Negotiation policies and coalition tools in e-marketplace environment

Computers & Industrial Engineering 59 (2010) 619–629

Contents lists available at ScienceDirect

Computers & Industrial Engineering journal homepage: www.elsevier.com/locate/caie

Negotiation policies and coalition tools in e-marketplace environment Paolo Renna * University of Basilicata, DIFA, Via dell’Ateneo Lucano, 10, 85100 Potenza, Italy

a r t i c l e

i n f o

Article history: Received 12 February 2009 Received in revised form 7 July 2010 Accepted 8 July 2010 Available online 14 July 2010 Keywords: Multi Agent Systems Negotiation E-marketplace Discrete-event simulation Coalition

a b s t r a c t The e-marketplace in Business To Business applications allows enterprises to make economic transactions in a virtual marketplace. Often Small and Medium Enterprises (SMEs) do not participate to this potential opportunity because of some distrust as: the benefits of e-marketplace participation are not perceived clearly; SMEs doubt that e-marketplace fits the products offered. The possibility to evaluate the real value added by an e-marketplace can be a driving force to adopting e-marketplace for SMEs. The research proposes negotiation policies, customer’s tactics and coalition tools as a value added services in e-marketplaces. The e-marketplace scenario is a private neutral linear owned by a third part with catalogue based procurement actions. A Multi Agent System methodology is used to implement the architecture of the e-marketplace. Furthermore, the author proposes the use of discreteevent simulation to test the suggested methodologies and to evaluate the economic value of adopting the proposed negotiation and coalition tools in e-marketplaces. The simulations are conducted in a very dynamic environment and a proper statistical analysis is performed. The simulation results show that the proposed methodologies lead to benefits both for suppliers and customers.  2010 Elsevier Ltd. All rights reserved.

1. Introduction The electronic marketplace is a virtual place where customers and suppliers can make economic transactions. The customers can get the advantage of a wide market and therefore, through the sellers’ competition a reduction of the transaction costs. Potential benefits for the suppliers include the access to more customers and reduce the marketing costs. The driving forces to use the e-marketplace solution are the following: – market: in this category, there are: the globalization of the market, product life cycle reduction, the enterprise value chain is more distributed, etc., – technology: the development of Information and Communication Technology provides the tools that support the e-marketplace applications. The e-marketplace can be defined (Greiger, 2003): ‘‘an e-marketplace brings multiple buyers and sellers together (in a ‘‘virtual” sense) in one central market space. If it also enables them to buy and sell from each other at a dynamic price which is determined in accordance with the rules of the exchange, it is called an electronic exchange; otherwise it is called a portal”. In this paper, the e-marketplace is a ‘‘catalogue marketsites”; catalogue based marketsites are planned to improve systematic sourcing of highly spe* Tel.: +39 0971 205143; fax: +39 0971 205160. E-mail address: [email protected] 0360-8352/$ - see front matter  2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.cie.2010.07.007

cialized items with high degree of product specification, by automating the entire process (Kaplan & Sawhney, 2000). From ‘‘centricity” point of view the ‘‘neutral marketsites” are considered; they are designed for improving efficiency in highly fragmented industries, by offering increased visibility and a neutral knowledge base for both buyers and sellers. Generally, these e-electronic marketplaces are established by third party, usually referred as an exchange owner, who sets-up the e-marketplace and gets a fee form e-marketplace transactions and services offered to e-marketplace participants. These e-marketplaces are linear when each actor behaves as a seller or as a buyer; on the other hand, they are called exponential when actors can behave as seller or as a buyer depending on the specific transaction. Neutral e-marketplaces are also referred to as Virtual Districts and they are specifically designed for Small and Medium Enterprises (Perrone, Bruccoleri, & Renna, 2005). In this paper, a neutral linear – catalogue based e-marketplace environment is investigated, according to the above definitions. The major barriers to participate in e-marketplaces for Small and Medium Enterprises (SMEs) are the following (The European e-Business Report, 2008): – Most of the SMEs are not able to develop ICT tools for the lack of skills inside the enterprise. – The investment efforts by the SME can be limited. – The low expectation on the effective volume increasing. – The fear of implementation costs. – Security of exchange.

620

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629

– The possibility to evaluate the real value added, if the SME wants to adopt an e-marketplace. The analysis supported by the survey of Barrat and Rosdahl (2002) that have located a set of value added services (VASs) that could improve e-marketplace applications spreading and profitability; among those, ‘‘link with distribution and logistic planning” and ‘‘settle the dispute among buyers and sellers” have received respectively a 90% and 78% of preferences among the research participants. In this paper, three VASs will be implemented: – The way to reach an agreement among customers and suppliers. The protocols investigated are: negotiation, qualified negotiation, auction and one proposal. For each protocol, three customer’s tactics are evaluated: temporal, imitative and hybrid. – Coalition approach allows suppliers to stay together in e-marketplaces. The benefits that the suppliers and customers can gain will be deeply investigated. – Link with a production planning algorithm. The production planning algorithm can provide information to perform an efficient negotiation. The benefits of e-marketplaces have to be highlighted by the following methodologies: – A Multi Agent architecture is developed in order to design the e-marketplace structure. – Open source tool is chosen to support the development of the simulation environment that allows to cut costs and time. – A proper design of experiments is developed in order to understand the real value added to suppliers and customers in different environmental conditions. This paper is organized as follows: in Section 2, a literature review of negotiation by Multi Agent System in Business To Business e-marketplaces is illustrated. In Section 3, the e-marketplace scenario is described, while in Section 4 negotiation approaches and customers’ tactics are illustrated. The coalition approach proposed is explained in Section 5. In Section 6 the simulation environment is described, while in Section 7 the simulation results are presented. Finally in Section 8 the conclusions are discussed. 2. Literature review Many authors have investigated the development of electronic services, like e-procurement, combined with intelligent decision support systems by the creation of intelligent distributed systems like Multi Agent Systems (MAS) (Kim & Lee, 2002; Maes, Guttman, & Moukas, 1999). Analyzing the literature about the intelligence agent in automated negotiation approach Bartolini, Preist, and Jennings (2002) developed a negotiation protocol adopting FIPA from a software engineering prospective in a MAS environment. The generalized protocol developed has been implemented in JADE and give examples of rules for an English auction. Benyoucef, Alj, Levy, and Keller (2002) proposed a rule-driven approach to represent, manage and explore negotiation strategies and coordination information. For that, they divided the behavior of negotiating agents into protocols, strategies and coordination. They developed simulation examples in the English and Dutch auctions. They also implemented simple coordination schemes across several auctions. Dumas, Aldred, Governatori, and ter Hofstede (2005) proposed an approach to develop bidding agents that participate in multiple alternative auctions, with the goal of obtaining an item with a given probability. The approach consists of a prediction method and a planning algorithm. The prediction method exploits the his-

