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A multi-agent framework for efficient food distribution in disaster areas Umar Manzoor* Department of Computer Science, National University of Computing and Emerging Sciences, Islamabad, Pakistan and Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia Email:
[email protected] *Corresponding author
Maria Zubair and Kanwal Batool Department of Computer Science, National University of Computing and Emerging Sciences, Islamabad, Pakistan Email:
[email protected] Email:
[email protected]
Bassam Zafar Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia Email:
[email protected] Abstract: Disasters have been causing huge damages to infrastructure as well as human life for a long time. The time immediately following a disaster needs to be managed efficiently to minimise the damage to the human population in that area. One of the key challenges faced when disaster hits an area is efficient distribution of resources such as medicine, food, water, etc. In this paper, we propose a solution particularly for distribution of food which can be extended for any other resource as well. We have proposed a solution in which a big disaster region is divided into smaller areas (regions) and multi-agent systems are used to deliver food in these regions. Agents use shortest path algorithms and coordination to deliver food within their particular region efficiently. In the experiment section, we have performed different experiments to verify how the proposed solution performs when one of the parameters is changed and how this system performs in different scenarios. Keywords: disaster management; food-distribution problem; multi-agent systems; coordination in multi-agent system.
Copyright © 2014 Inderscience Enterprises Ltd.
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U. Manzoor et al. Reference to this paper should be made as follows: Manzoor, U., Zubair, M., Batool, K. and Zafar, B. (2014) ‘A multi-agent framework for efficient food distribution in disaster areas’, Int. J. Internet Technology and Secured Transactions, Vol. 5, No. 4, pp.327–343. Biographical notes: Umar Manzoor received his BS in Computer Science and his MS in Computer Science from the National University of Computer and Emerging Sciences, and his PhD in Multi-Agent Systems from the University of Salford, Manchester, UK, in 2003, 2005 and 2011, respectively. In 2006, he joined the National University of Computer and Emerging Sciences, Islamabad, Pakistan, as a Lecturer and promoted after as an Assistant Professor. In 2012, he was promoted as an Associate Professor; currently, he is working at King Abdulaziz University, Jeddah, Saudi Arabia. He has published extensively in the area of multi-agent systems, autonomous systems, behaviour monitoring and network management/monitoring. Maria Zubair is currently doing her MS in Computer Science from the National University of Computer and Emerging Sciences, Islamabad, Pakistan. Kanwal Batool is currently doing her MS in Computer Science from the National University of Computer and Emerging Sciences, Islamabad, Pakistan. Bassam Zafar received his BS in Electronic Engineering and Communication, his MS in Information Technology and his PhD in Computer Science from De Montfort University, Leicester, UK, Manchester, UK, in 2003, 2004 and 2008 respectively. He is currently working as an Assistant Professor at the Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudia Arabia. This paper is a revised and expanded version of a paper entitled ‘Using multi-agent systems to solve food distribution problem in disaster area’ presented at IEEE ICITST, London, UK, 9–12 December 2013.
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Introduction
Anything creating sudden chaos during normal routine can be considered a disaster. The extent of disaster can be measured by the extent of chaos created. Disasters, both natural and artificial, have been part of Earth’s system for a long time. They have been a part of our life from as long as one can remember. It includes earthquakes, floods, droughts, limnic or volcanic eruptions, accidents, fires and explosions, etc. It is almost impossible to predict any kind of natural disaster so required immediate assistance is highly unlikely but proper aid afterwards can help save many lives, e.g. it is possible to save many lives if affective(s) are pull out of the fire in first 72 hours. People in most cases are not aware of the precautionary measures and hence fear overpowers them completely. They do not understand the dos and don’ts. Like in some disaster cases, people remained safe while they were at their homes, however being safe is not the only survival technique. There are other potential problems as well. People also need survival resources. In order to get those resources one needs to be able to get to those people or organisations that provide help and assistance in such scenarios. Hence, proper and timely communication is necessary but the network usually does not work in disaster areas. And if at all it works, it
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usually goes down at such times. In recent years, organisations all over the world are facing problems in disaster management issues because time and resource is critical. They have been trying to find out easy and efficient ways of getting help to the affected ones. They are not only focusing on the safety but providing aid is also one of their top priorities. Whenever a disaster hits an area, some basic tasks which are performed 1
searching and finding survivors
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providing them proper shelter
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ensuring communication between different helping organisations
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estimating damage
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getting emergency supplies and ensuring that those supplies reach people.
