Search results

In this chapter, we consider the model of call center incoming call forecasting and staffing-level optimization. We first present the structure of the model and how an agent-based modeling technique could enrich the decision rule and the model. A matrix layout is introduced to present the model so that it can be understood in an efficient way from the perspective of a programmer. The agent-based queuing model will be used in forecasting. We then utilize the bisection method and stepwise method to optimize the staff level to satisfy a target range service-level criteria. Call center management could use the model in practice for their management forecasting and optimization decision-making process in terms of how many agents they need to achieve the target business efficiency goal.

The purpose of this study to improve the efficiency of customer acquisition and retention through the design of salary information disclosure mechanism.

This study develops a stylized game-theoretic model of delegating customer acquisition and retention, focusing on how firms choose delegation and wage information disclosure strategy.

The results confirm the necessity for enterprises to disclose salary information. When sales agents are risk neutral, firms should choose multi-agent (MA) delegation and disclose their wages. However, when agents are risk averse, firms may disclose the wages of acquisition agents or both agents in MA delegation, depending on the uncertainty of the retention market.

This paper contributes to the literature on delegation of customer acquisition and retention and demonstrates that salary disclosure can be used as a supplement to the incentive mechanism.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

This study aims to explore how managers exercise agency in strategic investment decisions (SIDs) by drawing on their knowledgeability of the strategic context. Specifically, the authors address the role of position–practice relations and irresistible causal forces in this conduct.

The authors examine SID-making (SIDM) practices in four case organisations operating in highly competitive markets, conducting interviews with managers at various levels and analysing company documents. Drawing on strong structuration theory, the authors show how managerial decision makers draw upon their knowledge of organisational context when exercising agency in SIDs.

The authors provide insights into how SIDM behaviour, specifically agents’ conduct, is shaped by a combination of position–practice relations and the agents’ comprehension of their organisation’s context.

The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice.

The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice. Particularly, the authors contribute to this literature by identifying irresistible causal forces and illuminating why actors might not resist in SIDM processes, despite having the potential to do so.

The purpose of this paper is to create a platform framework based on agent for virtual enterprise (VE) with the characteristic of supply chain by adopting multi‐agent technology.

According to the system hierarchy theory and the features of VE with the characteristic of supply chain, the conception and organization structure of supply chain‐oriented VE are proposed. Combined with characters of multi‐agent, a platform framework based on agent for VE with the characteristic of supply chain is created. Aiming at complexity of net node of running platform framework, two‐layer architecture mode, which are information alternation layer and basic function layer inside net node based on agent, are designed. The modes are proved to be reasonable in the management of the system and networks resources.

The theory base of realization of the VE based on agent with the characteristic of supply chain is found.

The reasonable basic function design of various agents are main limitations.

The paper presents a very useful tool for the operation and management of VE.

A new approach and scheme for VE with the characteristic of supply chain is presented. This paper is aimed at researchers and engineers.

First this paper introduces the concepts of virtual manufacturing system (VMS). The host enterprise and the multiple numbers of supply and distribution enterprises that make up a VMS, and the hierarchical and horizontal relationship that exists between these enterprises are explained. The steps involved in formation and operation of a VMS are then analyzed in detail. Second, we present a three view based methodological approach to make a multi‐agent model of VMS. Finally, with the help of a testing prototype, we show how to develop an autonomous Internet based data collection system for operation of VMS in accordance with the proposed methodological approach.

This article describes the use of intelligent agents to filter and categorise information and concludes with case studies of select Verity partners who have built unique applications using Verity agent technology.

The mythological ‘Daily Me’. At the recent NetMedia 97 conference at City University I watched a presentation by Steve Yelvington, Editor of the Star Tribune Online in Minneapolis. He was describing the development of an intelligent agent system called MOM (My Own Matrix) that the Star Tribune is developing to serve the needs of its readers.

Fairly rapid environmental changes call for continuous surveillance and on‐line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi‐agent system for monitoring and assessing air‐quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air‐quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data‐driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real‐world test case is demonstrated.