sis techniques are still lacking. This paper enables concisespecification by using BPMN to document clinical protocolconstraints in the healthcare domain. It involves not only basiccontrol-flow patterns but also advanced synchronizations. Thispaper presents conformance analysis techniques in BPMNfirstly, which facilitates conformance monitoring and analysison advanced semantic patterns and evaluates them on complexconformance patterns from the cardiology domain. We showthat General Synchronizing Merge can be specified by BPMNand that a state of art conformance check technique can beapplied to it.

A* based log replay method can assign different weightsto different activities according to the importance for furtheranalysis. Though non-conformant behavior is not encouragedin clinical domain, some “violations” can have a perfectclinical reason and explanation. Violation of the rule doesnot necessarily mean harm to the patient. On the contrary,sometimes deviation from the rule is required to keep thepatient safe. Analysis of conformance using A* based algo-rithm to detect where deviations happen will help to figureout the reason behind it and to optimize the clinical protocols.Work in [13] support more than the semantics we mentionedin this paper such as subprocess and compensation. Furthermore, there are other dimensions to model compliance rules,for instance, data-flow dimension, organization perspective andtime-related perspective [10]. Future work aims at extendingthe compliance rules for other dimensions to model patternsin the clinical domain.

ACKNOWLEDGMENT

We thank Shan Nan and Lonneke Vermeulen for theirsubstantial support in writing this paper. The research leading

TABLE IITEST CASES OF CONFORMANCE CHECK

Basic control-flow Test case

Sequence

Three tasks in one sequence pattern vs. 10 evententries in one traceFour tasks in one sequence pattern vs. 10 evententries in one trace

Parallel Split

Two branches with three tasks vs. 10 evententries in one traceThree branches with four tasks vs. 10 evententries in one trace

Synchronization

Two branches with three tasks vs. 10 evententries in one traceThree branches with four tasks vs. 10 evententries in one trace

Exclusive Choice

Two branches with three tasks vs. 10 evententries in one traceThree branches with four tasks vs. 10 evententries in one trace

Simple Merge

Two branches with three tasks vs. 10 evententries in one traceThree branches with four tasks vs. 10 evententries in one trace

Multi-choice

Two branches with four tasks vs. 10 event entriesin one traceThree branches with five tasks vs. 10 evententries in one trace

StructuredSynchronizingMerge

Two branches with three tasks vs. 10 evententries in one traceThree branches with four tasks vs. 10 evententries in one trace

Multi-merge

Two branches with three tasks vs. 10 evententries in one traceThree branches with four tasks vs. 10 evententries in one trace

General Synchro-nizing Merge

Two branches with five tasks vs. 10 event entriesin one trace

to these results has received funding from the Brain BridgeProject sponsored by Phillips Research.

REFERENCES

[1] A. Bottrighi, F. Chesani, P. Mello, M. Montali, S. Montani, andP. Terenziani, “Conformance checking of executed clinical guidelines inpresence of basic medical knowledge,” in Business Process ManagementWorkshops. Springer, 2012, pp. 200–211.

[2] M. R. Chassin, “Practice guidelines: best hope for quality improvementin the 1990s.” Journal of occupational medicine. : official publicationof the Industrial Medical Association, vol. 32, no. 12, pp. 1199–206,Dec. 1990.

[3] J. Mei, H. Liu, G. Xie, and G. T. Lakshmanan, “An engine forcompliance checking of clinical guidelines.” Studies in health technologyand informatics, vol. 180, pp. 416–20, Jan. 2012.

[4] P. de Clercq, Guideline-based Decision Support in Medicine: ModelingGuidelines for the Development and Application of Clinical DecisionSupport System. Technische Universiteit Eindhoven, 2003.

[5] M. C. Vissers, A. Hasman, and C. J. van der Linden, “Impact of a pro-tocol processing system (ProtoVIEW) on clinical behaviour of residents

and treatment.” International journal of bio-medical computing, vol. 42,no. 1-2, pp. 143–50, Jul. 1996.