Time management and coordination is very crucial in situations deriving from natural disasters because there are a large number of people involved and any kind of delay can lead to rise in casualties. Taking care of oneself is also very important because failing to do so you will ultimately fail in helping others as well. Currently, it becomes extremely difficult to manage rescue and help processes in disaster hit areas because there is lack of coordination and information sharing amongst different organisations that are working together. Firstly the organisations are not informed enough about any natural calamity until it is very late. Secondly, if they are they do not have proper resources to contact them and even after all these organisations manage to get there in time they do not know where exactly to start with. We need a system that ensures that all these tasks are done efficiently in order to provide maximum help to survivors. One of the key problems is distribution of resources such as food, clothes, medicines, etc., in the disaster area but in limited time and resources. In this paper, we focused on food distribution problem particularly, however, this solution can be applied to other distribution problems as well. The food is mostly not distributed fairly and there are some groups of people which do not get food at all. The supplies are needed to be divided in appropriate portions according to number of survivors in different regions. Multi-agent systems are used for solving many real-life problem solving applications (Manzoor et al., 2013; Nakajima et al., 2007; Ejaz et al., 2012; Fiedrich, 2006; Kopena et al., 2008; Nefti et al., 2010; Basak and Mazumdar, 2012; Domnori et al., 201). These systems consist of software entities known as agents who have ability to learn, coordinate and perform autonomous actions in order to perform a given task. The agent performs all these functionalities by perceiving the environment through sensors, analysing the conditions, choosing the right action to perform and then acting on that environment using actuators (García Coria et al., 2014; Manzoor and Nefti, 2011, 2008; Ijaz et al., 2008). We can also consider a human as an agent as it has five senses which act as sensors through which he can intercept the environment and changes occurring in it. He also has capability to analyse those changes and act according to his own objectives. Another example of an agent is a robotic agent which notices a certain change in a condition and act by performing suitable action using motor (Friedrich et al., 1997; Manzoor and Nefti, 2010). Learning process in an agent is very important to make it intelligent and capable of performing autonomous action. This learning process includes maintaining a history of experiences and using those historical patterns to decide best possible future actions
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to achieve a given goal efficiently (Marey et al., 2014; Manzoor and Nefti, 2012; Masegosa et al., 2014). Multi-agent systems can have independent or cooperative agents for solving a problem. The agent observes its environment for changes as well as behaviour of other agents as both are important in successful working of overall system. By using these observations and experiences from history, the overall task performance of the system can be improved (Manzoor et al., 2010; Manzoor and Nefti, 2010). Coordination amongst agents is used when there is a common goal which agents in a system want to achieve. By efficient coordination, the agents can work together to complete the goal in less time with increased reliability and better resource usage. Therefore, multi-agents can provide an excellent solution where there is need to solve a problem with limited resources and time constraints. Researchers have already used multi-agent systems in disaster management to cater different kinds of problems which are briefly discussed below. Ejaz et al. (2012) proposed a multi-agent system approach to formulate disaster evacuation guide. This guide is very useful when it comes to saving human life in tough time of disaster. Fiedrich (2006) devised a multi-agent system which models resource allocation tasks after strong earthquake hits a place. It highlights different problems in resource allocation. Kopena et al. (2008) discussed different problems which are faced when distributing resources in disaster area. One of the main problems is resources distribution which is normally not performed in optimal fashion which means that food is either wasted or there are some groups of people which do not get food in appropriate amount. Basak and Mazumdar (2012) discussed the use of coordination and cooperation in a multi-agent system in disaster scenario. These factors play a very important role when it comes to time constraint problems. Domnori et al. (2011) discuss a multi-agent framework for coordination between the different operative units functioning during rescue operations. In this paper, we have presented a multi-agent-based solution for solving food distribution problem in disaster hit area(s). Single agent or a single software entity fails for this purpose as it will not be efficient and will take too much time to find survivors and distribute food amongst them. Furthermore, it will not be able to take care of updates in varying conditions during its task. This is because of the altering nature of disaster hit environment. The real world has a habit of changing itself again and again and the structure of the problem demands a multi-agent system. A coordination-based multi-agent system for disaster stuck environment is a solution which promises efficient resource distribution in timely manner. This is because of many advantages provided by multi-agent systems. A multi-agent system consists of more than one agent that interacts with each other to achieve their common goals (Domnori et al., 2011). A multi-agent system gives the advantages of modularity, reliability, scalability, flexibility and individual goal specificity. Multi-agents based solution improves the efficiency of the solution by 1
reducing the time taken to perform different actions
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making better decisions.
