Self-organising socio-technical description in service systems for supporting smart user decisions in public transport

Self-organising socio-technical description in service systems for supporting smart user decisions in public transport

Expert Systems with Applications 42 (2015) 6329–6341 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: ww...

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Expert Systems with Applications 42 (2015) 6329–6341

Contents lists available at ScienceDirect

Expert Systems with Applications journal homepage:

Self-organising socio-technical description in service systems for supporting smart user decisions in public transport Monica Dra˘goicea a,⇑, João Falcão e Cunha b, Monica Pa˘trasßcu a a University Politehnica of Bucharest, Faculty of Automatic Control and Computers, Department of Automatic Control and Systems Engineering, 313 Splaiul Independentei, 060042 Bucharest, Romania b University of Porto, Faculty of Engineering – FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

a r t i c l e

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Article history: Available online 28 April 2015 Keywords: Service systems Service interactions Service design Agent based simulation Self-organisation

a b s t r a c t This paper describes an exploration towards the transposition of service science principles into design guidelines. This aims at capturing value co-creation service interactions embedding customer experience in service design and delivery activities. The new Socio-Technical Systems Engineering (STSE) process is proposed to guide improved design, and it is exemplified with a new real time service that provides integrated information for trip planning in a city. The STSE process supports a high level visual modelling approach assisted by model execution and simulation tools. From a service engineering perspective, the outcomes of this process are artefacts that automatically support consistency among design steps and effective integration of customer experience and stakeholder requirements through iterative cycles related to service design. The application of the STSE process in the design of an exploratory case study of a real time information and travel planning service is validated through simulation using an executable representation of requirements. It can be executed over more complex transport service offerings, with different resource allocation algorithms, or different public transport planning services over a sample of real users requesting information. Results of such a simulation are beneficial for the users, for the service providers, and for the authorities managing public transport in city or metropolitan areas. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Service organisations are introducing today new business models that are more and more oriented towards the service customer, whose experience is supposed to be embedded in the new service offerings and enterprise design through social computing and social software (Cunha & Galvão, 2014; Neville, Fasli, & Pitt, 2015). Therefore, the society – as a whole – is facing fundamental changes in the way its members interact with the service organisations, make informed (smart) decisions, work, and access new service offerings. Different methods and technologies are proposed to be used to implement social business solutions and some of them are into current practice already. Crowdsourcing approaches (Cunha & Galvão, 2014; Patrício, Fisk, & Cunha, 2008; Predic & Stojanovic, Abbreviations: ABS, agent based simulation; MBSE, model-based systems engineering; M&SBSE, modelling and simulation-based systems engineering; STSE, Socio-Technical Systems Engineering. ⇑ Corresponding author. Tel.: +40 74 501 5646. E-mail addresses: [email protected] (M. Dra˘goicea), [email protected] pt (J.F. e Cunha), [email protected] (M. Pa˘trasßcu). 0957-4174/Ó 2015 Elsevier Ltd. All rights reserved.

2015; Xintong, Hongzhi, Song, & Hong, 2014), agent based social simulation (Macbeth, Pitt, & Busquets, 2015; Davidsson, 2002; Macal & North, 2010; Moss & Edmonds, 2005), cloud technologies, pervasive mobile smart devices (Pitt, 2012), customer-centred design for services (Karwowski, Salvendy, & Ahram, 2010; Peng, 2012; Pinho, Beirão, Patrício, & Fisk, 2014; Teixeira et al., 2012), to name but a few, have recently emerged as powerful perspectives to support enhanced customer experience integration. Actual service systems can be described as complex socio-technical systems, being approached in an interdisciplinary vision that integrates business functions, technology and human resources, with the final aim of creating value and benefit through the generated services (Beaumont, Bolton, McKay, & Hughes, 2014; Carroll, 2012; Edvardsson, Tronvoll, & Gruber, 2011). A service has special characteristics, like intangibility, inseparability, perishability and simultaneity (Sampson, 2010; Sampson & Froehle, 2006). This requires a special approach to organise service systems’ activities in order to customise service offerings based on the customer’s requirements. Service systems are dynamic value co-creation configurations of resources (people, organisations, shared information, and technology) (Maglio, Vargo, Caswell, & Spohrer, 2009; Spohrer,


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Anderson, Pass, & Ager, 2008), where at least one resource is an operant resource, specifically a person with rights, and capable of interacting and judging service outcomes (Dra˘goicea et al., 2014). Service procedure automation and innovation are possible for a service system that relies on IT technology. Performance is supervised by means of Service Key Performance Indicators, while value is provided through close interactions between service provider and service customer. Therefore, there is a need for new service business models and new service systems engineering skills and tools (Lopes & Pineda, 2013; Pineda, Lopes, Tseng, & Salcedo, 2012) to integrate the above mentioned aspects in the service systems development cycle. They have to enhance collaboration at their core processes and to support the development of a new generation of IT-enabled services driven by customer requirements (Bithas, Kutsikos, Sakas, & Konstantopoulos, 2015; Borangiu, Oltean, Draˇgoicea, Cunha, & Iacob, 2014). Recent multidisciplinary research directions on service, service systems and service innovation reveal many opportunities generated in close encounters of service and technology. From an integration perspective, the service dominant logic perspective adds value to technology-based developments, advancing further priorities related to service design, improvement of service delivery processes, and understanding of the service value co-creation process (Demirkan et al., 2008; Ostrom, Parasuraman, Bowen, Patrício, & Voss, 2015; Ostrom et al., 2010; Wu & Wu, 2015). In the service dominant logic perspective (Vargo & Lusch, 2004), service systems modelling can be approached from four different points of view on: 1. Activity modelling. This perspective takes into consideration the flow of activities for service implementation: service contracting, service set-up and configuring, service delivery and monitoring, and follow-up and performance evaluation. Value co-creation mechanisms and value perception and estimation metrics have to be also highlighted (Borangiu et al., 2014). 2. Resource allocation modelling. This perspective highlights the flow of resources for service realisation and their allocation on service activities. It takes into consideration operand and operant resources, as well as information and methodologies for service realisation. 3. Service networking modelling. This is the external realisation of the service, in a value chain integrating suppliers, clients, partners and compliance (environmental constraints and regulations) (Cardoso, Lopes, & Poels, 2014; Cardoso & Pedrinaci, 2015). 4. Value co-creation interactions modelling. Service interactions refer to value co-creation interactions and governance interactions. Therefore, this stage should highlight value co-creation elements and the value proposition design process (Maglio et al., 2009). Therefore, a further exploration on transposing the principles of service science into guiding principles for service systems design and configuration is required today (Maglio & Spohrer, 2013). Research topics to advance service innovation should also foster the service orientation concept related to the (human and technological) resource description and allocation, control and support methods, IT tools and organisations. This would further support decision making for resource allocation, integration and interconnection. In this context, this work explores the possibility to integrate three main research directions using a service systems engineering process – named Socio-Technical Systems Engineering process – involving customers in the value co-creation chain: (a) service science research directions (modelling customer involvement in service design and delivery); (b) socio-technical systems research

