Multiagent Based Simulation Tool for Transportation

and Logistics Decision Support

Janis Grundspenkis and Egons Lavendelis

Riga Technical University

Faculty of Computer Science and Information Technology

Department of Systems Theory and Design

1/4 Meza Street, Riga

Latvia LV-1048

Abstract. A transportation and logistics domain belongs to complex problems

domains because there are many geographically distributed companies who

may enter or leave the system at any time. Analysis of the great number of

publications reveals that although traditional mathematical modelling and

simulation techniques still dominate, new approaches start to appear. Agent

technologies and multiagent systems emerge into transportation and logistics

domain only recently. The paper proposes the developed multiagent based

simulation tool for decision support in transportation and logistics domain. The

multiagent system consists from clients’ agents and logistics companies agents

which may participate in four types of auctions, namely, English auction, Dutch

auction, First-price sealed-bid auction and Vickrey auction. A client is an

auctioneer who is making decision about the best offer of delivering goods. The

simulation tool is implemented using Borland C++ Builder and MS Access

database.

1 Introduction

A transportation and logistics domain with many involved companies that are

geographically distributed belongs to complex problem domains. The logistics

domain is dynamic where logistics goals, companies’ capabilities and beliefs are

continually changing throughout the planning process. Moreover the logistics domain

is an open domain where organizations may enter or leave the system at any time [1].

Different methods and techniques are used for problem solving in transportation and

logistics. Analyses of the great number of publications reveals that traditional

mathematical modelling and simulation techniques still dominate for searching of

solutions, but new approaches start to appear, such as web- and knowledge based

systems, intelligent agents for distributed and mobile solutions., etc. [2].

Agent technologies start to penetrate into transportation and logistics domain only

recently. Intelligent agents represent organizations within the logistics domain, and

model their logistics functions, processes, expertise, and interactions with other

organizations. Some agents simulate users involved in traffic; others are means of

transport (trucks, trains, planes, ships), or elements of the traffic infrastructure [3].

Multiagent systems offer such useful features as parallelism, robustness and

Grundspenkis J. and Lavendelis E. (2006).

Multiagent Based Simulation Tool for Transportation and Logistics Decision Support.

In Proceedings of the 3rd International Workshop on Computer Supported Activity Coordination, pages 45-54

DOI: 10.5220/0002479000450054

 SciTePress

scalability. Multiagent based approaches are well suited for domains, which require

the integration and interaction of multiple sources of knowledge, the resolution of

interest and goal conflicts or time bound processing of data [4]. Applications of

intelligent systems in transportation and logistics cover such problems as multiagent

simulation for traffic modelling, decision support systems for letter transportation,

logistics planning, sea freight transportation, vehicle dispatching, railway

transportation scheduling, and others [5], [6], [2]. Several successful projects have

been described, for example, TELETRUCK system [7] and DIAL system [8]. During

a design of agent-based systems for transport a new agent technology has been

introduced – a holonic agent or holon. [9]. Two generic meta-types of agents, namely,

management and service agents have been introduced in the logistics domain [10].

At the same time investigations of different operation modes in multiagent systems

in the context of multimodal transportation and logistics problems are not intensive

enough. The paper deals with the development of the prototype of multiagent based

simulation tool. The multiagent system consists from clients’ agents and logistic

companies agents which allow simulating four different types of auctions. At the end

of the auction the client can make a deal with the winning agent.

2 Example of the Multimodal Transportation Route

One very popular way how to deliver goods from Asia to Europe is by using the

following supply chain: “Asian Deep Sea Port ↔ Western Europe Deep Sea Port ↔

Baltic and Mediterranean feeder ports ↔ European Costumers”. In this paper we

suppose that at first goods are transported to Shanghai Port by railway, then by deep

sea shipping lines to Hamburg, by feeder shipping lines to Mediterranean and Baltic

feeder ports, and finally by trains to clients. This multimodal transportation chain is

shown in Figure 1, and it is used as an example to show basic concepts of multiagent

systems for selecting the best company and route for transporting goods.

In this paper we discuss the possibility to simulate cooperation between all

interested parties. When client wants some good to be delivered, he does not go

directly to shipping and/or railway companies, but goes to logistic companies, who

contact their partner carriers. This process involves a lot of competitors and is time

and money consuming if done by humans. The proposed multiagent based simulation

tool is an attempt to automate this process and to support decision about optimal

solution.

3 The Architecture of Multi-Agent System

The purpose of building the multi-agent system is to provide more easy deal making

between clients and logistic companies.

The multiagent system is built as follows: each client has his agent and each

logistic company has its agent, too. Clients make deals with logistic companies using

auctions, where client is an auctioneer and logistic companies are bidders.

