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Decisions on the number of surgeries to perform during a typical week in an operating room (OR) generally affect downstream resources such as intensive care units (ICU), recovery rooms, or regular inpatient wards. Since capacity of e.g. ICUs is highly restricted, this bottleneck plays an important role in the management of an efficient flow of surgical patients through the system. This paper aims to provide hospital decision makers with easy-to-follow guidelines for the daily patient scheduling decisions and presents a novel chance-constraint optimisation model to develop efficient admission templates for elective surgeries on a tactical level. With a given patient mix, we decide on the number of surgeries that can realistically be treated during a typical week, with respect to limited resources of OR and ICU and considering stochastic demand. The benefit of such templates is exemplified using a dedicated simulation model in a realistic case study. The simulation model rebuilds daily operations in OR and ICU and allows to mimic typical decision making in OR and ICU. Computational studies show that continually applying such templates as admission guidelines leads to a well-utilised OR and efficiently used ICUs. The extensive simulation studies confirm that the template plans obtained from the chance-constraint optimisation model can provide guidance for an operating room planner.

Shorter product life cycles in the chemical industry have led to uncertain demand in terms of volume, location and type. Modular production concepts can be used to serve such volatile customer demand more efficiently. Modular plants increase the tactical flexibility of the supply chain substantially compared to centralized production with large-scale plants. Furthermore, production facilities can be flexibly opened, deactivated and reactivated. Modular plants can be relocated in the medium term, and their production capabilities can be reconfigured via process module changes. This work proposes new mixed-integer programming formulations for the tactical planning of production networks in the chemical industry; these formulations prescribe the volume, location, and process of modular plants in the production network over a multi-period planning horizon. The economic value of modular flexibility is investigated in a case study from the chemical industry under the consideration of diverse demand structures. The results have several managerial implications for the technical implementation and development of modular production technology in the chemical industry.

Usually, all operating rooms in a hospital have the same daily capacity although differing operating hours might be advantageous to match the demand. We propose and evaluate an optimization model that tactically distributes the total available operating time over the different operating rooms in order to improve performance. The probability of a perfect day without overtime or cancellations serves as objective criterion. Hence, the solution obtained has a maximum probability of no overtime or cancellations in all operating rooms. Uncertainties handled are mainly the surgery duration of patients and the daily number of patients to treat. To assess the number of patients, a method for sampling from multidimensional distribution functions for health care purposes is introduced. It is demonstrated that optimal operating hours for operating rooms can significantly influence key performance indicators such as overtime, rescheduling of patients and utilization. Implications for research and practical purposes can be deduced from an extensive simulation study with realistic data.

Set Covering Problems are one of the classical combinatorial optimization problems and part of many different practical applications, e. g., the location of emergency service facilities. In many real-world problems the required statistical parameters are not precisely known and the obtained solutions may reveal a non-adequate performance. We introduce a robust formulation of the uncertain/probabilistic Set Covering Problem which combines the concepts of robust and probabilistic optimization by introducing ’Γ-robust α-covering’ constraints. This Robust Uncertain Set Covering Problem can be stated as a compact mixed-integer linear programming model. Additionally, two non-compact integer linear model formulations are developed. As the strength of these formulations is not known a priori, we analyze the performance of these formulations in an extensive computational study. A case study for the location of emergency service facilities highlights the benefits of our approach in comparison to a formulation neglecting these uncertainties.

During the last years, operators of combined heat and power (CHP) plants are challenged by changing circumstances in the electricity markets. While ensuring a stable heat and power supply for customers, CHP plant operators seek to maximise the profitability of the CHP plant operation. We propose a comprehensive concept and illustrate its potential for increasing the profitability when operating a CHP plant with heat storage by participating in multiple electricity markets. For this purpose, the structure of the decision-making process consisting of bid submission, market clearing and CHP plant operation planning is represented. A multistage stochastic mixed-integer linear programming (MILP) model is developed that simultaneously optimises the operation of the CHP plant with heat storage and bidding in sequential electricity markets. Price uncertainty is captured by means of stochastic processes. The proposed concept is applied to the real case of a municipal energy supply company that participates in the German day-ahead spot and balancing market. Results of the case study illustrate how trading in sequential electricity markets can result in an increased profitability of the CHP plant operation, where additional revenues from trading offset higher generation costs. Furthermore, we exemplify how the optimal operation of the CHP plant and heat storage device are adjusted according to revenue potential in multiple electricity markets.

Cybercrime, one of the most important security topics, will continue to emerge as a more critical security threat within the next years. Among the different attacks, phishing is of special interest because of its negative impact for the economy. In this paper, we develop a simulation study based on the work of Fultz and Grossklags. To extend their analysis of cybercrime by an economic view, we customized their model and used it as basis for our analysis. Based on the data from recent literature, our assessment gives insights into the perpetrator's behavior and allows us to quantify the effectiveness of countermeasures. Due to the fact that mainly risk-seeking persons are responsible for these attacks, countermeasures aiming at increasing the penalties are not very effective. We discovered that better control of dark markets to prevent the trading of stolen data has a much higher impact. In general, results of our simulation can be used to analyze the perpetrator's economic motives and to establish a basis for effective countermeasures.

