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Abstract

The negotiation between agent and multi-agent is an important method for solving the conflict and realizing the cooperation in the multi-agent system of manufacturing enterprise supply chain. In this article, we will take manufacturing enterprise supply chain as the research object, analyze negotiation process of multi-agent, study the negotiating model and tactics as well as the steps, and then an illustration is discussed for validating the negotiation model. The research can provide theoretical and operational methods for manufacturing enterprise supply chain management, is helpful to coordinate and control the manufacturing enterprise supply chain, and realize the efficient, flexible, and quick operation of manufacturing enterprise supply chain.

Keywords

Multi-agent negotiation mechanism negotiation model and tactics supply chain management

Introduction

It has become an important research method using the agent and multi-agent system that own reasoning and decision-making capabilities to simulate, optimize, and monitor manufacturing enterprise supply chain operation.¹ The manufacturing enterprise supply chain comprises various entities such as suppliers, core enterprise, distributors, and customers.^2–5 These entities can be abstracted as autonomous agents. The agents can complete the related activities of the supply chain entities. The manufacturing enterprise supply chain can be seen as a multi-agent system composed of agents belonging to different enterprises or departments. In the multi-agent system of the manufacturing enterprise supply chain, the negotiation between agents is an important method to realize the cooperation and solve the conflict for the multi-agent system. Consultation parties promote their private interests to maximize by taking appropriate consultation model, analyzing and improving consultation strategies, selecting the appropriate consultation, and arranging a reasonable consultation agenda.^6–8

The research of agent and multi-agent is originated from distributed artificial intelligence, which is the inevitable result of the development of distributed artificial intelligence, modern computer technology, and communication technology.^9,10 Agent and multi-agent are commonly used to solve the problems of decision-making and collaboration in supply chain management. In view of the limited ability of single agent to solve complex problems, it is possible to form a multi-agent system by coordinating multiple independent agent behaviors to compensate for the lack of a single agent function. In multi-agent system, each agent coordinates each other’s goals and completes specific task or achieves goals through cooperation.

The research of multi-agent system is concentrated on multi-agent model, architecture, consistency and coordination, multi-agent planning, and conflict processing.¹¹ The negotiation process can be viewed as a process which agents coordinate their respective actions through communications to take joint action. Multi-agent communication technology is a key foundation and service guarantee for negotiation. Knowledge query and manipulation language (KQML) is an agent communication language based on speech act theory, which has become the fact standard of agent communication language.¹² Implementing agent negotiation in addition to agreeing on communication languages, there is necessary to have a negotiation mechanism to regulate the collaboration process. Contract network protocol (CNP) is part of the most well-known and widely used consultative mechanisms.^13,14

Durfee et al.¹⁵ think that the negotiations are two or more parties to use the relevant structured information exchange, forming planning and consistent views. Kraus¹⁶ argue that negotiation is an important part of determining whether electronic transactions are successful. Huang and Sycara¹⁷ divide negotiation into time-dependent, resource-dependent, and behavior-dependent. It is pointed out that the main factors influencing the convergence of the negotiation process are time, resources, and behavior, and based on these factors, the negotiation subject can generate various negotiation strategies. Jennings et al.¹⁸ study the division of research areas, and concluded that the scope of consultation study mainly includes the category of consultation language, decision category, and process category.