Fig. 1. Supply chain: “Asian Deep Sea Port (Shanghai) ↔ Western Europe Deep Sea Port

(Hamburg) ↔ Baltic and Mediterranean feeder ports ↔ European Costumers”.

Clients’ agents do not cooperate with each other. Two auctions organized by

different agents also do not affect each other. This allows to simplify systems

architecture by viewing system with only one client (and his agent) and many logistic

companies (and their agents). Figure 2 shows business links between all actors and

also shows which of them have agents.

Client’s agent gets all necessary parameters from client and makes an auction. In

the developed tool the following parameters are used:

• Starting point and destination,

• Auction type,

• Starting price per unit (for some auction types),

• Other parameters for auctions and their weight coefficients (if available).

Each logistics company’s agent has knowledge base containing all carriers his

owner (logistic company) collaborates with and all information about routes which

they operate. Similarly, the knowledge base contains all information about terminals

with which logistic company collaborates.

Fig. 2. Business links between all actors and their agents.

After receiving an offer to participate in auction, the first thing that agent has to do

is to calculate his private evaluation. To do it, agent has to find all possible paths how

to transport containers from starting point to destination. The transportation system is

represented by a graph, where nodes are terminals and edges are possible routes. Each

edge and also each node have weights. Weights correspond to transportation costs per

transportation unit and costs per unit of goods (containers) that are kept in terminals.

In this case it is easy to use some very simple path finding algorithm (for example,

depth search) to find all possible paths. The minimal paths costs plus some percents

for minimal companies’ profit is agent’s private valuation.

As mentioned before, a client is an auctioneer and logistic companies are bidders.

There is one difference from traditional auction interpretation: traditionally auctioneer

maximizes price, but bidders – minimize, but if we are auctioning the possibility to

sell something (in this case carrying service), we (auctioneer) need to minimize the

price, but bidder – to maximize. It is worth to stress that there are not critical changes

in basic auction protocols: only the price changes to opposite direction.

Agent knowledge bases contain their strategies in different types of auctions. There

are four types of auctions. Auctioneer starts an auction by sending all auction

parameters (listed above) to bidders. There are four types of auctions [11] that are

implemented in the proposed simulation tool:

• English auctions (the most commonly known type of auction) that are first-price,

open cry, ascending auctions. The auctioneer starts off by suggesting a reservation

price. If no agent (bidder) is willing to bid more than the reservation price, the

good is allocated to auctioneer for this amount. In other case, bids are then invited

from agents who must bid more than current highest bid, and then the winner is

agent who has made the current highest bid. In English auctions dominant strategy

is to bid a small amount more than the current price, if it is less than private

valuation.

• Dutch auctions are open-cry descending auctions. The auctioneer starts out by

offering some artificially high price. The auctioneer then continually lower the

current price by some small value until some agent makes a bid and wins the

auction. There is no dominant strategy for Dutch auctions in general.

• First-price sealed-bid auction is an example of one shot auction. There is a single

round in which bidders submit to the auctioneer a bid. The winner is an agent that

made the highest bid. Agents use the dominant strategy – to bid a bit less than true

valuation.

• Vickrey auctions are the most unusual and perhaps counterintuitive of all

considered auction types because these auctions are second price sealed-bid

auctions. There is a single negotiation round, during which each bidder submits a

single bid; bidders do not get to see the bids made by other agents. The winner is

an agent who made the highest bid, however he pays the price of the second

highest bid. Agents use the dominant strategy – to bid his true valuation. This is the

main advantage of this protocol for the auctioneer .

After receiving auction’s parameters each agent calculates his private valuation.

Then, if his strategy says him to bid, he bids according to his strategy. In the

developed simulation tool all agents have identical strategies which are the dominant

ones in corresponding auctions. In real life these strategies can differ. After receiving

each bid the auctioneer informs other bidders about this bid (if auction is open-cry).

At the end of auction client can make a deal with the winning agent.

4 Implemented Simulation Tool

All four types of auctions are used in the simulation tool which is implemented using

Borland C++ Builder and MS Access database. The simulation tool only shows the

mechanism how these auctions can be carried out. At the moment there are big

differences from real deal making system. First, our system runs on one computer and

it is not possible to connect to it through Internet. A real system should have one

server and many client’s and many logistic company’s computers each having one

agent. Second, our system has common database for agents. Agents get from it only

their knowledge defined by relationships in this database. At the same time, it is

needed to stress that these differences are only technical realization details, and they

do not affect the main algorithms and ideas. That is only a matter of programming

client server applications to implement a real system.

Interface of the simulation tool consists from three main parts:

• Auction parameters input part. This part allows client to input routes and auctions

parameters (see Figure 3.)

