Combining Process Simulation and Agent Organizational Structure Evaluation in Order to Analyze Disaster Response Plans

Then, we study the agent organizational structure involved in the plan by analyzing the role graph of actors and notably the power, coordination and control relations among them ac-cordi

Organizational Structure Evaluation in order to

Analyze Disaster Response Plans

Nguyen Tuan Thanh LE, Chihab HANACHI⋆, Serge STINCKWICH⋆⋆, and

Tuong Vinh HO⋆ ⋆ ⋆

nguyen.le@irit.fr hanachi@univ-tlse1.fr serge.stinckwich@ird.fr ho.tuong.vinh@ifi.edu.vn

Abstract This paper shows how to simulate and evaluate disaster re-sponse plans and in particular the process and the organization set up

in such situations We consider, as a case study, the tsunami resolution plan of Ho Chi Minh City, Vietnam We firstly examine the process model corresponding to this plan by defining three scenarios and analyzing sim-ulations built on top of them Then, we study the agent organizational structure involved in the plan by analyzing the role graph of actors and notably the power, coordination and control relations among them ac-cording to the Grossi framework These evaluations provide recommen-dations to improve the response plan

Keywords: agent organization evaluation, crisis management, process simulation, role graph, decision support system

1 Introduction

In crisis situations (tsunami or earthquake), coordination among the implied stakeholders (rescue teams and authorities) is of paramount importance to ease the efficient management and resolution of crises Coordination may be sup-ported by different related means such as plans, processes, organizational struc-tures, shared artifacts (geographical maps), etc [3]

Most often, coordination recommendations to manage crisis are available in

a textual format defining the actors, their roles and their required interactions

in the different steps of crisis life-cycle: mitigation, preparedness, response and recovery

⋆

Nguyen Tuan Thanh LE and Chihab HANACHI are with Toulouse 1 University and members of the IRIT Laboratory (SMAC Team), France

⋆⋆

Serge STINCKWICH is with UCBN & UMI UMMISCO 209 (IRD/UPMC), France

⋆ ⋆ ⋆

Tuong Vinh HO is with Institut Francophone International, Vietnam National Uni-versity (VNU) & UMI UMMISCO 209 (IRD/UPMC), Hanoi, Vietnam

While coordination recommendations, in a textual format, are easy to ma-nipulate by stakeholders, taken individually, they do not provide direct means

to be analyzed, simulated, adapted, improved and may have various different interpretations, so difficult to manage in real time and in a distributed setting

In [4], we propose an approach to transform a textual coordination plan into

a formal process in order to have an accurate representation of the coordination,

to reduce ambiguity and ease an efficient preparedness and resolution of tsunami

at Ho Chi Minh City

Formalizing coordination and producing models are a first step toward a bet-ter understanding and masbet-tering of coordination Then, it is also important to evaluate coordination models in order to provide recommendations to authority

to help them improving coordination within resolution plans Most of the time, authorities make real-world exercises to validate their plans but do not formally validate them Unfortunately real-world exercises are not always possible (cost, impossibility to reproduce reality, etc.) Therefore simulation and formal valida-tion become unavoidable

Given these observations, it becomes useful to make formal evaluation of coordination models used during crisis situations This is the approach followed

in this work (see lifecycle of Fig 1) Notably, our contribution consists in the definition of a framework to evaluate both the underlined process and the agent (actor) organization set up in a resolution plan The two evaluation dimensions, process and organization, are complementary since the first one abstracts the coordinated behavior of the actors while the second abstracts the relationships (control, coordination, power ) between actors Both are to be evaluated since they influence the efficiency, the robustness and the flexibility of the disaster response plans Even if our work considers a concrete case study (i.e the Ho Chi Minh City tsunami response plan), our approach is general enough to be applied

to any crisis management plan

Fig 1 Evaluation lifecycle of disaster response plans

The paper is organized as follows We first recall the formal process model that we have proposed in [4] corresponding to the Ho Chi Minh City rescue plan Related works about business process simulation and organizational structure evaluation are presented in section 3 We then define three scenarios and analyze simulations built on top of them Afterward, we evaluate the agent organizational structure involved in this plan by analyzing the role graph of the actors and notably the power, coordination and control relations among them according to the Grossi framework [8] These evaluations provide recommendations to improve the response plan Finally, we discuss the results and conclude our work

2 Background

Response plans used during crisis situations involve the interactions of many actors and tasks organized in a flowchart of activities with interleaving decision points, that can be roughly be seen as a specific business process We would like to apply business process techniques in crisis management Therefore in [4], we have presented a process-based model to analyze coordination activities extracted from tsunami response plan proposed by People’s Committee of Ho Chi Minh City This conceptual model (Fig 2), described with a Business Process Model and Notation (BPMN) diagram, has been built by analyzing an official textual plan provided by the suitable authorities