In this paper, a combinatorial optimization model is proposed to efficiently select security safeguards in order to protect IT infrastructures and systems. The approach is designed to provide very concrete decision support for an organization as a whole or separately for specific systems. It can be applied in practice without requiring the decision maker himself to collect extensive input data. This is accomplished by using an existing comprehensive and highly accepted knowledge base as a basis for decision making. For our analysis, we use the publicly available IT baseline protection catalogues of the German Federal Office for Information Security (BSI). The catalogues contain more than 500 threats and over 1200 safeguard alternatives to choose from. Applying our model, it is possible to make use of this knowledge and determine optimal selections of safeguards according to given security requirements. The approach supports the decision maker in establishing an effective baseline security strategy.

Volunteer fire departments have been founded to ensure public safety in case of fire and to provide support for professional firefighters. Most of the current stations date back to the beginning of the 19th century. Today, volunteer fire departments face numerous challenges, such as reduced number of following young volunteers or decreasing public budgets. We quantify these effects and identify different alternatives, such as relocating or closing of stations to minimize the impact on rescue service supply. Based on comprehensive case studies in Bochum, Germany it is apparent that these recommendations provide an excellent toolset for a prospective planning approach.

Competitiveness in global markets requires the ongoing identification and full utilization of improvement potential. To cut costs by means of process optimization, logistics service providers need to focus on efficient planning — not only for external but also for internal transports. This contribution will demonstrate that significant reductions in internal transports at one of the Deutsche Post World Net’s main parcel sorting centers can be achieved by applying the robust solution of a modified three-dimensional linear assignment model. The proposed MILP model minimizes necessary manual transportation effort by layout modifications. Additionally, it takes into account the specific characteristics and requirements of the parcel sorting center. The large-scale model is hierarchically decomposed and all resulting models are solved using MILP software. Uncertain data concerning the structure and quantities of transportation demand over time are considered adequately by applying robustness criteria. The transportation effort of the robust solution is close to every scenario-optimal objective value. The achieved reduction of manual transportation effort leads to a significant increase in runtime quality, to a decrease in costs and to a balanced utilization of the sorting and distribution unit. The approach proposed is not only valuable for parcel sorting centers operated by Deutsche Post World Net; it can also be generally applied to develop robust solutions in an uncertain dynamic decision situation.

Over the last few years, there has been wide discussion in the field of economics research with regard to the process-oriented approach. Extensive restructuring took place in practice. To improve business processes, a generally applicable, object-oriented, simulation-based decision support system abbreviated as GEPSIS has been developed. It is used to model business processes and to evaluate different process alternatives quantitatively. The aim is to determine the optimal process. Within a business process context, tasks, work flows and decisions are influenced by persons whose behavior is not deterministic. Such a behavior cannot be described adequately taking crisp approaches. For this reason, consideration of uncertainty and vagueness is of special importance. Both stochastic and linguistic types of uncertainty are considered. The vagueness of verbal formulations is modeled using linguistic variables. Several specific procedures are developed for process control. They result from specific priority rules for the object classes. Furthermore, a knowledge-based procedure comprising approximate inference is developed. In this way, rules for the sequencing of workflow objects can be modeled approximating reality. The suitability of GEPSIS and the effects of the application of different procedures to process control are demonstrated. This is achieved by considering a mail order book and record store and analyzing its actual business - e.g. the order process. The example covers some typical flow structures which can be found in many order processing situations.

In application and theory, great importance is attached to the optimisation of business processes, mostly without explaining into detail the criteria and the alternatives considered for an optimisation. Especially, quantitative criteria can be valuable to evaluate improvements of business processes, then quantitative methods of operations research are of special importance. Many problems of business processes have similarities to problems in the context of project management or production process planning which have already been solved successfully. Particularly simulation-based approaches have been proven to be appropriate in supporting decision making with respect to complex dynamic systems with uncertain data as is the optimisation of business processes. In the following, a generally applicable, object-oriented, simulation-based decision support system GEPSIS is introduced which is specifically developed for the improvement of business processes. The objects are designed in such a way that they are sufficient to model a great variety of different business processes. The pictorial representation assists in analysing the static structure of the process, the simulation system allows the quantitative evaluation of the dynamic behaviour. A case study shows its advantages.