• Knowledge editing part consists of buttons in main window (see Figure 4) and

simple database editing forms. These forms allow user to edit agent list, their

attributes and all agent knowledge bases. Although this program is developed for

container flow optimisation in one special supply chain, it is very simple to use it

for any other routes not only in Europe and China but worldwide, because it is

possible to add and remove terminals at any place on the map.

• Bid and winner information output part. This part consists of Europe’s and China’s

maps, where during the auction all terminals and also routs are drawn. There is also

result table, where detailed information about price, path and time is printed.

Whole window with first bid for route “Shanghai – Liepaja” using English auction

and starting price 500 is shown in Figure 5.

Fig. 3. Auction parameters input part .

The first bid of auction in route “Shanghai – Liepaja” is shown in Figure 5 but it is

not the last one. This is English auction and the dominant strategy for this auction

type is to bid just a bit more than current price. So there are quite a big number of bids

even between two agents. The winning bid contains much less price than starting

price and also much more reasonable path is chosen (result table is shown in Figure

6).

Fig. 4. Knowledge editing part. (All simple database editing forms are not shown).

But there is an open question: which auction will give the lowest price for us? In

case of risk-neutral bidders it is not important which protocol to choose. But for risk-

averse bidders Dutch and First-price sealed-bid auctions are the best for auctioneer.

Risk-averse auctioneers, however, do better with Vickrey or English auctions [11],

Fig. 5. Whole window with first bid for route “Shanghai - Liepāja”.

Fig. 6. Winning offer in English auction for route “Shanghai-Liepāja” and starting price 500.

Results of experiments carried out with the simulation tool shows that there is no

big influence of auction protocols on price (price did not vary more than 10% in any

case) because the agents were risk-neutral. In real world it seems that large number of

small companies are risk-averse, because they perceive every loss very painfully,

while big companies may take a risk and that should be taken into consideration when

choosing auction protocol.

5 Cooperation between Logistic Companies and Carriers

In previous chapters we discuss only automation of cooperation between client and

logistic companies, but cooperation between logistic companies and carriers was only

mentioned. It is possible to automate this communication, too. In this case all actors

shown in Figure 2 have agents. Though automation of communication between carrier

agents and logistic company agents can be done using auctions, we must take into

consideration that these auctions should have more than one winner because each

carrier cooperates with more than one logistic company and vice versa (it can be

organized also using some other negotiation protocols).

Fig. 7. Multi-multi-agent system and holons.

In this case the proposed multi-agent system is transformed into a multi-multi-

agent (or holonic multiagent) system, because for clients a logistic company and

carriers with which it cooperates is one whole object (holon) represented by one agent

– a logistic company’s agent. There are 3 holons, but only one is marked in Figure 7

(other holons contain other 2 logistic companies with their carriers) which illustrates

the same situation as in Figure 2; only carriers have their agents and the logistic

companies and carriers with which they cooperate make holons.

In fact, it is possible to continue by automating also carrier company

communication with ships, trains and trucks, but in this situation auctions definitely

will not be needed, because these are units of the same company, and they just need to

be coordinated but no deal making is needed. Thus, there is simple hierarchy between

carrier’s main agent and its ships’ agents.

6 Conclusion

The developed simulation tool demonstrates that it is quite simple to implement a

multi-agent system for automation of communication between clients and logistics

companies. Also it is possible to make deals between carriers and logistic companies

automatically. The price may be determined using different types of auctions. That

minimizes efforts for finding the best way to deliver some goods: the client instead of

contacting all known logistic companies could just enter his wills and in few moments

get deal with one company. Logistic companies, in their turn, need not to make

negotiations with all clients, they can just announce their company’s politics to

corresponding agents and these agents will make deals with possible clients.

Simulation results show that if both auctioneer and bidders are risk-neutral, there is

no big difference, which auction protocol is used. In real situation we must take into

consideration that small companies are risk-averse, while big companies can afford a

risk. As a consequence, we must choose the appropriate auction protocol.

It is possible to include in this system also carriers and automate their

communication with logistic companies. This is one of the directions of future work.

Then it will be a multi-multi-agent system and each logistic company and carriers

with which it cooperates make a holon. For client this holon is represented by a

logistic companies agent.

Multiagent system is advanced and quite cheap solution for communication

problem solving between logistic company and their clients, and also carriers.

The future work is to make our systems more realistic. There are no big difficulties

to implement a real deal making system. That is only a matter of programming of

client server mechanisms, because all complicated algorithms are already

implemented in the developed simulation tool. It is also possible to make these

auctions a legal instrument by using electronic signatures. In this case all deals should

be made online and a lot of human resources should be saved.

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