Bachelor and Master thesis

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Bachelor theses

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Master theses

Topic: Markdown optimization for fresh categories (Kaufland)

For a retailer, ordering the right quantity of products is a crucial task on a daily level – too much quantity leads to food waste whereas too little leads to out–of–stock situations, lost sales, and dissatisfied customers. Excess stocks are particularly critical in fresh categories since the items must be sold off within a very short time due to their short best–before date. In order to sell the items before they perish (e.g., before reaching the best before date), targeted discounts are given by the employees in the stores. If a price discount is too high, the remaining stock may be salvaged quickly, and the margin losses may be too high. On the other side, if the price discounts are too low, the risk of food waste increases.

The aim of this master’s thesis is to develop an approach for profit–optimized markdown pricing in fresh categories (bakery, dairy and/ or counter) on a market–specific and item–specific level, with the goal to minimize the total write–offs and waste (losses from price reduction and remaining stock).

For a more detailed description, please follow this link: PDF: Markdown optimization for fresh categories

The tasks include:

• Complete a structured literature review about markdown policies for overstocks
• Structurally derive decision problem and formalize it as a stochastic–dynamic optimization model
• Implement the decision problem as a simulation model (e.g., in Python or AnyLogic)
• Collect data from Kaufland and prepare it for the application
• Complete numerical studies to generate some managerial insights
• Develop plan for potential implementation of the tool at Kaufland

A real–world dataset will be provided

Topic: Food waste in the hospitality and food service sector

Catering for a large number of people, as is the case in canteens, at events, or in hotels, for example, poses major challenges for service providers. Consumers require an attractive offer of dishes, while their preferences and hunger are hardly known. At the same time, the provider must prevent empty buffets and sold-out meals at all costs, a setting that practically provokes food waste. We want to approach this problem from the providers’ point of view, analyze the current way of planning the quantities and meals in facilities such as hotels, canteens, mass catering, etc. and find strategies for improvement.

The Master thesis should cover the following tasks:

• Literature overview of planning approaches in this area (both empirical and modeling-based)
• Development of an interview guide based on open research questions appearing from the literature review (in coordination with the supervisor)
• Finding and contacting suitable interviewees
• Conducting the interviews and analysis of the gathered information via qualitative content analysis
• Derivation of improvement measures

Topic: The Stochastic Patient-to-Bed Assignment Problem

Patients who are admitted to a hospital as inpatients must be assigned a bed while respecting certain constraints. For instance, gender-mixed rooms are usually to be avoided, and it is desirable to assign patients of similar age to a room since this has been shown to increase patient satisfaction. Transfers of a patient to a different room during their stay are possible but should be minimized in order to reduce work for the nurses and stress for the patients. The goal of the master’s thesis is to go beyond a rolling horizon approach by formulating the patient-to-bed assignment problem with stochastic arrivals and lengths of stay of patients using Markov decision processes (MDPs).

For a more detailed description, please follow this link: PDF: The Stochastic Patient-to-Bed Assignment Problem

The specific tasks to be performed include:

• Generate explicit distributions and test instances from the real data set
• Set up an automated instance generator
• Formulate an MDP for the case with a stochastic length of stay
• Formulate an MDP for the case where both length of stay and patient arrivals are stochastic