(agent-based modelling of socio-technical systems); and (c) modelling and simulation based service systems engineering concerns. Agent technology is a powerful instrument used today to implement complex distributed applications and distributed intelligent systems, with many facets like coordination (Bedrouni, Mittu, Boukhtouta, & Berger, 2009), self-organisation (Bernon, Gleizes, Migeon, & Serugendo, 2011), functionality composition (Coria, Castellanos-Garzón, & Corchado, 2014; Luo, Li, Liu, Zheng, & Dong, 2010), to name but a few in the present context. Agent based models are proposed to be integrated in incipient development strategies for socio-technical systems (Macbeth et al., 2015; Moon, 2015; Nikolic & Ghorbani, 2011; Pitt, Busquets, & Riveret, 2013b; Van Dam, Nikolic, & Lukszo, 2013) to model different aspects of social behaviour and social – technical interactions. The approach presented in this paper intends to explore from an information integration point of view how agent based simulation (ABS) can be used to evaluate different aspects in IT-enabled services, co-produced through ad-hoc collaboration. They should be taken into consideration when proposing innovated services that integrate customer experience and support users to make smart decisions along with these services. Currently there are no real time services that provide integrated information on planning a trip by bus or alternatively by taxi. The working solution proposed here raises different questions regarding real world implementation of such services. The agent-based collaborative and self-organising approach that is presented here allows an easy open integration of several systems, enabling the creation of a ‘‘virtual’’ service enterprise. It supports a rapid and on-demand creation of a complex virtual service network made up of different service providers that collaborate through complex interactions to fulfil empowered customer needs. Different value propositions can be expressed and evaluated along with this architecture. The agent based simulation of the mobile real-time information and travel planning service can be extended to account for more complex situations. The simulation model can be executed over more complex transport service offerings, using different taxi and bus service providers, different allocation algorithms, different public transport planning services over a sample of real users requesting for information. Results of such simulation are beneficial for the users, for the service providers, and for the authorities that manage public transport in city or metropolitan areas. In this respect, the paper exemplifies the Socio-Technical Systems Engineering process extending a public transport information service (Cunha & Galvão, 2014) where users select different ways to reach their destinations, such as walking, taking a bus, hiring a taxi, or a mixture of these. Information regarding transport resources is gathered from various sources through the service providers’ information systems. Integration of various types of information allows compilation of personalised tracking offers. On-line availability of the transport information service through smart devices is possible, supporting open expansion with other service providers based on customer experience. In Section 2 the specific purpose of this exploration endeavour is depicted and the STSE process is described. It is proposed as a valuable guiding approach that defines the required steps to generate modelling and simulation artefacts to formally visualise service entities interactions, as UML and agent based executable models, using a socio-technical description in service systems. In Section 3 the MOVE-ME service scenario introduced in Cunha and Galvão (2014) is extended to be validated using ABS for future implementation solutions. The steps in the STSE process related to city daily travel planning are illustrated. Its usefulness, related to a real world problem concerning public transport service development, can be further evaluated considering two points of view:

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(a) user perspective (is it possible to consider user needs and to transform them into pertinent guidelines for operational efficiency of the service providers’ business processes?); and (b) service provider perspective (is it possible to address operational efficiency at the service provider level as an emergent property of the system, based on self-organising features of the agent based implementation model?). The role of the development platform in transposing the above mentioned principles in practice is emphasised and integration guidelines of the STSE process steps with the Presage2 multiagent development platform (Neville & Pitt, 2008) are specified. Section 4 presents practical aspects concerning the creation of modelling and simulation artefacts along with the STSE process activities. Step-through simulation was performed for the UML and agent based executable models. An agent based implementation model for service prototyping was created with Presage2 and qualitative simulation results as well as quantitative evaluation aspects are presented. An what if type analysis based on several simulations scenarios was performed to evaluate and to understand how operational changes may impact service delivery. Section 5 draws final conclusions, emphasising open research questions.

2. Socio-Technical Systems Engineering process When trying to design models of socio-technical systems, the designer has to deal with many aspects related to the specific characteristics of these types of systems:  Interactions: different interactions among social elements (for example, businesses, customers, governmental institutions, and regulatory compliance) and technical systems (for example, manufacturing factories, and utility plants) arise in socio-technical systems; the interactions among customers and businesses on a technical basis support the idea of customer-centred design of services (Karwowski et al., 2010; Peng, 2012).  Big data: socio-technical systems are complex systems in which big data is involved, in volume, diversity of sources, variety, velocity and veracity (Chmieliauskas, Davis, & Bollinger, 2013).  Resource access: according to the service dominant logic (Vargo & Lusch, 2004), socio-technical systems can be also approached as dynamic configurations of resources where value co-creation processes can be highlighted (Maglio et al., 2009; Spohrer et al., 2008).  Competition and cooperation: the complex nature of interactions arising when stakeholders access resources in service systems is central to the development of effective policies for resource management (Pitt, Schaumeier, Busquets, & Macbeth, 2012b; Schindler, 2012); for socio-technical systems recent research directions show how human societies form self-governing institutions, in which actors compete and cooperate in an attempt to self-regulate the provision and appropriation of resources according to mutually-agreed, conventional rules (Ostrom, 1990; Ostrom, 2005).  Openness: from an information management perspective, socio-technical systems can be approached as open systems (Hewitt, 1986), as they have to deal with diverse sources of information embedded in their environment.  Changeability: socio-technical systems are designed and they are evolving in changing environments through different types of interactions among social and technical elements.  Adaptability: to be effective and sustainable, activities and interactions evolving within a socio-technical system should adapt to the changing requirements of their environment.  Empowerment: different interactions within current socio-technical systems are initiated by empowered customers using different information channels to make smart decisions