[6] R. Blaser, M. Schnabel, C. Biber, M. Baumlein, O. Heger, M. Beyer,E. Opitz, R. Lenz, and K. A. Kuhn, “Improving pathway compliance andclinician performance by using information technology.” Internationaljournal of medical informatics, vol. 76, no. 2-3, pp. 151–6, 2007.

[7] T. Forsner, A. A. Wistedt, M. Brommels, and Y. Forsell, “An approachto measure compliance to clinical guidelines in psychiatric care.” BMCpsychiatry, vol. 8, no. 1, p. 64, Jan. 2008.

[8] R. G. Brindis and C. Sennett, “Physician adherence to clinical practiceguidelines: does it really matter?” pp. 13–5, Jan. 2003.

[9] J. F. Sucher, F. A. Moore, S. R. Todd, R. M. Sailors, and B. A. McKinley,“Computerized clinical decision support: a technology to implement andvalidate evidence based guidelines.” The Journal of trauma, vol. 64,no. 2, pp. 520–37, Feb. 2008.

[10] E. Ramezani, D. Fahland, and W. M. P. van der Aalst, “Where did imisbehave? diagnostic information in compliance checking,” in BusinessProcess Management. Springer, 2012, pp. 262–278.

[11] H. T. D. Beer and B. F. V. Dongen, “Process mining and verification ofproperties: An approach based on temporal logic,” in On the Move toMeaningful Internet Systems 2005: CoopIS, DOA, and ODBASE: OTMConfederated International Conferences, CoopIS, DOA, and ODBASE2005, volume 3760 of Lecture Notes in Computer Science. Springer-Verlag, 2005, pp. 130–147.

[12] N. Lohmann, E. Verbeek, and R. Dijkman, Transactions on Petri Netsand Other Models of Concurrency II-Petri Net Transformations forBusiness Processes A Survey, K. Jensen and W. M. Aalst, Eds. Berlin,Heidelberg: Springer-Verlag, 2009.

[13] P. Van Gorp and R. Dijkman, “A visual token-based formalization ofBPMN 2.0 based on in-place transformations,” Information and SoftwareTechnology, vol. 55, no. 2, pp. 365–394, Feb. 2013.

[14] “BUSINESS PROCESS MODEL AND NOTATION (BPMN),” 2011.[15] T. Allweyer, BPMN 2.0: Introduction to the Standard for Business

Process Modeling. Books on Demand GmbH, 2010.[16] R. Muller and A. Rogge-Solti, “BPMN for healthcare processes,”

Services und ihre Komposition Proceedings of the 3rd Central-Europeanworkshop on Services and their Composition, vol. 705, pp. 65–72, 2011.

[17] H. Scheuerlein, F. Rauchfuss, Y. Dittmar, R. Molle, T. Lehmann,N. Pienkos, and U. Settmacher, “New methods for clinical pathways-Business Process Modeling Notation (BPMN) and Tangible Busi-ness Process Modeling (t.BPM).” Langenbeck’s archives of surgery /Deutsche Gesellschaft fur Chirurgie, vol. 397, no. 5, pp. 755–61, Jun.2012.

[18] W. M. P. van der Aalst, Process Mining - Discovery, Conformance andEnhancement of Business Processes. Springer, 2011.

[19] A. Rozinat, W.M.P. van der Aalst(Promotor), and A. W. (Co-promotor),“Process mining : conformance and extension,” 2010.

[20] W. van der Aalst, A. Adriansyah, and B. van Dongen, “Replayinghistory on process models for conformance checking and performanceanalysis,” Wiley Interdisciplinary Reviews: Data Mining and KnowledgeDiscovery, vol. 2, no. 2, pp. 182–192, Mar. 2012.

[21] R. Dechter and J. Pearl, “Generalized best-first search strategies and theoptimality af A*,” Journal of the ACM, vol. 32, no. 3, pp. 505–536, Jul.1985.

[22] N. Mulyar, W. M. P. van der Aalst, and M. Peleg, “A pattern-based anal-ysis of clinical computer-interpretable guideline modeling languages.”Journal of the American Medical Informatics Association : JAMIA,vol. 14, no. 6, pp. 781–787, 2007.