At present, the popular multi-agent negotiation mechanism mainly includes the blackboard system negotiation mechanism, the service-oriented negotiation mechanism, the market-driven negotiation mechanism, the auction agreement negotiation mechanism, and the contract network negotiation mechanism.^19,20 The basis of the negotiation support system is to construct a suitable negotiation model. The negotiation model mainly includes the topics of negotiation, objectives, negotiation protocols, and interaction mechanisms. Kraus et al.²¹ propose a generic multi-agent negotiation model, which explores the multi-agent negotiation under incomplete information conditions, and the model mainly discusses the negotiation of two-person single-objective. Fatima and Wooldridge²² give a computational function of fuzzy value based on agent negotiation problem and validate the validity of computational function through experimental data and simulation. Fatima and Wooldridge²³ present the calculation method of price utility function using fuzzy mathematics method and discussed the price negotiation model and strategy based on multi-agent. Faratin et al.^24,25 study consultations between the two sides based on the price and utility of the web, and proposed multi-agent negotiation model and strategy for two commodity prices with incomplete information. Gao and Zeng²⁶ and Gao and Zhou²⁷ study the multi-agent negotiation based on contract network, applied the Bayesian learning mechanism for agent negotiation, solved the conflict problem, and proved the validity of the model by simulation. Zhiping et al.²⁸ study the bilateral multi-objective autonomous consultation model, which applied the vector to express the problem in the negotiation and applied the Bayesian learning mechanism for the autonomous learning of agent negotiation. Park and Yang²⁹ and Shao and Zhang³⁰ propose a multi-objective negotiation model for the e-commerce environment and consider how to make the negotiation agent reach a satisfactory solution or an optimal solution. Li and Sheng³¹ discuss a multi-agent negotiation model with uncertainty information and proposed a B2C e-commerce negotiation model that includes information collection, search, negotiation, and evaluation. Hernández et al.³² and Siqi and Gerhard³³ present an automated negotiation strategy that can adjust utility based on adaptive concession making mechanism by acquiring an opponent model. And provided an empirical analysis based on game theory that showed the robustness of the automated negotiation strategy. Wang et al.³⁴ present a hybrid multi-agent negotiation protocol to tackle the negotiation problem. The ontology-based method and operation protocol are adopted in the agent negotiation process, and a negotiation case is given and verified the validity and efficiency of the operation protocol. Khalid and Ryszard³⁵ consider a one-to-many negotiation approach by the buyer agent and addressed the bidding strategy among negotiation process and proposed novel dynamic negotiation tactics. The experimental results approved the effectiveness and robustness of dynamic negotiation tactics in different negotiation environments. Manupati et al.³⁶ describe a negotiation approach based mobile-agent and presented the functions and fundamental framework of the method, an illustrative example was presented to validate the feasibility of the approach.

The above literature provides lots of methods for solving the negotiation problem and application of agent and multi-agent in manufacturing enterprise supply chain, for example, the works reported in the literature^19,26–30 study the multi-agent negotiation by applying Q-learning and Bayesian learning mechanism. The works reported in Kraus et al.²¹ explore the multi-agent negotiation model of two-person single-objective under incomplete information conditions. And, the works reported in the literature^22,23,31 explore agent negotiation problem using fuzzy mathematics method. Although these works studied the negotiation problems from different aspect, the influence of different risk preferences on negotiation process is not taken into account. However, in the actual negotiation process, different risk preferences will affect the negotiation number, price, and utility, and the purpose of this article is to explore the impact of different risk preferences on negotiation process. Based on the above research, we will build the the bidding tactics model of both parties, which take into account the impact of different risk preference and discuss how different risk preferences affect the negotiation number, price, and utility by experiment. We discuss the multi-agent collaborative framework in section “Multi-agent collaborative framework.” We present the bidding tactics model in section “Negotiation process” and analyze the negotiation procedure. In section “Empirical illustration,” we apply the concrete example for analyzing the influence of different risk preferences on the negotiation process, which can provide the method for solving agent negotiation problem and demonstrate the validity and correctness of the models. We finish by a conclusion and some future work.

Multi-agent collaborative framework

The negotiation process will be finished based on the multi-agent collaboration. In order to realize multi-agent collaboration, that need sharing ontology for transferring message between agents, agent communication language needs KQML communication language, and communication protocol can use the common Transmission Control Protocol/Internet Protocol (TCP/IP) protocol. And, based on the contract net negotiation, multi-agent collaborative framework can be built just as shown in Figure 1.

Figure 1.

Multi-agent collaborative framework based on contract net.

