Simulation Framework to Analyse Operating Room
Release Mechanisms
Rimmert van der Kooij, Martijn Mes, Erwin Hans
Beta Working Paper series 454
BETA publicatie
ISBN
ISSN
NUR
Eindhoven
WP 454 (working
paper)
982
April 2014
Simulation Framework to Analyse Operating Room Release
Mechanisms
Rimmert van der Kooija , Martijn Mesa,∗, Erwin Hansa
a Centre
for Healthcare Operations Improvement and Research, University of Twente, The Netherlands
Abstract
The block time (BT) schedule, which allocates Operating Rooms (ORs) to surgical specialties,
causes inflexibility for scheduling outside BT, new surgeons, new specialties, and for specialties
that have fluctuation in the number of surgeries. For this inflexibility, we introduce the concept
of releasing ORs, and present a generic simulation and evaluation framework that can be used by
hospitals to evaluate various release mechanisms. The simulation and evaluation framework is illustrated by a case study at Vanderbilt Medical Center and University (VUMC) in Nashville. The
results show that introducing a release policy has benefits in decreasing the number of unscheduled patients and decreasing access time, without affecting the specialties originally assigned to
the released rooms.
1. INTRODUCTION
Most hospitals assign Operating Rooms (ORs) to specialties (e.g., Orthopedics, Gynecology)
using a Block Time (BT) schedule, where BT can be assigned to a specialty for the whole day or
part of the day. This schedule is typically cyclical (e.g., cycles of 1 or 2 weeks). Hospitals want
to make best use of their resources, conduct surgeries safely, and above all want to keep patient
satisfaction high. Arrival rates of patients vary per specialty and over time. An exact fit between
the number of patients and a cyclical division of BT is impossible without generating waiting
lists. According to Baugh and Li (2012), when there are enough hospitals around, patients already walk away when the access time is higher than a couple of days. In order to reduce waiting
times, specialties will have to go into overtime, even though there might be other specialties that
do not have all their BT utilized.
To create more flexibility in the BT schedule, a release mechanism can be introduced. According to Dexter and Macario (2004), releasing a room can be defined as: making allocated but
unfilled block time on the short term available to other surgeons or specialties. Releasing a room
is a breech with the principal of BT scheduling where a specialty owns a block. Releasing the
remaining time creates flexibility for specialties that have utilized all of their own BT without
having to extend beyond their own BT into overtime. The patients also benefit from this practice
since an earlier date for their surgery can be planned.
The motivation for this article is the request by Vanderbilt University and Medical Center
(VUMC) to evaluate their current OR scheduling approach, to assess the limitations of the current
∗ Corresponding
author. E-mail: m.r.k.mes@utwente.nl; Tel.: +31 534894062; Fax: +31 534892159.
April 9, 2014
system, and to propose interventions to improve it. VUMC has a 7 day release program that
releases all unscheduled BT of specialties in the coming 7 days to all specialties.
To research, evaluate, and test other approaches for the releasing of ORs, we will use simulation. In the literature there has been an increase in articles that describe simulation in healthcare
(Fone et al., 2003; G¨unal and Pidd, 2010) and, more specifically, simulation of OR scheduling
(Ferrin et al., 2004; Lowery and Davis, 1999). However, these models cannot easily be adapted
towards other hospitals. Simulation model reuse is done infrequently (Robinson et al., 2004).
The objective of this article is (i) to present a generic discrete event simulation (DES) framework
to evaluate the scheduling of ORs with a releasing mechanism, and (ii) to apply this simulation framework to the case study of VUMC to provide insight into the effects of various release
mechanisms.
In the remainder of this article, we subsequently describe the problem (Section 2), the conceptual simulation model (Section 3), and the application of this simulation model to the case of
VUMC (Section 4). We end with conclusions in Section 5.
2. PROBLEM DESCRIPTION
To demarcate the scope of our research, we use the framework for health care planning and
control (Hans et al., 2011). Our scope is limited to the managerial area “resource capacity planning” and to the hierarchical fields “tactical and offline operational” control, i.e., we concentrate
on the tactical and offline operational planning of resources, and therefore exclude the strategic
and online operational decisions. This means that emergency patients are not taken into account.
This decision is made because we focus on the analysis of various release strategies, which has
a minor impact on the way we can handle the emergency patients.
Dexter and Macario (2004) discuss when to release OR time based on maximizing efficiency.
They describe eight conditions from previous work, which we summarize here. Here, we use
the term case to denote a patient that will undergo surgery. Relevant conditions are: (i) allocate
time to maximize OR efficiency; (ii) specialties with released BT should still be able to schedule
additional cases; (iii) future OR allocations should not be affected by whether the OR is released;
(iv) when a specialty has filled all its OR time, but wants to schedule another case, it is beneficial
to perform this case in underutilized time of another specialty than in overtime, and only release
a room if this is the case; and (v) room time should be released based on the expected under
utilization on the day of surgery, because of different arrival rates between specialties.
According to Shaneberger (2003), at least four other hospitals in the US have a strategy for
releasing ORs and the strategy differs from the suggestions made by Dexter et al. (2003) in the
interview and his articles (Dexter and Epstein, 2005; Dexter and Macario, 2004; Dexter et al.,
2003; Shaneberger, 2003). In the literature, two ways to release ORs are discussed: (i) release
the room on a certain day before surgery (Shaneberger, 2003) and (ii) release the room that
is the least utilized, or has the lowest expectation to be utilized when a new case needs to be
scheduled outside the BT (Dexter et al., 2003). In this article we review option (i), releasing ORs
on a certain day before surgery, e.g., 7 days before the day of surgery (DoS). When scheduling
according to the release policy is applied, there can be a request to schedule the case on a certain
day that is pre-arranged with the patient (request day). Relaxing this request day is one of the
possibilities, e.g., by scheduling a day sooner or later. These are strategies that can be evaluated
in a simulation model and are bounded to the release policy.
In practice scheduling happens with or without a waiting list, the presence or absence of a
waiting list is not influenced by the absence or presence of a release policy, but depends mostly
2
on country “preference” (Dexter et al., 1999). In the US “Any workday” (Dexter et al., 2003)
scheduling is usual, which means that the surgeon or surgeon scheduler, schedules the surgery
with the patient in their office. In Europe it is usual that, in the clinic the surgeon determines that
the patient needs surgery. The patient is then is placed on a waiting list. Waiting time or access
time between countries varies significantly (Dexter et al., 1999) also due to the strategy taken.
There is a difference between the scheduling of regular elective cases and the release cases,
for both different strategies can be applied. For either one of them a waiting list can be used.
The use or absence of a waiting list has implications for the scheduling policies. To schedule the
cases, “on-line bin packaging” (OBP) algorithms can be used (Dexter et al., 1999). Online here
means that the patient is given a surgical date the moment the surgery is requested. Dexter et al.