[23] N. Russell, A. H. M. T. Hofstede, and N. Mulyar, “Workflow controlflowpatterns: A revised view,” Tech. Rep., 2006.

Working Papers Beta 2009 - 2013 nr. Year Title Author(s) 435 434 433 432 431 430 429 428 427 426 425 424

2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013

Analyzing Conformance to Clinical Protocols Involving Advanced Synchronizations Models for Ambulance Planning on the Strategic and the Tactical Level Mode Allocation and Scheduling of Inland Container Transportation: A Case-Study in the Netherlands Socially responsible transportation and lot sizing: Insights from multiobjective optimization Inventory routing for dynamic waste collection Simulation and Logistics Optimization of an Integrated Emergency Post Last Time Buy and Repair Decisions for Spare Parts A Review of Recent Research on Green Road Freight Transportation Typology of Repair Shops for Maintenance Spare Parts A value network development model and Implications for innovation and production network management Single Vehicle Routing with Stochastic Demands: Approximate Dynamic Programming Influence of Spillback Effect on Dynamic Shortest Path Problems with Travel-Time-Dependent Network Disruptions

Hui Yan, Pieter Van Gorp, Uzay Kaymak, Xudong Lu, Richard Vdovjak, Hendriks H.M. Korsten, Huilong Duan J. Theresia van Essen, Johann L. Hurink, Stefan Nickel, Melanie Reuter Stefano Fazi, Tom Van Woensel, Jan C. Fransoo Yann Bouchery, Asma Ghaffari, Zied Jemai, Jan Fransoo Martijn Mes, Marco Schutten, Arturo Pérez Rivera N.J. Borgman, M.R.K. Mes, I.M.H. Vliegen, E.W. Hans S. Behfard, M.C. van der Heijden, A. Al Hanbali, W.H.M. Zijm Emrah Demir, Tolga Bektas, Gilbert Laporte M.A. Driessen, V.C.S. Wiers, G.J. van Houtum, W.D. Rustenburg B. Vermeulen, A.G. de Kok C. Zhang, N.P. Dellaert, L. Zhao, T. Van Woensel, D. Sever Derya Sever, Nico Dellaert, Tom Van Woensel, Ton de Kok

423 422 421 420 419 418 417 416 415 414 413 412 411

2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013

Dynamic Shortest Path Problem with Travel-Time-Dependent Stochastic Disruptions: Hybrid Approximate Dynamic Programming Algorithms with a Clustering Approach System-oriented inventory models for spare parts Lost Sales Inventory Models with Batch Ordering And Handling Costs Response speed and the bullwhip Anticipatory Routing of Police Helicopters Supply Chain Finance. A conceptual framework to advance research Improving the Performance of Sorter Systems By Scheduling Inbound Containers Regional logistics land allocation policies: Stimulating spatial concentration of logistics firms The development of measures of process harmonization BASE/X. Business Agility through Cross- Organizational Service Engineering The Time-Dependent Vehicle Routing Problem with Soft Time Windows and Stochastic Travel Times Clearing the Sky - Understanding SLA Elements in Cloud Computing Approximations for the waiting time distribution In an M/G/c priority queue

Derya Sever, Lei Zhao, Nico Dellaert, Tom Van Woensel, Ton de Kok R.J.I. Basten, G.J. van Houtum T. Van Woensel, N. Erkip, A. Curseu, J.C. Fransoo Maximiliano Udenio, Jan C. Fransoo, Eleni Vatamidou, Nico Dellaert Rick van Urk, Martijn R.K. Mes, Erwin W. Hans Kasper van der Vliet, Matthew J. Reindorp, Jan C. Fransoo S.W.A. Haneyah, J.M.J. Schutten, K. Fikse Frank P. van den Heuvel, Peter W. de Langen, Karel H. van Donselaar, Jan C. Fransoo Heidi L. Romero, Remco M. Dijkman, Paul W.P.J. Grefen, Arjan van Weele Paul Grefen, Egon Lüftenegger, Eric van der Linden, Caren Weisleder Duygu Tas, Nico Dellaert, Tom van Woensel, Ton de Kok Marco Comuzzi, Guus Jacobs, Paul Grefen A. Al Hanbali, E.M. Alvarez, M.C. van der van der Heijden