The multi-agent collaborative framework is based on contract net that includes the manager, bidder, agent name server (ANS), and Facilitator (intermediary agent).

As agent, the manager and tender of contract net compose task processor, knowledge base and contract processor. Task processor is responsible for handling and solving task, accepting the task form contract processor, and resolving task by means of local knowledge base and sending the results to contract processor. The local crunodes history knowledge, currently negotiation status, and information of solving problem process have been stored in the knowledge base. Contract processor will verdict the mission, send bids and confirm the contract, analyze and explain arrived messages, and harmonize the total crunodes.

ANS can maintain the agent registry table and will realize the mapped from agent name to its physical address in the network. When one ANS starts, it will tell all the others agent and ANS in the network by broadcasting. Agent will decide whether use this ANS, and other ANS will contact with this ANS and exchange the registry information and requesting message. Similarly, when agent comes into the system and sends request to all the ANS by broadcasting, all the ANS will respond to the request and then agent will decide the registering ANS by itself. An agent can inquire about one or more ANS that have known the results (i.e. the so-called client pull mode). An agent can inquire about only one ANS, and the ANS will find the way to inquire about the physical addressing of agent (i.e. the so-called server pull way). In addition, agent can also use the two ways to inquire about.

Intermediary agent (Facilitator) is responsible for managing the Yellow Pages of management system, build the mapped from agent functions for name in order to quickly find the right agent and interact with it. The handling capacity of ANS and Facilitator can be evaluated by two aspects including the quantity of handling request and the number of responding agent at the same time. Finding agent from ANS is the overall service of system level, and finding agent from Facilitator is based on the ability of knowledge level.

When facing the purchasing mission, manufacturing agent will negotiate with suppliers’ agent A, B, C, and D. At first, manager is not to hurry to send bids, it will check its knowledge base, and rationalize based on examples by means of the historical information. If there is the appropriate example, manager will send the purchasing request. Otherwise, Manager sends a message to the Facilitator for finding the agent possessing this capability, Facilitator will return to the agent name list, and then Manager will request these ANS agents of the physical address, and according to the returning results by ANS, Manager will negotiate with bidder. If the negotiations succeed, then the purchasing mission will be distributed.

Negotiation process

In the multi-agent system of manufacturing enterprise supply chain, the agents represent different enterprises and organizations, and cooperate in order to realize the fast and efficient operation of the supply chain. According to the common negotiation type among agents, the negotiation between multi-agents is mainly cooperative. From the essence of collaboration, the purchasing business of core enterprise in the manufacturing enterprise supply chain purchases some kind of part from the supplier agent. The two sides negotiate the price, the quality, the delivery time, and the supply quota, and finally reach the unanimous goal.

Negotiation hypothesis

In the negotiation process, participants in the negotiation would be necessary communicated in order to complete the specific negotiation project at a certain time, and this communication is built on a comprehensive negotiated agreement. The negotiation tactics are a target or multiple targets on the basis of the offer or offer combination, which is helpful to improve the efficiency of conflict resolution and resource allocation by means of a multi-step decision. In order to solve the negotiation problem, it is necessary to make some hypothesizes about the different agents involved in the negotiation:

Agents seeking to maximize their own interests will not accept less than their deserving benefits solution. Let ( $u_{s}$ , $u_{c}$ ) as the final negotiation results, so $u_{s} \geq c_{s}$ and $u_{c} \geq c_{c}$ . And, when consensus is reached, $c_{s}$ and $c_{c}$ are the accepted lowest points of the two sides.

Agent is a rational individual, that is, each individual involved in negotiation is negotiated by the rules of doing anything, then all agents have individual rationality.

Agent also has joint rational, that is to say when there is a negotiation outcome $N_{p}$ enabling participants to gain greater utility, negotiation participants would not choose another result $N_{Q}$ as the final results of the consultation, and $A_{u}$ is the whole outcome.

Negotiation among agents is carried out under incomplete information. For example, they do not even know each other’s bidding tactics and risk preferences.