(1999) describe four possibilities: Next Fit, First Fit, Best Fit or Worst Fit. When making use
of a waiting list in the simulation study, Regret Based Random Sampling (RBRS) (Hans et al.,
2008) with an extra optimization via Random Exchange Method (REM) can be used to schedule
the cases. The combination of a waiting list for elective cases and applying an OBP algorithm for
the released cases with-out a waiting list is also a possibility or vice versa. This would mean that
a waiting list is used for the elective patients and that when the rooms are released the requested
cases are planned directly by using the OBP algorithms. To discuss the different scheduling
options, we refer in the remainder of this paper to elective block time scheduling and scheduling
through a release policy by elective cases and staged cases respectively.
To evaluate the scheduling options (for both the elective cases and the staged cases), and
the scenarios (maximum capacity, request day and scheduling policies) bounded to staged case
scheduling, we use simulation. To evaluate the simulated scenarios, we use the following performance indicators:
• Utilization rate: total time available in the BT schedule divided by the total time required
by the cases, including turnaround time.
• Overtime: total surgery time past the official close time of the OR in the BT, including the
cleanup or turnover time after the last surgery.
• Undertime: total non-utilized OR time within regular BT, which is not scheduled with
surgeries.
• Access time: time between a request for surgery and the first possibility to schedule the
case.
• Number of cases scheduled.
• Number of unscheduled cases: due to resource constraints some surgeries will be unable
to accommodate in the schedule.
3. SIMULATION MODELLING
This paper proposes a generic DES framework for the performance evaluation of various
ways of OR scheduling. In this model, we incorporate the possibility for a release mechanism.
We describe the three main components of our simulation model: entities, resources, and processes. In the simulation model the entities will be represented by the patients that undergo the
surgery. They are the moving parts through the simulation model. The resources will be represented by the physical resources in the hospital, e.g., operating rooms, the medical equipment,
3
and medical staff. The processes describe how the entities and the resources are connected and
how the patient is scheduled and undergoes the surgery. In the upcoming subsections, we present
the individual components of our simulation framework (patients, resources, and processes), after which we present the complete framework.
3.1. Patients
The attributes of the patient determine when and where the patient will be scheduled. The
main attributes of the patient are: the arrival process and the surgery type. To attribute a surgery
type to a patient, we need to determine the case mix share of the specialty, and the surgery types
performed by the specialties. The case mix share is used to determine the number of patients
that need to be generated per specialty, and the number of surgery types per specialty is used
to determine which surgery type to attribute to the patient. Per surgery type, a procedure time
distribution can be generated.
To schedule and simulate the cases, we need the following information: number of cases per
week, distribution of case times, turnaround time and arrival rate of the cases (Lowery and Davis,
1999). Cases for different specialties have different arrival rates and differ in case time. More
details are obtained when the distributions of case times are obtained on procedure level instead
of specialty level, this can only be done when enough data is available to fit distributions per case
type. For each case, we use the following attributes: ID (to track the case), arrival date and time,
requested surgery date, specialty, surgery type of the specialty, surgeon, expected duration (time
between wheels in wheels out), real duration, expected turnover time, real turnover time, delay
and downtime, and patient type (inpatient or outpatient). Which case is generated is based on the
share the surgery type has within the specialty and which share the specialty has among the total
specialties.
3.2. Resources
There are several resources bound to the ORs. We distinguish between: staff, rooms, and
equipment. We distinguish three different staff types: anesthesia staff, OR nurses, and the surgeons. We assume that the anesthesiologists and the nursing staff are assigned to the ORs, and
the surgeons are bounded to the OR where the surgery of their patient takes place. For the ORs,
we assume there is a BT division between the different specialties in the hospital and that the
opening hours per day of the week are known. Also limitations of certain OR rooms need to be
taken into account. For example, certain surgeries can only take place in a negative pressure OR.
This is also tied to the equipment: some ORs have fixed equipment needed for certain surgeries,
whereas other equipment can be moved around between ORs.
As input we use the number of hours a surgeon or specialty has certain BT, and the number
of blocks a surgeon or specialty has per week (Dexter et al., 1999; Lowery and Davis, 1999). In
our case study at VUMC, the BT schedule repeats itself every week; the blocks are allocated per
room, per day to a surgeon, and the ORs have different opening hours. The BT schedule also
defines which rooms are allowed to be released (see Section 4.1).
3.3. Processes
Figure 1 shows the processes involved from the moment the patient arrives at the hospital to
the moment the patient leaves the hospital. The patient initiates the scheduling process by either
going to a clinic or to the ER. In the clinic or the ER, the physician determines whether the case
is urgent or elective. Urgent cases leave the elective scheduling system and are handled in the
4
online operational planning processes, which are excluded from this simulation framework (see
Section 2).
To include the release policy, we include the decision to determine whether the case needs to
be scheduled according to the release policy or that the case will be scheduled according to the
regular BT scheduling. In Figure 1, we included also the option of using the waiting list. The
waiting list can be used either to generate a list for the release policy or for regular elective BT
scheduling.
For scheduling with or without waiting list, different strategies for scheduling cases can be
applied as discussed in Section 2. Also a different scheduling strategy can be used to schedule
staged cases or elective cases, represented by the two different processes in Figure 1. The patient
thereafter is notified and admitted for surgery according to the schedule and surgery is performed.
Thereafter the patient leaves the system, is discharged, or transferred (e.g., ICU, PACU, ward, or
morgue).
The way the processes are organized in the hospital are used as input for the simulation
model. Besides the regular processes of scheduling and surgery, we also include downtime and
delays since they influence the performance heavily. According to Lowery and Davis (1999),
15-20% downtime is a realistic target in most operating rooms. In our model, the delay can be
attributed to the patient or can be generated at the event trigger end task, as shown later on in
Figure 2 and Figure 3.
Patient
Hospital entry
Arrives at ER
Clinic visit
Urgent case?
Elective scheduling
Yes
Add-on case or
urgent. Online
operational
handling
OR Department
No
Scheduling
with waiting
list?
No
Releasing
policy
effectuated
Patient notified for
surgery
Patient admitted
for surgery
Yes
Yes
Waiting list
No
Schedule
according to policy
in BT/OR
Waiting list
scheduling
effectuated
Schedule
according to
releasing policy in
BT/OR
Surgery
Patient
discharged,
transferred or
deceased
Figure 1: Processes for scheduling elective patients
5
3.4. Events
The simulation is driven by events that trigger the processes, such as patient arrival, patient
scheduling, surgery, and departure. Our conceptual model is intended for DES, where an System
Dynamics Simulation or Agent Based Simulation does not give the required visual representation
of the individual patients, processes and queues (G¨unal, 2012). To simulate the, in our opinion,
two main different approaches to scheduling, the situation with and without a waiting list, we
present two versions of the model. In the two conceptual simulation models we made the tradeoff between level of detail and generalizability. The conceptual simulation model is designed to
support the comparison of various strategies for releasing rooms.