tory of past auctions in order to build probability functions capturing the belief that a bid of a given price may win a given auction. The planning algorithm computes a price, such that by sequentially bidding in a subset of the relevant auctions, the agent can obtain the item at that price with the desired probability. The approach addresses the case where the auctions are for substitutive items with different values. Experimental results show that the approach increases the payoff of their users and the welfare of the market. Wurman, Wellman, and Walsh (2002) examined the design space of auction mechanism and identify the core activities. Formal parameters qualifying the performance core activities enable precise specification of auction rules. This specification constitutes an auction description language that can be employed in the implementation of configurable marketplaces. Oliver (1997) and Choi, Liu, and Chan (2001) developed a genetic algorithm to find offers for agents to negotiate with other agents. The approaches based on genetic algorithms have a disadvantage because they require many trials to achieve good strategies in each round of negotiation. Loutaa, Roussakib, and Pechlivanosc (2008) proposed a dynamic multi-lateral negotiation model and a negotiation strategy based on a ranking mechanism. The contract generation algorithm of the Seller is coupled with a Buyer ranking mechanism that entails identification of the most suitable contract among the contracts proposed. The framework developed is limited to price and due date and is tested between one buyer and one seller. Cheng, Chan, and Lin (2006) presented a formal heuristic model for making trade-offs in automated negotiations in a third-partydriven e-marketplace. The tactics that the agents are to employ when making trade-offs are explicitly formulated as fuzzy inference systems, which are used to infer new offers at each round of negotiation. The experimental results demonstrate that the proposed automated negotiation algorithm is efficient in terms of the number of offers exchanged, the joint utility obtained, and the Pareto-efficiency of the negotiated contracts. The automated negotiations formulated in this study do not consider the quantities that a buyer demands or the quantities that a supplier can provide. Lopes, Wooldridge, and Novais (2008) discussed the literature review concerning the automated negotiation in supply chains and e-marketplaces. The main observation is that few papers focus on the operational and strategic process of preparing and planning for negotiation, and thus, define and characterize the key activities that negotiators should attend to before actually starting to negotiate. Among recent researches about coalition in e-marketplace, Li, Chawla, Rajan, and Sycara (2004) proposed a mechanism design problem of coalition formation and cost sharing in a group-buying electronic marketplace, where buyers can form coalitions to take advantage of volume based discounts. Simulation results show positive correlation between stability and incentive compatibility (which is in turn related to efficiency). Renna, Argoneto, Lo Nigro, Perrone, and Noto La Diega (2005) proposed two approaches to support coalition in e-marketplace environment by Multi Agent Architecture. The approaches proposed are based on game theory; in particular one on Nash Equilibrium and other on Shapley Value. The approaches are discussed for (a) coalition existence conditions; (b) coalition operation; and (c) coalition profit sharing. The simulation results showed how the possibility to make coalition among suppliers is a real value added both for customers and suppliers. Jin and Wu (2006) investigated the formation of supplier coalitions in on-line reverse auctions. An auction mechanism with supplier coalitions is proposed, which allows suppliers to form coalitions with one another for the purpose of enhancing their profitability and providing them incentives to participate in online reverse auctions. Basic requirements are identified for a valid coalition mechanism, and the requirements include individual

621

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629

rationality, market efficiency compatibility, maintaining competition, observability and controllability, and financial balancedness. The proposed coalition mechanism is well defined and satisfies all validity requirements. The stable coalition structure under this mechanism is also studied, and it is proved that under symmetric information there exists one unique strongly stable coalition structure. Nagarajan and Sosic (2006, 2007) studied dynamic alliance formation among agents in competitive markets. Their model considers price competition among n agents selling substitutable products and facing both deterministic and stochastic demand. The authors highlighted that cooperative bargaining between coalitions is still an important but relatively unexplored area of study. Chandrashekar and Narahari (2007) addressed the problem of forming procurement networks for items with value adding stages that are linearly arranged. We model the problem of Procurement Network Formation (PNF) for multiple units of a single item as a cooperative game where agents cooperate to form a surplus maximizing procurement network and then share the surplus in a stable and fair manner. Argoneto and Renna (2010) proposed an approach for coalition formation based on NASH equilibrium in an e-marketplace. The model is tested in an environment in which negotiation, production planning and Multi Agent are integrated. From the analysis of the literature, it is possible to extrapolate some useful highlights: – most research papers concerning only price or only one item in negotiation; – few research studies concern a link between the negotiation process and the production planning activity; – in most research to test the approaches has been developed a numerical example, in few papers a distributed agent architecture has been developed in order to test the approaches in a dynamic environment; – few studies concern genetic algorithms, fuzzy logic, but these applications imply high computational time incompatible with real applications; and – coalition approaches proposed are characterized by exchange of information among the coalition participants. In a real case, the independent enterprises do not exchange information. The research presented in this paper is focused on three innovative subjects: – Concerning negotiation protocol, it is proposed a sequential negotiation based on qualification of the suppliers and an auction approach. Moreover, the decisions during the negotiation are based on the information provided by a production planning model. Therefore, the environment is more realistic, because the suppliers’ actions are related to the state of the manufacturing system (costs, capacity available in ordinary, over time and sub-furniture, resources allocation, etc.). – The possibility to form coalitions among the suppliers. The proposed approach is based on independent suppliers that decide in autonomy if make an effort to form a coalition and reducing the exchange of information. Therefore, the coalition approach is more realistic. Moreover, the competition can be played among single supplier and coalitions. – The Simulation environment developed allows to test the proposed methodologies in very dynamic market conditions. The customer demand is not known at priori, and it has been considered three uncertain issues: overlap degree among the orders that affects the workload of the manufacturing system; demand fluctuation; price fluctuation.