No fraud in negotiation, that is, negotiation participants have sincerity in the negotiation process.

Agent owes certain environmental awareness.

In the process of offer and counter-offer, the bid of the supplier of single target based on multi-agent in the manufacturing enterprise supply chain is higher than the bid of core enterprise purchasing agent. The supplier agent is $G (x^{i}) \geq G (y^{i - 1})$ and the purchasing agent is $N (y^{i}) \leq N (x^{i - 1})$ , eventually the price of two sides is tending to agreement. Where $G$ and $N$ represent the utility functions of supplier and buyer, and $x$ and $y$ represent the offer combinations of suppliers and buyer.

Here, rationality means that behavior of negotiation participants should be taken to improve the individual interests and does not refer to the actual action taken.

Negotiation model

For ease of understanding, the basic explanation of the parameters used in this article is as follows:

$P_{c}^{i}$ : offer price of core enterprise purchasing agent at time i.

$P_{s}^{i}$ : offer price of supplier agent at time i.

$a_{c}$ : degree of risk appetite or patience of core enterprise purchasing agent.

$a_{s}$ : degree of risk appetite or patience of supplier agent.

$U_{Union}^{p}$ : joint utility function of the two parties.

$P^{*}$ : ultimate price after bargaining successfully.

The pricing negotiation of the supply chain in the manufacturing enterprise is a single-objective negotiation. In the manufacturing enterprise supply chain, core enterprise purchasing agent and supply agent make offer and bargain on the price, till they reach consistent goal, and finally the two parties have the most benefit. This single-objective negotiation between agents is widespread in manufacturing enterprise supply chains.

In order to facilitate the analysis, this article solves the problem expressed as a manufacturing enterprise in the supply chain of core enterprise to purchase a component, and the supplier can provide goods, so core enterprise purchasing agent and supplier agent negotiate on the price of such parts. The offer tactics defined are as following. The offer tactics of core enterprise purchasing agent in the manufacturing enterprise supply chain is

P_{c}^{i} = P_{c}^{i - 1} + a_{c} (P_{s}^{i - 1} - P_{c}^{i - 1}) \times \frac{P_{c}^{i - 1}}{P_{s}^{i - 1}}

In the formula, i = 2, …, n. The higher for risk appetite of core enterprise purchasing agent, the bigger for $a_{c}$ . The greater for the degree of patience, the smaller for $a_{c}$ . When $i = 1$ , the offer of core enterprise purchasing agent in the manufacturing enterprise supply chain is $P_{c}^{1}$ . The offer tactics of supplier agent is

P_{s}^{i} = P_{s}^{i - 1} - a_{s} (P_{s}^{i - 1} - P_{c}^{i - 1}) \times \frac{P_{c}^{i - 1}}{P_{s}^{i - 1}}

In the formula, i = 2, …, n. The higher for risk appetite of supplier agent, the bigger for $a_{s}$ . The greater for the degree of patience, the smaller for $a_{s}$ . When $i = 1$ , the bid of supplier agent is $P_{s}^{1}$ .

The ultimate goal of the parties involved in the negotiation is to realize the maximization of the utility of core enterprise purchasing agent and the supplier agent. The joint utility function of the two parties $U_{Union}^{p}$ defines as follows

U_{Union}^{p} = \frac{| P^{*} - P_{c}^{1} | \times | P_{s}^{1} - P^{*} |}{{(P_{s}^{1} - P_{c}^{1})}^{2}}

Negotiation procedure

According to the quotation strategy proposed above, this article considers that the specific negotiation steps of multi-step negotiation of multi-agent in the supply chain of manufacturing enterprises are reproduced below:

Step 1. In the manufacturing enterprise supply chain, the purchasing agent of core enterprise and supplier agent will negotiate for a certain part. They have the first quotation of $P_{c}^{1}$ and $P_{s}^{1}$ . The two parties will exchange the first quotation by communication language. If $P_{c}^{1} = P_{s}^{1}$ and the joint function of price utility $U_{Union}^{p}$ is the maximum, then turn to step 3.