3.4.1. Conceptual model with waiting list
Following the framework from Mes and Bruens (2012), we identify four events that trigger
processes or decisions: New Hour, New Patient, End Task and End Delay (Figure 2). The
processes in Figure 2 are connected with solid lines; dotted lines are used to indicate information
flow between processes and databases.
After a new hour has started, the event ‘New Hour’ checks whether the release policy is
active, and ask whether it is time to schedule the elective waiting list or the staged waiting list.
This event is ended when this decision is answered with ‘No’. A waiting list can be scheduled,
e.g., at the end of the day or at the start of a day. Every hour, the event New Hour checks
whether it is time to schedule the elective waiting list and staged waiting list. When it is time
to schedule the waiting list, the process ‘scheduling elective patients into the schedule’ or the
process ‘scheduling the staged cases into the schedule’ is put into effect. This process saves the
schedule, erases the scheduled patient from the waiting lists, and might consult the BT schedule
to create the appropriate schedule.
The event New Patient triggers the process that creates attributes to this patient (see Section
3.1). The decision whether the release policy is active for this patient determines whether the
patient is placed on the elective waiting list or the staged waiting list, and End event is called to
close this process.
The End Task event is triggered every time the OR finishes a case. It determines whether
a delay is required. It is critical that this delay or downtime is modeled accurately since it
influences the outcomes (Lowery and Davis, 1999). When a delay is required, this delay is
started, after which the End Delay event is triggered. When no delay is required, the process of
calling the next patient from the schedule is initiated.
The End Delay event in Figure 2 triggers also the process of calling the next patient from
the schedule. Using common random numbers, a time from the distribution is drawn that will
be used to simulate the patient/case. The case is started and the End Task is called. The patient
leaves the system when all its tasks are finished.
3.4.2. Conceptual model without waiting list
The conceptual model without waiting list, as shown in Figure 3, differs from the conceptual
simulation model with waiting list (Figure 2), in the process that takes place after the event New
Hour or New Patient is triggered. When the decision whether the release policy is active is
triggered at the event of a New Patient, the patients are scheduled directly into the schedule, and
are stored in the table ‘scheduled cases’, it omits the waiting list as shown in Figure 2. The event
new hour in Figure 3 triggers the decision whether the current schedule needs to be optimized.
6
New Hour
New Patient
End Task
Release policy
active?
Create attributes,
arrival rate,
surgery type and
distribution of OR
time duration
Delay
required?
No
Yes
Yes
Time to
schedule waiting
list?
No
Call next patient
from scheduled
cases
Yes
Time to
schedule staged
cases?
No
End Delay
Yes
No
Yes
Release policy
active?
Place patient on
staged waiting list
(call end)
No
Start delay
(call end delay)
Draw with CRN
time from surgery
time distribution
(different than
expected time/
duration)
Place patient on
elective waiting list
(call end)
Simulate surgery
(Call end task)
Waiting list
elective
patients
Waiting list
staged patients
Block
Schedule
Schedule elective
waiting list into
schedule
Schedule staged
waiting list into
schedule
Send patient Home/
PACU/Ward/ICU
Scheduled
cases
End
Figure 2: Scheduling with a waiting list
4. A CASE STUDY
To illustrate our simulation and evaluation framework, we perform a case study in VUMC.
In the case study, we show that the input parameters and processes described are sufficient to
simulate the OR department of this hospital. The case study shows the application and results of
the simulation. We first present the experimental settings (Section 4.1) after which we present
the experimental results (Section 4.2).
4.1. Experimental settings
In VUMC, the elective scheduling is done according to the ‘Any Workday’ scheduling (Dexter et al., 2003) principle. The patient leaves the clinic with a date and time for surgery. VUMC
is releasing the rooms 7 days ahead of the DoS. When a room is released this means: blocking
the ability to schedule directly in the specialty own BT for the upcoming 7 days, instead a day is
requested for the surgery, and the cases are put on a staged cases list, and by the end of the day
these cases are scheduled on a first come first served (FCFS) basis for the DoS. The staged cases
list is scheduled at the end of the day. Scheduling cases longer than 7 days ahead of the DoS
happens according to the regular BT scheduling.
In this case study, we modeled the 50 ORs of VUMC that are ‘on campus’. Of these 50
ORs, there are 47 inpatient ORs and 3 outpatient ORs (VUMC has more outpatient ORs but
they are located ‘off campus’). Of the 50 ORs, 11 in total are not released, e.g., cardiac surgery
and orthopedic trauma rooms are not released under the release policy. Not releasing means the
specialties are still allowed to plan cases in these rooms without having to put them on a staged
case list.
7
New Hour
New Patient
End Task
Optimize
schedule with
local search?
Create attributes,
arrival rate,
surgery type and
distribution of OR
time duration
Delay
required?
End Delay
No
Call next patient
from scheduled
cases
Yes
No
End
Yes
Apply local search
to schedule /
scheduled cases
Yes
Release policy
active?
Schedule patient
based on releasing
policy
No
Start delay
(call end delay)
Schedule patient
based on regular
Block Time
scheduling
Draw with CRN
time from surgery
time distribution
(different than
expected time/
duration)
Simulate surgery
(Call end task)
Block
Schedule
Send patient Home/
PACU/Ward/ICU
Scheduled
cases
Figure 3: Scheduling without a waiting list
To validate the simulation model, we used the verification and validation tools described
by Law (2007). For example, we compared the historic data to the simulated results and the
utilization rate was 2.1% lower in the simulation with the same number of surgeries performed
on a yearly schedule. This was acceptable given the choice made in the fitting of the statistical
distributions and level of the detail that was put into the simulation model. The assignment of
cases was also similar to the historic schedules, so VUMC trusts the model as a sufficiently
accurate representation of the OR department.
In the simulation, we first create the OR schedule with the attributes assigned to a new patient
event as shown in Figure 4. The top schedule of Figure 4 gives an overview of how the schedule
looks like based on the scheduling heuristics applied, where the scheduling is done based on the
expected duration. The bottom schedule shows how the day would look like when the actual
schedule is put into effect and all the patients show up. The bottom schedule also shows that
before certain surgeries, there is a delay before the surgery takes place. Examples of non-released
rooms are VOR13 and VOR8, which show underutilized time. In the other ORs, we can clearly
see that, after the specialty scheduled their own cases, the rooms were released and cases of a
different specialty are added. For example OR3 has two colors, first otolaryngology (OTO) cases
in light green, then a plastic surgery case is scheduled in dark gray. The bottom schedule also
shows that rooms can run into overtime due to variation in the surgery duration (the black arrows
indicate the regular time for various ORs).
