Identifying Key Factors in Agent Based Simulation

Identifying Key Factors in Agent Based Simulation Model on Processes in Time Constrained Environment Yim Ling Loo

Azhana Ahmad

College of Information Technology, Universiti Tenaga Nasional, Email : [email protected]

College of Information Technology, Universiti Tenaga Nasional, Email : [email protected]

Alicia Y.C. Tang College of Information Technology, Universiti Tenaga Nasional, Email : [email protected] Abstract – Processes in time constrained environment which could be known as works or tasks with deadlines became a part of life in most areas. Efficiency of life is even measured by the ability of an individual in meeting deadlines. Due to this reality, agent-based simulation systems ventured into simulating tasks with deadlines as a measure to maximize the efficiency of work. However, due to propriety nature of the existing research works, similar research works could find no means of reusing or replicating results from the existing models developed. This paper discusses the existing research works together with limitations that are hindering the evolution of agent-based simulation modelling for tasks with deadlines. Within this paper, key factors in agent-based model for simulating tasks with deadlines are identified, which further refine the existing conceptual model that address the limitations found.

Keywords – generic agent-based simulation, simulation modelling, time-constrained environment, processes I. INTRODUCTION Simulation technology emerged exponentially as a tool to comprehend and predict particular situation or phenomenon in different domains such as business, health science, military and especially social science [1]. As a trusted and proven approach for understanding and predicting different phenomenon, different approaches of simulation had been introduced in order to further improve the robustness and reliability of the simulation. As such, approaches such as cellular automata, discrete event, object-oriented and recently, agent-based approach were used for simulation modelling [2]. Agent-based simulation modelling are mostly developed in modelling simulation of different phenomenon which involves not only objects but individuals that are adaptive and is able to discern, in other words, mimicking real human behaviours and nature [3],[4],[5]. Agent-based approach enables simulation of dynamic phenomenon to be more accurate, where the simulation is expected to be as similar as the real life situation. This inspired integration of the approach in simulations of different phenomenon which dynamic is the main attribute, for instance, social science [3]. This work is sponsored by Kementerian Pengajian Tinggi, Malaysia under the Fundamental Research Grant Scheme (FRGS).

Past research works of agent-based simulation modelling have shown that the nature of the research works are either ad-hoc or domain specific based [6]. This has led to certain level restriction to the growth of agent-based simulation modelling [5]. However, recent research works have ventured into the nature of generic modelling in different domains such as agriculture [7], business [8] and transportation [9]. These research works has similarities of addressing to different limitations of the previous works in agent-based simulation modelling because of the ad-hoc and domain specific nature [6]. As the limitations were not completely addressed even from the mentioned research works that embrace the nature of genericity, a generic agent-based simulation model was introduced in expectation to address to the limitations [6]. The simulation model was developed to be able to be implemented in different domains, yet similar phenomenon in order to address to the limitations of inflexibility to be reused, replicated, verified and validated against and especially domain specific nature that disabled the ability of the model to be further refined. In order to have the generic model to be further developed and verified, the model need to be tested for its objectives or expectations. This led to the investigations on the implementation of generic agent-based simulation model for simulation of processes in time constrained environment. Processes in time constrained environment can be known as tasks with attached deadlines to be met as described by research works in [13],[14]. In daily lives, meeting tasks’ deadlines are unavoidable for an individual. In emergency department of a hospital, the whole process of registering a new emergency outpatient to passing the patient to the attendance of designated physician until the assignation of ward and bed is a process or task to be fulfilled within an alleged allocation of time. The whole process is restricted to a period of time to avoid delays or more to be known as inefficiency [10]. This paper reviews the recent research works which revolve around processes in time constrained environment and discusses the identification of key factors that the research works consider to be implemented in the simulation systems. In Section II of this paper, a detailed discussion of

related research works will be discussed, followed by the motivations that lead to this research work. The identification of key factors and refinement on the conceptual generic agent-based simulation model focusing on simulating processes in time constrained environment are discussed and drafted in Section III of this paper. II.