Finally, the negotiation protocols, tactics and coalition model proposed are tested in order to evaluate the value added services to e-marketplace by these methodologies; who among customers and suppliers get the major benefits related to the market conditions. 3. E-marketplace scenario Here the context is represented by a private neutral linear e-marketplace owned by a third part where a set of registered buyers (or customers), and a set of registered sellers (or suppliers), are allowed to play procurement actions. Buyers and sellers do not establish such marketplaces, which are usually set-up by an independent company whose aim is to put together a separate group of agents in order to establish a sort of ‘‘procurement virtual district”. The seller benefits generally comes from getting access to more buyers, expanding in this way its own market, while the buyer benefits come from the possibility to get lower procurement costs, wider choice of products and better quality. The exchange owner usually gets its income from the transaction fees and eventually from some VAS fees such as secure transactions or financial services. In such e-marketplace, procurement actions are usually catalogue-based and the relation among the traders is generally based on repetitive ‘‘one-off” trades, even if several transactions can take place among the same partners in forthcoming periods (Perrone et al., 2005). Fig. 1 shows the e-marketplace use cases and its interaction with external actors. As the reader can notice, three kinds of actors have been located: – system manager: it is the independent party that sets and manages the e-marketplace. He sets the e-marketplace tools (‘‘Set e-marketplace tools” use case) and provides negotiation constraints (‘‘Provides Negotiation Constraints” use case) that bounds the negotiation behavior of both suppliers and customers;

E-marketplace Provides negotiation strategies Inputs order specification

Customer

Provides catalogue information

Provides negotiation strategies

Provides planning constraints

Sets e-markeplace tools

System Manager

Provides negotiation constraints

Fig. 1. E-marketplace use cases.

Supplier

622

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629

– customer: it is the generic registered e-marketplace client, who is allowed to provide its negotiation strategies and to input a catalogue based order; and – supplier: it is the generic registered e-marketplace supplier, who provides catalogue information, negotiation strategies and planning information.

evaluates the counter offer and, if it does not achieve a utility threshold, a negotiation process will start. Once the customer inputs its order, the procurement transaction proceeds automatically and it is handled by the customer and supplier agents.

In here, the following scenario has been considered: let N be the set of suppliers and M the set of customers; each customer can input orders in the market: they can be fulfilled from each supplier. The orders consist of an array (i, V, dd, p), being i, the supplier product index, V the required quantity, dd, the requested delivery date, and p, the asked price; each supplier s(s 2 N) is allowed to submit a counter offer for the order by fixing volume, due date and price (Vs, dds, ps) in order to maximize its own utility. Customer

Fig. 2 shows, through an UML activity diagram, the details of the negotiation process (Perrone et al., 2003) involving three agents: Customer Negotiation Agent (CNA), Supplier Negotiation Agent (SNA) and Supplier Planning Agent (SPA). The SPA agent performs a production planning algorithm proposed in Perrone et al. (2003) that provides the production alternatives. The negotiation process is characterized by the following constraints (Negotiation constraints):

CNA

4. Negotiation policies

SNA

SPA

Waits for order proposal

Trasmits order

Waits for production planning request

Estimates order importance Runs PrP Algorithm

Computes Order Proposal Constraints Computes Production Alternatives

Provides Order Proposal Constraints Provides production alternatives

Computes utility tresholds

Waits for SNA answer

Wait for production data

Updates utility thresholds

Computes counter-proposal

Evalutes counter-proposal Trasmits counter-proposal

Positive?

Signs Contract

r>r max? Waits for CNA answer Quits negotiation

Asks for another counter-proposal

Updates profit limits

Updates customer database

Updates supplier database

Fig. 2. Negotiation process activity diagram.

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629

 the negotiation is one-to-many and it involves exclusively one customer and many suppliers;  the negotiation is an iterative one with a maximum number of rounds, rmax; after that an agreement is reached or the negotiation fails;  during each round the supplier is allowed to submit a new counter-proposal to the customer, while the last can only accept (A), reject (R) or ask for a new counter-proposal (N), therefore the customer answer at the round r can be referred as (A, R, N)r;  the agreement is reached if the customer accepts the supplier counter-proposal at round r < rmax; in this case customer and supplier sign an electronic contract;  supplier and customer behavior is assumed to be rationale according to their utility functions; and  supplier utility function is not known to the customer and vice versa; however supplier and customer can argue, by applying proper learning algorithms, the behavior of their counterparts. The negotiation process starts with the order submission by the customer. The order is processed through the Customer Order Inputting Menu and it is delivered to the CNA. The order consists of the array (i, Vi, ddi, pi)0 being i, the selected product from the supplier catalogue, Vi the required quantity, ddi, the requested delivery date, and pi, the asked price. By the activity diagram of Fig. 2 the following actions are carried out: Transmits order: the CNA transmits the order array (i, Vi, ddi, pi)0 to the SNA. Provides Order Proposal Constraints: the above values are transmitted to the SPA and they will constraint production planning activities. Runs PrP: the SPA runs the production planning (PrP) algorithm that is proposed and deeply described in Perrone et al. (2003, 2005). Computes Production Alternatives: as output of the PrP algorithm the SPA computes an array of production planning alternatives PAj (j = 1    n) that associates a supplier profit (Prj) and an offered volume (Vj) to each combination of offered due date (ddj) and price (pj): that is PAj = (Prj, Vj, ddj, pj) "j, where Vj 6 Vi. Provides production alternatives: PAj is transmitted to the SNA. Computes counter-proposal: If r = 1, the SNA builds the set K0 = {1, 2, . . . , k, . . . , n} of alternatives such as:

Prk ¼ Prmax ¼ max fPrj g 8k 2 K 0 j¼1;...;n

ð1Þ

and it searches within K0 for the alternative j such as:

J  j min j2K 0

  jddj  ddi j þ jpj  pi j þ jV j  V i j 3

PRmax  PRmin r r max

8k 2 K r

 ThuðrÞ ¼ Thumax  1 

2   r1 r1 þF r max 1 r max 1    2 r1 r1  1 þ Thumin  r max 1 r max 1

ð5Þ

The above expression is a spline function that decreases through the negotiation process (r increases) controlled by the parameter F (function slope) that allows to set the slope of the curve between the values Thumax and Thumin (that represent respectively the maximum and minimum threshold values). Evaluates counter-proposal: the CNA evaluates the utility of the counter-proposal:

U cp ¼ U v þ U dd þ U p r

ð6Þ

where Uv, Udd, Up are respectively the utilities of the volumes, the due date and the price, computed as:

Uv ¼

  V j ; Vi

ð7Þ

    ddj  ddmin ddmax  ddj U dd ¼ Max Min ;0 ; ddi  ddmin ddmax  ddi U p ¼ Min

! ! pi ;1 pj

ð8Þ

ð9Þ

where ddmax and ddmin, are respectively ddi ± 3. These values represent the interval of due dates in which the suppliers can formulate a counter-proposal. The values are limited to ±3 in order to limit the computational time of production planning alternatives. However, this assumption does not affect the comparison of the proposed approaches. If the U cp P ThuðrÞ the CNA accepts (A) the counter-proposal r and it signs the agreement with SNA; afterwards they update their database with agreement data. Conversely, if the U cp < ThuðrÞ and r r < rmax, CNA asks for a new counter-proposal (N), otherwise if r > rmax, CNA rejects the proposal and quits the negotiation. The above negotiation approach is one-to-many; in the next paragraphs are described the following approaches: supplier qualification, auction approach and one proposal. 4.1. Qualified negotiation

ð3Þ

Afterwards SNA builds the set of production alternatives Kr = {1, 2, . . . , k, . . . , m} such that:

Prk P Prr

Updates utility thresholds: the CNA updates the utility function thresholds at the round r according to the following expression:

ð2Þ

On the other hand, if r > 1, the SNA applies a profit reduction strategy according to the customer importance and the negotiation round, that is it computes the new acceptable profit at the round r as in (3):

Prr ¼ Prmax 

623

ð4Þ

and it finds the alternative j that minimizes the relation (2) with  j 2 Kr. The array ðddj ; pj ; V j Þ, both in cases r = 1 and r > 1, represents the supplier counter-proposal.  Transmits counter-proposal: ðddj ; pj ; V j Þ is transmitted to the CNA. At this time the SNA remains waiting for a CNA answer.

The one-to-many negotiation approaches have some limits. The customer negotiates with all suppliers at the same time; the e-marketplace management becomes difficult with the increase of suppliers involved. During the negotiation process, customer and suppliers exchange several messages; a high number of suppliers can lead to communication overhead. Finally, a high number of suppliers can increase significantly the time to reach an agreement. For the above reasons, it can be performed a one-to-one negotiation process. The customer classifies the suppliers in a ranked list. The customer negotiates with the first supplier of the list; if the negotiation fails, the customer negotiates with the supplier ranked in the next position. The methodology to classify the suppliers is based on negotiation history. The customer computes a modified price based on two components: price (pricepastorders) and utility (Upastorders) that the customer obtains in the past negotiation (the negotiations that reach an agreement). The modified price is computed by the following expression:

624

price mod ¼

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629

X

pricepastorders  U pastorders

ð10Þ

pastorders

The expression (10) means that the price is related to the utility that the customer obtains. The pricemod is used to compute the ranking list of the suppliers. The motivations of the expression (10) are the following: – The price indicates the weight of the generic past agreement between customer and supplier. However, a high price can be related to elevated volume or high production costs of the supplier. – The utility denotes how the supplier satisfied the volume and due date requested by the customer. The customer updates the ranking lists each time a negotiation reaches an agreement with a supplier using the expression (10). This approach allows to reduce the impact of the negative characteristics of one-to-many negotiation models. 4.2. Auction-based protocol The negotiation process used in this paper forces the suppliers to improve the customer utility at each round. In this case, a generic supplier improves the customer utility, even if it submits the better proposals to the customer. In order to consider the competition issue among the suppliers, the customer communicates to all suppliers who among them submitted the best counter-proposal. The generic supplier tries to improve the counter-proposal, if its proposal was not the better at previous round. Otherwise, the supplier does not improve the counter-proposal. At the first round of the auction, the suppliers formulate the counter-proposal as described for the negotiation process. During the auction process, if the generic supplier needs to improve the counter-proposal, it uses the expressions (1)–(4) to compute the counter-proposal. The auction protocol ends when the maximum number of rounds is reached; the customer signs the contract with the supplier that submits the best counter-proposal, if the utility computed by the customer is greater than the minimum threshold requested by the customer (Thumin). Otherwise, the auction fails. This approach increases the time to reach the agreement, because it extends for all rounds of negotiation. The main benefit is the improvement of customer utility with limited impact on profit reduction of the suppliers.

the proposal, if the proposal satisfies the minimum threshold value, or reject the proposal. In the last case, no agreement is possible among customer and suppliers. This approach leads to minimum time possible to reach an agreement, because it works with only one round. The impact on the customer utility and suppliers’ profit has to be investigated. 4.4. Customer behavior The customer evaluates the suppliers’ counter-proposal using a threshold function. The values of the threshold function computed in each round of negotiation depend on the tactic implemented. The following tactics are proposed (Renna, 2010): – Time dependent: the customer reduces the threshold function during the time available for the negotiation. The shape of the threshold function computed between the maximum and minimum values define the tactic. Fig. 3 shows three different shapes for this tactic. The tactic proposed as time dependent (round of negotiation) is reported in expression (5). The function slope F is used to modify the shape of the threshold function. The value of F used in this paper leads to a tactic between neutral and slowly-reduction (see Fig. 3a and c). – Imitative: the customer tries to imitate the suppliers reducing the threshold function proportionally to the improvement of the suppliers’ counter-proposal. The threshold function of this tactic is computed by the following expressions:

Thuð1Þ ¼ Thumax ; at first round of negotiation:     c-p ThuðrÞ ¼ Max Thuðr  1Þ  Uc-p for r > 1: r  U r1 ; Thumin

ð12Þ   The value Ucp  U cp r r1 is the improvement of the generic supplier counter-proposal between two successive rounds. It can be noticed that the threshold function is different for each supplier. – Hybrid: the customer combines the time and imitative tactics. The threshold function is computed by the following expression:

"

    ThuðrÞ ¼¼ Min Max Thuðr  1Þ  Urcp  U cp r1 ; Thumin ; Thumax

4.3. One proposal

  1

2     r1 r1 r1 þF  1 r max 1 r max 1 r max 1  2 # r1 ð13Þ þ Thumin  r max 1

This approach is proposed as a benchmark to compare the performance of the above described approaches. The suppliers reply to the customer submitting the best proposal computed using the expressions (1)–(4) with r = rmax. The customer can only accept