Current results of revenue management and pricing policy research enable suppliers to identify and realize optimal sales strategies and decisions. In response, a corporation which has to deal with results of pricing policies is compelled to optimize its purchase. New developments in the aviation sector and airline industry along with a highly competitive flight ticket market and the importance of strategic procurement reveal the necessity of creating new decision support solutions based on quantitative models. The developed decision support system assists the decision making process of a corporation in annual contract negotiations with airlines to minimize its flight travel costs. Considering provided quantity and volume discounts with varying tiers and targets both operational and strategic decision support is provided. It combines the developed decision process with the use of a mathematical model. The proposed decision support system leads to optimal airline selection and flight contingent allocation considering traditional airlines and low-cost carriers as suppliers. It is addressed to a multinational corporation with numerous global host countries, branch offices, trading partners and customers with world-wide flight demand.

Empirical studies considering the location and relocation of emergency medical services (EMS) vehicles in an urban region provide important insight into dynamic changes during the day. Within a time period of one day the demand, travel time/speed of ambulances and covered areas change cyclically. Nevertheless, most existing approaches in literature ignore these variations and require a (temporally and spatially) fixed (double) coverage of the planning area. Neglecting these variations and fixation of the coverage could lead to a false estimation of the time dependent fleet size and individual positioning of ambulances. Through extensive data collection, nowadays it is possible to precisely determine the required coverage of demand areas. Based on data driven optimization a new approach is presented, maximizing the flexible, empirically determined required coverage, which has been adjusted for variations due to day-time and site. This coverage prevents the EMS system from unavailability of ambulances due to parallel operations to ensure an improved coverage of the planning area closer to realistic demand. An integer linear programming model is formulated in order to locate and relocate ambulances. Based on a comprehensive case study it is shown, that the approach achieves these objectives and leads to cost savings and improvement in the quality of emergency care.

In Germany, a large number of volunteer fire departments (VFD) exist that have been founded in order to provide regional support for professional firefighters. The current locations of these volunteer fire departments might be suboptimal with respect to costs and availability. Due to historical developments the city of Bochum (Germany) also hosts a large number of volunteer fire departments. A mixed-integer linear program is presented in order to investigate the consequences of partial relocating or closing of volunteer fire departments. Timeliness is one of the most important objectives since it reflects the quality of emergency services such as firefighting systems. Timeliness means that in most instances the emergency can be reached within a predefined time limit after reception of the emergency call. A sufficient number of firefighting facilities have to be established and located in order to provide high quality services over a wide area with spatially distributed demands. Different criteria are taken into account in order to find optimal locations. Minimizing the average travel time and minimizing the maximum travel time are goals which are in conflict with minimizing costs for establishing facilities and for transport. A multi-criteria approach is applied and different solutions for an urban area with several volunteer firefighting departments are analyzed. The tradeoff between maximizing the quality of emergency services and cost reduction is illustrated using a real world case study with 18 existing volunteer fire departments.

Operating room (OR) schedules are regularly influenced by uncertain demands such as surgery durations or randomly arriving emergency patients. The performance of these schedules depend on the information about these uncertainties when designing the schedules. We focus on an oine operational planning level at which assignments of patients to days and rooms are made without focusing on the intra-day sequence. At this level a sucient amount of time is to be reserved for every elective surgery. At the same time we observe that the performance of a given schedule influences several stakeholders’ interests. We therefore combine the aspects uncertain planning parameters and multiple interests and investigate the performance of schedules for ORs using a dedicated robust multi-criteria optimization approach. We compute dierent schedules focusing on fluctuating surgery times, randomly arriving emergency demand, and dierent objectives. In order to evaluate the schedules’ quality, we perform a simulation study and demonstrate to what extend robust schedules achieve the mentioned goals. The consequences of significant changes in assumptions about possible deviations in surgery times and how they aect the level of achievement of dierent objectives is investigated in a second step.

The importance of research and development has been increasing steadily during the last decades and it will grow further in the future. As a consequence, models which can support the planning process for R&D become also more numerous and sophisticated. This contribution first reviews the existing literature in these areas. Starting from a rather basic model, the structure of planning models for R&D is developed. The main focus is on models which use the mathematical programming framework. Special attention is given to the modelling of uncertainty which is particularly important in R&D planning. Future developments which seem to be desirable and necessary are considered as extensions of models described before.

An interactive decision support system is introduced which aids in solving multiple objective programming problems subject to strict and flexible constraints. Integral part is an extension of a well-known fuzzy sets approach evaluating possible solutions by their degrees of membership to objectives and constraints. This approach is linked to classical multiple objective programming models. If the decision maker cannot determine membership functions a priori the system suggests functions dependent on the given information and interactive modifications are allowed.

An interactive system is introduced which supports a decision maker in solving programming models with crisp or fuzzy constraints and crisp or fuzzy goals. One part of the system is the determination of membership functions representing goals. To this purpose fuzzy extreme solutions are computed and are presented to the decision maker. These and each of the proposed compromise solutions are fuzzy-efficient.