Step 2. If $P_{c}^{i} > P_{s}^{i}$ , the two parties will come to an agreement, and the final price is $P_{c}^{i}$ , at this time, $U_{Union}^{p} = (| P_{c}^{i} - P_{c}^{1} | \times | P_{s}^{1} - P_{c}^{i} |) (P_{s}^{1} - P_{c}^{1})^{2}$ , then turn to step 3.

If $P_{s}^{i} > P_{c}^{i}$ , core enterprise purchasing agent of the manufacturing enterprise supply chain will propose a counter-offer $P_{c}^{i + 1}$ , then turn to step 2.

① If $P_{c}^{i + 1} > P_{s}^{i}$ , the two parties will come to an agreement and the final price is $P_{c}^{i + 1}$ , at this time, $U_{Union}^{p} = (| P_{c}^{i + 1} - P_{c}^{1} | \times | P_{s}^{1} - P_{c}^{i + 1} |) (P_{s}^{1} - P_{c}^{1})^{2}$ , then turn to step 3.

② If $P_{s}^{i} > P_{c}^{i + 1}$ , supplier agent cannot agree the counter-offer $P_{c}^{i + 1}$ of purchasing agent and persists its quotation $P_{s}^{i}$ . Then, core enterprise purchasing agent rejects quotation $P_{s}^{i}$ , which will lead the current negotiation to end, and turn to step 4.

Step 3. The core enterprise purchasing agent and supplier agent will sign a contract, and the supplier agent will accept the mission.

Step 4. The negotiation is to end.

The negotiation process is shown in Figure 2.

Figure 2.

Negotiation flowchart.

In the consultation process, the two sides do not want to get failure result for negotiation, both of whom not only want to reach an agreement but also hope to loss a little in the bilateral negotiation. In other words, both sides will face a dilemma of choice. So, we made the following assumptions: the probability that core enterprise purchasing agent in the manufacturing enterprise supply chain accepts the offer $P_{S}^{i}$ of the supplier agent is defined as 1 − P, then P is the probability which the purchasing agent would refuse this offer $P_{S}^{i}$ of supply agent. At the same time, the earnings of the purchasing agent are denoted by $U_{C} (P_{s}^{i})$ . As the above situation, the rational decision-making behavior of supply agent is as follows: when $U_{S} (P_{C}^{i}) > (1 - P) U_{S} (P_{S}^{i})$ , that is, $(U_{S} (P_{S}^{i}) - U_{S} (P_{C}^{i})) (U_{S} (P_{S}^{i})) < P$ , the supply agent should accept the counter-offer of the purchasing agent. Whereas, the supply agent will insist on his offer $P_{s}^{i}$ , when $(U_{S} (P_{S}^{i}) - U_{S} (P_{C}^{i})) (U_{S} (P_{S}^{i})) > P$ . In formula, $(| U_{s} (P_{s}^{i}) - U_{s} (P_{c}^{i}) |) (U_{s} (P_{s}^{i}))$ is shown as the determination of the supply agent strived on his offer $P_{S}^{i}$ .

In the same reason, $(| U_{c} (P_{s}^{i}) - U_{c} (P_{c}^{i}) |) (U_{c} (P_{c}^{i}))$ is known as the determination of the purchasing agent strived on his counter-offer $P_{C}^{i}$ . If one agent found that another agent determination strived on the agreement is greater than his, he would compromise in bilateral negotiation. If the determinations of the two sides are equal, both of them would compromise at the same time. They will continue to negotiate after compromised until Nash equilibrium point is obtained, that is to say, a unique rational solution $(u_{S}, u_{C})$ can be obtained in the bilateral negotiation. At the same time, $u_{S} \geq c_{S}$ , $u_{C} \geq c_{C}$ , and $[u_{S} - c_{S}] \times [u_{C} - c_{C}]$ reaches to its maximum. The joint function of price utility $U_{Union}^{P}$ also reaches to the maximum.

