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You searched for +publisher:"Delft University of Technology" +contributor:("Van Cranenburgh, S."). Showing records 1 – 2 of 2 total matches.

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Delft University of Technology

1. Bouwmeester, A. Understanding Communication Preferences of Banking Customers:.

Degree: 2016, Delft University of Technology

Banks and service companies in general are facing problems with multi-channel management, especially in the context of outbound communication. Problems are the high costs of the multi-channel systems, unsatisfied cus-tomers, and few customers who interact with companies. Personalization of the selection of communication channel to reach a customer is seen a solution to these problems. However, a complication is that currently no insight exists in what factors can explain channel preferences of customers. These factors are required for estimating the channel preferences of customer. In order to identify these factors a survey has been used to collect channel preferences of banking customers in the context of outbound contact. Furthermore, hypotheses about what factors are expected to explain channel preferences were constructed. These hypotheses have been tested through multinomial logistic regression models. Multiple relations between channel preference and predictors were identified. To assess the impact of the findings on the presented problems, it is recommended to start pilots in which the selection of a channel to reach a customer is based on the identified predictors. Advisors/Committee Members: Verbraeck, A., Cunningham, S.W., Van Cranenburgh, S..

Subjects/Keywords: multi-channel management; channel preferences; multinomial logistic regression; outbound communication; financial sector

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bouwmeester, A. (2016). Understanding Communication Preferences of Banking Customers:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:4d4a8da3-369d-4c06-92b8-d91f3522e7de

Chicago Manual of Style (16th Edition):

Bouwmeester, A. “Understanding Communication Preferences of Banking Customers:.” 2016. Masters Thesis, Delft University of Technology. Accessed November 12, 2019. http://resolver.tudelft.nl/uuid:4d4a8da3-369d-4c06-92b8-d91f3522e7de.

MLA Handbook (7th Edition):

Bouwmeester, A. “Understanding Communication Preferences of Banking Customers:.” 2016. Web. 12 Nov 2019.

Vancouver:

Bouwmeester A. Understanding Communication Preferences of Banking Customers:. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2019 Nov 12]. Available from: http://resolver.tudelft.nl/uuid:4d4a8da3-369d-4c06-92b8-d91f3522e7de.

Council of Science Editors:

Bouwmeester A. Understanding Communication Preferences of Banking Customers:. [Masters Thesis]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:4d4a8da3-369d-4c06-92b8-d91f3522e7de

2. Brouns, M. Operational Scheduling in a Multi-Actor Environment using Multiagent Systems:.

Degree: 2015, Delft University of Technology

Liquid bulk is one of the largest industries of the modern world, and efficient scheduling is needed for liquid bulk terminals to remain competitive. A common solution for the planning efficiency problem is to apply planning algorithms, rather than human planners for creating schedules. However, replacing human planners by algorithms is often a difficult problem and its implementation is often obstructed both by management and the planning departments. In order to still achieve efficient schedules, attention has shifted from planning algorithms intended to replace the human planner, to scheduling support systems which aim to assist the planner in making efficient schedules. A core aspect of both automated scheduling systems as well as scheduling support systems is an optimization engine, to allow the human planner to quickly generate alternative schedule options. In recent years, literature has emerged introducing the concept of Multiagent systems for planning and scheduling. Multiagent scheduling seems to be a suitable approach for scheduling in liquid bulk terminals since the agents provide a natural metaphor for the scheduling problem in a liquid bulk terminal. This is mainly because a terminal serves vessels which are owned by a set of customers which are largely uninterested in the vessels of other customers. Multiagent systems can, in such a scenario, offer flexibility with regards to optimality criteria which traditional scheduling systems cannot. The main goal of this study was to determine whether Multiagent Systems (MAS) could be effectively applied for proposing schedule allocation options to assist schedulers in liquid bulk terminals. The research started with an extensive background study, in which it was found that there are a number of stakeholders involved in the scheduling process, which have partially conflicting goals. The main relevant stakeholders are the terminal and its customers, both consisting of several independent departments or entities. The main goals of the terminal are achieving a high terminal utilization, having a low impact on surroundings, having low pressure on operations, and not paying demurrage costs. The main goals from the customer side are achieving short turnaround times, and maintaining product quality. By combining the stakeholder requirements with information regarding the processes, infrastructure, and products at liquid bulk terminals, it was found that the scheduling problem resembles a specific version of the job-shop problem: JMPT | pmtn, prec, ri, sij | multi. Since this problem is strongly \NP-hard it is generally infeasible to find an optimal solution as the size of the problem instance grows, and efficient algorithms are needed to find solutions. After formally defining the scheduling problem as present in liquid bulk terminals, this thesis moved on to determine the applicability of several common Multiagent scheduling solutions based on four dimensions: whether the solution provides a good analogy to the scheduling problem, whether the solution… Advisors/Committee Members: Chorus, C.G., Aldewereld, H.M., Van Cranenburgh, S., De Weerdt, M.M., Jansen, M..

Subjects/Keywords: multiagent systems; multiprocessor tasks; multi-actor scheduling; scheduling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Brouns, M. (2015). Operational Scheduling in a Multi-Actor Environment using Multiagent Systems:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:dfa0ef97-a72a-47f5-88ff-c2d10872faec

Chicago Manual of Style (16th Edition):

Brouns, M. “Operational Scheduling in a Multi-Actor Environment using Multiagent Systems:.” 2015. Masters Thesis, Delft University of Technology. Accessed November 12, 2019. http://resolver.tudelft.nl/uuid:dfa0ef97-a72a-47f5-88ff-c2d10872faec.

MLA Handbook (7th Edition):

Brouns, M. “Operational Scheduling in a Multi-Actor Environment using Multiagent Systems:.” 2015. Web. 12 Nov 2019.

Vancouver:

Brouns M. Operational Scheduling in a Multi-Actor Environment using Multiagent Systems:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2019 Nov 12]. Available from: http://resolver.tudelft.nl/uuid:dfa0ef97-a72a-47f5-88ff-c2d10872faec.

Council of Science Editors:

Brouns M. Operational Scheduling in a Multi-Actor Environment using Multiagent Systems:. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:dfa0ef97-a72a-47f5-88ff-c2d10872faec

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