Resource Management for Complex, Dynamic Environments

Raunak, Mohammad; Osterweil, L. J.

doi:10.1109/tse.2012.31

Cited by 24 publications

(14 citation statements)

References 27 publications

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“…Along these lines, we attempt to obtain better insights into jointly utilizing Little-JIL and hADL. Although there is strong support for using either language independently [14,10,9] our hypothesis is that applying both languages in combination will provide more intuitive results in interaction-intensive environments (Sec. 4).…”

Section: Introductionmentioning

confidence: 92%

“…This allows for linking Little-JIL steps to hADLs model elements, in a way that allows the two specifications to be orthogonal. Expressive, extensible, orthogonal resource specification and management [14] is thus one of the main reasons for choosing Little-JIL over workflow languages such as BPEL or YAWL. The way in which Little-JIL supports implementation of abstraction, based upon semantics rigorously defined using finite state machines, also facilitates clear specification of both activities and communication, as well as their relations to each other.…”

Section: Specifying Human Flexible Behaviormentioning

confidence: 99%

“…Where flexibility and human initiative needs to be emphasized and understood, there she opts for hADL. In this use case, we analyze the potential for human flexibility in an exemplary emergency department (ED) process (see also [14]). Efficient and effective ED processes rely on optimal resource allocation.…”

Section: A Hospital Patient Handling Use Casementioning

confidence: 99%

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Specifying Flexible Human Behavior in Interaction-Intensive Process Environments

Dorn

Dustdar

Osterweil

2014

Lecture Notes in Computer Science

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Abstract. Fast changing business environments characterized by unpredictable variations call for flexible process-aware systems. The BPM community addressed this challenge through various approaches but little focus has been on how to specify (respectively constrain) flexible human involvement: how human process participants may collaborate on a task, how they may obtain a joint decision that drives the process, or how they may communicate out-of-band for clarifying task-vital information. Experience has shown that pure process languages are not necessarily the most appropriate technique for specifying such flexible behavior. Hence selecting appropriate modeling languages and strategies needs thorough investigation. To this end, this paper juxtaposes the capabilities of representative human-centric specification languages hADL and Little-JIL and demonstrate their joint applicability for modeling interaction-intensive processes.

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Section: Introductionmentioning

confidence: 92%

Section: Specifying Human Flexible Behaviormentioning

confidence: 99%

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Specifying Flexible Human Behavior in Interaction-Intensive Process Environments

Dorn

Dustdar

Osterweil

2014

Lecture Notes in Computer Science

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“…We use the ROMEO resource manager [12]. Every step requires at least one resource, specially designated as the step's agent.…”

Section: Process Modelingmentioning

confidence: 99%

Resource Specification for Prototyping Human-Intensive Systems

Shin

Brun

Osterweil

et al. 2015

Fundamental Approaches to Software Engineering

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Abstract. Today's software systems rely heavily on complex resources, such as humans. Human-intensive systems are particularly important in our society, especially in the healthcare, financial, and software development domains. One challenge in developing such systems is that the system design must account for the constraints, capabilities, and allocation policies of their complex resources, particularly the humans. The resources, their capabilities, and their allocation policies and constraints need to be carefully specified, and modeled. Toward the goal of supporting the design of systems that make effective use of such resources, we introduce a resource specification language and a process-aware, discrete-event simulation engine that simulates system executions while adhering to these resource specifications. The simulation supports (1) modeling the resources that are used by the system, and the ways in which they are used, (2) experimenting with different resource capability mixes and allocation policies, and (3) identifying such undesirable situations as bottlenecks, and inefficiencies that result from these mixes and policies. The joint use of detailed resource specifications and simulation supports rapid evaluation of human-intensive system designs. We evaluate our specification language and simulation framework in the healthcare domain, on a software system for managing a hospital emergency department.

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“…The rich semantics of Little-JIL make it easy to capture the complex nature of an emergency department process, and is especially effective in supporting the clear specification of what kinds of resources are needed by each activity in a process. Management of the resources available for allocation to the activities in the process specification is separated into the ROMEO [16] component, which facilitates specifying the characteristics of different resources, and constraints on those resources' allocation and availability. This separation makes it easy to keep track of resource allocations, utilizations, waiting times, and other properties.…”

Section: Resource-aware Simulationmentioning

confidence: 99%

Resource scheduling through resource-aware simulation of emergency departments

Shin¹,

Balasubramanian²,

Brun³

et al. 2013

2013 5th International Workshop on Software Engineering in Health Care (SEHC)

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Abstract-This paper proposes using resource-aware, discreteevent simulation to measure the effects of resource scheduling in hospital emergency departments. Determining staffing and resource allocation is a complex constraint-optimization problem that has significant impact on hospital costs and patient care quality. We developed detailed models of the emergency department process of caring for patients, the resources available to support that process, and the scheduling constraints on the deployment of those resources. We then ran a battery of discrete-event simulations of this process, varying details of process, resource mixes, and scheduling constraints, to analyze the effects of resource availability (e.g., staffing patterns) on patient length of stay. Our simulation approach proved to be particularly adept at supporting the systematic investigation of two issues of particular interest to domain experts: (1) an excessive focus on minimizing the average length of stay (the objective most typically used for optimizing emergency department staffing) can have undesirable, previously unappreciated effects, (2) too strong a focus on one particular kind of resource as the preferred vehicle for decreasing patient length of stay can tend to obscure the value of considering other kinds of resources. The unexpected nature of some of our results raises open questions about how to validate the results of complex simulations.

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Resource Management for Complex, Dynamic Environments

Cited by 24 publications

References 27 publications

Specifying Flexible Human Behavior in Interaction-Intensive Process Environments

Specifying Flexible Human Behavior in Interaction-Intensive Process Environments

Resource Specification for Prototyping Human-Intensive Systems

Resource scheduling through resource-aware simulation of emergency departments

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