and interact with companies (for example, service providers) through various communication channels, hoping to get a superior customer experience (Cunha & Galvão, 2014; Ostrom et al., 2015; Pinho et al., 2014). Considering the above mentioned observations, it can be considered that the benefit of designing new service systems as being characterised as socio-technical systems mainly refers to the definition of best practices to understand internally the configurations and interdependencies of their components, and externally the interactions of their stakeholders in a value co-creation network. Developing a socio-technical description in service systems that naturally explores value co-creation interaction evaluation would eventually foster service innovation on a multidisciplinary perspective that integrates people, technology and new service business models and processes. Recent literature has proposed the extension of the systems engineering body of knowledge for service systems engineering (Mott, 2010; SEBoK, 2014) to develop new processes, methods and tools to design complex service systems of the future. The INCOSE systems engineering vision for 2020 describes the evolution of the systems engineering endeavours in a ‘‘model-based’’ or ‘‘model driven’’ context, defined as the model-based systems engineering (MBSE) initiative (Estefan, 2008). In this perspective powerful MBSE processes were developed and integrated in industry, for example, the Harmony/SE process (Hoffmann, 2008, 2011). The modelling and simulation-based systems engineering (M&SBSE) initiative supports an effective systems engineering process in which the model is still central, but it reinforces that this model should be executable (Gianni, D’Ambrogio, & Tolk, 2015; MBSE, 2015). Therefore, besides centralising experiences and successful best practices in the systems engineering endeavour, an M&SBSE process should naturally integrate design options to validate design using an executable representation of requirements. Agent technology is used today more and more as a powerful tool to support evaluation, implementation, and exploitation of new IT-based applications, from e-commerce, telecommunications, healthcare, to emergency management (Helbing, 2012; Uhrmacher & Weyns, 2009; Van Dam et al., 2013), and so on. The Agent Directed Simulation initiative (Barry, Koehler, & Tivnan, 2009; Yilmaz & Ören, 2009) describes different forms of agent-related research, expressing the use of simulation for agents (agent simulation) and the use of agents for simulation (agent supported-simulation, and agent-based simulation). Overall, through orchestration, composition, and coordination of agent-based soft-wired components, the purpose of agent-based system modelling and simulation relates to understanding system response, predicting behaviour and defining emergent behaviours (Yih & Chaturvedi, 2010). In this research and application perspective, the STSE process is described as a means to further explore the utilisation of the M&SBSE point of view along with the multiagent technology in order to express value co-creation interactions in service systems design and to guide the service design activities. The organisation view in the STSE process (an M&SBSE process) reflects the sequence of steps that are needed to be fulfilled in order to construct a detailed executable model of the specific system based on the observed social need (Fig. 1). In these steps, observed social phenomena are recorded and value co-creation interactions are defined based on specific user needs. The steps that are defined in the STSE process aim at creating specific outputs in terms of model artefacts to be used later for implementation and bottom-up integration to support new service development. They define the top-down design perspective in the organisation view, and the final output of this pathway is the agent based implementation model that is used to evaluate the emergent

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Fig. 1. STSE process – organisation, implementation and integration.

properties though collaboration among interacting entities. The four steps in the organisation view account for the creation and reuse of the requirements based scenarios. These scenarios will be used also to assist the following bottom-up integration activities. Specific stages in the implementation and integration view reflect implementation activities, in terms of software and hardware development, following a normal systems engineering process, for example, the Harmony Integrated Systems and Software Development Process (Hoffmann, 2008, 2011). Table 1 presents a comparison between the STSE process and a V-model for a systems engineering process (Haskins, 2011; Shamieh, 2012). Step 1. Social need identification. The stakeholders’ needs are identified, and value co-creation interactions are defined. The service provider may communicate through welcome non-service interactions with customers, suppliers, partners, authority and competition, based on user needs, service system business goals, other service data, and information regarding potential business advantages and constraints (Borangiu et al., 2014). A requirements document is created to define overall system functionalities and performance criteria for system validation if a new service process development is motivated. Step 2. Scenario definition. Value propositions are defined and a use case model is created to comply with the user identified needs. The business scenario is created. Based on it, the agent based simulation scenarios will be further defined in an environment that allows validating different aspects of social interaction between the service consumer and the service provider. The output of this stage is the use case model. Step 3. Formal model design. This is a high-level design stage where a formal model of the proposed working scenario is created. The proposed architecture of the agent based model should meet the service delivery requirements extracted from the user needs and integrating the value propositions created by the service

provider. The output of this stage is the proposed architecture of the agent based model, consisting of a set of agents, the environment in which they operate, agent communication protocol, and the set of general rules according to which they execute their actions and access resources. Step 4. Agent based operational modelling. The functional requirements defined in the previous stages are transformed into a coherent description of the service functionalities through model execution. This is a value-proposition-based interactions phase, in which the customer and provider may negotiate in terms of value propositions. Two aspects can be investigated at this stage: (a) the level of service quality promised by the provider against the needs of the customer, and (b) the cost of the service utilisation against the requested price that the customer has to pay (Borangiu et al., 2014). Following the initial negotiation, the value-proposition may not be accepted. If the value proposition can be improved, then a new service offer is generated and the actors return to the value proposition-based interaction phase, for a new negotiation. If the value proposition cannot be modified, then the negotiation fails. If the value proposition is accepted, the service is accepted too, and is subsequently used by the service customer.