RELATED WORKS

Almost all areas of life are filled with processes in time constrained environment or better known as tasks to be completed within a designated deadline. Ranging from fast food industry which have demands in accomplishment of food order to food delivery in limited time to accomplish draft of a building in engineering industry, processes in time constrained environment are obligatory. Furthermore, fast paced and high demand of level of efficiency in life made not only every different industry but every individual to adopt daily objective of being efficient and optimized. Level of efficiency and optimization is measured mostly by the length of time used to accomplish a task [10]–[20]. This caused emergence of numerous approaches to make sure efficiency and optimization take place such as developing systems with goals of time and activity scheduling [16]–[20], resource scheduling [10]–[12] and planning optimization [13]–[20]. In order to furthermore validate the reliability of these scheduling and allocation systems, simulation systems were introduced to simulate phenomenon according to the scheduling systems. Amongst different available approaches of simulation systems, agent-based simulation system is one of the most used simulation approach for the simulation of such dynamic environment [10]–[20]. Agent-based simulation modelling has rather been well known and used in different areas of simulation, simulating time and resources scheduling and allocation. Areas that are typically reviewed in this paper are health services [10]–[12], facility management [13]–[15], software scheduling [16],[17] and transportation scheduling [18]–[20]. Similar to the limitations mentioned in [6], the existing research works of agent-based simulation modelling as mentioned above were ad-hoc or domain specific in nature. Simulation models in [10]–[12] shows significant limitation for having ad hoc and domain specific development of simulation model. The agent-based simulation models for process in time constrained environment for pediatric [10], emergency [11] and orthopaedic department [12] can be developed generically to be used in those three research works. These three research works embrace similar objective of decreasing patient waiting time and increasing efficiency in health care services, with similar parameters of having physicians, patients, and limitations of time, resources and number of tasks. With so much similar attributes, simulation model developed for one of the research work can be reused by the other. Instead of reusing, models were developed from scratch. Ad hoc or domain specific nature has further led to the incapability of the simulation models to be further

customized or extended for simulation of other phenomenon in the similar domain or different domains. Research works of scheduling facility management services [13], logistic services [14] and overhead crane service [15] falls in the similar simulation of logistic services scheduling with similar objectives and parameters. Instead of developing a simulation model that could that could be further customized to support simulations of the specific phenomenon, a different simulation model was developed for each phenomenon. Hence, unnecessary large consumption of resources in terms of time and cost. Substantially, similar limitations found in the research work of [16]–[20]. The incapability of extension and reuse for the model developed caused validation and verification for the model to be voided as well, as replication of results based on reuse of the model in other application domains are inevitable to justify the reliability of simulation results and robustness of the model [1],[2]. The different limitations found in recent research works which focus on simulation of processes in time constrained environment were similar of those mentioned in [6]. Meanwhile, in order to further refine the conceptual generic agent-based simulation model in [6], a specific scope or constraints need to be applied to determine simulation details which are objectives, directions and parameters of simulation for model refinement. Thus, simulation of processes in time constrained environment, where efforts of developing generic model has not been implemented yet. Thus the motivation of further refinement and identification of key factors to be integrated in the conceptual generic agent based simulation model in [6] with the mentioned constraint or scope. The following section discusses on how the conceptual generic agent based model is further refined according to the area of simulating processes in time constrained environment and which part of the model is being further refined. III.

SIMULATION OF PROCESSES IN TIME CONSTRAINED ENVIRONMENT

In order to address the limitations of being ad-hoc or domain specific, having incapability of extensibility, deficiency in validation and verification as well as reuse and replication, a conceptual generic agent-based simulation model was introduced in [6]. The conceptual model could be seen in Figure 1.