410 409 408 407 406 405 404 403 402 401 400 399

2013 2013 2013 2013 2013 2013 2013 2013 2013 2012 2012 2012

To co-locate or not? Location decisions and logistics concentration areas The Time-Dependent Pollution-Routing Problem Scheduling the scheduling task: A time Management perspective on scheduling Clustering Clinical Departments for Wards to Achieve a Prespecified Blocking Probability MyPHRMachines: Personal Health Desktops in the Cloud Maximising the Value of Supply Chain Finance Reaching 50 million nanostores: retail distribution in emerging megacities A Vehicle Routing Problem with Flexible Time Windows The Service Dominant Business Model: A Service Focused Conceptualization Relationship between freight accessibility and Logistics employment in US counties A Condition-Based Maintenance Policy for Multi-Component Systems with a High Maintenance Setup Cost A flexible iterative improvement heuristic to Support creation of feasible shift rosters in Self-rostering

Frank P. van den Heuvel, Karel H. van Donselaar, Rob A.C.M. Broekmeulen, Jan C. Fransoo, Peter W. de Langen Anna Franceschetti, Dorothée Honhon,Tom van Woensel, Tolga Bektas, GilbertLaporte. J.A. Larco, V. Wiers, J. Fransoo J. Theresia van Essen, Mark van Houdenhoven, Johann L. Hurink Pieter Van Gorp, Marco Comuzzi Kasper van der Vliet, Matthew J. Reindorp, Jan C. Fransoo Edgar E. Blanco, Jan C. Fransoo Duygu Tas, Ola Jabali, Tom van Woensel Egon Lüftenegger, Marco Comuzzi, Paul Grefen, Caren Weisleder Frank P. van den Heuvel, Liliana Rivera,Karel H. van Donselaar, Ad de Jong,Yossi Sheffi, Peter W. de Langen, Jan C.Fransoo Qiushi Zhu, Hao Peng, Geert-Jan van Houtum E. van der Veen, J.L. Hurink, J.M.J. Schutten, S.T. Uijland

398 397 396 395 394 393 392 391 390 389 388

2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012

Scheduled Service Network Design with Synchronization and Transshipment Constraints For Intermodal Container Transportation Networks Destocking, the bullwhip effect, and the credit Crisis: empirical modeling of supply chain Dynamics Vehicle routing with restricted loading capacities Service differentiation through selective lateral transshipments A Generalized Simulation Model of an Integrated Emergency Post Business Process Technology and the Cloud: Defining a Business Process Cloud Platform Vehicle Routing with Soft Time Windows and Stochastic Travel Times: A Column Generation And Branch-and-Price Solution Approach Improve OR-Schedule to Reduce Number of Required Beds How does development lead time affect performance over the ramp-up lifecycle? Evidence from the consumer electronics industry The Impact of Product Complexity on Ramp- Up Performance

K. Sharypova, T.G. Crainic, T. van Woensel, J.C. Fransoo Maximiliano Udenio, Jan C. Fransoo, Robert Peels J. Gromicho, J.J. van Hoorn, A.L. Kok J.M.J. Schutten E.M. Alvarez, M.C. van der Heijden, I.M.H. Vliegen, W.H.M. Zijm Martijn Mes, Manon Bruens Vasil Stoitsev, Paul Grefen D. Tas, M. Gendreau, N. Dellaert, T. van Woensel, A.G. de Kok J.T. v. Essen, J.M. Bosch, E.W. Hans, M. v. Houdenhoven, J.L. Hurink Andres Pufall, Jan C. Fransoo, Ad de Jong Andreas Pufall, Jan C. Fransoo, Ad de Jong, Ton de Kok Frank P.v.d. Heuvel, Peter W.de Langen, Karel H. v. Donselaar, Jan C. Fransoo Frank P.v.d. Heuvel, Peter W.de