To represent the current situation in VUMC, we applied a ‘first fit’ rule for the regular elective
cases that are scheduled in regular BT, this is in accordance with the ‘Any Workday’ scheduling.
The scheduling of regular cases happens directly into the BT schedule, which is release period
plus one day, in this case the 8th day from the arrival of the patient (first come first served).
Staged cases have a request day. When a staged case appears, we first try to apply a ‘first
fit’ rule to the rooms of the specialty on the requested day. The rooms of the specialties will not
always give a possible solution. When none of the specialty rooms is available, then a ‘worst fit’
8
Figure 4: Simulated schedule
rule is applied to all of the released rooms. We schedule according to the ‘worst fit’ rule in order
to leave the most room for surgeries of the specialty that appear later in time. When a ‘worst fit’
was not possible on the request day, the case is placed on the unable to schedule list (one of the
key performance indicators).
In our simulation, we have also applied alternative strategies to the scheduling of the cases.
For VUMC we first determined when the maximum capacity of the ORs was reached, then we
simulated the various prospective solutions for VUMC. The prospective solutions are: (i) varying
volume, (ii) varying request day constraint, and (iii) varying scheduling policy. In Section 4.2
we discuss how we simulated the prospective solutions and present the results.
4.2. Experimental results
In this section, we present the results for how the system performs with an increasing number
of cases, and the performance of the prospective solutions.
9
4.2.1. Varying number of patients
Table 3 show the results for increasing number of patients per week, where VUMC currently
has 630 patients per week. We conclude that (i) foremost the 7-day release patients cannot be
scheduled, (ii) the number of canceled cases increases more rapidly after 700 cases per week,
(iii) overtime is relatively stable and bounded to the variance of the surgeries, but undertime
decreased, and therefore the utilization rate increases, (iv) with around 760 patients scheduled in
a week the utilization rate of 98% is reached, (v) access time remains relatively stable, only for
the elective patients it increases, and (vi) the average duration of the unscheduled cases decreases
when the number of unscheduled cases increase.
Table 1: Results with varying number of patients
Volume (cases per week):
Utilization rate (%) without turnover
Utilization rate (%) with turnover
Overtime (% of available time)
Undertime (% of available time)
Access time (days) 7-day release patients
Access time (days) regular elective patients
Nr of unscheduled cases
Nr of unscheduled 7-day release cases
Avg duration (hours) unscheduled cases
610
67%
79%
10%
22%
3.5
7
103
78
9.3
620
68%
81%
11%
21%
3.5
7.1
109
89
8.9
630
69%
82%
11%
20%
3.5
7.1
169
139
8.4
640
70%
84%
12%
19%
3.5
7.2
149
128
7.7
650
71%
84%
11%
18%
3.5
7.5
146
124
8.2
680
74%
88%
12%
16%
3.6
7.4
272
249
6.8
710
78%
92%
14%
13%
3.6
7.3
483
461
6
4.2.2. Varying request day constraint
Currently, cases are labeled as unscheduled when on the request day the case cannot be
scheduled. We choose to alter this approach and simulate other possibilities. The possibilities
we choose: extend the request day with the day after (+1Day), extend with one day before and
one day after (±1Day), extend with 2 days after (+2Day), and an all day approach (AllDay).
The result in Table 5 show that one day, one day before and after, and two days after have
almost the same performance. The all days approach gives a boost to the number of unscheduled
surgeries. We also tested the scenarios in the case of 730 scheduled surgeries per week, and
observe similar results (results not shown here). Scheduling the volume of 730 cases according
to the current scheduling policy results in 654 unscheduled cases, where the all days approach
results in 42 unscheduled cases.
Implication for the patients of changing the request day to multiple days is that they do not
know up front on which of the coming 7 days their surgery is to be scheduled. This means
that there is a trade-off between the number of cases that can be scheduled and uncertainty with
respect to the surgery date. We advise only to put the policy of scheduling all days into effect
when the number of cases increases and there is a need to schedule more patients.
4.2.3. Varying scheduling policy
To show the impact of the 7-day release policy for VUMC, we simulate the following policies: current number of patients without using a release policy (NoRelease), current situation
with the 7-day release program (7Day), the current situation with the 7-day release program and
a waiting list of one day (7DayList), situation with the 7-day release program with 730 cases
(7Day730), and the situation with the 7-day release program and a waiting list of one day with
10
740
80%
95%
15%
11%
3.6
7.9
810
784
5.3
Table 2: Results with varying request day constraint
Variation from request day:
Utilization rate (%) without turnover
Utilization rate (%) with turnover
Overtime (% of available time)
Undertime (% of available time)
Access time (days) 7-day release patients
Access time (days) regular elective patients
Nr of unscheduled cases
Nr of unscheduled 7-day release cases
Avg duration (hours) unscheduled cases
Current
69%
82%
11%
20%
3,5
7,1
169
139
8,4
+1Day
69%
82%
11%
20%
3,5
7,2
96
78
8,7
±1Day
70%
83%
11%
20%
3,5
7,1
94
73
8,4
+2Day
70%
83%
11%
20%
3,5
7,1
93
71
8,2
AllDay
69%
82%
11%
20%
3,6
7,4
35
14
11,6
730 cases (7DayList730). For the policies that use a waiting list, we sort the cases on this waiting
list on decreasing expected duration. We then apply ‘first fit’ for the specialty rooms and ‘worst
fit’ when specialty rooms are not available.
Table 3 shows that scheduling without the 7-day release policy does not lead to an improvement: with 3738 unscheduled cases it performs poorly. Also the other key performance indicators show a decrease in performance. The use of a waiting list of only one day (7DayList
and 7DayList730) leads to an increased performance. The number of unscheduled cases drops
from 169 to 77 and in the case of 730 scheduled cases a week, it drops from 640 to 449. We
recommend VUMC to implement this approach.
Table 3: Results with varying scheduling policy
Scenario:
Utilization rate (%) without turnover
Utilization rate (%) with turnover
Overtime (% of available time)
Undertime (% of available time)
Access time (days) 7-day release patients
Access time (days) regular elective patients
Nr of unscheduled cases
Nr of unscheduled x-day release cases
Avg duration (hours) unscheduled cases
NoRelease
62%
74%
12%
23%
0,0
8,4
3738
0
2,9
7Day
69%
82%
11%
20%
3,5
7,1
169
139
8,4
7DayList
69%
82%
12%
20%
3,5
7,2
77
57
9,5
7Day730
79%
95%
15%
12%
3,6
7,9
654
640
5,4
7DayList730
80%
95%
15%
11%
3,6
8,0
449
432
5,2
5. DISCUSSION & CONCLUSION
In this paper, we presented a generic discrete event simulation framework. We showed that
the simulation model is capable of evaluating the performance of the OR department on key
performance indicators. We showed that different what-if approaches could be simulated. The
simulation model is also applicable to other hospitals.