The performance of multi-agent system depends on two important factors, i.e., learning and coordination. Learning focuses on planning and predicting possible future events by
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maintaining history of previous actions/results whereas coordination on the other hand ensures agents coordinate with each other to share the available resources/knowledge to achieve common goals. In this paper, we have also investigated how these two factors impact the performance of the multi-agent system independently and together. The food distribution problem includes discussion how food and similar resources can be appropriately managed and delivered to survivors in disaster area using multi-agents. For this purpose, we have proposed the use of different types of agents for different purposes. Using this solution, food distribution problem can be solved efficiently using coordination and learning in multi-agent systems. The rest of the paper is organised as follows. This section is followed by system architecture design which includes complete description of systems modules and their working. After that the system model is discussed, followed by experimentation and conclusion.
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System architecture
The architecture of system is defined in three different steps. First, there is an abstract design which describes the main entities required in the system. Secondly, we define architectural design which is inspired from the layered architecture used for multi-agent systems. After that, detailed architecture is discussed where each module of system is opened in further details, describing each task required for performing the given module. Each design along with diagram is described in the following subsections.
2.1 Zero-level diagram Zero-level architecture diagrams are used to define an introductory view of the system’s architecture. Figure 1 shows the zero-level diagram of the proposed system (i.e., overview of the main modules of the system). It shows all the main entities that are part of the system and which interact with core multi-agent system to perform food distribution tasks. We have a disaster area map which shows all the regions where disaster has hit. It has information about the area of the region as well as the population effected by it. The area of each region and the population affected metrics can be used for making decision about food required for that region. Food warehouse presents the place where food is stored and is used to monitor the food available and how it should be divided to cover maximum population. In database all the information related to disaster area is maintained. Using this, statistical charts and reports can be created which are useful for end-user to view and maintain various processes involved in food distribution process. For example, this data can be used to analyse patterns for time to reach maximum possible survivors and repeated statistics can be formulated to give efficient solutions for next disaster. The multi-agent system itself consists of the different agents for performing various tasks and this system continuously interacts with rest of the entities to perform various tasks. Knowledge base is used to maintain all the agent related data which can be used by agents to perform intelligent and autonomous decisions.
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U. Manzoor et al. Zero level architecture diagram showing the major entities like disaster area map, food warehouse, knowledge base and database and statistics units which all perform together to achieve the solution of the problem (see online version for colours)
2.2 System architecture Figure 2 shows the architecture of the system and this architecture is inspired from the layered architecture used for multi-agent systems. Different modules of system are divided into layers and these layers interact with each other to perform different tasks/ actions. The actions in each layer depend on the actions performed in previous layer. This design is suitable for our system because there are different types of roles for agents and humans which can be divided into layers. For example, humans have two roles, i.e., users of the system and the survivors which require food. Similarly, agents can be divided into administrator, coordinator and food distributor roles. On top-most layer, we have our human user who is managing the whole system. He interacts with the administrator agent for different tasks that he needs to be performed. These tasks include giving input about a new disaster region and to view statistics/ reports of regions where food distribution is completed. Administrator agent is responsible for various actions such as receiving disaster information from human user, storing and sending agent team. This agent interacts with database and knowledge base to take different types of decisions.