Predefined Application Domains Env1 Generic Agent-based Simulation Model Env2

Simulation

Application Domains (different datasets) Env1a

Env3 Repository

Replication

External Application Domains

Env2a Validation

Env4

TABLE I. PARAMETERS FOR SIMULATION OF PROCESSES IN TIME CONSTRAINED ENVIRONMENT Environmental Parameters Agent Parameters Time, T Capacity, C Resources, R Attributes, A Task Size, TS Behaviours, B Task Type, TT Number of Tasks, TN Workflow, W

Envi dataset Envia

Env5 Envi

Fig. 1. Conceptual generic agent-based simulation model [6]

Referring to the conceptual model, there are important elements and components; environment, Envij which consists of the tested application domains, while components consists of repository, simulation, validation and replication. The elements and components were discussed thoroughly in [6] where further works of refinements were suggested for the evolvement of the conceptual model. Through thorough reviews done on the recent research works of agent-based simulation models developed for simulating processes in time constrained environment crucial information were gathered for this research work to evolve. Recent research works in developing simulation model for processes in time constrained environment were found to have similar objectives. The simulation systems were implemented in different application domains ranging from health services to transportation scheduling for similar objectives of maximizing resources and efficiency [10]–[20]. In order to achieve the objectives, particular key factors that will affect simulation need to be specified. In certain simulation models, the particular key factors are called variables, stakeholders and parameters. These key factors directly affect the accuracy of simulation and reliability of simulation results [10],[14],[19]. Thus, the particular key factors which will be translated consistently as parameters within this paper, need to be specified in the conceptual generic model [6], in which, further refines the conceptual model. Parameters adopted for this research work will be thoroughly discussed in the next section. A. Simulation Parameters Amongst the reviewed research works [10]–[20], similar or common parameters were found and were found to be appropriately divide into two categories; environment and agent parameters. Environmental parameters depicts the particular details of the application domains that needed to be considered in simulation. Agent parameters depicts the particular details of individuals involved in the real life system that is to be simulated. Within these two categories of parameters, similar specific parameters were found across the different research works. TABLE I summarized all the similar parameters within their categories.

The parameters listed in TABLE I affect and interact between each other despite of environmental or agent parameters. Within the defined time constrained environment, time is the most important factor to be considered in the simulation of the environment, therefore the parameter time, T, is inevitable to be one of the environmental parameter. The factor of time depicts the duration a task should be accomplished, which is when a task is expected to begin and when it is expected to end. It is worthy of noting that “tasks” is used instead of “processes” throughout this research work to avoid ambiguous use of “processes” to describe other areas of the simulation model. The unit of measurement for time, T, can vary from seconds to years, depending on the nature of the task, Task which consists of task size, TS, task type, TT, and number of tasks, TN, as well as workflow, W, designated for the task. Resources, R, in an environment depict the availability of equipment and facility. Unit of measurement might vary according to the nature of application domain, for example, availability of overhead crane to accomplish transportation can be measured as units and availability of medicine drops to accomplish medical check-ups can be measured by millilitres. The attributes of task which consists of task size, TS, task type, TT, and number of tasks, TN, affects the other parameters such as amount of time, T, and resources, R, to be used to complete the task. Task attributes measurement similar to the other parameters, varies according to the nature of application domain. Workflow, W, of the environment affects the other parameters in the environment as well, as workflow determines how much time a task should begin and end as well as the flow of one task to other and the rules or regulation to abide. Workflow of an environment varies from another, depending on the application domain. Capacity, C, is a critical parameter that depict the availability of individuals, a type of resource to accomplish the task. Different task attributes need determines different capacity of individuals to be made available to accomplish the task. Integral numbers can be used as measurement to agent capacity parameter. Agent attributes, A, depict the role of the individual, to determine different individual in handling different task type. Behaviours of individual, B, depicts the norms of individuals in accomplishing tasks given. Behaviours of individual, B, affects the capacity, C and also the resources, R, as a late-comer will affect the availability of overall resource and capacity of worker. The common parameters found in the research works reviewed across the different application domains can be applied in the conceptual generic model for further investigation. As illustrated in Fig. 1, simulation component

will be the area for further investigation of the conceptual model with common objectives and parameters of simulation gathered and listed in Section III. As simulation component consists of algorithms as well as environment and agent parameters, it is best that the parameters are integrated into this component, ultimately for further refinement of the conceptual model. B. Refinement of Simulation Component with Simulation Parameters In the initial conceptual generic agent-based simulation model, simulation component was conceptually developed to contain the elements of algorithm and environment which were essential to be included in simulation component. Algorithm plays the role of being the main engine that govern the execution of simulation for the desired environment or application domain while environment contains the parameters to be determined and executed by the governing algorithm [6]. From the observations on the current research works of agent-based simulation for processes in time constrained environment that is thoroughly discusses in Section II and III, the conceptual simulation component can be further refined as shown in Fig. 2. Simulation Component