3. Case study – simulating value co-creation interactions for customer experience integration This section depicts Step 2 and Step 3 in the STSE process. The exploratory case study develops around the scenario and research perspectives on smart mobile traveller information services introduced in Cunha and Galvão (2014) for the MOVE-ME smartphone mobile service application (MOVE-ME, 2015). Specifically, the working perspective accounts for simulating value co-creation interactions for customer experience integration and enhancing city transport user experience. The STSE process is used in order to guide the creation of design artefacts to support improved service design, highlighting aspects of value co-creation through the interaction among service customers and service providers in the process of service delivery. It integrates both a value-in-exchange model and a value-in-use model for service delivery. This working perspective expresses different design aspects proposed along with the STSE process, taking into consideration both the user and the service provider perspectives (Fig. 2). User perspective. A service customer (tourist) uses a smartphone mobile service application in order to fulfil his needs, trying to make smart decisions on daily travelling regarding easy access to current information through the mobile device, getting pertinent advices taking into consideration personal budget (time and money), being able to change personal travelling plans while interacting on-line with different transport service providers (for example, bus, taxi) through the mobile service application. Service provider perspective. Service providers can adhere to an open virtual service enterprise environment available to the service customer through the mobile information service. Service

Table 1 The STSE process and the V-model for a systems engineering process.

STSE: Socio-Technical Systems Engineering process Output: V-model for a systems engineering process Output:

Step 1

Step 2

Step 3

Step 4

Social need identification Requirements document Concept of Operations Requirements document

Scenario definition

Formal model design

Agent based operational modelling

Use case model System Specification

Agent based architectural model High level design

Agent based implementation model (executable) Detailed design

System requirements document and use case model

Architectural analysis model

Software implementation model (executable)

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Fig. 2. Transport information service in the open virtual service enterprise environment.

providers can gather information about utilisation degrees of travel routes in the city, utilisation degrees of transport vehicles, evaluation of peak hours, seasonal trends, etc. Based on this, smart decision that involves new value propositions is possible, such as evaluation of new public transport routes availability, extension of available routes, improvement of working shifts, acquisition of supplementary vehicles and improvement of existing business plans. 3.1. Business scenario – developing a new public transport information service using STSE The following business scenario was formulated in order to demonstrate the above mentioned purpose of exploration. This business scenario accounts for Step 2 in the STSE process. A tourist arrives in a new city, eager to visit many interesting locations. He uses a map of the city with main tourist attractions, but he does not have enough information about local transport (routes for bus, metro, costs of travel by taxi) that would allow him to schedule a daily trip based on his preferences about cost and time. He would prefer to use public transport in certain situations, but he needs assistance on defining a smart decision based on his preferences. He can ask hotel reception for hints, but he would like to interact on-line with a mediator, as long as his preferences might also change based on weather, location, time frame, visiting preferences. Now he has the possibility to access a new application on his mobile phone that connects to a special service offered by the city transport service system. This special service prepares a customised offer for the tourist based on his cost and time preferences. Through his mobile application, this service will provide him with real-time traffic information, as public transport vehicles and taxis, and pertinent information regarding costs and utilisation. Having this information available through the transport integrated information system, the tourist can make a smart decision on choosing the best transport means to reach his destination (walking, bus or taxi). Considering practical implementation aspects of this business scenario, agent technology is used to define a complete simulation perspective describing interacting entities (software agents taking the roles of users and service providers, such as public transport and taxi companies). The role of the transport information service (mediator service) was modelled also as a software agent. This mediator service is available through the application installed on


the mobile device to assist people to travel in the city, receives customer preferences and negotiates on behalf of the customer with different service providers that offer transport solutions in the city. The agent-based model for this business scenario is defined and executed with Presage2, a general purpose platform for developing animation and simulations of collective adaptive systems (Macbeth, Pitt, Schaumeier, & Busquets, 2012; Neville & Pitt, 2008) that allows implementation of self-organising principles in sustainable institutions as originally defined by Ostrom (1990). Step 3 in the STSE process can be further explained based on the definition of an agent-based architectural model. The components that are described as follows are the agents, the agent communication strategy and the environment in which they interact, and the set of general rules that apply to the agents’ behaviours. Self-organising capabilities are obtained considering the system as a whole. The agent-based simulation environment prepared for the proposed case study is defined based on a 5-tuple description of the agents and the environment where they operate (Table 2). The description takes into consideration four elements (initial destination, desired destination, available resources in terms of money and time, and personal preferences). The user accesses the information service through the mobile application and he receives a transport plan. The transport plan is generated by the mediator and it consists of the required steps to fulfil in order to travel between the desired points in the city, taking into consideration user preferences [P]. Different transport plans can be implemented along with the proposed simulation scenarios and they account for available value propositions in the open virtual service enterprise environment: (a) a transport plan with taxis; (b) a transport plan with buses; (c) walking; and (d) a combined plan (walking, bus, taxi).

3.2. Definition of the agent types Six types of agents are integrated in the agent based simulation model: user, mediator, taxi dispatcher, taxi, bus dispatcher and bus (Table 3). The User agent interacts with the Mediator agent through a transport request, specifying its preferences (Fig. 3). To this request, the Mediator agent will answer with a list of travel plans. In order to select among these travel proposals, the User agent will apply a set of declarative rules (Table 5 in Section 3.4). If none of the offers is considered to be acceptable, the User agent can wait or modify the request parameters, relaxing the constraints (time and travel cost). If the travel proposal from the Mediator agent is accepted, the User agent has to send a confirmation to the Mediator agent and to follow the agreed transport plan. The Taxi Dispatcher agent interacts with the Mediator agent to receive taxi requests, it selects those requests it considers being able to fulfil, it creates taxi offers for them and it sends these offers back to the Mediator agent (Fig. 4). After the Mediator agent acknowledges offer acceptance, the Taxi Dispatcher agent sends this request to the selected Taxi agent, waiting then for the final notification of service accomplishment. If one of the Taxi agents cannot fulfil its assignment (waiting for confirmation or during service delivery), the Taxi Dispatcher agent will try to replace it; otherwise, it will ask the Mediator agent to cancel the offer. The Taxi agent is a reactive type agent that receives transport orders to be fulfilled from the Taxi Dispatcher agent. Each transport order consists of a destination location and a client identification code. After receiving the order, the Taxi agent will pick up its client from the specified location. After the order is fulfilled, the Taxi agent will notify the Taxi Dispatcher agent, waiting for other orders. The Bus Dispatcher agent interacts with the Mediator agent to inform about the bus routes and schedules in the city.