Threshold function

Threshold function

round

(a) Neutral

ð11Þ

Threshold function

round

(b) rapid-reduction Fig. 3. Customer’s tactics.

round

(c) slowly-reduction

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629

5. Coalition approach The coalition model is carried out by a Coalition Agent (CA) which knows the current processing order and collects the supplier counter offers (Lo Nigro, Argoneto, Bruccoleri, Perrone, & Renna, 2005). The CA evaluates the possible coalition among the suppliers; as deeply discussed in Tombusß and Bilgiç (2004) the coalition formation problem is a set partitioning problem; since the number of variables of possible coalitions grows exponentially with the number of partners, here, the author has considered coalitions with two or three suppliers. Moreover, in the considered case, the set of partners’ combination S does not represent a partition of N; indeed, indicating with SN,2 and with SN,3 the set of possible suppliers’ combination with two and three elements respectively, it will be S ¼ SN;2 [ SN;3 . Let us indicates with (i, j) or with (i, j, k) the generic set sl belonging to S; sl 2 S can compete for the order acquisition if

X

Vl ¼

Vz 6 V

ð14Þ

z¼i;j=z¼i;j;k

expresses that for each combination of suppliers (two or three suppliers), the coalition l is created if the offered volume (Vl) is less than the volume requested by the customer; each single supplier is also allowed to compete for the order as a particular case of coalition. In the subsequent step, the CA computes the coalition counterproposal through the following steps: The coalition due date (ddl) is the maximum due date among those proposed by the suppliers participating in the coalition:

ddl ¼

max fddz g

ð15Þ

z¼i;j=z¼i;j;k

The coalition volume is the sum of Eq. (14); The coalition price is computed as it follows: first a weighted price (pr_mpl) is computed through the following expression:

P pr mpl ¼

z¼i;j=z¼i;j;k P

pz  V z

ð16Þ

z¼i;j=z¼i;j;k V z

then, the CA computes an index (dl) measuring the distance between the proposed counteroffer and the customer request: 

dz ¼

V   V z pz  p ddz  dd þ þ  V p dd  t arr

ð17Þ

afterwards an indifference price pr_indl, representing the price that with ddl and Vl guarantee the customer with an offer as good as the best one among those generated by suppliers i, j and k, is computed as the price satisfying the following equation: 

dl ¼

V   V l pr indl  p ddl  dd þ þ  ¼ min ðdz Þ: V p dd  t arr z¼i;j=z¼i;j;k

ð18Þ

Finally the best coalition price pr_bestl is computed as the price satisfying the following expression: 

dl  fs ¼

V   V l pr bestl  p ddl  dd þ þ  ¼ min ðdz Þ V p dd  t arr z¼i;j=z¼i;j;k

ð19Þ

where fs < 1 (in this paper the value is fixed to 0.8). pr_bestl represents a price that with ddl and Vl will guarantee to the customer an offer better than the best alternative offer among

625

those generated by supplier i, j and k, therefore, it represents the added value the coalition l provides to the customer. The coalition price pr_coal is computed by the following expression:

pr coal ¼ fif pr mpl > pr bestl ; maxðpr mpl ; pr indl Þ; pr bestl g ð20Þ pr_coal, as computed in Eq. (20), assures all the coalition participants at least the same profit they would have achieved by competing alone (in case of pr_coal = pr_mpl); moreover, whether possible, they gain an extra-profit (in case of pr_coa = pr_indl) and, when the best alternative is considered, also the customer gains a benefit from the coalition formation (in case of pr_coa = pr_bestl). The CA collects the coalition and the single supplier proposals and it evaluates the index d of Eq. (17) for each proposal (coalition and single supplier). The CA submits to the customer the proposal with minimum value of d. The customer evaluates the proposal and, as explained in Section 4, it accepts the counter-proposal and signs the agreement with the supplier whether the related utility is greater than a threshold value. Conversely, if the utility associated with the counter-proposal is lower than the threshold value, the customer evaluates if a negotiation with the supplier can be activated; in particular, if negotiation time is not over, the customer asks for a new counter-proposal, otherwise if negotiation time is over the customer rejects the proposal and quits the negotiation. In case the contract is signed with a coalition two alternatives are possible: – the price of the coalition is the same of the pr_mp and there is not extra-profit; and – the price of the coalition is more than the pr_mp; in this case the surplus of expression (21) will be shared among the coalition suppliers:

extra  profit l ¼ ðpr coal  pr mpl Þ  V l

ð21Þ

6. Simulation environment It has been developed a test environment of the e-marketplace context. It consists of a simulation environment capable of testing and evaluating the proposed approaches and to understand benefits of added value services in e-marketplace. In order to cut times and costs for the development of the actual e-marketplace environment, the simulation environment has been developed directly in open source architecture by using Java Development Kit package. The modeling formalism here adopted is a collection of independent objects interacting via messages. This formalism is quite suitable for Multi Agent Systems development. In particular, each object represents an agent and the system evolves through a message sending engine managed by a discrete event scheduler. In particular, the following agents have been developed: the CNA, the SNA, the SPA, CA, the scheduler agent, the order agent and the statistical analysis agent. The former four agents have been deeply described in the previous sections. The scheduler agent is in charge with system evolving by managing the discrete event of the simulation engine for each replication. The order agent generates the orders data that the customers input in the e-marketplace for each replication. Finally, the statistical analysis agent collects the data of each replication and generates the report of the simulation when the results reach the statistical significativity level. The customer and supplier agents have a local database, which is used to store and manage information and messages received from other agents. A proper interface has been developed to con-

626

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629

nect the Java code to the database. The production planning algorithm performed by the SPA has been developed in Lingo solver; an opportune java class interface has been developed to connect the SPA and lingo solver. The test case consists of one customer and five suppliers. Orders enter the supplier system during a time horizon of 120 periods (days) divided in buckets of 30 periods, where is possible to make re-planning. The customer’s order is defined by the following data: – order: it is the unique identification number of the order. – ta is the time when the customer inputs the order in the e-marketplace. ta is randomly generated to guarantee that, at least, four orders within the same re-planning time bucket are assured. – ddi is the order due date. It is randomly generated following a uniform distribution with lower bound ta and upper bound given by the end of the re-planning time bucket. – Vi is the order volume. It is randomly generated following a uniform distribution with lower and upper bound depending on the capacity of the suppliers. – pi is the order price. It is randomly generated following a uniform distribution that depends on the mark-up value, the average cost for each unit of product and the order volume. – C_num is a customer identification; it is extracted by uniform distribution among all the customers types. – Type is the product identification; it is extracted by uniform distribution among all the product types. The simulations have been conducted under different scenarios, characterized by the following parameters:  workload of the suppliers; it depends on the volume requested by the customers;  the price level of the orders submitted by the customer; and  the overlapping among the orders.