387 386 385 384 383 382 381 380 379 378 377 375

2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012

Co-location synergies: specialized versus diverse logistics concentration areas Proximity matters: Synergies through co-location of logistics establishments Spatial concentration and location dynamics in logistics:the case of a Dutch province FNet: An Index for Advanced Business Process Querying Defining Various Pathway Terms The Service Dominant Strategy Canvas: Defining and Visualizing a Service Dominant Strategy through the Traditional Strategic Lens A Stochastic Variable Size Bin Packing Problem With Time Constraints Coordination and Analysis of Barge Container Hinterland Networks Proximity matters: Synergies through co-location of logistics establishments A literature review in process harmonization: a conceptual framework A Generic Material Flow Control Model for Two Different Industries Improving the performance of sorter systems by scheduling inbound containers

Langen, Karel H. v.Donselaar, Jan C. Fransoo Frank P. v.d.Heuvel, Peter W.de Langen, Karel H.v. Donselaar, Jan C. Fransoo Zhiqiang Yan, Remco Dijkman, Paul Grefen W.R. Dalinghaus, P.M.E. Van Gorp Egon Lüftenegger, Paul Grefen, Caren Weisleder Stefano Fazi, Tom van Woensel, Jan C. Fransoo K. Sharypova, T. van Woensel, J.C. Fransoo Frank P. van den Heuvel, Peter W. de Langen, Karel H. van Donselaar, Jan C. Fransoo Heidi Romero, Remco Dijkman, Paul Grefen, Arjan van Weele S.W.A. Haneya, J.M.J. Schutten, P.C. Schuur, W.H.M. Zijm H.G.H. Tiemessen, M. Fleischmann, G.J. van Houtum, J.A.E.E. van Nunen, E. Pratsini Albert Douma, Martijn Mes

374 373 372 371 370 369 368 367 366 365 364 363 362 361

2012 2012 2012 2012 2012 2012 2011 2011 2011 2011 2011 2011 2011 2011

Strategies for dynamic appointment making by container terminals MyPHRMachines: Lifelong Personal Health Records in the Cloud Service differentiation in spare parts supply through dedicated stocks Spare parts inventory pooling: how to share the benefits Condition based spare parts supply Using Simulation to Assess the Opportunities of Dynamic Waste Collection Aggregate overhaul and supply chain planning for rotables Operating Room Rescheduling Switching Transport Modes to Meet Voluntary Carbon Emission Targets On two-echelon inventory systems with Poisson demand and lost sales Minimizing the Waiting Time for Emergency Surgery Vehicle Routing Problem with Stochastic Travel Times Including Soft Time Windows and Service Costs A New Approximate Evaluation Method for Two-Echelon Inventory Systems with Emergency Shipments Approximating Multi-Objective Time-Dependent Optimization Problems

Pieter van Gorp, Marco Comuzzi E.M. Alvarez, M.C. van der Heijden, W.H.M. Zijm Frank Karsten, Rob Basten X.Lin, R.J.I. Basten, A.A. Kranenburg, G.J. van Houtum Martijn Mes J. Arts, S.D. Flapper, K. Vernooij J.T. van Essen, J.L. Hurink, W. Hartholt, B.J. van den Akker Kristel M.R. Hoen, Tarkan Tan, Jan C. Fransoo, Geert-Jan van Houtum Elisa Alvarez, Matthieu van der Heijden J.T. van Essen, E.W. Hans, J.L. Hurink, A. Oversberg Duygu Tas, Nico Dellaert, Tom van Woensel, Ton de Kok Erhun Özkan, Geert-Jan van Houtum, Yasemin Serin Said Dabia, El-Ghazali Talbi, Tom Van Woensel, Ton de Kok Said Dabia, Stefan Röpke, Tom Van Woensel, Ton de Kok