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Table 2 Formal model design – the agent-based architectural model. Simulation scenario component



Agent-based simulation environment: 5-tuple hU, TS, M, R, Ci

U – users of the system

Travel agent requesting transport

TS – available transport services M – mediator service R – rules governing the system

Public transport (buses); taxis Mediator agent LR – Legal rules (national, international) OR – organizational rules Implemented in the CityMap module Points that define the desired travel Time, money Walking, travel by bus, travel by taxi Transport by bus

User (U): 4-tuple hS, D, r, Pi

Transport system (TS): 3-tuple hs, LR, ORi

C – environment (city map) S – start location; D – destination r – user’s available resources P – personal travel preferences s – transport services offered by an integrated transport system

Transport by metro Transport by taxi Information on bus and metro routes Information on travel duration Information on delayed routes Regulatory compliance Internal organisational rule of the transport service provider Customised transport plan based on user preferences

LR – legal rules for the transport system OR – organisational rules Mediator (M): M: hS, D, r, Pi ? s

Associates users and transport services

Table 3 Types of agents in the agent based simulation model. Agent type


Interacts with




Travel between two points; has personal preferences (time, costs) Mediates interaction among user and other transport services


Travel need


Transport request

Transport request, including personal preferences List of travel plans

Taxi dispatcher Bus dispatcher Mediator Taxi Mediator Mediator Bus Bus dispatcher

Taxi offer request Bus schedule request Taxi request Taxi service selection Duty plan Bus route request Bus scheduling plan Receive route

Taxi request Transport plan with bus Taxi offer Duty plan Service accomplishment notification Bus route Dispatch route Travel on route


Taxi dispatcher

Coordinates the activities of the Taxi agents

Taxi Bus dispatcher

Transport user by taxi Dispatch Bus agents on available routes


Travel on planned route

Fig. 3. User – mediator interaction (UML sequence diagram).

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Fig. 4. User – taxi interaction (UML sequence diagram).

The Bus agent is a reactive type agent that receives the route to drive on from the Bus Dispatcher agent. In this case, a bus route consists of a set of stop locations and a direction to follow. After receiving the current route, the Bus agent heads towards the closest bus station on this route starting its normal duty cycle. The Bus agent will continue its duty on the specified route till the Bus Dispatcher agent sends another route assignment. 3.3. Agent conversation definition This section briefly discusses the agent communication strategy implementation. For this purpose, the message exchange among participants was modelled in Presage2 as a finite state machine, where state transitions are triggered by messages and guarded by conditions (Macbeth et al., 2015). A state-machine based conversation protocol was developed (Stan, 2014) to allow agent Table 4 The formal model of the communication protocol. Protocol name: UserWithTaxiCommunicationProtocol Actions: RequestDestinationAction, TakeMeToDestinationAction, DestinationReachedAction States: WAITING_REQUEST, State Type: START REQUEST_DESTINATION, State Type: ACTIVE TAKE_ME_TO_DESTINATION, State Type: ACTIVE DESTINATION_REACHED, State Type: END Transitions: ‘‘RequestingDestination’’: reqDestCond, WAITING_REQUEST, REQUEST_DESTINATION, RequestDestinationAction ‘‘SendingDestination’’: takeMeToDestCond, REQUEST_DESTINATION, TAKE_ME_TO_DESTINATION, TakeMeToDestinationAction ‘‘DestinationReached’’: destReachedCond, TAKE_ME_TO_DESTINATION, DESTINATION_REACHED, DestinationReachedAction Messages: RequestDestinationMessage TakeMeToDestinationMessage DestinationReachedMessage

interaction. This communication protocol defines different sets of messages that can be exchanged by agents. Table 4 presents as an example a possible definition of the communication protocol between the User agent and a Taxi agent. This communication protocol defines the following User agent – Taxi agent conversational states:  WAITING_REQUEST – waiting for the Taxi agent to request destination.  REQUEST_DESTINATION – the Taxi agent requesting the destination from the User agent, then waiting for the answer.  TAKE_ME_TO_DESTINATION – the User agent sending a message specifying the destination where to be transported.  DESTINATION_REACHED – the Taxi agent notifying the User agent about reaching the destination. The required communication protocol has to be specified, and an instance of this protocol and the corresponding actions are created when a new agent is added in the agent based model. The corresponding conditions are specified for each state transition, and the action specified at the initialization of the protocol is executed. 3.4. Decision making and rules Multi-agent systems are created as artificial societies that attempt to mimic human and social behaviour in terms of judgment, behaviour, interactions or decision making. These aspects are always governed by rules, freely developed in communities (Ostrom, 1990) or imposed on a regulatory compliance perspective (for example, laws). This distinction is presented in Table 2, where two stakeholder perspectives are considered in the agent-based architectural model:  Regulatory compliance is the perspective of the Authority, the stakeholder of the transport service system that formulates legal rules (LR) that govern the way in which transport services are delivered to the customer (for example, safety, labour legislation).