A multi-agent framework for efficient food distribution in disaster areas Figure 2
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System architecture showing the system modelled as layered architecture (see online version for colours)
The administrator agent sends a team to a disaster area and for doing this it first manipulates number of agents required for that particular area using population size and area. A coordinator is chosen from the team for communication with administrator agent. The coordinator is provided with all the necessary information to manage team and deliver food to human population. The coordinator is responsible for leading team to the destination area, distributing food according to population and managing food distribution agents to perform their tasks. In the next layer, we have food distribution agents which are responsible for carrying and delivering food. These agents, with the guidance from coordinator, pick food from the warehouse, carry food to given area and deliver it to human population. A food distribution agent can interact with single or multiple members of human population. In the final layer, we have the human population which represents the survivors of the disaster to whom food needs to be delivered.
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2.3 System process model The proposed system has three main modules which are input module, food distribution module and statistics module. The input module involves two actors, the human user and administrator agent. The human user provides input in form of XML which is parsed and manipulated to get complete information of region and stored in appropriate form in database. Another alternative way for input is using dialog box which takes the input in form of text fields. The administrator agent is responsible for the task of creating regions and assigning coordinator agents in them. It is also responsible to share the appropriate data passed by the user with the coordinator agent which uses this data to calculate the number of agents required for the task of distribution in different regions as per the requirement of that region. The required food is first collected from warehouse then the team led by coordinator moves to the exact disaster location. The coordinator monitors the food distribution agents by dividing and leading them to different human population groups and distributing food in parallel. Shortest-path algorithms are used to reach closest groups. The third module is statistical analysis and report generation. Statistical reports are generated for end-user so that he can view the entire work of the system and can choose to perform tasks for improvement.
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System model
The multi-agent system for food distribution is divided in different parts and different agents are involved to complete the different tasks. The different agents involved in the system can be represented as following: •
AA: Administrator agent.
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CA: Coordinator agent.
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FA: Food distribution agent.
The input to the system is a map containing information of disaster region. This information contains •
PRx: Population in region X.
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ARx: Area of region X.
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FRx: Food required in region X.
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SRx: Status of food distribution task e.g. not started, in process, completed.
Therefore, a particular region can be represented as a set ‘Rx’ which is defined as follows: R x = {PR x , AR x , FR x , SR x }
Each region has a unique ID and all the regions can be represented using a set ‘Regions’ which is represented as follows: Regions = {R1 , R 2 , R 3 … R N }
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When the system receives region set as input, it initiates the process of creating team to send to that region. Administrator agent computes the number of required agents required for the task based on area and population information. The initial team can be represented as: Team = {A1 , A 2 , A3 … A M }
After that, the system performs an election between the team members to choose one of them as coordinator. Rest of the agents are classified as food distribution agents. The team is represented as: Team = {C, FA1 , FA 2 … FA M −1}
This team collects required food and is then sent to population in that region to distribute food. The coordinator is responsible for dividing the food in the region to ensure it is distributed properly. These food distribution agents find population and deliver food to them. This task can be performed without coordination, i.e., each agent randomly travels and finds destination or this can be performed using coordination where each agent travels to different population segment. When the entire region is covered, the team reports all the activity to administrator agent which updates the database accordingly. Figure 3 shows the calling sequence of the agents. Figure 3
Calling sequence of different types of agents in the system
The detailed flow of the system can be represented as follow: Step 1
Get input from user in XML format.
Step 2
Parse input and apply formatting where required.
Step 3
Divide given region into number of regions mentioned in the input.