We expect different results and policies for different hospital characteristics. Small hospitals
might achieve higher benefits compared to larger hospitals because it is harder to find a good fit
between demand and need for the specialties in regular BT assignment. We displayed a simula11
tion framework that is able to answer the question whether it is beneficial to implement a release
policy for a hospital and a model that is able to simulate the best release policy.
We illustrated our simulation model using a case study at VUMC. We showed that for VUMC
it is beneficial to use the 7-day release policy, because of a reduction in access time, a reduction
in the number of unscheduled surgeries, and increased flexibility for the surgeons. We therefore
advise that other hospitals take into consideration to implement the releasing ORs. This article
gives a guideline on how to simulate and evaluate other OR departments.
ACKNOWLEDGMENTS
We like to express our gratitude to Vanderbilt University and Medical Center in general and
in particular to Jesse Ehrenfeld, Vikram Tiwari, and Beth Adame without whose support and
information this research would not have been possible.
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12
Working Papers Beta 2009 - 2014
nr. Year Title
Author(s)
454 2014 Simulation Framework to Analyse Operating Room Rimmert van der Kooij, Martijn Mes,
Release Mechanisms
453 2014 A Unified Race Algorithm for Offline Parameter
Tuning
452 2014 Cost, carbon emissions and modal shift in
Erwin Hans
Tim van Dijk, Martijn Mes, Marco
Schutten, Joaquim Gromicho
Yann Bouchery, Jan Fransoo
intermodal network design decisions
Josue C. Vélazquez-Martínez, Jan C.
451 2014 Transportation Cost and CO2 Emissions in
Location Decision Models
450 2014 Tracebook: A Dynamic Checklist Support System
Fransoo, Edgar E. Blanco, Jaime MoraVargas
Shan Nan, Pieter Van Gorp, Hendrikus
H.M. Korsten, Richard Vdovjak, Uzay
Kaymak
449 2014 Intermodal hinterland network design with multiple Yann Bouchery, Jan Fransoo
actors
448 2014 The Share-a-Ride Problem: People and Parcels
Sharing Taxis
447 2014 Stochastic inventory models for a single item
at a single location
446 2014 Optimal and heuristic repairable stocking and
expediting in a fluctuating demand environment
445 2014 Connecting inventory control and repair shop
control: a differentiated control structure for
Baoxiang Li, Dmitry Krushinsky,
Hajo A. Reijers, Tom Van Woensel
K.H. van Donselaar, R.A.C.M.
Broekmeulen
Joachim Arts, Rob Basten,
Geert-Jan van Houtum
M.A. Driessen, W.D. Rustenburg,
G.J. van Houtum, V.C.S. Wiers
repairable spare parts
444 2014 A survey on design and usage of Software
Samuil Angelov, Jos Trienekens,
Reference Architectures
Rob Kusters
Extending and Adapting the Architecture Tradeoff
Samuil Angelov, Jos J.M. Trienekens,
443 2014 Analysis Method for the Evaluation of Software
Paul Grefen
Reference Architectures
A multimodal network flow problem with product
442 2014 Quality preservation, transshipment, and asset
management
Maryam SteadieSeifi, Nico Dellaert,
Tom Van Woensel
441 2013 Integrating passenger and freight transportation:
Model formulation and insights
440 2013 The Price of Payment Delay
Veaceslav Ghilas, Emrah Demir,
Tom Van Woensel
K. van der Vliet, M.J. Reindorp,
J.C. Fransoo
439 2013 On Characterization of the Core of Lane Covering Behzad Hezarkhani, Marco Slikker,
Games via Dual Solutions
438 2013 Destocking, the Bullwhip Effect, and the Credit
Crisis: Empirical Modeling of Supply Chain
Tom van Woensel
Maximiliano Udenio, Jan C. Fransoo,
Robert Peels
Dynamics
437 2013 Methodological support for business process
Redesign in healthcare: a systematic literature
review
436 2013 Dynamics and equilibria under incremental
Horizontal differentiation on the Salop circle
435 2013 Analyzing Conformance to Clinical Protocols
Involving Advanced Synchronizations
434 2013 Models for Ambulance Planning on the Strategic
and the Tactical Level
Rob J.B. Vanwersch, Khurram
Shahzad, Irene Vanderfeesten, Kris
Vanhaecht, Paul Grefen, Liliane
Pintelon, Jan Mendling, Geofridus G.
Van Merode, Hajo A. Reijers
B. Vermeulen, J.A. La Poutré,
A.G. de Kok
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
433 2013 Mode Allocation and Scheduling of Inland
Container Transportation: A Case-Study in the
Netherlands
Stefano Fazi, Tom Van Woensel,
Socially responsible transportation and lot sizing:
432 2013 Insights from multiobjective optimization
Yann Bouchery, Asma Ghaffari,
431 2013 Inventory routing for dynamic waste collection
Martijn Mes, Marco Schutten,
Jan C. Fransoo
Zied Jemai, Jan Fransoo
Arturo Pérez Rivera
Simulation and Logistics Optimization of an
430 2013 Integrated Emergency Post
N.J. Borgman, M.R.K. Mes,
I.M.H. Vliegen, E.W. Hans
Last Time Buy and Repair Decisions for Spare
429 2013 Parts
A Review of Recent Research on Green Road
428 2013 Freight Transportation
Typology of Repair Shops for Maintenance
427 2013 Spare Parts
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
A value network development model and
426 2013 Implications for innovation and production network B. Vermeulen, A.G. de Kok
management
Single Vehicle Routing with Stochastic Demands:
425 2013 Approximate Dynamic Programming
Influence of Spillback Effect on Dynamic Shortest
424 2013 Path Problems with Travel-Time-Dependent
Network Disruptions
C. Zhang, N.P. Dellaert, L. Zhao,
T. Van Woensel, D. Sever
Derya Sever, Nico Dellaert,
Tom Van Woensel, Ton de Kok
423 2013 Dynamic Shortest Path Problem with Travel-Time- Derya Sever, Lei Zhao, Nico Dellaert,
Dependent Stochastic Disruptions: Hybrid
Tom Van Woensel, Ton de Kok
Approximate Dynamic Programming Algorithms
with a Clustering Approach
System-oriented inventory models for spare
R.J.I. Basten, G.J. van Houtum
422 2013 parts
Lost Sales Inventory Models with Batch Ordering
T. Van Woensel, N. Erkip, A. Curseu,
421 2013 And Handling Costs
J.C. Fransoo
420 2013 Response speed and the bullwhip
Maximiliano Udenio, Jan C. Fransoo,
Eleni Vatamidou, Nico Dellaert
419 2013 Anticipatory Routing of Police Helicopters
Rick van Urk, Martijn R.K. Mes,
Erwin W. Hans
418 2013 Supply Chain Finance: research challenges
ahead
Kasper van der Vliet, Matthew J.