Each experiment class has been replicated in order to achieve a confidence degree equal to the 95% and 10% confidence intervals for each performance index considered. Three values of overlap have been considered: ‘‘0” means that the orders are generated in order to avoid overlap periods among them. ‘‘5” means that the orders are generated with a maximum number of overlap periods equal to 5. ‘‘10” means that the orders are generated with a maximum number of overlap periods equal to 10. Thus, combining the level of three parameters (price, volume and overlap), 12 simulation classes of experiments have been obtained (see Table 2). The suppliers are characterized by the following parameters (see Table 3): – – – – – – –

CAPR: it is the capacity in ordinary time; CAPO: it is the capacity in overtime; CAPS: it is the capacity in outsourcing; FC: it is the fixed cost of the process plan; CRG: it is time unit cost in regular time; COV: it is time unit cost in overtime; and CSB: it is time unit cost in outsourcing.

The costs and the capacity are constant for each period t and resource j. In order to reduce the computational time, one process plan is considered for the production planning algorithm (Perrone et al., 2003). Table 4 reports the total hours for product unit in order to compute the average unit cost. 7. Simulation results For each experimental condition, the following performance indicators are reported: – Average customer utility: it is the average of Eq. (6) computed over all orders that reach an agreement.

The orders generation is obtained by the following steps: 1. ta is randomly generated; 2. ddi is randomly generated subject to the overlap constraint among the orders; 3. the volume of the order is obtained by the following expression:

V i ¼ ðddi  t a þ 1Þ  Volume

Table 2 Experiment classes.

ð22Þ

4. the price of the order is obtained by the following expression:

pi ¼ av eragecost unit  v olume  markup

ð23Þ

At the end of this process, it is generated the list of orders in terms of arrival time, due date, volume and price. Table 1 reports the values high and low for ‘‘volume” and ‘‘mark up”. The two levels of mark-up allow to investigate the e-marketplace with different degree of price fluctuations. The responsiveness of the network of suppliers to satisfy the volume requested by the customers is tested by the two levels of volume fluctuations.

Mark-up Volume Average capacity utilization

Low

High

Uniform [0.8–1.2] Uniform [65–105] 30%

Uniform [1–1.4] Uniform [115–165] 50%

Mark-up

Volume

Overlap

1 2 3 4 5 6 7 8 9 10 11 12

H L H L H L H L H L H L

H H L L H H L L H H L L

0 0 0 0 5 5 5 5 10 10 10 10

Table 3 Suppliers data. Supplier 1 CAPR CAPO CAPS CAPR

Table 1 Distribution parameters.

No.

FC CRG COV CSB

Supplier 2

Supplier 3

Supplier 4

Supplier 5

65 60 70 65

110 115 125 110

120 135 50 120

95 85 50 95

130 130 60 130

267 42 73 62

214 46 90 66

90 50 69 66

136 50 86 62

154 49 90 59

627

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629 Table 4 Total hours for product unit.

Table 7 Unbalanced suppliers’ index.

Product type

Total hours

Product type

Total hours

1 2 3 4 5

10 10 10 10 20

6 7 8 9 10

20 20 40 40 40

Imitative Temporal Hybrid

Negotiation

Qualified

Coalition

Auction

One proposal

0.67 0.62 0.66

0.75 0.84 0.94

0.36 0.37 0.38

0.67 – –

0.56 – –

Negotiation

Qualified

Coalition

Auction

One proposal

151.06 157.06 140.55

32.05 50.07 47.73

176.49 190.12 179.44

281.83 – –

60 – –

Table 8 Average number of rounds. Table 5 Customer utility.

Imitative Temporal Hybrid

Negotiation

Qualified

Coalition

Auction

One proposal

24.18 25.48 25.82

21.40 23.96 23.93

21.20 25.87 26.72

26.74 – –

16.35 – –

– Average total suppliers utility: It is the sum of the profit for all suppliers. – Suppliers unbalanced index: this index computes the distribution of the profit among the suppliers of the e-marketplace. The index is calculated as:

 N  X 1 utility supplier i   unbalanced ¼ N  total utility supplier

ð24Þ

i¼1

N is the number of suppliers. If the profit is equally distributed among the suppliers, the expression (24) is equal to 0, otherwise the expression (24) is greater than 0. This index allows to highlight if some suppliers gain high profit, while other suppliers gain low profit. The capability of the e-marketplace management to distribute profit to all participants is attractive for the potential participants. – Average time to reach an agreement in terms of rounds of negotiation. The number of rounds is used as an index of the total time consumed by the customers and suppliers in bargaining protocols. Tables 5–8 report the performance measures for each policy tested. From the analysis of the table the following issues can be drawn: – Customer utility: the auction approach that forces the suppliers to improve the counter-proposal until the last round of negotiation leads to obtain the best performance of customer utility. As the reader can notice, the coalition approach leads to a customer utility very close to auction approach in case of hybrid behavior of the customer. The hybrid customer’s behavior leads to better performance for negotiation and coalition, while the imitative leads to worst performance measure. – Suppliers’ utility: the coalition approach outperforms all the other approaches in terms of total profit of the suppliers. It is obviously, because the coalition approach allows to increase the total volume supplied to the customers for two reasons:

Imitative Temporal Hybrid

the coalition approach reaches more agreement than other approaches; coalition approach increments the volume utility of the customers. As the customer utility the imitative customer behavior leads to worst performance. – Unbalanced index: the coalition approach leads to better performance measure, while the qualified approach leads to worst performance measure. The customer’s behavior has low impact on coalition approach, while the temporal is the best strategy for negotiation and imitative strategy for qualified. – Consumed time: the auction approach is characterized by the higher time to reach the agreement, this is because the suppliers are forced to negotiate until the last round. The value is minor of 300 (the maximum number of rounds possible) because in some cases the suppliers submit a counter-proposal that satisfies all the attributes requested by the customer before the last round. The negotiation one-to-one using the qualification of the suppliers allows to reduce the consumed time under the time consumed by the one proposal approach. This result highlights the goodness of the qualification approach. Coalition approach is comparable with the negotiation approach. The performance measures are combined in order to obtain a single value of performance of the proposed approaches. Therefore, the performance values are normalized and Table 9 reports the normalized value of the performance measures. The normalization is computed by the following expressions:

v alue  minðv alueÞ maxðv alueÞ  minðv alueÞ

ð25Þ

It is used, if the higher value of the index means better performance.

maxðv alueÞ  v alue maxðv alueÞ  minðv alueÞ

ð26Þ

It is used, if the higher value of the index means worst performance. Therefore, for each performance index the value 1 identifies the best performance measure, instead the value 0 identifies the worst performance measure. Assume that, each performance measure has the same importance, the average of the performance measures ([0, 1] interval) assigns the goodness of the approach. The best compromise among the performance measures is reached by the hybrid customer’s tactic and the coalition approach.

Table 6 Suppliers’ utility.

Imitative Temporal Hybrid

Negotiation

Qualified

Coalition

Auction

One proposal

6,792,964 6,636,737 6,999,910

4,994,201 5,572,544 5,535,495

34,088,922 103,868,822 102,537,140

5.726.018 – –

6.136.527 – –

628

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629

Table 9 Normalized performance index. Negotiation

Table 11 Volume analysis.

Qualified

Coalition

Auction

One proposal

Normalized Imitative Temporal Hybrid

– customers’ utility 0.750 0.490 0.880 0.730 0.910 0.730

0.470 0.920 0.998

1.000

0.000

Normalized Imitative Temporal Hybrid

– suppliers’ utility 0.018 0.000 0.017 0.006 0.020 0.005

0.294 1.000 0.987

0.007

0.012

Normalized Imitative Temporal Hybrid

– unbalanced suppliers’ index 0.47 0.33 1.00 0.55 0.17 0.98 0.48 0.00 0.97

Normalized Imitative Temporal Hybrid

– number of rounds 0.52 1.00 0.50 0.93 0.56 0.94

0.42 0.37 0.41

0.00

0.89

Normalized Imitative Temporal Hybrid

– total performance 0.44 0.46 0.49 0.46 0.49 0.42

0.55 0.82 0.84

0.37

0.39

0.47

0.66

Customer utility Suppliers’ utility Unbalanced index Negotiation rounds

Volume high

Volume low

Difference (%)

20.48 27,828,305 0.60 150.75

25.01 14,067,880 0.64 137.88

22.12 49.45 +6.67 8.54

Mark-up high

Mark-up low

Difference (%)

23.30 22,992,394 0.62 142.22

23.13 19,201,275 0.62 146.41

0.73 16.49 0.00 2.95

Table 12 Mark-up analysis.

Customer utility Suppliers’ utility Unbalanced index Negotiation rounds

8. Conclusions

Table 9, also, shows the value added services that each participant (supplier or customer) can gain by e-marketplace participation. Table 10–12 report the performance measures in order to analyze how the parameters overlap, volume and mark-up affect the performance. The percentage difference is computed in comparison to ‘‘0” overlap. – The main effect of the overlap is on the suppliers’ utility. The increment of the overlap periods lead to increase the suppliers’ profit. This is due to the opportunity for the suppliers to negotiate more orders overlapped among them, and therefore, with higher volume provided. The increment of overlap leads to increase the pressure on the suppliers. Therefore, the customer utility decreases and the time to reach the agreements increases. Finally, the unbalanced index does not affect by the overlap degree. – The main effect of the volume is on the suppliers’ utility. The profit of the suppliers is proportionally to the volume of the orders. The high volume of the orders increases the pressure on the suppliers with the reduction of customers’ utility and increment of time to reach the agreements. The distribution of the profit among the suppliers is inhomogeneous with low volume of the orders. – The only performance affected by the mark-up is the profit of the suppliers. The other performance measures have a low variation when the mark-up changes. The above information allows to evaluate what are the effects of the market changes on the performance indexes. Moreover, the actors of the e-marketplace can select the opportune strategy (negotiation protocol, tactics and coalition protocol) using the information of the simulations.

This paper deals with real added value services in e-marketplaces for Business To Business applications. In particular, the value added services investigated regard: the negotiation protocol among suppliers and customers, the customer’s tactics and the coalition among suppliers. Results of this research can be located at two levels. Concerning the problem of agreement among customers and suppliers, the following conclusions can be drawn: – Assumed the performance with the same importance, the coalition approach and hybrid customer’s tactic lead to the better results. Therefore, these tools provide the higher value added services. – The qualified approach reduces the time consumed to reach the agreements, under the time consumed by the one proposal approach. – Among the protocols investigated, the negotiation is the more robust protocol to the variation of the tactics performed by the customer. The other protocols have high variability of performance measures when the customer changes the tactic. – Concerning the market parameters, the following conclusions can be drawn: – The suppliers’ profit is affected mainly by the overlap among the orders. The overlap among the orders increases the pressure on the production planning activities increasing the time to reach the agreements. – The amount of volume impacts on the average customer utility. The increment of the volume forces the suppliers to use all the capacity available (for example overtime capacity) with the reduction of the satisfaction obtained by the counter-proposals submitted. – The only influence of the mark-up is on the total profit of the suppliers. The other performance measures have low variation when the mark-up changes.

Table 10 Overlap analysis.

Customer utility Suppliers’ utility Unbalanced index Negotiation rounds

Overlap 0

Overlap 5

Overlap 10

Overlap 5–0 (%)

Overlap 10–0 (%)

24.85 14,511,174 0.62 138.16

22.79 19,413,770 0.64 144.91

22.01 29,286,580 0.61 152.14

8.29 33.78 3.22 4.89

11.43 101.82 1.61 10.12

P. Renna / Computers & Industrial Engineering 59 (2010) 619–629

At more strategic level, the research shows that the platform developed based on Multi Agent paradigm allows to support real e-marketplace environment. In particular, this is relevant for the suppliers’ point of view. In fact, the generic supplier is connected with two important components of an enterprise. The production planning algorithm that allows to generate the production orders for the manufacturing system and the database that allows to supervise the performance of the enterprise. This reduces the risk of the investment related to the participation in an e-marketplace, because the platform is close the real behavior of enterprises. This issue is related to several factors influencing diffusion rate of e-marketplaces (White, Daniel, Ward, & Wilson, 2007). Further researches will investigate the following aspects in depth: – The customers and the suppliers can be provided with a local inferential engine that will enable the agents to select their best strategy in order to maximize their utility when the environmental conditions change. – How a learning algorithm, based on past performances, can improve the agents’ utility. The agent will be able to elaborate the past information coming from the negotiation output, and will try to forecast the opposite agents’ behavior.