Step 4
Set a counter variable remaining coordinator agents equal to number of regions
Step 5 While remaining coordinator agents ! = 0 Administrator Agent performs steps 6 to 12. Step 6
Estimate required food for the given region. This decision is made on the basis of population size.
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Step 7 Estimate number of agents that are required using information about population groups in area. Step 8
Collect the food from the warehouse.
Step 9 Form a team of agents. The number of agents for each area are selected according to the region area. Step 10 Distribute food in that region by visiting the area. Step 11 Team reports back to the coordinator agent which forwards the summary to administrator agent. Step 12 Decrement remaining coordinator agents counter Step 13 Update database and statistics. The above steps run for all the regions that user provides and ensures that food distribution task is performed completely. The statistics section is maintained to ensure that user can manage and maintain the activities of agents. Figure 4 summarises the major entities that interact with the system while carrying out the above algorithm. Figure 4
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Entities that interact with the system (see online version for colours)
Experimental setup
The experiments are carried out to find an optimum way to solve the food distribution problem. This part is divided into three sub-parts in which separate experiments and their results are discussed. The results of each of these experiments play significant role in designing a distribution mechanism for food in disaster area.
4.1 Assumptions Some basic assumptions are made about the environment and the system while carrying out these experiments. The first assumption is that the disaster region can be considered as a 2D map which can be mapped as a 2D grid. Each cell in the grid can
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represent obstacles, agent or people. A single cell in grid can have either one of the above. The second assumption is that the population exists in form of groups as studies suggest that survivors in disaster region form groups. For a single group, a single agent is used to deliver food. Therefore, when a team of agents is formed for a given area, the number of agents is always equal to the number of population groups in that area.
4.2 Experimental framework The experiment is carried on using JADE toolkit and Java platform. Three types of agents are created, i.e., administrator agent, coordinator agents and food distribution agent each with their own code to perform required tasks. •
administrator agent is responsible for taking input from human user, process it and passes it to coordinator of each region
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coordinator agent uses this information to create suitable number of agents for regions and distribute food in those regions using food distribution agents
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food distribution agents follow shortest path to population groups and deliver food they are carrying.
4.3 Inputs and outputs of the system The input is taken using an input dialog box which includes grid size, number of regions, obstacle ratio, population groups, food, number of agents, etc. For the experimental purposes, the grid is initialised with random values. This means that obstacles and people groups will be dynamically placed anywhere in the area of that grid. The output of every run (i.e., time taken to distribute food, etc.) is taken for a number of times called trials and stored in database for analysis purpose.
4.4 User interface The user interface is kept simple to avoid extra computational costs. A region is represented as N × N matrix. Each box in grid represents a unit of area. Figure 5 shows the grid of size 20 × 20. The blue blocks represent the obstacles and red blocks represent the target population groups. Black box represents agents which are going to deliver food. For simplicity only food distribution agents are shown in simulations and the coordinator and administrator agent only perform their actions at the backend system. The random setup of the environment help in analysing how system reacts in different possible scenarios for example, where obstacles are more concentrated towards one part of region or when they are evenly distributed. Figure 4 shows how the region looks like in the given user interface.
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Figure 5
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The user interface of a grid of 20 × 20 with 30% obstacles ratio and five population groups (see online version for colours)
Experimentation and results
We have performed several experiments with different variables to identify how each variable has an impact on the performance of the system. This helps in analysing estimated time and how it can be improved in varying conditions. As time is the most important factor in distribution of any resource in disaster area, we compare the effect of varying different variables in the system with respect to time.
5.1 Number of regions vs. time In this experiment we investigated does number of region effect completion time and is it important to divide a given area into regions. In this experiment, constant grid of 200 × 200 size is used. Furthermore, other parameters like number of obstacles are also kept constant. In this case, the disaster area is assumed to have 20 population groups and the obstacles ratio is set to be 30%. In the first iteration the whole grid is treated as a single region, in second iteration it is divided into two regions and afterwards for each iteration the number of regions is incremented by 2. It was observed that as the number of regions increased, the complexity of the problem is reduced as shown in Figure 6.