Reindorp, Jan C. Fransoo
Improving the Performance of Sorter Systems
417 2013 By Scheduling Inbound Containers
Regional logistics land allocation policies:
416 2013 Stimulating spatial concentration of logistics
S.W.A. Haneyah, J.M.J. Schutten,
K. Fikse
Frank P. van den Heuvel, Peter W. de
Langen, Karel H. van Donselaar,
firms
Jan C. Fransoo
The development of measures of process
Heidi L. Romero, Remco M. Dijkman,
415 2013 harmonization
BASE/X. Business Agility through Cross-
Paul W.P.J. Grefen, Arjan van Weele
Paul Grefen, Egon Lüftenegger,
414 2013 Organizational Service Engineering
Eric van der Linden, Caren Weisleder
The Time-Dependent Vehicle Routing Problem
413 2013 with Soft Time Windows and Stochastic Travel
Times
Duygu Tas, Nico Dellaert, Tom van
412 2013 Clearing the Sky - Understanding SLA
Elements in Cloud Computing
Marco Comuzzi, Guus Jacobs,
411 2013 Approximations for the waiting time distribution
In an M/G/c priority queue
A. Al Hanbali, E.M. Alvarez,
410 2013 To co-locate or not? Location decisions and
logistics concentration areas
Frank P. van den Heuvel, Karel H. van
Donselaar, Rob A.C.M. Broekmeulen,
Jan C. Fransoo, Peter W. de Langen
409 2013
408 2013
407 2013
406 2013
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
Woensel, Ton de Kok
Paul Grefen
M.C. van der van der Heijden
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
405 2013 Maximising the Value of Supply Chain Finance
Kasper van der Vliet, Matthew J.
Reindorp, Jan C. Fransoo
404 2013 Reaching 50 million nanostores: retail
distribution in emerging megacities
Edgar E. Blanco, Jan C. Fransoo
403 2013 A Vehicle Routing Problem with Flexible Time
Windows
Duygu Tas, Ola Jabali, Tom van
Woensel
402 2012 The Service Dominant Business Model: A
Service Focused Conceptualization
Egon Lüftenegger, Marco Comuzzi,
401 2012 Relationship between freight accessibility and
Logistics employment in US counties
Frank P. van den Heuvel, Liliana
Rivera,Karel H. van Donselaar, Ad de
Jong,Yossi Sheffi, Peter W. de Langen,
Jan C.Fransoo
400 2012 A Condition-Based Maintenance Policy for MultiComponent Systems with a High Maintenance
Qiushi Zhu, Hao Peng, Geert-Jan van
Paul Grefen, Caren Weisleder
Houtum
Setup Cost
399 2012
A flexible iterative improvement heuristic to
E. van der Veen, J.L. Hurink,
Support creation of feasible shift rosters in
J.M.J. Schutten, S.T. Uijland
Self-rostering
Scheduled Service Network Design with
398 2012
397 2012
Synchronization and Transshipment Constraints
K. Sharypova, T.G. Crainic, T. van
Woensel, J.C. Fransoo
For Intermodal Container Transportation Networks
Destocking, the bullwhip effect, and the credit
Maximiliano Udenio, Jan C. Fransoo,
Crisis: empirical modeling of supply chain
Robert Peels
Dynamics
396 2012
395 2012
Vehicle routing with restricted loading
J. Gromicho, J.J. van Hoorn, A.L. Kok
capacities
J.M.J. Schutten
Service differentiation through selective
E.M. Alvarez, M.C. van der Heijden,
lateral transshipments
I.M.H. Vliegen, W.H.M. Zijm
A Generalized Simulation Model of an
Martijn Mes, Manon Bruens
394 2012 Integrated Emergency Post
393 2012 Business Process Technology and the Cloud:
Vasil Stoitsev, Paul Grefen
Defining a Business Process Cloud Platform
392 2012 Vehicle Routing with Soft Time Windows and
Stochastic Travel Times: A Column Generation
D. Tas, M. Gendreau, N. Dellaert,
T. van Woensel, A.G. de Kok
And Branch-and-Price Solution Approach
391 2012 Improve OR-Schedule to Reduce Number of
Required Beds
390 2012 How does development lead time affect
J.T. v. Essen, J.M. Bosch, E.W. Hans,
M. v. Houdenhoven, J.L. Hurink
Andres Pufall, Jan C. Fransoo, Ad de
performance over the ramp-up lifecycle?
Jong
Evidence from the consumer electronics
Andreas Pufall, Jan C. Fransoo, Ad de
389 2012 industry
The Impact of Product Complexity on Ramp388 2012 Up Performance
Jong, Ton de Kok
Frank P.v.d. Heuvel, Peter W.de
Langen,
Karel H. v. Donselaar, Jan C. Fransoo
Co-location synergies: specialized versus diverse
387 2012 logistics concentration areas
Frank P.v.d. Heuvel, Peter W.de
Langen,
386 2012 Proximity matters: Synergies through co-location
of logistics establishments
Frank P. v.d.Heuvel, Peter W.de
Langen,
385 2012 Spatial concentration and location dynamics in
logistics:the case of a Dutch province
384 2012 FNet: An Index for Advanced Business Process
Querying
Karel H. v.Donselaar, Jan C. Fransoo
Karel H.v. Donselaar, Jan C. Fransoo
Zhiqiang Yan, Remco Dijkman, Paul
Grefen
W.R. Dalinghaus, P.M.E. Van Gorp
383 2012 Defining Various Pathway Terms
Egon Lüftenegger, Paul Grefen,
Caren Weisleder
382 2012 The Service Dominant Strategy Canvas:
Defining and Visualizing a Service Dominant
Stefano Fazi, Tom van Woensel,
Jan C. Fransoo
Strategy through the Traditional Strategic Lens
381 2012 A Stochastic Variable Size Bin Packing Problem
With Time Constraints
K. Sharypova, T. van Woensel,
J.C. Fransoo
Frank P. van den Heuvel, Peter W. de
380 2012 Coordination and Analysis of Barge Container
Hinterland Networks
Langen, Karel H. van Donselaar, Jan
C.