Empirical illustration

In order to better illustrate the impact of the risk appetite on the negotiation process in the supply chain of the manufacturing enterprise, it is assumed that the purchasing agent and the supplier agent in the manufacturing enterprise supply chain will negotiate the price of a certain component. The two sides will negotiate to complete the offer and counter-offer according to the consultation steps, eventually the two sides agree on the price, and realize the largest income, or the smallest loss, and finally tend to Nash equilibrium. In the negotiation process of the two parties, the two sides will offer and court-offer by their offer strategy, but the degree of risk appetite or patience of two sides, that is, $a_{c}$ and $a_{s}$ defined in the bid strategies of the negotiated both sides is different, which led to the number of consultations and transaction prices, is different, and at the same time, the joint utility of price in two sides is also different.

According to changes of the degree of risk preference or patience of both parties, that is, $a_{c}$ and $a_{s}$ defined in the bid strategy of the negotiated both sides, the initial quotation for the negotiation parties is (400,480), which is discussed in three cases, namely, a_c = 0.2, a_s = 0.2, 0.5, 0.6; a_c = 0.5, a_s = 0.2, 0.5, 0.6; and a_c = 0.6, a_s = 0.2, 0.5, 0.6. Using MATLAB to simulate the negotiation process, we can see more clearly the changes in the transaction price and the number of consultations.

From Figures 1 –3, when the risk appetite $a_{c}$ of the purchasing agent does not change and the risk appetite $a_{s}$ of the supplier agent becomes larger gradually, the absolute value of the slope (when each additional one consultation is added and how much the price decrease) of the supplier’s price function is bigger, that is, the more risk-prone, the greater the price reduction rate is. Likewise, when the risk appetite of the supplier is unchanged, and the risk appetite of the purchasing agent is gradually increasing; also in line with the above analysis, this is no longer listed in detail discussed.

Figure 3.

Negotiation result (a_c = 0.2, a_s = 0.2, 0.5, 0.6).

In order to illustrate the impact of the risk appetite of both parties on the number of consultations and the joint effect of consultation, the results of number of consultations and negotiated joint utility of three different situations of $a_{c} = a_{s} = 0.2$ , $a_{c} = 0.2, a_{s} = 0.6$ , and $a_{c} = 0.6, a_{s} = 0.6$ are calculated (Figures 4 and 5), respectively, by the initial quotation combination $(P_{c}^{1}, P_{s}^{1}) = (400, 480)$ . Table 1 gives the final results.

Table 1.

Computer result of negotiation number, final price, union utility, and $α$ .

$(a_{c}, a_{s})$	(0.2,0.2)	(0.2,0.6)	(0.6,0.6)
Negotiation number	16	7	2
Final price	439.98	420	440
Union utility	0.25	0.1875	0.25

Figure 4.

Negotiation result (a_c = 0.5, a_s = 0.2, 0.5, 0.6).

Figure 5.

Negotiation result (a_c = 0.6, a_s = 0.2, 0.5, 0.6).

Analysis and comparison

Different from works in the literature,^{19,21–23,26–28,31} we mainly discuss the influence of different risk preferences of agent on the negotiation process:

In this article, the price function of core enterprise in the supply chain of the manufacturing enterprise is the ascending function, which will slowly increase the price over time, while the supplier’s price function is a decreasing function, which will slowly decrease the price over time. So, in the manufacturing enterprise supply chain, the prices of core enterprises and suppliers in the same coordinate plane will be more and closer, and eventually intersect and become a straight line, indicating the success of the negotiations of the two sides. If there is no intersection, the two sides will not reach an agreement, which means the end of the negotiations.

