Namhun Kim

Agent-based simulation of affordance-based human behaviors in emergency evacuation

Abstract Complex cognitive processes corresponding to human control behaviors cannot be easily inferred using (1) a logical rule-based model, (2) a statistical model, or (3) an analytical predictive model. Predicting human behaviors in complex and uncertain environments like emergency evacuation is considered almost impossible (at least NP hard) in systems theory. In this paper, we explore simulating human behaviors using affordance-based finite state automata (FSA) modeling, based on the ecological concept of affordance theory. To this end, we introduce the conceptual and generic framework of affordance-based human behavior simulation developed through our previous work. Following the generic framework, formal simulation models of affordance-based human behaviors are developed, especially for emergency evacuation, to mimic perception-based dynamic human actions interacting with emergent environmental changes, such as fire. A “warehouse fire evacuation” case is used to demonstrate the applicability of the proposed framework. The human action planning algorithms in the simulation model are developed and implemented using the Adjusted Floor Field Indicators, which represent not only the evacuee’s prior knowledge of the floor layout but the perceivable information about dynamic environmental changes. The results of our simulation study verify that the proposed framework accurately simulates human fire evacuation behavior. The proposed framework is expected to capture the natural manner in which humans behave in emergency evacuation and enhance the simulation fidelity of analyses and predictions of perceptual human behaviors/responses in the systems by incorporating cognitive intent into human behavior simulations.

Using finite state automata (FSA) for formal modelling of affordances in human-machine cooperative manufacturing systems

Modelling complex systems poses significant challenges on how one represents the system components and interactions among them. In order to provide a systematic approach to represent human participation as a part of a dynamic system, this paper presents a formal automata model of human-machine cooperative systems that incorporates human capabilities with respect to system conditions. Specifically, we propose a control model for human-involved shop floor systems based on discrete event-based systems (DES) and an environmental concept known as an affordance. When modelling human-involved systems where a human operator is considered a crucial system component, it is necessary to analyse the model complexity that increases significantly due to a humans behavioural patterns. From the perspective of the temporal and physical state domains a human operators behaviour is usually limited by attention and resource constraints. We investigate these limitations and map them into constrained system affordances, and then construct a formal human-machine cooperative model based on the finite state automaton (FSA) model. The proposed model can provide a framework to combine human activities into systems operations in consideration of humans effectivities and system affordances. A detailed application example is provided to illustrate that the proposed model can effectively be applied to manufacturing control systems.

Environmental and economic assessment of closed-loop supply chain with remanufacturing and returnable transport items

Environmental impacts of closed loop supply chain are studied.A hybrid model and reusable containers are considered.Ignoring transportation and emission costs results to obtain false optimal values.Hybrid policy is the optimal one while pure remanufacturing is the expensive one.Optimal policies for container management are decided. Devastating environmental impacts of supply chain(s) (SC) have resulted in government legislation, customer awareness, and pressure from various stakeholders to implement environmentally sustainable strategies in SC. The most important objectives of the sustainable supply chain management (SCM) are enhancement of value creation over product life-cycle by reuse and considerations of environmental impacts. Environmental issues arising from manufacturing and logistic operations affect the economic growth as well as sustainability of the SC which must be considered during policy making. To achieve the economic goals and to improve the sustainability of SC, this paper proposes a multi-echelon closed-loop supply chain (CLSC) model with a third party logistics (3PL) that provides transportation and collection services. The proposed model investigates environmental impacts from production and transportation in a hybrid manufacturing-remanufacturing system which uses returnable transport items (RTI) for product transportation. Objectives of the model are to study the impacts of transportation and carbon emission costs in a hybrid CLSC, and to devise best RTI management policies under the influence of these costs. The developed mathematical model falls under the category of mixed-integer non-linear programming (MINLP) problems, for which an improved solution methodology has been proposed. The total cost is minimized with simultaneous optimization of container capacity, required number of containers, shipment sequence of retailers, cycle time, and remanufacturing rate. Robustness of the model is illustrated through a numerical example with five different cases, graphical representations, and sensitivity analysis. Results prove that ignoring these factors not only produce negative environmental impacts but also lead to non-optimal solutions and ultimately cause huge economic loss.

Performance assessment in an interactive call center workforce simulation

Abstract In this paper, a new performance assessment methodology for human-in-the-loop call center systems at the level of customer-agent interactions (CAI) is proposed. We develop a team-in-the-loop simulation test bed, to analyze CAI-level performance of a service system using a temporal performance measure with time windows. The proposed framework should allow researchers to collect and analyze individual as well as team performance at a finer granularity than current call center efforts which focus on queue-centered analysis. The software framework is object-oriented and has been designed to be configurable. A sample simulation study in different scenarios is illustrated to provide the usages and advantages of the proposed method with index of Interactive Service Performance.

