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Genetic algorithm for schedule optimization in the homecare industry
University of Skövde, School of Engineering Science.
2018 (English)Independent thesis Advanced level (degree of Master (One Year)), 15 credits / 22,5 HE creditsStudent thesis
Abstract [en]

In the last couple of years, the development within the Artificial Intelligence, AI, has rapidly improved with more and more applications to ease daily live. These applications of AI are countless and can lead to a more sustainable and efficient world. AI is there to solve complex problems which are hard to handle by hand for people and give solutions which would otherwise be not reachable in the same timeframe by considering the same variables. One of the applications of AI is to solve scheduling problems, such as the scheduling optimization problem within the homecare industry. Scheduling within the homecare industry is highly demanding since the problem is very complex due to the many different variables which are dependent on each other. Next to this the industry is dynamic in which members and clients come and go. Within the homecare industry employees, from now on called members, are visiting clients for treatments. These visits are at the clients’ home with a certain length and frequency a week, which is different per client. Next to this a client can have multiple assigned members in which they alternate each other. Next to this the client has his or her private schedule with their availability, which are constraints the schedule maker has to take into account. As an understanding of the problem the case study of Malmo stad is used to receive data and develop an algorithm around. The main objective is to develop a programme which solves the scheduling problem within the homecare industry. For this feasible results are needed which thereby meet the constraints of availability, treatment lengths and frequencies, with high importance of the objective of minimizing the travel time. To get sustainable and efficient schedules the algorithm should find the schedule in which the travel time is the lowest. To address the problem, the research strategy of design and development is used. In this the literature in combination with the case study is used to develop and verify the program Within this project an algorithm is developed on the base of a Genetic Algorithm, GA, within MATLAB. Within the GA the fitness of the individuals is determined by feasibility and if the population has reached the maximum fitness, which is that the schedule is feasible and meets the constraints, goes into a fitness which graded by travel time in which minimum travel time is the most optimum. The programme gives as result the schedule per member within the Area for one week. Overall can be concluded that GA can be a solution to address the scheduling optimization problem for the Homecare industry. The algorithm gives solutions which are sufficient and logic. Nevertheless, with a lack of previous data within the industry and no comparison algorithms the verification of the algorithm is hard. Future work has to determine the performance of the GA with respect to this problem.

Place, publisher, year, edition, pages
2018. , p. 67
Keywords [en]
Artificial Intelligence, Genetic Algorithm, Homecare industry, Scheduling problem
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:his:diva-15946OAI: oai:DiVA.org:his-15946DiVA, id: diva2:1231894
Subject / course
Automation Engineering
Educational program
Industrial Systems Engineering - Master’s Programme
Supervisors
Examiners
Available from: 2018-07-11 Created: 2018-07-09 Last updated: 2018-07-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • Other locale
More languages
Output format
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