Employee Scheduling with Uncertain Demand

In the service industry, employee scheduling is one of the most important tasks and cost components. Therefore, saving only small percentages in employee-related costs results in significant total cost reduction. One of the problems that managers face in retail stores is the short-term demand fluctuations. One of the approaches to deal with this issue is to assign overtime work by extending shifts to cope with a lack of employees in real-time.

The goal of this thesis is to describe and analyze the optimization problem addressed in the base paper in detail. The student is expected to carry out a literature review on the model specifications related to multi-skill workforce scheduling of the stream of literature and to position the base paper in it. The proposed method must be implemented in a modeling language (e.g. docplex), critically assessed and extended. The student is also expected to conduct numerical experiments both on the base and extended model to generate some managerial insights. Critical assessment of the contribution of the proposed problem and model will conclude this thesis.

Requirements: Knowledge of a modeling language (e.g., OPM 662 Business Analytics: Modeling and Optimization).

Literature:
Bürgy, R., H. Michon-Lacaze, and G. Desaulniers (2019). Employee scheduling with short demand perturbations and extensible shifts. Omega 89, 177 – 192.

Allocation of check-in counters considering time-varying passenger arrivals

Check-in counters are the first point of contact for many passengers at airports. Airline managers and airport authorities face the decision how many counters should be opened at which time to account for the time-varying passenger arrival process. Optimal plans have to balance the trade-off between personnel costs or long waiting times and customers that cannot check-in on time. Parlar and Sharafali (2008) propose an analytical model to dynamically allocate check-in counters based on a stochastic dynamic programming formulation.

The goal of the thesis is to gain insights regarding the model of Parlar and Sharafali (2008) and its limitations. Therefore, an implementation of the approach is required which allows conducting a comprehensive numerical study. Sensitivity analyses with respect to key parameters and an extension of the approach to a generalized queueing system are expected to build the basis for a critical discussion of the model assumptions.

Requirements: Sound knowledge of queueing theory (e.g., from OPM 661 Business Analytics: Robust Planning in Stochastic Systems), knowledge of a programming language (e.g., Python) or willingness to acquire basic programming skills.

Literature:
Parlar, M., & Sharafali, M. (2008). Dynamic Allocation of Airline Check-In Counters: A Queueing Optimization Approach. Management Science, 54(8), 1410–1424.
Parlar, M., Rodrigues, B., & Sharafali, M. (2012). On the allocation of exclusive-use counters for airport check-in queues: static vs. dynamic policies. Opsearch, 50(3), 433–453.

Managing the transient phase: Decision models and insights

Many business processes are analyzed with queueing models. Before such queueing systems reach a steady state, they pass a transient phase. In this phase, the performance of the queueing system changes over time even though all parameters are constant. This phase can be observed in many real-world applications in which the jobs waiting in the queue are frequently cleared, e.g., at airport check-in counters, supermarkets, or production systems starting without inventory. A solid understanding of the transient phase is of managerial importance because the performance may substantially deviate from its steady-state behavior. Hence, the analysis of the transient phase serves as a basis for the support of design and control decisions in queueing systems before they reach a steady state.  Moreover, a sound understanding of the length of the transient period is also a prerequisite for a justified application of steady-state models, which dominate in the scientific literature.

The goal of the thesis is to provide a literature review of business processes and decision models related to the transient phase. Based on this a numerical investigation of drivers for the length of the transient period, e.g., the size of the system and the degree of variability, has to be conducted. Existing implementation of analytical transient results and a discrete-event simulation can be used.  A comparison with existing approximations of the relaxation time and the development of an own approximation based on regression analysis are expected.

Requirements: Sound knowledge of queueing theory, e.g., from OPM 661 Business Analytics: Robust Planning in Stochastic Systems.

Literature:
Gwiggner, Claus, und Sakae Nagaoka. 2014. „Data and queueing analysis of a Japanese air-traffic flow“. European Journal of Operational Research 235 (1):265–75.
Abate, Joseph, und Ward Whitt. 1987. „Transient behavior of the M/M/1 queue: Starting at the origin“. Queueing Systems 2 (1):41–65.

Appointment Systems and Truck Arrival Management: A Literature Review and Research Agenda

Innovative demand management mechanisms have emerged in the past decade to improve the performance of truck handling operations in different areas of application along the supply chain, such as production plants, distribution centers, seaport container terminals, and air cargo terminals. In particular, the implementation of terminal appointment systems has received increasing attention in practice and research. Such a system's overall objective is to smooth demand by shifting truck arrivals from peak to off-peak periods. Appointment systems are prevalent in a variety of other areas, in particular applied for health care and service operations.

The goal of the thesis is to provide a comprehensive literature review, combining the streams of truck arrival management and appointment systems. The student analyzes and classifies the design characteristics of the various fields of application and their corresponding quantitative analysis and planning approaches in the academic literature in order to identify research gaps for potential future research efforts.