360 359 358 357 356 355 354 353 352 351 350 349 348 347 346 345

2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011

Branch and Cut and Price for the Time Dependent Vehicle Routing Problem with Time Window Analysis of an Assemble-to-Order System with Different Review Periods Interval Availability Analysis of a Two-Echelon, Multi-Item System Carbon-Optimal and Carbon-Neutral Supply Chains Generic Planning and Control of Automated Material Handling Systems: Practical Requirements Versus Existing Theory Last time buy decisions for products sold under warranty Spatial concentration and location dynamics in logistics: the case of a Dutch provence Identification of Employment Concentration Areas BOMN 2.0 Execution Semantics Formalized as Graph Rewrite Rules: extended version Resource pooling and cost allocation among independent service providers A Framework for Business Innovation Directions The Road to a Business Process Architecture: An Overview of Approaches and their Use Effect of carbon emission regulations on transport mode selection under stochastic demand An improved MIP-based combinatorial approach for a multi-skill workforce scheduling problem An approximate approach for the joint problem of level of repair analysis and spare parts stocking Joint optimization of level of repair analysis and spare parts stocks

A.G. Karaarslan, G.P. Kiesmüller, A.G. de Kok Ahmad Al Hanbali, Matthieu van der Heijden Felipe Caro, Charles J. Corbett, Tarkan Tan, Rob Zuidwijk Sameh Haneyah, Henk Zijm, Marco Schutten, Peter Schuur M. van der Heijden, B. Iskandar Frank P. van den Heuvel, Peter W. de Langen, Karel H. van Donselaar, Jan C. Fransoo Frank P. van den Heuvel, Peter W. de Langen, Karel H. van Donselaar, Jan C. Fransoo Pieter van Gorp, Remco Dijkman Frank Karsten, Marco Slikker, Geert-Jan van Houtum E. Lüftenegger, S. Angelov, P. Grefen Remco Dijkman, Irene Vanderfeesten, Hajo A. Reijers K.M.R. Hoen, T. Tan, J.C. Fransoo G.J. van Houtum Murat Firat, Cor Hurkens R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten Ton G. de Kok

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2011 2011 2011 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010

Inventory control with manufacturing lead time flexibility Analysis of resource pooling games via a new extenstion of the Erlang loss function Vehicle refueling with limited resources Optimal Inventory Policies with Non-stationary Supply Disruptions and Advance Supply Information Redundancy Optimization for Critical Components in High-Availability Capital Goods Analysis of a two-echelon inventory system with two supply modes Analysis of the dial-a-ride problem of Hunsaker and Savelsbergh Attaining stability in multi-skill workforce scheduling Flexible Heuristics Miner (FHM) An exact approach for relating recovering surgical patient workload to the master surgical schedule Efficiency evaluation for pooling resources in health care The Effect of Workload Constraints in Mathematical Programming Models for Production Planning Using pipeline information in a multi-echelon spare parts inventory system Reducing costs of repairable spare parts supply

Frank Karsten, Marco Slikker, Geert-Jan van Houtum Murat Firat, C.A.J. Hurkens, Gerhard J. Woeginger Bilge Atasoy, Refik Güllü, TarkanTan Kurtulus Baris Öner, Alan Scheller-Wolf Geert-Jan van Houtum Joachim Arts, Gudrun Kiesmüller Murat Firat, Gerhard J. Woeginger Murat Firat, Cor Hurkens A.J.M.M. Weijters, J.T.S. Ribeiro P.T. Vanberkel, R.J. Boucherie, E.W. Hans, J.L. Hurink, W.A.M. van Lent, W.H. van Harten Peter T. Vanberkel, Richard J. Boucherie, Erwin W. Hans, Johann L. Hurink, Nelly Litvak M.M. Jansen, A.G. de Kok, I.J.B.F. Adan Christian Howard, Ingrid Reijnen, Johan Marklund, Tarkan Tan H.G.H. Tiemessen, G.J. van Houtum F.P. van den Heuvel, P.W. de Langen,