Fransoo
379 2012 Proximity matters: Synergies through co-location
of logistics establishments
Heidi Romero, Remco Dijkman,
378 2012 A literature review in process harmonization: a
conceptual framework
S.W.A. Haneya, J.M.J. Schutten,
377 2012
Paul Grefen, Arjan van Weele
P.C. Schuur, W.H.M. Zijm
A Generic Material Flow Control Model for
H.G.H. Tiemessen, M. Fleischmann,
Two Different Industries
G.J. van Houtum, J.A.E.E. van Nunen,
E. Pratsini
Improving the performance of sorter systems by
375 2012 scheduling inbound containers
Albert Douma, Martijn Mes
Strategies for dynamic appointment making by
374 2012 container terminals
Pieter van Gorp, Marco Comuzzi
MyPHRMachines: Lifelong Personal Health
373 2012 Records in the Cloud
E.M. Alvarez, M.C. van der Heijden,
372 2012 Service differentiation in spare parts supply
through dedicated stocks
Frank Karsten, Rob Basten
W.H.M. Zijm
X.Lin, R.J.I. Basten, A.A. Kranenburg,
371 2012
Spare parts inventory pooling: how to share
the benefits
G.J. van Houtum
Condition based spare parts supply
Martijn Mes
370 2012
369 2011 Using Simulation to Assess the Opportunities of
Dynamic Waste Collection
J. Arts, S.D. Flapper, K. Vernooij
J.T. van Essen, J.L. Hurink, W.
368 2011 Aggregate overhaul and supply chain planning for Hartholt,
rotables
B.J. van den Akker
367 2011
Operating Room Rescheduling
Kristel M.R. Hoen, Tarkan Tan, Jan C.
Fransoo, Geert-Jan van Houtum
Switching Transport Modes to Meet Voluntary
366 2011 Carbon Emission Targets
Elisa Alvarez, Matthieu van der Heijden
On two-echelon inventory systems with Poisson
365 2011 demand and lost sales
J.T. van Essen, E.W. Hans, J.L. Hurink,
364 2011 Minimizing the Waiting Time for Emergency
Surgery
Vehicle Routing Problem with Stochastic Travel
Times Including Soft Time Windows and Service
363 2011 Costs
A New Approximate Evaluation Method for Two362 2011 Echelon Inventory Systems with Emergency
Shipments
361 2011 Approximating Multi-Objective Time-Dependent
Optimization Problems
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
A.G. Karaarslan, G.P. Kiesmüller, A.G.
Branch and Cut and Price for the Time Dependent de Kok
360 2011 Vehicle Routing Problem with Time Window
Analysis of an Assemble-to-Order System with
359 2011 Different Review Periods
Interval Availability Analysis of a Two-Echelon,
358 2011 Multi-Item System
357 2011 Carbon-Optimal and Carbon-Neutral Supply
Chains
Ahmad Al Hanbali, Matthieu van der
Heijden
Felipe Caro, Charles J. Corbett, Tarkan
Tan, Rob Zuidwijk
Sameh Haneyah, Henk Zijm, Marco
Schutten, Peter Schuur
Generic Planning and Control of Automated
356 2011 Material Handling Systems: Practical
Requirements Versus Existing Theory
355 2011
Last time buy decisions for products sold under
warranty
M. van der Heijden, B. Iskandar
Frank P. van den Heuvel, Peter W. de
Langen, Karel H. van Donselaar, Jan
C. Fransoo
354 2011 Spatial concentration and location dynamics in
logistics: the case of a Dutch provence
Frank P. van den Heuvel, Peter W. de
Langen, Karel H. van Donselaar, Jan
C. Fransoo
353 2011 Identification of Employment Concentration Areas
Pieter van Gorp, Remco Dijkman
352 2011 BOMN 2.0 Execution Semantics Formalized as
Graph Rewrite Rules: extended version
Frank Karsten, Marco Slikker, GeertJan van Houtum
351 2011 Resource pooling and cost allocation among
independent service providers
E. Lüftenegger, S. Angelov, P. Grefen
350 2011
A Framework for Business Innovation Directions
Remco Dijkman, Irene Vanderfeesten,
Hajo A. Reijers
K.M.R. Hoen, T. Tan, J.C. Fransoo
The Road to a Business Process Architecture: An
349 2011 Overview of Approaches and their Use
G.J. van Houtum
348 2011 Effect of carbon emission regulations on transport
Murat Firat, Cor Hurkens
mode selection under stochastic demand
347 2011 An improved MIP-based combinatorial approach
for a multi-skill workforce scheduling problem
R.J.I. Basten, M.C. van der Heijden,
J.M.J. Schutten
346 2011 An approximate approach for the joint problem of
level of repair analysis and spare parts stocking
R.J.I. Basten, M.C. van der Heijden,
J.M.J. Schutten
Joint optimization of level of repair analysis and
345 2011 spare parts stocks
Ton G. de Kok
Inventory control with manufacturing lead time
344 2011 flexibility
Frank Karsten, Marco Slikker, GeertJan van Houtum
Analysis of resource pooling games via a new
343 2011 extenstion of the Erlang loss function
Murat Firat, C.A.J. Hurkens, Gerhard J.
Woeginger
342 2010 Vehicle refueling with limited resources
Bilge Atasoy, Refik Güllü, TarkanTan
Optimal Inventory Policies with Non-stationary
341 2010 Supply Disruptions and Advance Supply
Information
Kurtulus Baris Öner, Alan Scheller-Wolf
Geert-Jan van Houtum
Redundancy Optimization for Critical Components
339 2010 in High-Availability Capital Goods
Joachim Arts, Gudrun Kiesmüller
338 2010 Analysis of a two-echelon inventory system with
two supply modes
Murat Firat, Gerhard J. Woeginger
335 2010 Analysis of the dial-a-ride problem of Hunsaker
and Savelsbergh
Murat Firat, Cor Hurkens
334 2010
Attaining stability in multi-skill workforce scheduling A.J.M.M. Weijters, J.T.S. Ribeiro
333 2010
Flexible Heuristics Miner (FHM)
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.
332 2010 An exact approach for relating recovering surgical Boucherie, Erwin W. Hans, Johann L.
patient workload to the master surgical schedule
Hurink, Nelly Litvak
Efficiency evaluation for pooling resources in
331 2010 health care
M.M. Jansen, A.G. de Kok, I.J.B.F.
Adan
The Effect of Workload Constraints in
330 2010 Mathematical Programming Models for Production Christian Howard, Ingrid Reijnen,
Johan Marklund, Tarkan Tan
Planning
329 2010 Using pipeline information in a multi-echelon spare
H.G.H. Tiemessen, G.J. van Houtum
parts inventory system
Reducing costs of repairable spare parts supply
328 2010 systems via dynamic scheduling
F.P. van den Heuvel, P.W. de Langen,
K.H. van Donselaar, J.C. Fransoo
Identification of Employment Concentration and
327 2010 Specialization Areas: Theory and Application
Murat Firat, Cor Hurkens
326 2010 A combinatorial approach to multi-skill workforce
scheduling
Murat Firat, Cor Hurkens, Alexandre
Laugier
325 2010
Stability in multi-skill workforce scheduling
M.A. Driessen, J.J. Arts, G.J. v.