Fig 2 Conceptual model of tsunami response plan proposed by Ho Chi Minh City

We can identify in the model above seven organizations (represented by lanes) involved with their flow of tasks and mutual interactions In BPMN, a task (like T1: Detect tsunami risk) is represented by a rounded-corner rectangle Several control structures are possible to coordinate the different tasks: sequence (arrow), parallelism (diamond including “+”) or alternatives (diamond with “X”) We can notice, in Fig 2, that Military and Police organizations are supposed to perform tasks in parallel In this case, each organization members should be distributed over the parallel tasks according to a given policy (proportional distribution, distribution according to the importance given to each task ) The Health & Red Cross organization has to choose to carry out only one task among two possible ones

This model has been transformed and executed within a workflow system, namely Yet Another Workflow Language1

(YAWL), to demonstrate the feasibil-ity of managing the plan in a distributed setting However, this transformation not only dropped lots of details of our conceptual model, but also did not pro-vide process simulation functions, notably what-if simulation and performance analysis, useful for decision makers in charge of defining and updating plans We will provide later in section 4 a more elaborate model by having more realistic scenarios and organizational structure evaluations, that will allow more complex analysis of rescue plans

3 Related Works

This section will situate our contribution according to three complementary points of view: coordination in Multi-Agent Systems (MAS), simulation of dis-crete event systems and organizational perspective

The problem of coordinating the behaviour of MASs has been regularly ad-dressed [2] A coordination model is useful in crisis context since it helps in supporting interdependence between stakeholders, the achievement of common goals (e.g saving victims), and the sharing of resources (vehicles, food, houses for victims, ) and competencies (medical, carriers, ) A coordination model can exploit and/or combine different techniques: 1) organizational structuring 2) contracting 3) negotiating 3) planning 4) shared artefacts We follow in this paper a process-oriented technique which can be considered as a combination of plans within an organizational structure The advantage of process-oriented co-ordination is to provide visibility on the whole crisis evolution: past, present and future activities and their relationships [1] proposes a very detailed review of process management systems supporting disaster response scenarios However, one main drawback of these systems is to support the real time managing of the crisis, while we consider the whole life cycle of the process and in this paper the simulation and validation steps

From a simulation point of view, a computer-based simulation of processes can be done following a discrete-event simulation, where a crisis evolution can be

1

http://www.yawlfoundation.org/

represented as a sequence of events This approach has been applied successfully

in workflow and business processes [5] Process simulation helps to identify the bottlenecks in the flow of tasks and then optimize them with alternative ones or find out the better resource management solution Rozinat et al in [6] proposed

an approach by analyzing the event logs (in structured format), then extracting automatically the useful information about: 1) control flow, 2) decision point, 3) performance, and 4) roles Using these information, the authors constructed

a four-facets simulation model and simulated it with a Petri nets tool, namely CPN2

Unlike [6], our model is created from an unstructured textual guideline so

we cannot use an event miner such as ProM3

tool to extract automatically the useful information In our case, we have observed manually the necessary infor-mation by studying the textual plan, extracting the actors, their activities, and finally designed a corresponding conceptual model due to our comprehension

In [7], the authors combined three types of information to generate a more accurate simulation model: 1) design information used to form model structure, 2) historic information (event logs) used to set model parameters (such as arrival rate, processing time) and 3) state information used to initialize the model In our work, we have only used the design information to create our simulation model We then added the necessary parameters like resource quantity, time constraints extracted from the official textual plan to the model

From an organizational point view, Grossi et al proposed in [8] a framework

to evaluate the organizational structure based on a role graph with three di-mensions: power, coordination and control They introduced the concepts and the equations involved into the evaluation Using these equations, we compared our results with the standard values proposed by Grossi in order to assess the robustness, the flexibility and the efficiency of our organization

The novelty of our work is to evaluate resolution plans through a formal representation and to consider both process and organizational aspects at the same time and in a coherent framework

4 Rescue Plans Assessment by Process Simulation

In this section, we will describe how to evaluate a rescue plan by using busi-ness process simulations In order to perform these simulations, a conceptual model (such as Fig 2) is not sufficient Therefore, we need to add extra informa-tion (quantity of resources, time constraints) that will allow us to define more accurate scenarios

4.1 Definition of Simulation Parameters

Related to business process essence, we consider four input parameters as follows [9]: 1) the Arrival process expresses the arrival rate of new cases (i.e., process