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 The service provider itself is a stakeholder of the service systems, providing internal organisational rules (OR) to define internal business processes (for example maintenance rules for the vehicles, working shifts scheduling). The transport service customer (User agent) is a stakeholder that uses the service in specific conditions, based on its own preferences (for example, time, costs, comfort). Along with the service itself, the user has access to different resources integrated by the service system (for example, non-physical resources without rights, like shared information, or physical resources without rights, like transport vehicles, roads). The way in which these resources are accessed is specified by rules. Presage2 supports a formal characterisation of these governance rules that can be implemented as collective choice rules used to compute for resource allocation (Neville & Pitt, 2008; Macbeth et al., 2015). These four stages of resource provisioning, demanding, allocation and appropriation were briefly exemplified in Section 3.3, along with the definition of the agent communication protocol. Two short examples describing the set of declarative rules governing the self-organisation mechanism for the proposed agent-based simulation model are presented in Table 5. These basic rules apply when the user has to select a transport offer based on his preferences, and when the maintenance activities must be scheduled, as organisational rules (OR).

Table 5 Declarative rules in the agent-based simulation model. Stakeholder perspective



rule ‘‘Accept the top transport offer if it meets the time constraint’’ when User in State.LOOKING_FOR_TRANSPORT, topOffer: analyze ReceivedTransportOffers (topOffer.getTravelTime() < getTravelTimeTarget())) then selectTransportOffer(topOffer); end rule ‘‘Accept the top transport offer if we are approaching the deadline’’ when User in State.LOOKING_FOR_TRANSPORT, topOffer: analyze ReceivedTransportOffers (getTravelTime() > getTravelTimeTarget()/2)) then selectTransportOffer(topOffer); end

Service provider (general rules)

rule ‘‘Send taxis to maintenance’’ when DistanceTraveled() > DISTANCE_BETWEEN_REVISIONS, then goToRevision(); end rule ‘‘Send taxis back to work after maintenance’’ when Taxi.Status.IN_REVISION and RevisionComplete then resetDistanceTraveled() and goToWork(); end

4. Results and discussions This section depicts Step 4 in the STSE process. The executable agent based implementation model is used in order to evaluate the proposed exploration aim. 4.1. Simulation configuration According to Step 2 depicted in Section 3, four simulation scenarios were defined, integrating user preferences for travelling, such as taxi, bus, or any other kind of transport. For each simulation set-up, different parameters were taken into consideration: (a) user parameters (number of users in simulation, transport preference, time constraints); (b) available transport methods (walking, taxi, bus); (c) taxi and bus parameters (parameterization in terms of walking/bus/taxi average velocity and cost/distance unit); and (d) specific simulation and GUI parameters. Table 6 describes the three categories of modules included in the simulation environment. In a control scenario (S1), all the users prefer walking, instead of using other transport services. They have to adapt their requirements for this transport method. Considering that all the users start the journey at a reasonable time, they can reach their destination at the desired moment. In a taxi-preference scenario (S2), users prefer walking or using taxis. The taxis can be allocated efficiently when users are closer, so the allocation algorithm strives to get the closest taxi for each user. In a bus-preference scenario (S3), users may choose walking or travelling by bus. In this case, the algorithm uses a city map and tries to determine the optimal bus routes and the number of necessary buses on these routes. In an all transport-preference scenario (S4), all possible transport modes considered are included. 4.2. Considerations on qualitative and quantitative evaluation of simulation results This section provides a discussion on qualitative and quantitative evaluation of simulation results. Each scenario can be initialized with a set of simulation parameters associated to different service providers’ value propositions (Table 7). Users may opt to

Table 6 Module definition and integration in the Presage2 agent-based simulation. Scope


Description and role


Presage2 module library

CityMap – representation of the city map

User defined modules

Declarative rules (Drools) Agents


User Mediator Taxi dispatcher Taxi Bus dispatcher Bus Network module

TransportMoveHandler – module to allow agents to move on the map NetworkModule – allows agents’ communication RuleModule – allows agents to receive rules GUIModule – for graphical visualisation of simulation and results User rules Service provider rules Reactive agent, declarative rules applied Coordinator Coordinator Reactive agent Coordinator Reactive agent Communication protocol that allows agents to communicates through custom defined messages

select available transport methods (walking, taxi, bus), preference-based resource allocation method (random allocation, All prefer walking, All prefer taxi, All prefer bus), and time constraint upper limit. Operational efficiency on the service provider side is evaluated based on allocated service resources (number of bus routes and

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Table 7 Simulation parameters in different simulation scenarios. Transport method

No. users

Resource allocation

Time constraint upper limit

No. Taxi disp.

No. Taxis/taxi disp.

Taxi allocation method

No. Bus route

No. Buses/route

(S1) Walking (S2) Walking, Taxi

23 23 23 23 23 23

Random Random Random Random Random Random

Rule Rule Rule Rule Rule Rule

– 2 3 – – 3

– 3 3 – – 3

– Proximity Proximity – – Proximity

– – – 1 2 2

– – – 4 4 4

(S3) Walking, Bus (S4) Walking, Taxi, Bus

3x 3x 3x 3x 3x 3x

Table 8 Quantitative analysis - simulation on the specified scenarios. Scenario

Time constraint upper limit

% of users (destination ok)

% of users (time ok)

% of taxi users

% of users walking

% of bus users

(S1) control (S2) walking taxi

Rule Rule Rule Rule Rule Rule Rule Rule

100 100 100 100 100 100 100 100

100 52 61 13 45 69 76 82

– 57 59 – – 65 69 78

– 43 41 6 4 31 6 4

– – – 94 96 4 25 18

(S3) walking, taxi (S4) Walking taxi, Bus

3x 3x 3x 3x 3x 3x 3x 2x

Table 9 Evaluation of a (S4) scenario. Time step

State description

State details

User state





(5; 13; 0)

0 3

Sending message Transport offer received


(5; 13; 0) (5; 13; 0)


Transport offer received


(5; 13; 0)


Transport offer received


(5; 13; 0)

57 60 134 134 233 234

Transport offer selected Taxi message Taxi message Sending message Taxi message Destination reached