Generic Algorithm

Algopi

Algoei EnvPei

Appi

Apei

Configure

EnvPpi

Environment, Envij

Predefined environment

Domain specific environment

EnvPpi Resources

EnvPei Workflow

Tasks

Resources

Size

Time

Number

Appi Capacity

Workflow

Tasks

Type

It is worthy of noting that due to the generic nature of the conceptual simulation model, predefined parameters are labelled with subscript p and domain specific parameters are labelled with subscript e. The purpose of predefined parameters is that this generic model will be initially investigated with case study of a few domains for implementation and validation. Then, the generic model is expected to be further customized and extended to extensively simulate tasks in time constrained environment of other domains in the future where domain specific parameters allocation is needed. The purpose of growing parameters with subscript i is due to the generic nature of the conceptual simulation model, where the simulation is expected not only for one domain, but a few domains and more in the future. Generic algorithm governs and configures the simulation of environment, which consists of parameters. However, further investigation needed to be done to repository component before the refinement of generic algorithm element, as the data collected in repository crucially affects details within generic algorithm. Further refinement to the environment element, Envij, is done in the architecture of simulation component, reflecting the detailed parameters to be considered within environmental parameters, EnvPpi and EnvPei and agent parameters, APpi and APei as illustrated in Figure 2. As shown in Figure 2, both predefined and domain specific environmental parameters, EnvPpi and EnvPei, consist of time, T, resources, R, tasks, Task and workflows, W, while tasks, Task is comprised of task size, TS, task type, TT and number of tasks, TN. Thus, environmental parameters can be denoted using the formula below; 𝐸𝑛𝑣𝑃𝑝𝑖 = {𝑇𝑝𝑖 , 𝑅𝑝𝑖 , 𝑇𝑎𝑠𝑘𝑝𝑖 , 𝑊𝑝𝑖 } (3) 𝐸𝑛𝑣𝑃𝑒𝑖 = {𝑇𝑒𝑖 , 𝑅𝑒𝑖 , 𝑇𝑎𝑠𝑘𝑒𝑖 , 𝑊𝑒𝑖 } (4) Where; 𝑖 = {1, 2, 3,4, … }

Type

Size

Time Number

Apei

Behaviours

Behaviours

Capacity

Attributes

𝑖 = {1, 2, 3,4, … }

Attributes

Fig. 2. Further refinement of simulation component in conceptual generic agent based simulation model.

As described in [6], the governing generic algorithm consists of predefined algorithms, Algopi and domain specific algorithms Algoei. A slight change was made to the illustration of generic algorithms element, Algoi, in the architecture of the simulation component, reflecting the relationship of both environmental and agent parameters within the sets of algorithms, as described in both of the formulas below, which is stated in [6]; 𝐴𝑙𝑔𝑜𝑝𝑖 = {𝐸𝑛𝑣𝑃𝑝𝑖 , 𝐴𝑃𝑝𝑖 } (1) 𝐴𝑙𝑔𝑜𝑒𝑖 = {𝐸𝑛𝑣𝑃𝑒𝑖 , 𝐴𝑃𝑒𝑖 } (2) Where;

Meanwhile, both predefined and domain specific agent parameters, APpi and APei, consist of capacity, C, attributes, A and behaviours, B. Hence, the agent parameters can be denoted using the formulas below; 𝐴𝑃𝑝𝑖 = {𝐶𝑝𝑖 , 𝐴𝑝𝑖 , 𝐵𝑝𝑖 } 𝐴𝑃𝑒𝑖 = {𝐶𝑒𝑖 , 𝐴𝑒𝑖 , 𝐵𝑒𝑖 } Where; 𝑖 = {1, 2, 3,4, … }

(5) (6)