Requirements: Basic knowledge of business analytics approaches (e.g., OPM 661 Business Analytics: Robust Planning in Stochastic Systems or OPM 662 Business Analytics: Modeling and Optimization).

Peak management of time-dependent demand: Data analysis and insights

Waiting on hold is the biggest problem in the service centre industry. Service centres need to decide whether they increase the number of agents or accept longer waiting times. virtualQ offers virtual waiting service to eliminate waiting on hold for call centers, so callers don’t have to wait on the phone until an agent finally picks up. virtualQ's peak management algorithms analyze the overall pattern of call volume and agent staffing level and adjust the call-back period accordingly to smoothen load peaks, i.e., bringing callers back in when the call center is less busy and when most agents are available.

The student is expected to analyze real-world call center data from various industries and to provide in-depth analysis about queueing behavior under different peak situations. Industry specific queueing problems shall be identified and described. 

Requirements: Skills in data analysis and basic knowledge of queueing systems (e.g., OPM 661)
Collaboration:  virtualQ

Please contact raik.stolletz@uni-mannheim.de for further information.

Variability analysis in digitalized semiconductor supply chains

Robert Bosch GmbH is one of the world’s leading semiconductor manufacturers for sensors used, e.g., in smartphones and the automotive industry. The production processes are subject to stochastic and dynamic effects. In particular, yield and process availability change over time due to process improvements. Digitalized production systems increase the availability of data and allow for a systematic analysis of different types of variability.

The student is expected to gather and analyze available shop floor data of different production steps. The data serves as a basis for an in-depth analysis of different sources of variability. The student has to apply statistical methods to gain insights into the magnitude and interdependencies between the different sources of variability.

Requirements: Knowledge of stochastic system (e.g. OPM 661 Business Analytics: Robust Planning in Stochastic Systems), knowledge in statistics is recommended (e.g. CC 502 Applied Econometrics), knowledge in MySQL is advantageous for the collection of data.
Collaboration:  Robert Bosch GmbH

Personaleinsatzplanung in Wohnheimen für Menschen mit Behinderung: Optimierungsmodelle und heuristische Lösungsansätze

Die LEBENSHILFE Dillenburg e.V. ist Träger verschiedener Einrichtungen der Behindertenhilfe im nördlichen Lahn-Dill-Kreis. Neben stationären und ambulanten Wohnangeboten unterhalten wir mehrere Werkstätten für Menschen mit Behinderungen, einen familienentlastenden Dienst sowie ein Kinderzentrum. Insgesamt sind bei uns ca. 400 Mitarbeiterinnen und Mitarbeiter beschäftigt, die etwa 1000 Menschen betreuen.

Unsere Wohnheime für Menschen mit Behinderungen bieten ein Zuhause für Menschen mit zumeist hohem Hilfebedarf. Menschen die Unterstützung in der Bewältigung des Alltags in Form von Anleitung, Begleitung und Entwicklungsförderung benötigen. In unseren Wohnheimen leben mehrere Menschen in einem Haus in unterschiedlichen Wohngruppen zusammen. Sie arbeiten zumeist in einem unserer Betriebe, auf einem Außenarbeitsplatz des ersten Arbeitsmarktes, sind Rentner oder sind aufgrund des hohen Hilfebedarfs in einer Tagesbetreuung.
Für viele Menschen mit Behinderung ist es sinnvoll, in einem Wohnheim zu wohnen und nicht in ambulante Wohnformen überzugehen. Grund dafür ist vor allem ein hoher Hilfebedarf. Das Betreuungspersonal ist ständig vor Ort und kann begleiten und anleiten. Eine besondere Bedeutung hat deshalb das Betreuungspersonal und damit die Personaleinsatzplanung. Sie muss den hochgradig volatilen Personalbedarf decken, Mitarbeiter mit verschiedenen Arbeitsverträgen verplanen, individuelle Einsatzwünsche berücksichtigen und komplexe rechtliche Nebenbedingungen beachten.

Die Masterarbeit soll die problemspezifischen Annahmen und Ziele herausarbeiten und in die wissenschaftliche Literatur einordnen. Ein ganzzahliges Optimierungsmodell soll aufgestellt und in Python implementiert werden. Basierend auf zu erhebenden Realdaten, sollen Testrechnungen zur Lösbarkeit mit Standardsolvern durchgeführt und geeignete heuristische Lösungsverfahren entwickelt, sowie getestet werden.  Sensitivitätsanalysen unterstützen die Ausarbeitung von generellen Einsichten in das Optimierungsproblem.

Voraussetzungen: Kenntnis einer Modellierungssprache für ganzzahlige Probleme (z.B. docplex) wie sie z.B. in OPM 662 Business Analytics: Modeling and Optimization erlernt wird.
Praxispartner: Lebenshilfe Dillenburg