328 327 326 325 324 323 322 321 320 319 318 317 316 315

2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010

systems via dynamic scheduling Identification of Employment Concentration and Specialization Areas: Theory and Application A combinatorial approach to multi-skill workforce scheduling Stability in multi-skill workforce scheduling Maintenance spare parts planning and control: A framework for control and agenda for future research Near-optimal heuristics to set base stock levels in a two-echelon distribution network Inventory reduction in spare part networks by selective throughput time reduction The selective use of emergency shipments for service-contract differentiation Heuristics for Multi-Item Two-Echelon Spare Parts Inventory Control Problem with Batch Ordering in the Central Warehouse Preventing or escaping the suppression mechanism: intervention conditions Hospital admission planning to optimize major resources utilization under uncertainty Minimal Protocol Adaptors for Interacting Services Teaching Retail Operations in Business and Engineering Schools Design for Availability: Creating Value for Manufacturers and Customers

K.H. van Donselaar, J.C. Fransoo Murat Firat, Cor Hurkens Murat Firat, Cor Hurkens, Alexandre Laugier M.A. Driessen, J.J. Arts, G.J. v. Houtum, W.D. Rustenburg, B. Huisman R.J.I. Basten, G.J. van Houtum M.C. van der Heijden, E.M. Alvarez, J.M.J. Schutten E.M. Alvarez, M.C. van der Heijden, W.H. Zijm B. Walrave, K. v. Oorschot, A.G.L. Romme Nico Dellaert, Jully Jeunet. R. Seguel, R. Eshuis, P. Grefen. Tom Van Woensel, Marshall L. Fisher, Jan C. Fransoo. Lydie P.M. Smets, Geert-Jan van Houtum, Fred Langerak. Pieter van Gorp, Rik Eshuis. Bob Walrave, Kim E. van Oorschot, A.

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2010 2010

Transforming Process Models: executable rewrite rules versus a formalized Java program Getting trapped in the suppression of exploration: A simulation model A Dynamic Programming Approach to Multi-Objective Time-Dependent Capacitated Single Vehicle Routing Problems with Time Windows

Georges L. Romme S. Dabia, T. van Woensel, A.G. de Kok

312 2010 Tales of a So(u)rcerer: Optimal Sourcing Decisions Under Alternative Capacitated Suppliers and General Cost Structures

Osman Alp, Tarkan Tan

311 2010 In-store replenishment procedures for perishable inventory in a retail environment with handling costs and storage constraints

R.A.C.M. Broekmeulen, C.H.M. Bakx

310 2010 The state of the art of innovation-driven business models in the financial services industry

E. Lüftenegger, S. Angelov, E. van der Linden, P. Grefen

309 2010 Design of Complex Architectures Using a Three Dimension Approach: the CrossWork Case R. Seguel, P. Grefen, R. Eshuis

308 2010 Effect of carbon emission regulations on transport mode selection in supply chains

K.M.R. Hoen, T. Tan, J.C. Fransoo, G.J. van Houtum

307 2010 Interaction between intelligent agent strategies for real-time transportation planning

Martijn Mes, Matthieu van der Heijden, Peter Schuur

306 2010 Internal Slackening Scoring Methods Marco Slikker, Peter Borm, René van den Brink

305 2010 Vehicle Routing with Traffic Congestion and Drivers' Driving and Working Rules

A.L. Kok, E.W. Hans, J.M.J. Schutten, W.H.M. Zijm

304 2010 Practical extensions to the level of repair analysis R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten

303 2010 Ocean Container Transport: An Underestimated and Critical Link in Global Supply Chain Performance

Jan C. Fransoo, Chung-Yee Lee

302 2010 Capacity reservation and utilization for a manufacturer with uncertain capacity and demand Y. Boulaksil; J.C. Fransoo; T. Tan

300 2009 Spare parts inventory pooling games F.J.P. Karsten; M. Slikker; G.J. van Houtum

299 2009 Capacity flexibility allocation in an outsourced supply chain with reservation Y. Boulaksil, M. Grunow, J.C. Fransoo

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2010

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297 2009 Responding to the Lehman Wave: Sales Forecasting and Supply Management during the Credit Crisis

Robert Peels, Maximiliano Udenio, Jan C. Fransoo, Marcel Wolfs, Tom Hendrikx

296 2009 An exact approach for relating recovering surgical patient workload to the master surgical schedule

Peter T. Vanberkel, Richard J. Boucherie, Erwin W. Hans, Johann L. Hurink, Wineke A.M. van Lent, Wim H. van Harten