Within the environment element of simulation for tasks in time constrained environment, time, T, and task, Task, are a crucial parameters to be included in the simulation model. Time, T can be denoted by; 𝑇𝑝𝑖 = {𝑇𝑝1 , 𝑇𝑝2 , 𝑇𝑝𝑛 } (7) 𝑇𝑒𝑖 = {𝑇𝑒1 , 𝑇𝑒2 , 𝑇𝑒𝑛 } (8) Where; 𝑖 = {1, 2, 3,4, … } 𝑛 = {1, 2, 3,4, … }

Time, T, is the parameter that sets main constrain for the completion of task, Task. Time within this research work is measured in units, progressively and according to the workflow, W, set by the environment. Time parameter is the main governance of expected beginning and ending of task, Task. Task parameter comprises of task size, task type and number of tasks, which is denoted by the formulas below; 𝑇𝑎𝑠𝑘𝑝𝑖 = {𝑇𝑆𝑝𝑖 , 𝑇𝑇𝑝𝑖 , 𝑇𝑁𝑝𝑖 } 𝑇𝑎𝑠𝑘𝑒𝑖 = {𝑇𝑆𝑒𝑖 , 𝑇𝑇𝑒𝑖 , 𝑇𝑁𝑒𝑖 } Where; 𝑖 = {1, 2, 3,4, … }

(9) (10)

Task size, type and number of task define the attribute or nature of the task to be completed. Similar to time, task is also affected by workflow parameter, as workflow determines both time, task and resources to be used at a given phenomenon. Environmental resources, R, parameter consists of facilities and equipment available to be used to complete the assigned task. Resources parameter affects time and task, where if resources were to be unavailable or limited at a certain point of time, length of time may need to be lengthen and task size, type and number to be handled at the point of time have to be changed. Within agent parameters, capacity, C, which depicts the number of individuals available for task completion. This parameter affects the other parameters in both environment and agent elements. If given capacity is more than expected, time of task completion may be shorten, larger task size, type and number could be handled at one point of time and more environmental resources needed. Agent parameter Attributes, A, depicts the role that the individual is expected to commit. This parameter affects all the other parameters as well. For instance, if manager agent halted giving order to subordinate agent to finish a task in an expected time, the task becomes overdue, time of completion lengthened and instant addition to resources and capacity have to be done for quick completion of the overdue. Behaviours, B, of an agent depicts the norms of individual in the environment. If some of the individuals in the environment effectively start working at 9am instead of the stated task routine at 8am, time to task completion lengthens as lack of capacity affects the task size, type and number to be handled, which ultimately affects the workflow. Hence, all the parameters present in the environment element, Envij, regardless whether it is environmental or agent parameters, are crucial and they affect one another. The parameters are interdependent and dynamically relating with one another, which suggest that agent-based simulation modelling is crucial, for the approach is meant for dynamic environment [1],[2],[3]. Based on the reviews of the related research works and the application of information gathered from the review, the conceptual generic agent-based simulation model which was introduced in [6] is further refined. The common parameters contributes to the refinement of the conceptual model especially to the simulation component. The identification of the parameters impacts the other components as repository,

validation and replication which relates closely with simulation component. IV. CONCLUSION AND FURTHER WORKS This paper reviewed current research works involving agentbased simulation of processes in time constrained environment. The research works were found to have limitations of being developed in ad hoc and domain specific nature, which led to the inflexibility of further extension or customization, lack of validation and verification as well as replication. Hence, the phenomenon of processes in time constrained environment is found suitable to further investigate a conceptual generic agent-based simulation model with efforts of addressing to the limitations. With information of objectives and parameters gathered from the different research works reviewed, simulation component in the conceptual generic model was further investigated. The overall conceptual generic model was further refined through the further investigation of phenomenon as well as simulation objectives and parameters to the refinement of simulation component. As the conceptual generic agent-based simulation model is in the process of refinement, other components such as repository, validation and replication will be further refined in further works. Repository component is expected to be explored next, as research methodology, the method data collection and data to be collected for the parameters listed in simulation component falls in repository component. Since generic algorithm interdepend with data collected in repository component, the generic algorithm is expected to be refined in detail after further investigation is done on the repository component. REFERENCES [1]

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