Acknowledgement Author wishes to thank the anonymous referees whose suggestions and comments contribute to improve the value of this paper. References Argoneto, P., & Renna, P. (2010). Production planning, negotiation and coalition integration: A new tool for an innovative e-business model. Robotics and Computer-Integrated Manufacturing, 26(1), 1–12. Barratt, M., & Rosdahl, K. (2002). Exploring business-to-business marketsites. European Journal of Purchasing & Supply Management, 8, 111–122. Bartolini, C., Preist, C., & Jennings, N. R., 2002. A software framework for automated negotiation. In Proceedings of AOSE 2002, international workshop on agentoriented software engineering, Bologna, Italy, LNCS 2585 (pp. 88–100), Springer Verlag. Benyoucef, M., Alj, H., Levy, K., & Keller, R. K. (2002). A rule-driven approach for defining the behaviour of negotiation software agents. In J. Plaice et al. (Eds.), Proceedings of DCW 2002, LNCS 2468 (pp. 165–181). Springer Verlag. Chandrashekar, T. S., & Narahari, Y. (2007). The core and shapley value analysis for cooperative formation of procurement networks. In The 9th IEEE international conference on e-commerce technology and the 4th IEEE international conference on enterprise computing, e-commerce and e-services (CEC-EEE 2007), pp. 175–184. Cheng, C., Chan, C. H., & Lin, K. (2006). Intelligent agents for e-marketplace: Negotiation with issue trade-offs by fuzzy inference systems. Decision Support Systems, 42, 626–638.

629

Choi, S. P. M., Liu, J., & Chan, S. P. (2001). A genetic agent based negotiation system. Computer Networks, 37, 195–204. Dumas, Marlon, Aldred, Lachlan. J., Governatori, Guido, & ter Hofstede, Arthur H. M. (2005). Probabilistic automated bidding in multiple auctions. Electronic Commerce Research, 5(1), 25–49. Greiger, M. (2003). Electronic marketplaces: A literature review and a call for supply chain management research. European Journal of Operational Research, 144, 280–294. Jin, M., & Wu, S. D. (2006). Supplier coalitions in on-line reverse auctions: Validity requirements and profit distribution scheme. International Journal of Production Economics, 100(2), 183–194. Kaplan, S., & Sawhney, M. (2000). E-Hubs: The new B2B marketplaces. Harvard Business Review, 78(3), 97–103. Kim, W., & Lee, J. K. (2002). Agent and e-business models. Decision Support System – Special Issue, 34. Li, C., Chawla, S., Rajan, U., & Sycara, K. (2004). Mechanism design for coalition formation and cost sharing in group-buying markets. Electronic Commerce Research and Applications, 3(4), 341–354. Lo Nigro, G., Argoneto, P., Bruccoleri, M., Perrone, G., & Renna, P. (2005). Horizontal co-operation in supply chain in an e-market environment. In Proceedings of the international manufacturing leaders forum on ‘‘global competitive manufacturing”, 27th February–2nd March 2005, Adelaide, Australia. Lopes, F., Wooldridge, M., & Novais, A. Q. (2008). Negotiation among autonomous computational agents: Principles, analysis and challenges. Artificial Intelligence Review, 29, 1–44. Loutaa, M., Roussakib, I., & Pechlivanosc, L. (2008). An intelligent agent negotiation strategy in the electronic marketplace environment. European Journal of Operational Research, 187(3), 1327–1345. Maes, P., Guttman, R. H., & Moukas, A. G. (1999). Agent that buy and sell: Transforming commerce as we know it. Communications of the ACM, 42(3), 81–91. Nagarajan, M., & Sosic, G. (2006). Game-theoretic analysis of cooperation among supply chain agents: Review and extensions. European Journal of Operational Research, 187(3), 719–745. Nagarajan, M., & Sosic, G. (2007). Stable farsighted coalitions in competitive markets. Management Science, 53(1), 29–45. Oliver, J. R. (1997). A machine learning approach to automated negotiation and prospects for electronic commerce. Journal of Management Information Systems, 13(3), 83–112. Perrone, G., Bruccoleri, M., & Renna, P. (Eds.). (2005). Design and evaluating value added services in manufacturing e-marketplace. Springer. Perrone, G., Renna, P., Cantamessa, M., Gualano, M., Bruccoleri, M., & Lo Nigro, G. (2003). An agent based architecture for production planning and negotiation in catalogue based e-marketplace. In Proceedings of the 36th CIRP-international seminar on manufacturing systems, 03–05 June, 2003, Saarbruecken, Germany. Renna, P. (2010). Negotiation policies for e-procurement by multi agent systems. In Nag Barin (Ed.), Intelligent systems in operations: Models, methods, and applications. 978-1-61520-605-6. IGI Publishing. Renna, P., Argoneto, P., Lo Nigro, G., Perrone, G., & Noto La Diega, S. (2005). Coalition protocols in manufacturing e-marketplaces: Creating and measuring the competitive advantage. In Proceeding of VII Convegno A.I.Te.M., Lecce, 7–9 September, 2005. The European e-Business Report, 2008. The impact of ICT and e-business on firms, sectors and the economy. 6th synthesis report of the sectoral e-business watch. Tombusß, O., & Bilgiç, T. (2004). A column generation approach to coalition formation problem in multi-agent systems. Computers and Operations Research, 31(10), 1635–1653. White, A., Daniel, E., Ward, J., & Wilson, H. (2007). The adoption of consortium B2B e-marketplaces: An exploratory study. The Journal of Strategic Information Systems, 16(1), 71–103. Wurman, P. R., Wellman, M. P., & Walsh, W. E. (2002). Specifying rules for electronic auctions. AI Magazine, 23(3), 15–23.