Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

Robotized Warehouse Systems: Developments and Research Opportunities

Robotized handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of robotized handling systems, such as the shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and OR modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Due to unique system features (such as autonomous control, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotized warehouse systems will form the next category of warehouses. All vital warehouse design, planning and control logic such as methods to design layout, storage and order picking system selection, storage slotting, order batching, picker routing, and picker to order assignment will have to be revisited for new robotized warehouses

Methods and Formal Models for Healthcare Systems Management

A healthcare system is an organization of people, institutions, and resources that deliver healthcare services to meet the health needs of target populations. The size of the systems, the huge number of agents involved and their different expectations make the management of healthcare systems a tough task which could be alleviated through the use of technology. In this thesis, new methods and formal models for healthcare system management are presented. Particularly, the thesis is divided in two main parts: the first one has to do with the modeling and analysis in hospitals by the use of clinical pathways while the second one deals with the planning and scheduling of patients in the operation rooms.Regarding the modeling and analysis of healthcare systems, depending on different visions and expectations, the system can be treated from different perspectives called facets. In chapter 2, the formal definition and characterization of two facets are given: (1) facet of resource management and (2) handshake between clinical pathways facet. They are obtained by applying to Stochastic Well-formed Nets (colored Petri Nets) modeling the healthcare system a set of relaxations, abstraction and modifications. In the first facet the subclass of S4PR is obtained which is a characteristic model of the resource allocation systems while in the second facet Deterministically Synchronized Sequential Process (DSSP) are considered. Both nets (S4PR and DSSP) are formal subclasses of Petri Nets where net level techniques can be applied.In chapters 3 and 4, we will focus on the liveness of the DSSP systems resulting from the facet of communication between clinical pathways. These kinds of nets are composed by agents (modeling clinical pathways) cooperating in a distributed way by the asynchronous messaging passing through the buffers (modeling the communication channels). In particular two approaches have been proposed.The idea behind the first approach is to advance the buffer consumption to the first conflict transition in the agents. Considering healthcare systems modeled by a DSSP, this means that before a patient starts a clinical pathway, all required information must be available. Unfortunately, this pre-assignment method only works in some particular DSSP structures which are characterized. A more general approach (than buffer pre-assignment) for liveness enforcing in non-live DSSP is given in Chapter. 4. The approach is formalized on two levels: execution and control. The execution level uses the original DSSP structure while for the control level we compute a new net system called the control PN. This net system is obtained from the original DSSP and has a predefined type of structure. The control PN will evolve synchronously with the non-live DSSP ensuring that the deadlock states will not be reached. The states (marking) of the control PN will enable or disable some transitions in the original DSSP, while some transitions in the control PN should fire synchronously with some transitions of the original DSSP.The second part of the thesis deals with surgery scheduling of patients in a hospital department. The Operating Rooms (ORs) are one of the most expensive material resources in hospitals, being the bottleneck of surgical services. Moreover, the aging population together with the improvement in surgical techniques are producing an increase in the demand for surgeries. So, the optimal use of the ORs time is crucial inhealthcare service management. We focus on the planning and scheduling of patients in Spanish hospital departments considering its organizational structure particularities as well as the concerns and specifications of their doctors.In chapter 5, the scheduling of elective patients under ORs block booking is considered. The first criterion is to optimize the use of the OR, the second criterion is to prevent that the total available time in a block will be exceeded and the third criterion is to respect the preference order of the patient in the waiting list. Three different mathematical programming models for the scheduling of elective patients are proposed. These are combinatorial problems with high computational complexity, so three different heuristic solution methods are proposed and compared. The results show that a Mixed Integer Linear Programming (MILP) problem solved by Receding Horizon Strategy (RHS)obtains better scheduling in lowest time.Doctors using the MILP problem must fix an appropriate occupation rate for optimizing the use of the ORs but without exceeding the available time. This has two main problems: i) inexperienced doctors could find difficult to fix an appropriate occupation rate, and ii) the uncertain in the surgery durations (large standard deviation) could results in scheduling with an over/under utilization. In order to overcome these problems, a New Mixed-Integer Quadratic Constrained Programming (N-MIQCP) model is proposed. Considering some probabilistic concepts, quadratic constraints are included in N-MIQCP model to prevent the scheduling of blocks with a high risk of exceeding the available time. Two heuristic methods for solving the N-MIQCP problem are proposed and compared with other chance-constrained approaches in bibliography. The results conclude that the best schedulings are achieved using our Specific Heuristic Algorithm (SHA) due to similar occupation rates than using other approaches are obtained but our SHA respects much more the order of the patients in the waiting list.In chapter 6, a three steps approach is proposed for the combined scheduling of elective and urgent patients. In the first step, the elective patients are scheduled for a target Elective Surgery Time (EST) in the ORs, trying to respect the order of the patients on the waiting list. In the second one, the urgent patients are scheduled in the remaining time ensuring that an urgent patient does not wait more than 48 hours. Finally, in the third step, the surgeries assigned to each OR (elective and urgent) are sequenced in such a way that the maximum time that an emergency patient should wait is minimized. Considering realistic data, different policies of time reserved in the ORs for elective and urgent patients are evaluated. The results show that all ORs must be used to perform elective and urgent surgeries instead of reserving some ORs exclusively for one type of patient.Finally, in chapter 7 a software solution for surgery service management is given. A Decision Support System for elective surgery scheduling and a software tool called CIPLAN are proposed. The DSS use as core the SHA for the scheduling of elective patients, but it has other features related to the management of a surgery department. A software tool called CIPLAN which is based on the DSS is explained. The software tool has a friendly interface which has been developed in collaboration with doctors in the “Lozano Blesa” Hospital in Zaragoza. A real case study comparing the scheduling using the manual method with the scheduling obtained by using CIPLAN is discussed. The results show that 128.000 euros per year could be saved using CIPLAN in the mentioned hospital. Moreover, the use of the tool allows doctors to reduce the time spent in scheduling to use it medical tasks.<br /