It can be seen from the above calculation results, when the parties agree on the risk, the number of consultations is the least, and even negotiate once to reach an agreement, at this point, the negotiation utility is maximum; when the parties not preferred risk, the two sides are very cautious to bid, the price change is very small, to go through more than one consultation can be agreed, and the utility is the smallest; when one party is risky and the other is not risk, the number of consultations and the effect should be between the two, but at this time, both sides of the utility has been reduced, and the risk of the party will pay a higher price than the risk of their own do not prefer. In the actual business survey, we found that in the consultation process, if a party quotation found that the trend of their offer is greater than the other, they will take the initiative to adjust their own pricing strategy; once again offer, the trend is less than the change trend that the price has been reported, that is, the two sides will adjust their own pricing strategy according to the other’s bid strategy.

Conclusion

The main work of this article is that we construct the negotiation model based on multi-agent in the supply chain of manufacturing enterprise, analyze the negotiation strategy and steps of the two sides, and then use the computer language tool to solve and simulate the negotiation process. In the negotiation model, the risk preference or patience of negotiation parties, that is, $a_{c}$ and $a_{s}$ defined in the bid tactics are different, and the change of $a_{c}$ and $a_{s}$ affects the number of consultations and the total utility. The research can provide theoretical and operational methods for the supply chain management of manufacturing enterprise, and help the coordination and control of the manufacturing enterprise supply chain, so that it can be operated efficiently, flexibly, and quickly.

About the future research, we mainly consider two aspects. First, on the basis of studying the two parties’ negotiations, we conduct three party consultations or multi-party consultations. In the actual supply chain operation, the negotiation about three parties or multi-parties is widespread. Second, with the improvement of consumption level and application of Internet technology, manufacturing enterprise personalized product supply chain has become a trend, the negotiation is not the same between the personalized product supply chain multi-agent system and traditional manufacturing enterprise supply chain multi-agent system, and this problem is worth studying. We would like to cooperate with researchers in this field to carry out such research together.

Footnotes

Handling Editor: Peter Nielsen

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper was supported by the National Natural Science Foundation of China (No. 71702174).

ORCID iD

Changhui Yang

References

Tang

Hayes

Shaw

. An intelligent-agent framework for concurrent product design and planning. IEEE T Eng Manage 1996; 243: 297–306.

. Research of distribution center location for regional E-supermarket. Inform Technol Manage Sci 2017; 2: 83–88.

Zhao

Song

Chang

et al . Research on coal production logistics efficiency of evaluation index system based on DEA. J Inform Technol Supp Chain Manage 2017; 2: 58–63.

Gunasekaran

Yang

. Constructing sustainable supply chain under double environmental medium regulations. Int J Product Econom 2014; 147: 211–219.

Tan

. A study on decision-making of food supply chain based on big data. J Syst Sci Syst Eng 2017; 26: 183–198.

Fan

Zhang

. Research on coal mine management information system of composite structure. Inform Technol Manage Sci 2017; 2: 73–77.

Han

Tan

. False failure returns: optimal pricing and return policies in a dual-channel supply chain. J Syst Sci Syst Eng 2018; 27: 1–30.

Chikhaoui

Lotfi

. Research on model of solving container stowage planning problem. J Inform Technol Supp Chain Manage 2017; 2: 71–77.

Lane

Mcfadzean

. Distributed problem solving and real-time mechanisms in robot architectures. Eng Appl Intell 1994; 7: 105–117.

10.

Yang

Sun

. Research on negotiation of manufacturing enterprise supply chain based on multi-agent. J Inter Technol, in press.

11.

Maturana

Norrie

. Multi-agent mediator architecture for distributed manufacturing. J Intell Manuf 1996; 7: 257–270.

12.

Labrou

Finin

. A proposal for a new KQML specification (DARPA knowledge sharing initiative), 1997, https://www.csee.umbc.edu/csee/research/KQML/papers/kqml97.pdf

13.

FIPA. FIPA contract net interaction protocol specification, 2002, http://www.fipa.org/specs/fipa00029

14.

Zhang

Liu

Duan

. Research on buy back contract application in supply chain coordination. J Inform Technol Supply Chain Manage 2017; 2: 93–97.

