The interaction view

In this research work, the agents exhibit two types of interactions, namely, conflictual and cooperative interactions. In MASs these interactions are often modelled through the contract net protocol (CNP) for agents’ negotiations and communications([326], [327]) as is the case in this work.

The subsections that follow will give a detailed description of the CNP before demonstrating the two types of the agents’ interactions in this work using the CNP.

III.2.3.1 THE CONTRACT NET PROTOCOL

As introduced above, the CNP is an approach to cooperation, coordination and task-sharing in multi-agent systems [328]. This approach is inspired by a market-like model whereby the system consists of nodes or software agents and each node on the network can, at different times or for different tasks, be a manager or a contractor [326]. According to Davis et al. [329], the CNP is not merely a means of

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transferring bits from one node to another but it rather provides a description of the content of the transmitted information. The negotiations in CNP happen in five stages [328] as indicated in Table III-1.

TABLE III-1:NEGOTIATIONS IN CNP

Stage Description

Recognition This is the stage whereby the agents recognizes

that it wants help with achieving its goal be-cause:

o It does not have the capabilities to achieve it.

o It does not want to achieve it in isola-tion.

o It wants to achieve the goal swiftly etc.

Announcement In this stage the agent sends out the goal de-scribed in recognition stage, its specifications, constraints and meta-tasks.

Bidding Other agents that receive the task decide

whether they should bid for it depending on their capabilities and the constraints attached to the task.

Awarding The agent that sent the announcement must

de-cide, upon receiving the bids, which agent to award the contract to.

Expediting This stage involves the possibility of other

sub-contracts in order to complete the contracted task.

III.2.3.2 CONFLICTING INTERACTION IN THE PROPOSED MAS

In conflicting interactions, agents have conflicting goals [74]. In the context of this research, the con-flicting situation occurs when the SA wants to repair the maximum number of CPAs in CBM group (group 2) while the MCA wants to ensure that enough CPAs are available to carry out the planned fleet

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operations within the specified horizon. These goals are conflicting because repairing many CPAs in group 2 might leave insufficient CPAs for the fleet operations during those CBM interventions. Hence the SA will try to find the best compromise between satisfying the fleet operations and at the same time deploying CBM to the remaining CPAs in group 2. Figure III-8 depicts the conflict resolution using a unified modelling language (UML) sequence diagram.

FIGURE III-8:SOLUTION TO CONFLICTUAL INTERACTION

III.2.3.3 COOPERATIVE INTERACTIONS IN THE PROPOSED MAS

These are the interactions among agents to reach a common goal [74]. Four cooperative interactions are identified in this research work as follows:

➢ Between the SA and the CPAs: With the objective of calculating the groups of CPAs as well as the maintenance priorities in the maintenance depots.

➢ Between the SA and the MCA: To verify the number of CPAs needed to satisfy the planned fleet operations within a given horizon (T).

➢ Between the SA and the MA: To verify the depots availability (i.e. the availability of the mainte-nance resources – Maintemainte-nance teams, infrastructure and the replacement parts).

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➢ Between the SA and the FSA: For confirmation of the proposed maintenance decisions.

The sequence diagram detailing these four cooperative interactions is depicted in Figure III-9.

FIGURE III-9:COOPERATIVE INTERACTIONS

III.3 SUMMARY

In this chapter a reactive CPSs FMSP model to be integrated in the model layer of the DSS specified in chapter II has been proposed. The formulation of this model is done using a multi-agent system (MAS) modelling approach. This MAS has been designed using ANEMONA design methodology which organ-izes the systems’ components into views or models. Through the proposed MAS approach, the aspects of the CPSs FMSP framework described in the previous chapter such as the CPSs, the maintenance

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depots, the fleet operator and the human-maintenance decision-maker (fleet supervisor) have been modelled as agents interacting with each other to achieve the model’s objectives. The interactions among these agents were handled by using the contract net protocol (CNP) which is inspired by mar-ket-like model.

The formulation of the MAS-based reactive CPSs FMSP model proposed in this chapter brings up the following interests and questions:

➢ Is the proposed MAS effective in satisfying the fleet’s availability and reliability expecta-tions as specified in chapter II? This question is also related to the primary concern on the limitations of heuristic-based MASs as pointed out in the section III.1.

➢ Is the proposed MAS operational in a dynamic environment? In other words, Is the pro-posed MAS reactive vis-à-vis the occurrences of unexpected events? Due to the domain-specific nature of MASs (as pointed out in the limitations of MASs in the section III.1), the definition of perturbative scenarios (unexpected events) in a dynamic environment, de-pends on the applicative case.

In the coming chapter, this research work will use numerical implementations and simulations to ad-dress the raised questions and the associated concerns following the proposition of the MAS for the reactive CPs FMSP in this chapter.

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NUMERICAL IMPLEMENTATIONS: MAS SIMULATIONS IN STATIC AND DYNAMIC ENVIRONMENTS

The previous chapter presented a model for the reactive CPSs FMSP decision-making to be integrated in the model layer of the DSS specified in chapter II. To design this model, a MAS modelling approach was chosen. The presented MAS mirrored different actors of the specified FMSP framework as coop-erating agents to reach the specified objectives (i.e. availability, reliability and reactivity).

The objective of this chapter is to simulate and provide the numerical implementations of the proposed MAS in order to answer the key questions raised in the previous chapter, namely, is the proposed MAS effective (capable of satisfying the fleet’s availability and reliability expectations)? Is the proposed MAS reactive in adapting the FMSP decisions after the occurrences of unexpected events? Meanwhile, since the presented MAS is fundamentally a heuristic-based approach, i.e. it is based on heuristic rules on the agents’ interactions ([330], [331], [332]), its overall effectiveness must be carefully studied and validated by more powerful and exact approaches but static optimization mechanisms ([333], [334], [137]), like mathematical programming ([335], [336]). This is especially true in the context of FMSP where performance expectations from different actors are high and must be ensured as much as pos-sible ([27], [74], [25]). Thus, in the context of this research, to validate the effectiveness of the pro-posed MAS in a static environment (i.e. absence of unexpected events), we formulate a mixed-integer linear programming (MILP) model ([337], [338]) and compare its solutions to those proposed by the MAS.

The remainder of this chapter is organized as follows, section IV.1 will present the framework used to implement the proposed MAS. Section IV.2 will present the simulation of the MAS in a static environ-ment. Moreover, a MILP model will be formulated in this section in order to validate the MAS model.

Section IV.3 will present the simulations of the MAS in a dynamic environment. In this section, the MAS will be put under simulated perturbations and observations will be made on how it reacts to mitigate these perturbations. A considered scenario for perturbations will be discussed in this section. In section IV.4, an illustrative example to demonstrate the capabilities of the MAS model in both static and dy-namic environments will be presented. Section IV.5 will put forward the limitations of the proposed MAS model. The last section will give the summaries and the conclusions drawn from this chapter as well as the perspectives.