Declarative techniques for modeling and mining business processes..

Organisaties worden vandaag de dag geconfronteerd met een schijnbare tegenstelling. Hoewel ze aan de ene kant veel geld geïnvesteerd hebben in informatiesystemen die hun bedrijfsprocessen automatiseren, lijken ze hierdoor minder in staat om een goed inzicht te krijgen in het verloop van deze processen. Een gebrekkig inzicht in de bedrijfsprocessen bedreigt hun flexibiliteit en conformiteit. Flexibiliteit is belangrijk, omdat organisaties door continu wijzigende marktomstandigheden gedwongen worden hun bedrijfsprocessen snel en soepel aan te passen. Daarnaast moeten organisaties ook kunnen garanderen dan hun bedrijfsvoering conform is aan de wetten, richtlijnen, en normen die hun opgelegd worden. Schandalen zoals de recent aan het licht gekomen fraude bij de Franse bank Société Générale toont het belang aan van conformiteit en flexibiliteit. Door het afleveren van valse bewijsstukken en het omzeilen van vaste controlemomenten, kon één effectenhandelaar een risicoloze arbitragehandel op prijsverschillen in futures omtoveren tot een risicovolle, speculatieve handel in deze financiële derivaten. De niet-ingedekte, niet-geautoriseerde posities bleven lange tijd verborgen door een gebrekkige interne controle, en tekortkomingen in de IT beveiliging en toegangscontrole. Om deze fraude in de toekomst te voorkomen, is het in de eerste plaats noodzakelijk om inzicht te verkrijgen in de operationele processen van de bank en de hieraan gerelateerde controleprocessen. In deze tekst behandelen we twee benaderingen die gebruikt kunnen worden om het inzicht in de bedrijfsprocessen te verhogen: procesmodellering en procesontginning. In het onderzoek is getracht technieken te ontwikkelen voor procesmodellering en procesontginning die declaratief zijn. Procesmodellering process modeling is de manuele constructie van een formeel model dat een relevant aspect van een bedrijfsproces beschrijft op basis van informatie die grotendeels verworven is uit interviews. Procesmodellen moeten adequate informatie te verschaffen over de bedrijfsprocessen om zinvol te kunnen worden gebruikt bij hun ontwerp, implementatie, uitvoering, en analyse. De uitdaging bestaat erin om nieuwe talen voor procesmodellering te ontwikkelen die adequate informatie verschaffen om deze doelstelling realiseren. Declaratieve procestalen maken de informatie omtrent bedrijfsbekommernissen expliciet. We karakteriseren en motiveren declaratieve procestalen, en nemen we een aantal bestaande technieken onder de loep. Voorts introduceren we een veralgemenend raamwerk voor declaratieve procesmodellering waarbinnen bestaande procestalen gepositioneerd kunnen worden. Dit raamwerk heet het EM-BrA�CE raamwerk, en staat voor `Enterprise Modeling using Business Rules, Agents, Activities, Concepts and Events'. Het bestaat uit een formele ontolgie en een formeel uitvoeringsmodel. Dit raamwerk legt de ontologische basis voor de talen en technieken die verder in het doctoraat ontwikkeld worden. Procesontginning process mining is de automatische constructie van een procesmodel op basis van de zogenaamde event logs uit informatiesystemen. Vandaag de dag worden heel wat processen door informatiesystemen in event logs geregistreerd. In event logs vindt men in chronologische volgorde terug wie, wanneer, welke activiteit verricht heeft. De analyse van event logs kan een accuraat beeld opleveren van wat er zich in werkelijkheid