2

http://cpntools.org/

3

http://www.processmining.org/prom/start

instances); 2) the Probabilities for choices indicates the probability of selecting one task to perform among several alternative tasks at a time; 3) the Service time expresses the required time for a task to complete its work; and 4) the Number of human resources specifies the kind of mobilized organizations and their quantity, as well as the allocated resources of tasks

These four parameters are insufficient in our context Indeed, BPMN simu-lation lacks some notions such as the actors’ capacities and the priorities or the important factors of tasks Hence, we have defined the notion of importance fac-tor of a task T as an evaluation number of the importance of this task regarding its capacity in term of rescues or good salvage The more this factor is high, the more its task can save persons or goods Hence, we must pay attention to

it since it influences the crisis resolution performance This factor will be used

in our context for allocating suitably the resources to parallel tasks, even if its use could be generalized to all types of tasks As we will demonstrate it, taking into account this new notion will improve the overall performance of our process model

To tune the arrival process and service time parameters, we could apply different kind of distributions such as Poisson distribution, Duration distribution, Normal distribution, Triangular distribution, etc

Different from a typical business process as flight ticket booking, whose arrival rate is frequent (time distance between two customers’ request is small), in crisis and disaster context, we do not meet the full queue or resource conflict problem

In our simulation, we have set the arrival process parameter to one, because we consider only one tsunami situation at a time

We have set the probabilities for choices (in number between 0 and 1) of alternative tasks and the importance factors (in percent) for parallel tasks as shown in Table 1 We allocate resources to tasks in the order of their importance: important tasks are first served with the maximum resources according to their needs

Tasks PC Tasks IF T12/T13 0.8/0.2 T4/T5 40/60

T18/T19 30/70 T18’/T19’ 70/30 T8/T9/T10 70/20/10 T8’/T9’/T11 10/10/80 Table 1 Probabilities for choices (PC) of alternative tasks and Importance factors (IF) of parallel tasks

We have also applied a Duration distribution for all tasks’ service time, as shown in Table 2 We assumed that the time span of a tsunami is three hours Furthermore, we have modeled seven roles (or actors) with their correspond-ing acronym: Institute of Geophysics (abbr IG), Local Administration (LA),

Task ST Task ST Task ST Task ST Task ST Task ST Task ST Task ST T1 10m T2 15m T3 10m T4 30m T5 30m T6 1h T7 30m T8 3h T8’ 3h T9 3h T9’ 3h T10 3h T11 3h T12 3h T13 30m T14 10m T15 15m T16 10m T17 30m T18 1h T18’ 1h T19 1h T19’ 1h T20 30m

Table 2 Service time (ST) of all tasks in tsunami response plan

Military (M), Police (P), Local Civil Defense Forces (LCDF), Communication Unit (CU), and Health & Red Cross (HR) The total number of human resources for each role is shown in Table 3 For the clarity purpose, we did not take into account other mobilized non-human materials such as the transport means (e.g., ambulances, fire trucks, canoes, etc), or the machines (e.g., sprayer epidemic prevention machine, GPS machine, etc)

Resource Quantity Resource Quantity Institute of Geophysics 5 Military 6836

Local Administration 160 Communication Unit 170

Local Civil Defense Forces 6700 Police 3700

Health and Red Cross 2600

Table 3 Human resources mobilized in our tsunami response plan

Our expected outputs of the process simulation are two-fold: a) the Time use representing the total time consumed by our tsunami response process, as well

as the average time, the average waiting time, the minimum or maximum time for each task; and b) the Resource use depicting the distribution of resources occupied by each actor

Practically, we use Bizagi tool4

to model and simulate our case study

4.2 Definition of Scenarios

Following [10], we could define a scenario by four components: the purpose, the content, the form and the cycle Regarding the purpose, crisis management sim-ulation aims at answering the two following questions: a) how could we allocate efficiently the human resources to tasks? and b) what is the best resources allo-cation strategy? The content and the form of our scenarios are defined by the tasks’ services time (in minutes), the number of mobilized actors (in positive integer values) and the probabilities for alternative tasks (in number) as well as the importance factor (in percent)

4

http://help.bizagi.com/processmodeler/en/index.html?simulation_levels htm

To demonstrate the efficiency of the importance factor notion, we have fixed the arrival process, the probabilities for choices, and the service time parameters

In a nutshell, we have shifted only the number of human resources allocated to tasks leading to the three scenarios:

– Scenario 1: We name it full-resource scenario For each task, we allocate to it the maximum number of human resources dedicated to it without considering any other aspects