295

2009

An iterative method for the simultaneous optimization of repair decisions and spare parts stocks

R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten

294 2009 Fujaba hits the Wall(-e) Pieter van Gorp, Ruben Jubeh, Bernhard Grusie, Anne Keller

293 2009 Implementation of a Healthcare Process in Four Different Workflow Systems

R.S. Mans, W.M.P. van der Aalst, N.C. Russell, P.J.M. Bakker

292 2009 Business Process Model Repositories - Framework and Survey

Zhiqiang Yan, Remco Dijkman, Paul Grefen

291 2009 Efficient Optimization of the Dual-Index Policy Using Markov Chains

Joachim Arts, Marcel van Vuuren, Gudrun Kiesmuller

290 2009 Hierarchical Knowledge-Gradient for Sequential Sampling

Martijn R.K. Mes; Warren B. Powell; Peter I. Frazier

289 2009 Analyzing combined vehicle routing and break scheduling from a distributed decision making perspective

C.M. Meyer; A.L. Kok; H. Kopfer; J.M.J. Schutten

288 2009 Anticipation of lead time performance in Supply Chain Operations Planning

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287 2009 Inventory Models with Lateral Transshipments: A Review

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286 2009 Efficiency evaluation for pooling resources in health care

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285 2009 A Survey of Health Care Models that Encompass Multiple Departments

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284 2009 Supporting Process Control in Business Collaborations

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283 2009 Inventory Control with Partial Batch Ordering O. Alp; W.T. Huh; T. Tan

282 2009 Translating Safe Petri Nets to Statecharts in a Structure-Preserving Way R. Eshuis

281 2009 The link between product data model and process model J.J.C.L. Vogelaar; H.A. Reijers

280 2009 Inventory planning for spare parts networks with delivery time requirements I.C. Reijnen; T. Tan; G.J. van Houtum

279 2009 Co-Evolution of Demand and Supply under Competition B. Vermeulen; A.G. de Kok

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2010 2009

Toward Meso-level Product-Market Network Indices for Strategic Product Selection and (Re)Design Guidelines over the Product Life-Cycle An Efficient Method to Construct Minimal Protocol Adaptors

B. Vermeulen, A.G. de Kok R. Seguel, R. Eshuis, P. Grefen

276 2009 Coordinating Supply Chains: a Bilevel Programming Approach Ton G. de Kok, Gabriella Muratore

275 2009 Inventory redistribution for fashion products under G.P. Kiesmuller, S. Minner

demand parameter update

274 2009 Comparing Markov chains: Combining aggregation and precedence relations applied to sets of states

A. Busic, I.M.H. Vliegen, A. Scheller-Wolf

273 2009 Separate tools or tool kits: an exploratory study of engineers' preferences

I.M.H. Vliegen, P.A.M. Kleingeld, G.J. van Houtum

272

2009

An Exact Solution Procedure for Multi-Item Two-Echelon Spare Parts Inventory Control Problem with Batch Ordering

Engin Topan, Z. Pelin Bayindir, Tarkan Tan

271 2009 Distributed Decision Making in Combined Vehicle Routing and Break Scheduling

C.M. Meyer, H. Kopfer, A.L. Kok, M. Schutten

270 2009 Dynamic Programming Algorithm for the Vehicle Routing Problem with Time Windows and EC Social Legislation

A.L. Kok, C.M. Meyer, H. Kopfer, J.M.J. Schutten

269 2009 Similarity of Business Process Models: Metics and Evaluation

Remco Dijkman, Marlon Dumas, Boudewijn van Dongen, Reina Kaarik, Jan Mendling

267 2009 Vehicle routing under time-dependent travel times: the impact of congestion avoidance A.L. Kok, E.W. Hans, J.M.J. Schutten

266 2009 Restricted dynamic programming: a flexible framework for solving realistic VRPs

J. Gromicho; J.J. van Hoorn; A.L. Kok; J.M.J. Schutten;

Working Papers published before 2009 see: http://beta.ieis.tue.nl