afspeelt binnen een organisatie. Om bruikbaar te zijn, moeten de ontgonnen procesmodellen voldoen aan criteria zoals accuraatheid, verstaanbaarheid, en justifieerbaarheid. Bestaande technieken voor procesontginning focussen vooral op het eerste criterium: accuraatheid. Declaratieve technieken voor procesontginning richten zich ook op de verstaanbaarheid en justifieerbaarheid van de ontgonnen modellen. Declaratieve technieken voor procesontginning zijn meer verstaanbaar omdat ze pogen procesmodellen voor te stellen aan de hand van declaratieve voorstellingsvormen. Daarenboven verhogen declaratieve technieken de justifieerbaarheid van de ontgonnen modellen. Dit komt omdat deze technieken toelaten de apriori kennis, inductieve bias, en taal bias van een leeralgoritme in te stellen. Inductief logisch programmeren (ILP) is een leertechniek die inherent declaratief is. In de tekst tonen we hoe proces mining voorgesteld kan worden als een ILP classificatieprobleem, dat de logische voorwaarden leert waaronder gebeurtenis plaats vindt (positief event) of niet plaatsvindt (een negatief event). Vele event logs bevatten van nature geen negatieve events die aangeven dat een bepaalde activiteit niet kon plaatsvinden. Om aan dit probleem tegemoet te komen, beschrijven we een techniek om artificiële negatieve events te genereren, genaamd AGNEs (process discovery by Artificially Generated Negative Events). De generatie van artificiële negatieve events komt neer op een configureerbare inductieve bias. De AGNEs techniek is geïmplementeerd als een mining plugin in het ProM raamwerk. Door process discovery voor te stellen als een eerste-orde classificatieprobleem op event logs met artificiële negatieve events, kunnen de traditionele metrieken voor het kwantificeren van precisie (precision) en volledigheid (recall) toegepast worden voor het kwantificeren van de precisie en volledigheid van een procesmodel ten opzicht van een event log. In de tekst stellen we twee nieuwe metrieken voor. Deze nieuwe metrieken, in combinatie met bestaande metrieken, werden gebruikt voor een uitgebreide evaluatie van de AGNEs techniek voor process discovery in zowel een experimentele als een praktijkopstelling.

MACHS: Mitigating the Achilles Heel of the Cloud through High Availability and Performance-aware Solutions

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their HA and satisfying the QoS requirements. Second, a Stochastic Petri Net (SPN) model is proposed to capture the stochastic characteristics of cloud services and quantify the expected availability offered by an application deployment. The SPN model is then associated with an extensible policy-driven cloud scoring system that integrates other cloud challenges (i.e. green and cost concerns) with HA objectives. The proposed HA-aware solutions are extended to include a live virtual machine migration model that provides a trade-off between the migration time and the downtime while maintaining HA objective. Furthermore, the thesis proposes a generic input template for cloud simulators, GITS, to facilitate the creation of cloud scenarios while ensuring reusability, simplicity, and portability. Finally, an availability-aware CloudSim extension, ACE, is proposed. ACE extends CloudSim simulator with failure injection, computational paths, repair, failover, load balancing, and other availability-based modules

Analyzing and improving air cargo terminal operations

Master'sMASTER OF ENGINEERIN