A multi-agent framework for efficient food distribution in disaster areas Figure 6
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Number of regions vs. time (see online version for colours)
5.2 Obstacles ratio vs. time In this experiment we investigated the effect of obstacles ratio in the region. Disaster regions usually lack a proper infrastructure and agents have to find their way through natural and human-made path to reach destined population. It is therefore important to know the impact of such obstacles on the performance. For this experiment, all parameters except obstacle ratio are kept constant. The grid size is kept 200 × 200 and number of regions is four. The obstacles ratio is initially set to zero and with every iteration it is increased by 5%. The experiment is run for a disaster area where population groups are 10. The time for each run is calculated. Figure 7 shows the impact of obstacles ratio on food distribution time. As shown by the result, the time for distributing the food increases as the obstacles ratio increases in area. Figure 7
Obstacles ration vs. time (see online version for colours)
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5.3 Grid size vs. time It is very important to know how the size of an area affects the time taken to distribute food. To know the effects of the size of area, we have performed this experiment which compares the grid size and the time required to solve it. For this experiment, obstacle ratio is kept constant at 30%, number of population group is kept 10 and the number of regions is kept 1. The first iteration starts with a grid size of 10 × 10. In each iteration, the rows and columns of the grid are increased by 10. The number of iterations is kept 20. Figure 8 presents graph which shows the time of the grid size ranging from 10 × 10 to 200 × 200. As shown the time increases in a linear manner as the grid size increases. As the grid size increases, the complexity of problem increases as well as the distance between source and destination points. This increases the time to solve the food distribution problem. Figure 8
Grid size vs. time (see online version for colours)
5.4 Number of population groups vs. time The next experiment is to check how population groups affect the time. In a disaster region, it’s common that populations are found in form of groups. The number of agents is kept equal to that number of groups to distribute food efficiently. For this experiment, a grid size of 100 × 100 is taken and obstacles ratio is kept 30. Iterations are performed and time is computed. In each iteration, the number of population groups is increased by 5. In Figure 9, the graph shows the results of the experiment can be seen.
A multi-agent framework for efficient food distribution in disaster areas Figure 9
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Food groups vs. time (see online version for colours)
As seen from the results, the time increases linearly as the number of food groups increases. This is because the team members are also increased according to the number of food groups and more time is required to deliver food in the region. Table 1 gives the summary of all the experiments and their results on the overall time for distributing the resource. Table 1
Impact of different factors on the overall time taken by system to distribute food in disaster hit area Impact on time when varying the factor
Factors
High
Low
Number of regions
Time is reduced as number of regions is increased. This is because these regions can be dealt in parallel.
Time is increased with less number of regions as parallel performance is reduced.
Obstacles ratio
As number of obstacles increase, time also increases as it takes time for agents to reach destination.
With lesser obstacles, finding path and reaching destination takes less time.
Grid size
As the grid size increases time also increases because of larger area to cover.
With smaller grid size, the task can be performed in lesser time.
As the number of population groups increases, there is a slight increase in time also.
As number of population groups decrease, time also decreases to some extent.
Number of population groups
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Conclusions
Distributing food (and other resources) is a difficult task in a disaster hit region. It is difficult to cover large areas and manage obstacles in territory using current solutions. Using multi-agents for food distribution makes the task easier and efficient as it divides the region into further regions and uses coordination and shortest-path algorithms to per-form better and deliver food more quickly. We have seen that using multiple agents in a distributed environment performs better than using a single agent in that
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environment. As shown by experiments, different parameters play an important role in the efficiency of the systems. By keeping such statistics, we can design a knowledge base system which can help in taking important decisions in food distribution problems. In the future work, different kinds of foods can be used and associated with types of agents. Also, we can use the same approach for distribution of other resources like medicine, clean water, etc., that is required by survivors in disaster regions. Another possible improvement can be to use better communication mechanisms between food distribution agents so that the agents do not move toward a common target. This will further save time and increase efficiency of system.
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