– Scenario 2: We call it importance-focus scenario It is based on a percentage distribution of human resources allocated to each parallel and alternative task These percentages are stated by the designer according to the im-portance factors or the probabilities for choices which he/she gives to each parallel or alternative task, respectively We allocate a maximum value of human resources to all the other tasks

– Scenario 3: It could be also called all-equal scenario For parallel and al-ternative tasks, the same number of human resources is allocated without regarding to the probabilities for choices or the importance factors of tasks The others tasks are allocated a maximum value

The number of human resources allocated to each task for the three previous scenarios are shown in Table 4

Task Scen 1 Scen 2 Scen 3 Task Scen 1 Scen 2 Scen 3

T3 160 160 160 T16 160 160 160

T4 160 64 80 T17 160 160 160

T5 160 96 80 T20 160 160 160

T6 6700 6700 6700 T7 170 170 170

T8 6836 4785 2278 T8’ 3700 370 1233

T9 6836 1367 2278 T9’ 3700 370 1233

T10 6836 683 2278 T11 3700 2960 1233

T18 6836 2050 3418 T18’ 3700 2590 1850

T19 6836 4785 3418 T19’ 3700 1110 1850

T12 2600 2080 1300 T13 2600 520 1300

Table 4 Number of human resources allocated to tasks in the three scenarios

4.3 Simulation & Analysis of three Scenarios

We compare the different scenarios through the utilization rate of the resources Fig 3 depicts the resource utilization (in percent) of each actor after the what-if simulation As we see, scenario 1 (full-resource scenario) spends more human re-sources than others for parallel tasks performed by Military or Police Otherwise

for the actors having only ordered tasks, scenario 1 consumes the less human resources Furthermore, except for the actor Health & Red Cross (in which we have an exclusive choice between two tasks: T12 and T13 ), we observe that the resource utilization of scenario 2 (importance-focus scenario) and scenario

3 (all-equal scenario) are identical For Health & Red Cross actor which has an alternative way, the resource utilization of importance-focus scenario is more efficient than the all-equal scenario

Fig 3 Utilization of human resources corresponding to three scenarios

We finally have computed the average of resource utilization of all actors as shown in Table 5 The best strategy is the Importance-focus scenario

M P HR LA CU LCDF IG Average Scen 1 79.69% 80.00% 21.82% 16.97% 3.64% 7.27% 6.06% 30.78% Scen 2 63.99% 64.00% 38.40% 29.33% 8.00% 16.00% 13.33% 33.29% Scen 3 63.99% 63.99% 24.00% 29.33% 8.00% 16.00% 13.33% 31.23% Table 5 Comparing the average of resources used in three scenarios

5 Rescue Plans Assessment by Agent Organization

Analysis

In this section, we evaluate the rescue plan organizational structure by using the framework provided by Grossi and al [8] This framework allows us to assess the robustness, flexibility and efficiency of our organization by using the power, coordination and control5

relations between each pair of roles

Grossi et al state that: a) the robustness means the stability of an organi-zation in the case of anticipated risks; b) the flexibility is the capacity of an organization to adapt to the environment changes; and c) the efficiency refers

to the amount of resources used by the organization to perform its tasks

In our case, we will show that the structure organization is efficient and suffi-ciently flexible but not enough robust Obviously, it is not possible to maximize simultaneously all criteria [8] Since our organization is devoted to the disaster response, thus we would like to focus on the amount of resources used by tasks (the efficiency)

As Grossi’s proposal, evaluating an organizational structure involves three steps: 1) building a role graph of the organization based on the three dimensions (power, coordination, control ); 2) measuring specific properties of the organiza-tional structure according to a set of formulas; 3) finally, comparing the obtained results with the optimum values proposed by Grossi in order to evaluate the qualities (robustness, flexibility and efficiency) of the organization

5.1 Building the Role Graph

According to three dimensions described above, we have built the role graph corresponding to our organizational model (seven roles) as seen in Fig 4 Each node corresponds to an organization while an arc corresponds to the relationship between two organizations We can identify three types of relationships: power, coordination and control

5.2 Computing the Metrics

Based on the role graph above, we have implemented isolation metrics (complete-ness, connected(complete-ness, economy, unilaterality, univocity, flatness) and interaction metrics (detour, overlap, incover, outcover and chain) as proposed by Grossi

5.3 Measuring the Qualities

In order to evaluate criteria of our organization, we have compared our results (right-hand table) with the proposed optimum values (left-hand table) in tables

6, 7 and 8

5

the power dimension defines the task delegation pattern; the coordination dimension concerns the flow of knowledge within the organization; and the control dimension between agent A and agent B means that agent A has to monitor agent B’s activities and possibly take over the unaccomplished tasks of agent B