Houtum, W.D. Rustenburg, B. Huisman
324 2010 Maintenance spare parts planning and control: A
framework for control and agenda for future
research
R.J.I. Basten, G.J. van Houtum
Near-optimal heuristics to set base stock levels in
323 2010 a two-echelon distribution network
M.C. van der Heijden, E.M. Alvarez,
J.M.J. Schutten
322 2010 Inventory reduction in spare part networks by
selective throughput time reduction
321 2010 The selective use of emergency shipments for
service-contract differentiation
E.M. Alvarez, M.C. van der Heijden,
W.H. Zijm
B. Walrave, K. v. Oorschot, A.G.L.
Romme
Heuristics for Multi-Item Two-Echelon Spare Parts
320 2010 Inventory Control Problem with Batch Ordering in
Nico Dellaert, Jully Jeunet.
the Central Warehouse
Preventing or escaping the suppression
319 2010 mechanism: intervention conditions
318 2010
317 2010
Hospital admission planning to optimize major
resources utilization under uncertainty
R. Seguel, R. Eshuis, P. Grefen.
Tom Van Woensel, Marshall L. Fisher,
Jan C. Fransoo.
Minimal Protocol Adaptors for Interacting Services Lydie P.M. Smets, Geert-Jan van
Houtum, Fred Langerak.
Teaching Retail Operations in Business and
316 2010
Engineering Schools
Pieter van Gorp, Rik Eshuis.
Design for Availability: Creating Value for
315 2010 Manufacturers and Customers
Bob Walrave, Kim E. van Oorschot, A.
Georges L. Romme
Transforming Process Models: executable rewrite
314 2010 rules versus a formalized Java program
S. Dabia, T. van Woensel, A.G. de Kok
313
Getting trapped in the suppression of exploration:
A simulation model
A Dynamic Programming Approach to MultiObjective Time-Dependent Capacitated Single
Vehicle Routing Problems with Time Windows
Tales of a So(u)rcerer: Optimal Sourcing Decisions
312 2010 Under Alternative Capacitated Suppliers and
Osman Alp, Tarkan Tan
General Cost Structures
In-store replenishment procedures for perishable
311 2010 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 K.M.R. Hoen, T. Tan, J.C. Fransoo,
mode selection in supply chains
G.J. van Houtum
307 2010
Interaction between intelligent agent strategies for Martijn Mes, Matthieu van der Heijden,
real-time transportation planning
Peter Schuur
306 2010 Internal Slackening Scoring Methods
305 2010
Vehicle Routing with Traffic Congestion and
Drivers' Driving and Working Rules
Marco Slikker, Peter Borm, René van
den Brink
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
Ocean Container Transport: An Underestimated
303 2010 and Critical Link in Global Supply Chain
Performance
Jan C. Fransoo, Chung-Yee Lee
302 2010
Capacity reservation and utilization for a
Y. Boulaksil; J.C. Fransoo; T. Tan
manufacturer with uncertain capacity and demand
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
298 2010
An optimal approach for the joint problem of level
of repair analysis and spare parts stocking
R.J.I. Basten, M.C. van der Heijden,
J.M.J. Schutten
Responding to the Lehman Wave: Sales
297 2009 Forecasting and Supply Management during the
Credit Crisis
Robert Peels, Maximiliano Udenio, Jan
C. Fransoo, Marcel Wolfs, Tom
Hendrikx
Peter T. Vanberkel, Richard J.
An exact approach for relating recovering surgical Boucherie, Erwin W. Hans, Johann L.
296 2009
patient workload to the master surgical schedule
Hurink, Wineke A.M. van Lent, Wim H.
van Harten
An iterative method for the simultaneous
295 2009 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
292 2009
Business Process Model Repositories - Framework Zhiqiang Yan, Remco Dijkman, Paul
and Survey
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
Analyzing combined vehicle routing and break
289 2009 scheduling from a distributed decision making
perspective
R.S. Mans, W.M.P. van der Aalst, N.C.
Russell, P.J.M. Bakker
C.M. Meyer; A.L. Kok; H. Kopfer; J.M.J.
Schutten
288 2009
Anticipation of lead time performance in Supply
Chain Operations Planning
Michiel Jansen; Ton G. de Kok; Jan C.
Fransoo
287 2009
Inventory Models with Lateral Transshipments: A
Review
Colin Paterson; Gudrun Kiesmuller;
Ruud Teunter; Kevin Glazebrook
286 2009
Efficiency evaluation for pooling resources in
health care
P.T. Vanberkel; R.J. Boucherie; E.W.
Hans; J.L. Hurink; N. Litvak
285 2009
A Survey of Health Care Models that Encompass
Multiple Departments
P.T. Vanberkel; R.J. Boucherie; E.W.
Hans; J.L. Hurink; N. Litvak
284 2009
Supporting Process Control in Business
Collaborations
S. Angelov; K. Vidyasankar; J. Vonk; P.
Grefen
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
281 2009
The link between product data model and process
J.J.C.L. Vogelaar; H.A. Reijers
model
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
Toward Meso-level Product-Market Network
278 2010 Indices for Strategic Product Selection and
(Re)Design Guidelines over the Product Life-Cycle
277 2009
R. Eshuis
B. Vermeulen, A.G. de Kok
An Efficient Method to Construct Minimal Protocol R. Seguel, R. Eshuis, P. Grefen
Adaptors
276 2009
Coordinating Supply Chains: a Bilevel
Programming Approach
275 2009
Inventory redistribution for fashion products under
G.P. Kiesmuller, S. Minner
demand parameter update
274 2009
Comparing Markov chains: Combining aggregation A. Busic, I.M.H. Vliegen, A. Schellerand precedence relations applied to sets of states Wolf
273 2009
Separate tools or tool kits: an exploratory study of
engineers' preferences
Ton G. de Kok, Gabriella Muratore
I.M.H. Vliegen, P.A.M. Kleingeld, G.J.
van Houtum
An Exact Solution Procedure for Multi-Item Two272 2009 Echelon Spare Parts Inventory Control Problem
with Batch Ordering
271 2009
Distributed Decision Making in Combined Vehicle
Routing and Break Scheduling
Dynamic Programming Algorithm for the Vehicle
270 2009 Routing Problem with Time Windows and EC
Social Legislation
Engin Topan, Z. Pelin Bayindir, Tarkan
Tan
C.M. Meyer, H. Kopfer, A.L. Kok, M.
Schutten
A.L. Kok, C.M. Meyer, H. Kopfer, J.M.J.
Schutten
269 2009
Remco Dijkman, Marlon Dumas,
Similarity of Business Process Models: Metics and
Boudewijn van Dongen, Reina Kaarik,
Evaluation
Jan Mendling
267 2009
Vehicle routing under time-dependent travel times:
A.L. Kok, E.W. Hans, J.M.J. Schutten
the impact of congestion avoidance
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