15.

Durfee

Lesser

Corkill

. Trends in cooperative distributed problem solving. IEEE T Know Data Eng 1989; 1: 63–83.

16.

Kraus

. Automated negotiation and decision making in multi-agent environments. Lect Notes Comput Sc 2001; 2086: 150–172.

17.

Huang

Sycara

. A computational model for online agent negotiation. In: Proceedings of the 35th annual HICSS 2002, Big Island, HI, 10 January 2002. New York: IEEE.

18.

Jennings

Norman

Faratin

et al . Autonomous agents for business process management. Appl Artif Intell 2000; 14: 145–189.

19.

Lichun

Shifu

. Multi agent multi problem negotiation model. J Software 2002; 13: 1637–1643.

20.

Xiaohong

Xuanhua

. Research on model management of decision support system based on multi-agent. Comput Eng Appl 2005; 13: 194–196.

21.

Kraus

Wilkenfeld

Zlotkin

. Multiagengt negotiation under time constraints. Artif Intell 1995; 75: 297–345.

22.

Fatima

Wooldridge

. Multi-issue negotiation under time constraints. In: Proceedings of the 1st international joint conference on autonomous agents and multi-agent systems, Bologna, 15–19 July 2002, pp.143–150. New York: ACM.

23.

Fatima

Wooldridge

. An agenda-based framework for multi-issue negotiation. Artif Intell 2004; 152: 1–45.

24.

Faratin

Sierra

Jennings

. Negotiation decision functions for autonomous agent. Robot Autonom Syst 1998; 24: 159–182.

25.

Faratin

Sierra

Jennings

. Deliberative automated negotiators using fuzzy similarities. In: Proceedings of the conference on fuzzy logic, 1999, pp.155–158, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.143.3537&rep=rep1&type=pdf

26.

Gao

Zeng

. Multi-agent based cooperation in virtual enterprises. Comput Integrat Manuf Syst 2003; 9: 85–89.

27.

Gao

Zhou

. Multi-agent learning negotiation research in virtual enterprise based on contract net. Comput Integrat Manuf Syst 2004; 4: 471–475.

28.

Zhiping

Hong

Qilun

. Research on a multilateral multi subject autonomous negotiation model. J Electron Inform 2007; 29: 733–738.

29.

Park

Yang

. An efficient multilateral negotiation system for pervasive computing environments. Eng Appl Artif Intell 2008; 21: 633–643.

30.

Shao

Zhang

. Research on business transformation methods based on E-business. J Inform Technol Manage Sci 2017; 2: 38–43.

31.

Sheng

. A multi-agent model for the reasoning of uncertainty information in supply chains. Int J Product Res 2011; 49: 5737–5753.

32.

Hernández

Mula

Poler

et al . Collaborative planning in multi-tier supply chains supported by a negotiation-based mechanism and multi-agent system. Group Decis Negot 2014; 23: 235–269.

33.

Siqi

Gerhard

. An efficient automated negotiation strategy for complex environments. Eng Appl Artif Intell 2013; 26: 2613–2623.

34.

Wang

Wong

Wang

. A hybrid multi-agent negotiation protocol supporting agent mobility in virtual enterprises. Inform Sci 2014; 282: 1–14.

35.

Khalid

Ryszard

. Coordinating the bidding strategy in multi-issue multi-object negotiation with single and multiple providers. IEEE T Cyb 2015; 45: 2261–2272.

36.

Manupati

Putnik

Tiwari

. Integration of process planning and scheduling using mobile-agent based approach in a networked manufacturing environment. Comput Ind Eng 2016; 94: 63–73.

Negotiation model and tactics of manufacturing enterprise supply chain based on multi-agent

Abstract

Keywords

Introduction

Multi-agent collaborative framework

Negotiation process

Negotiation hypothesis

Negotiation model

Negotiation procedure

Empirical illustration

Analysis and comparison

Conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References