Start location: (5; 13; 0) Target location: (20; 6; 0) Travel time target: 288 time units Transport preference: TAXI_PREFERENCE Transport sorting preference: PREFER_FASTEST Sending transport request to mediator 285 time units left Offer received: WALKING, costs: 120.0 currency units, 960.0 time units 281 time units left Offer received: TAKE_BUS, costs: 174 currency units, 303 time units WALKING, costs: 120 currency units, 960 time units 232 time units left Offer received: TAKE_TAXI, costs: 120 currency units, 168 time units TAKE_BUS, costs: 174 currency units, 303 time units WALKING, costs: 120 currency units, 960 time units TAKE_TAXI Taxi is coming! Please, specify destination! Target location: (20; 6; 0) Destination reached! On time: true


(5; 13; (5; 13; (5; 13; (5; 13; (20; 6; (20; 6;

number of buses on each route, taxi allocation based on distance, number of taxis allocated to each taxi dispatcher). This information is gathered on-line while the service is delivered. Several results on simulation model execution with different parameters are summarised in Table 8. Different pieces of information can be extracted and analysed, and further evaluation on operational procedures for each service provider is possible. Several simulations were run based on the executable agent based implementation model defined in Step 4 of the STSE process (23 users, 4 bus routes, 4 buses on each route, 3 taxi dispatchers with 8 taxis each, 2x time constraint upper limit, random transport preference allocation). A quantitative evaluation of the obtained results is presented in Table 9, based on the (S4) all transport-preference scenario. These results present the sequence of actions executed when a user accesses the service through his mobile device asking for transport, specifying his preferences:

0) 0) 0) 0) 0) 0)

target location, time required for arrival, transport preference and transport sorting preference. From the service provider perspective, the performance of the proposed solution is evaluated against two aspects: (a) the rate of the fulfilled transport requests (for example, number of users reaching on time to their final destination); and (b) the allocation rate of the transport resources. Different experiments were conducted to evaluate fair allocation of users per bus. As simulation results show, when the number of users in simulation is low, the average rate of travellers by bus was quite low. For example, the number of buses was reduced and the average number of passengers per bus increased, preserving the general performances on service delivery. The application of the STSE process to create model artefacts as multi-agent executable models supports also a qualitative evaluation through simulation of the requirements based


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Fig. 5. Agent interactions in a taxi request scenario (UML animated sequence diagram).

scenarios. This does not eliminate the need for a further disciplined engineering process, but helps the application designer to check model consistency, to follow more easily the requirements change, and to make new design decisions. Fig. 5 presents an excerpt of the animated sequence diagram describing the interaction among agent entities to fulfil a taxi order request. This is a model artefact related to the execution of the UML model describing the service interactions. This evaluation leads to the conclusion that the proposed strategy can be used in order to leverage user preferences against transport performance, such as the service systems provider (public transport, taxi companies). It is also useful to optimise operational activities and resource allocation for economic efficiency. 4.3. Design considerations for performance improvement Different aspects of value proposing and value realisation in a continuous interaction among transport service system stakeholders are expressed along with the simulation scenarios. Users have the possibility to access global information related to different means of transport (taxi, bus, walking), they can modify preferred travel destinations based on personal experience or other informed opinions (received through different media channels), they can express travel preferences (cost in terms of time and money),

and they can receive various travel plans according to personal preferences. Value is realised when the most practical solution presented to the user leads to the fulfilment of his expressed needs, for example, when the desired destination is reached within the cost constraints (time, money). Service providers formulate value propositions based on user expressed needs. For example, different service providers (public transport with bus, taxi companies) have the possibility to provide software interfaces towards integration with the transport information service (implemented here as a mediator service). Evenly, resource dispatchers, such as taxi dispatchers, may select requests that can be fulfilled in a convenient time frame. Effective service improvement requires both effective resource allocation algorithms and route design algorithms to be further integrated. Only for demonstration purposes in the scope of this exploration, a simple possible implementation for resource allocation used along with this case study is described in Table 10. At each time step, the Mediator agent checks the availability of the transport services (in terms of available transport resources) and associates them to the corresponding transport requests. The role of the Mediator agent is evaluated based on the efficient allocation of the transport resources (taxi, bus routes, etc.) among users and on the adequacy of the transport offer to the user constraints (time and money costs for the travel).

M. Dra˘goicea et al. / Expert Systems with Applications 42 (2015) 6329–6341 Table 10 Transport offer list generation (mediator agent). Input: transport request (TR), transport preference (TP), weighting preference (WP), transport offer (TO) Output: lists of transport offers (LoTO) For each transport request (TR) do Initialize new list of transport offers (LoTO) If recorded bus stations in the database then Request bus route from bus station agent Receive bus route from bus station agent add to the LoTO End If taxi recorded in the database then Request taxi for the actual TR Receive taxi offer and add to the LoTO End compute walking route and add to the LoTO Evaluate TP based on weighting preferences WP Generate sorting Preference (SP) for the current TR Sort the LoTO based on SP Store first N best preference for the current TR Send LoTO for the current TR Redistribute the other transport offers end

An improvement on resource allocation against user preferences was observed as more taxis are added, but the cost of the taxis’ utilisation will decrease. The analysis of time delays for each user shows that some of them arrived much earlier and others too late. This situation may be solved using a taxi allocation strategy to reduce the variation of the arrival times and to consequently increase the taxis’ utilisation rate. When taxis would have to travel longer distances to pick up the users, the costs for taxi dispatchers would increase accordingly. Results analysis show that users prefer travelling by bus at a lower cost, but their requirements are not always fulfilled (higher average delay). Additional simulations were performed with supplementary bus routes or taxis to improve the rate of fulfilling the travel request on user preferences. Considering the resource allocation modelling aspect for service realisation, new approaches, such as common pool of resources management (Deadman, Schlager, & Gimblett, 2000; Schindler, 2012), and new frameworks for resource allocation, such as computational justice (Pitt et al., 2013; Macbeth et al., 2012; Neville & Pitt, 2008), can be integrated in a natural way in the agent-based development cycle of service and service systems (García-Magariño & Gutiérrez, 2013; Yih & Chaturvedi, 2010). A schematic algorithm to be used by the Bus Dispatcher agent in order to find a bus route is presented in Table 11. In this implementation, just for demonstration purposes, the Bus Dispatcher agent uses a modified version of the A⁄ algorithm (Russell & Norvig, 2009) for route design in order to find the closest bus station to a specified location. While the A⁄ algorithm tries to find the shortest path between two locations, the implemented algorithm finds the best destination, based on the start location. This destination is chosen from a list of possible destinations (in this case, the bus stations on the desired route).