An affordance-based formalism for modeling human-involvement in complex systems for prospective control

We propose a predictive modeling framework for human-involved complex systems in which humans play controlling roles. Affordance theory provides definitions of human actions and their associated properties, and the affordance-based Finite State Automata (FSA) model is capable of mapping the nondeterministic human actions into computable components in modeling formalism. In this paper, we further investigate the role of perception in human actions and examine the representation of perceptual elements in affordance-based modeling formalism. We also propose necessary and sufficient conditions for mapping perception-based human actions into systems theory to develop a predictive modeling formalism in the context of prospective control. A driving example is used to show how to build a formal model of human-involved complex system for prospective control. The suggested modeling frameworks will increase the soundness and completeness of a modeling formalism as well as can be used as guide to model human activities in a complex system.

Modeling and Implementation of the Affordance-based Human-Machine Collaborative System

Yeong Gwang Oh․Ikchan Ju․Wooyeol Lee․Namhun KimThe Department of Human and Systems Engineering, UNIST(Ulsan National Institute of Science and Technology) Modeling and control of human-involved manufacturing systems poses a huge challenge on how to model all possible interactions among system components within the time and space dimensions. As the manufacturing environment are getting complicated, the importance of human in the manufacturing system is getting more and more spotlighted to incorporate the manufacturing flexibility. This paper presents a formal modeling methodo-logy of affordance-based MPSG (Message-based Part State Graph) for a human-machine collaboration system incorporating supervisory control scheme for flexible manufacturing systems in automotive industry. Basically, we intend to extend the existing model of affordance-based MPSG to the real industrial application of human- machine cooperative environments. The suggested extension with the real industrial example is illustrated in three steps; first, the manufacturing process and relevant data are analyzed in perspectives of MABA-MABA and the supervisory control; second, the manufacturing processes and task allocation between human and machine are mapped onto the concept of MABA-MABA; and the last, the affordance-based MPSG of human- machine collaboration for the manufacturing process is presented with UMLs for verification.

A modelling formalism for human-machine cooperative systems

This paper presents a collection of models of humans involved in complex systems with a focus on control of the system while allowing human participation in decisions for system operation. The existence of a formal model that captures human behaviour in a complex system allows for the efficient development of modelling and control software of man-machine systems. This paper provides a foundation for modelling and software development for complex systems that includes human activities. A modelling formalism, based on the automata theory for human-machine cooperative systems is demonstrated in this paper in consideration with the ecological concept of affordance.

Computational modelling of manufacturing choice complexity in a mixed-model assembly line

Manufacturing systems have evolved to adopt a mixed-model assembly line enabling the production of high product variety. Although the mixed-model assembly system with semi-automation (i.e. human involvement) can offer a wide range of advantages, the system becomes very complex as variety increases. Further, while the complexity from different options can worsen the system performance, there is a lack of quantifiable models for manufacturing complexity in the literature. Thus, in this paper, we propose a novel method to quantify manufacturing choice complexity for the effective management of semi-automated systems in a mixed-model assembly line. Based on the concept of information entropy, our model considers both the options mix and the similarities between options. The proposed model, along with an illustrative case study, not only serves as a tool to quantitatively assess the impact of choice complexity on total system performance, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput.

A Product Quality Prediction Model Using Real-Time Process Monitoring in Manufacturing Supply Chain

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

A stochastic model for the optimal batch size in multi-step operations with process and product variability

Virtually all manufacturing processes are subject to variability, an inherent characteristic of most production processes. No two parts can ever be exactly the same in terms of their dimensions. For machining processes such as drilling, milling, and lathing, overall variability is caused in part by machine tools, tooling, fixtures and workpiece material. Since variability, which can be accumulated from tolerance stacking, can result in defective parts the number of parts produced in a batch is limited. When there are too many parts in a batch, the likelihood of producing all acceptable parts in a batch decreases due to the increased tolerances. On the other hand, too small a batch size incurs an increase of manufacturing costs due to frequent setups and tool replacements, whereas the likelihood of acceptable parts increases. To address this challenge, we present a stochastic model for determining the optimal batch size where we consider part-to-part variation in terms of tool wear, which tends to be proportional to batch size. In this paper, a mathematical model is constructed based on the assumption that the process used for producing preceding parts affects the state of subsequent parts in a probabilistic manner.