Table 11 Schematic algorithm for route design (Bus Dispatcher agent). Input: start location (S), target location (T) Output: used bus route, start bus station (SBS), target bus station (TBS) For each bus route R do Find the closest bus station to S on route R (SBS) Find the closest bus station to T on route D (TBS) Compute the travel cost (C) If C is minimum then Store route R, SBS and TBS End End Reply with bus route (R, SBS, TBS)


It should be mentioned that many other competing algorithms which handle route design exist. To apply them in this specific case, difficulty arises in the presence of constraints (obstacle and collision avoidance mainly) and existence of performance and quality criteria. This leads to control problems which are difficult to solve exactly and which are sensitive to problem dimension and to the characteristics of the agents involved. Briefly, we can separate between heuristic methods which propose sub-optimal solutions, like the aforementioned A⁄ algorithm, genetic algorithms, potential field methods (Howard, Mataric´, & Sukhatme, 2002), and exact methods which try to solve exactly the optimisation problem. While the first category comes with no performance guarantees, in practice it provides adequate solutions. Still, we are interested in exact procedures which offer the optimal solution even if usually with computation penalties. From the latter category, mixed integer approaches which can handle discrete decisions can be mentioned (Vielma & Nemhauser, 2011). That is, the obstacle and avoidance conditions can be modelled in this framework and then an optimisation problem is to be solved whose solution is the optimal path to be followed by the agent. Such a problem can be written as:

ðx ; u Þ ¼ arg min Cðx; uÞ x;u




where the pair (x*, u*) denotes the optimal solution for the trajectory generation problem (with x – the internal state of the agent and u its control decisions); C(x, u) denotes the cost and x R O denotes the avoidance constraints. The difficulty resides mainly in writing the avoidance constraints and in the subsequent resolution of the optimisation problem. To this end, this can be considered as a promising technique which efficiently partitions the space into feasible regions such that overall the optimisation problem is simplified (Prodan, Stoican, Olaru, & Niculescu, 2012; Stoican, Prodan, & Olaru, 2013). Mainly, a collection of hyperplanes and their corresponding half-spaces may be considered (each of them divides the space into a positive and a negative region).

Hi ¼ fx 2 Rn : hi x ¼ ki g; Hi

Hþi ¼ fx 2 Rn : hi x 6 ki g;


¼ fx 2 R : hi x 6 ki g


together with all the possible intersections of positive and negative rðiÞ

 regions ðHþ i ; Hi Þ in order to obtain the disjoint cells AðrÞ ¼ \ Hi i2I


These can be labelled into admissible and interdicted cells. That means, a certain cell is either part of an obstacle (in O) or part of the feasible space ðin Rn n OÞ:

O ¼ [ Aðr Þ; r

Rn n O ¼ [ Aðr Þ r


This allows to enumerate the obstacles efficiently and to solve the associated mixed integer optimisation problem efficiently for route design. 5. Conclusions Today, empowered customers connect, share knowledge, and use information in new ways, interacting with businesses through pervasive technology. Therefore, new tools and solutions are needed to evolve, understand and innovate the way companies are transforming their business models to better respond to their customers. The socio-technical description in service systems that is approached along with new STSE process allows formulating a very important observation. Actually, it is not the technology itself that


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advance service innovation but the sustainable integrative effort to evaluate available multidisciplinary research outcomes on engineering disciplines, expert and intelligent systems, social sciences, arts and humanities. This exploration which is based on an M&SBSE process eventually helps advancing practical guidelines in the design, development, testing, implementation, and management of complex service systems in the future. From an exploratory point of view, specific questions can be formulated. What is the role of science behind all these operational issues? How can all these characteristics be methodologically formalized through modelling, executed in simulation, evaluated through use cases and scenarios, and validated against user requirements? In the larger perspective of socio-technical systems, is the chain of interactions between the stakeholders and the service system itself allowing value co-creation? Can the sequence of activities that accounts for value proposing, service delivery and value realisation through this complex chain of interactions be exposed and analysed from a modelling and simulation perspective? How easily can these models be transferred and integrated into real world operational solutions? From a research point of view, different directions to advance further priorities related to service design, improvement of service delivery processes, and understanding of the service value co-creation process can be proposed. The STSE process is introduced here as an M&SBSE process to support an overall perspective on the creation of different model artefacts in the given context. However, it would be worth to further explore how to evolve it towards a full development methodology, composed of the process, associated methods and tools, aiming to bring best practices from practitioners into service research, along with at-hand effective technologies. Two specific subsequent research directions may be outlined here, for methods and tools. The role of modelling and simulation perspective is always emphasised. Agent technology was used here towards the development of a method that specifies ‘‘how’’ to create executable model artefacts to evaluate value co-creation interactions. However, the proposed steps in the STSE process leaves the user with the choice to select most suitable algorithms in the given development context. For example, for optimal resource allocation and operational efficiency of the design effort should concentrate towards the integration of computational justice principles into service system modelling activities. Computational justice was advanced as an interdisciplinary research programme at an intersection between computer science and social sciences. Different competing algorithms which handle route design can be also integrated. The last two options were schematically formalized in discussions. Nevertheless, the role of the development platform in transposing the above mentioned aspects in practice is emphasised and integration guidelines of the STSE process steps with the Presage2 multiagent development platform were specified. Exploratory research on platform and tools integration to support activity modelling and service networking modelling would also advance many opportunities generated in close encounters of service and technology to make service processes more customer centric. Acknowledgements This research is developed and maintained by the EMAS – Emergent Multiscale Agents and Services for a Smarter World Research Group. We would like to thank our collaborators and students for their support and hard work, especially our colleagues Cristian Oara and Florin Stoican. We would also like to thank the reviewers for their constructive comments and valuable insight.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at

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