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Journal of business economics: JBE = Zeitschrift f��r Betriebswirtschaft : ZfB 91(6), 867���895 (2021). doi:10.1007/s11573-020-01019-4, Published by Springer, Berlin ; Heidelberg

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.

Health care management science (2021). doi:10.1007/s10729-021-09562-4, Published by Springer Science + Business Media B.V., Dordrecht [u.a.]

Chemical engineering & technology (2021). doi:10.1002/ceat.202000591, Published by Wiley-VCH Verl.-Ges., Weinheim

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.

Faced with new challenges, the chemical industry strives for more diversified product portfolios. Product life cycles are becoming shorter and lead to an increased uncertainty in product demands with regard to volume, location and type. To efficiently cope with highly individual demands, modular production concepts are considered. Modular plants can be installed in standard transportation containers and may operate at decentralized facilities in direct proximity to customers or suppliers. New modular plants can be added and existing plants can be relocated in the short-term to adapt to changing demand conditions. For an efficient utilization of the increased flexibility provided by modular plants, tactical planning must incorporate the new flexibility options. In this paper, using real data provided by a large chemical company, the economic benefits of modular production concepts in the specialty polymers market are assessed.

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.

In workforce scheduling for emergency medical services, it is important to ensure sufficient coverage at all time. Thus, planning has to consider unpredictable employee absences. To hedge against this type of uncertainty, on-call duties can be assigned to employees. In practice, these are often assigned ex-post based on the regular schedule. Little literature on workforce scheduling for ambulances or the planning of on-call duties exists. We present new set covering based integer programming formulations for cyclic stint-based staff scheduling with on-call duties. It is desirable for employees to work on consecutive days, called a stint, with a subsequent recovery period. On-call duties can be individually scheduled in-between two stints. Our model formulations integrate different cycle times for regular and on-call duties. A simple schedule that repeats quickly is devised for regular duties, while the on-call duty schedule rotates after each cycle to ensure fairness. The proposed models are applied to a local German emergency medical services provider. Using our stint-based model formulations, the planning complexity has been greatly reduced and reasonably large problem instances can be solved to optimality. Employee preferences, such as fairness, less work on weekends and longer recovery times, were taken into account to a high degree.

Process industries are facing strong global competition in recent years. In order to stay competitive, it is important to quickly react to new market developments and to serve specialized product demands. To meet these requirements, flexible production capacity is required. Recently, the use of modular plants has received much attention as an answer to these new challenges. Modular plants consist of standardized process modules, which allow for quick assembly, disassembly and relocation of production plants. Using modular production concepts, the flexibility of the production network is greatly increased (Seifert et al., Chemical Engineering and Technology 37(2):332–342, 2014, [6]). The technical feasibility of modular plant concepts has been proven by several projects, like the EU funded “\(F^3\)-Factory” project and the “CoPIRIDE” project (European Commission: Final report for flexible, fast and future production processes, 2014, [3]). Modular plants offer an increased manufacturing flexibility in type, volume and location of production capacities.

In the process industry markets are facing new challenges: while product life cycles are becoming shorter, the differentiation of products grows. This leads to varying and uncertain product demands in time and location. As a reaction, the research focus shifts to modular production, which allow for a more flexible production network. Using small-scale plants, production locations can be located in direct proximity to resources or customers. In response to short-term demand changes, capacity modifications can be made by shifting modular units between locations or numbering up. In order to benefit from the flexibility of modular production, the structure of the network requests dynamic adaptions in every period. Subsequently, once the customer demand realizes, an optimal match between disposed production capacities and customer orders has to be determined. This decision situation imposes new challenges on planning tools, since frequent adjustments of the network configuration have to be computed based on uncertain demand. We develop stochastic and robust mixed-integer programming formulations to hedge against demand uncertainty. In a computational study the novel formulations are evaluated based on adjusted real-world data sets in terms of runtime and solution quality.

Motorail transportation covers the loading of various types of cars and motorcycles onto trains. Order acceptance decisions are mainly driven by the availability of free capacity and have to respect several technical conditions. We focus on a dynamic situation where capacity demand is not known exactly but within certain bounds at the decision point. On the basis of proactive optimization, capacity bottlenecks are identified in advance. In particular, we develop a decision support system with mixed-integer programming to dynamically anticipate maximal available capacity. This key figure serves as an indicator to evaluate the underlying uncertainty and so achieve optimal train utilization. Numerical evaluations show that a variety of instances can be solved by standard commercial optimization software within reasonable time.

Since the liberalisation of the German energy markets, supply companies strive to gain additional revenues by trading in different electricity markets. Thus, trading strategies have to be adjusted to lower but more volatile energy prices caused by a rising share of renewable energy feed-in. Due to the increasing importance of combined heat and power (CHP) plants, an energy supply company is considered that operates a CHP plant with heat storage. A new modelling approach is presented to support generation companies scheduling their participation in two sequential electricity markets, the balancing market and the day-ahead spot market. In both markets, specific bidding procedures for trading exist. To derive optimal bidding strategies for CHP plants, we formulate the bidding problem as an innovative and detailed multistage stochastic programming model taking into account the sequencing of market clearing. The optimisation model simultaneously determines the operation of the CHP plant and heat storage device. An exemplary case study illustrates the benefit from coordinated bidding in sequential electricity markets.

One innovative production concept is currently highly discussed in the process industry: transformable, modular plant designs implemented in standardized transportation iso-containers Buchholz (Chem Eng Process: Process Intensif, 49(10):993–995, 2010, [3]).

For energy companies with a combined heat and power (CHP) plant and a heat storage device, the provision of balancing power provides additional revenue potential. Balancing power is needed to ensure a stable frequency if current generation differs from demand. In Germany, the responsible transmission system operator procures the needed type of reserve energy through an auction. To participate in the balancing market for minute reserve, energy companies have to submit a bid for a certain time frame of the following day that comprises a price and the amount of electricity at which power generation can be increased or decreased considering the expected load. Therefore, capacity allocation for the balancing market has to be considered simultaneously with the uncertain next day’s unit commitment of the CHP plant and the heat storage device. To support energy companies planning their bids, an optimization model is developed to determine the optimal bidding amount based on a forecasted market clearing price.

The importance of information security is constantly increasing with technology becoming more pervasive every day. As a result, the necessity and demand for practical methods to evaluate and improve information security is particularly high. The aim of this paper is to apply mathematical optimization techniques tool improve information security. According to the identified problem structure, a combinatorial optimization model is established. The objective of the presented approach is to maximize system security by choosing the best combination of security controls limited by available budget. In addition, by performing a What-If analysis and systematic budget variations, the decision maker can get improved insights and thus determine an ideal budget proposition yielding the highest benefit among all possible control configurations. An exemplary case study demonstrates how this approach can be used as a tool within the risk management process of an organization.

Increasing energy prices present a challenging task for small and medium-sized enterprises (SMEs). Since the availability of energy plays a crucial role in running the day-to-day business, this paper focuses on energy procurement of SMEs. Due to limited financial and human resources, SMEs find it difficult to implement measures to reduce energy costs. It is shown that a sustainable reduction of energy purchase prices can be achieved by choosing an appropriate procurement strategy. We develop a quantitative optimisation model that takes into account specific needs of SMEs. The aim of the model is to minimise energy purchase costs while assuring that demand is fulfilled at all times. Uncertainty regarding future energy prices and consumption is modelled by considering different scenarios for the evolution of uncertain parameters. A minimax regret approach is used to determine a robust selection of purchase contracts. For strategic decision support, a robust optimisation model is particularly well-suited to choose a risk-averse procurement strategy. In an exemplary case study, different procurement strategies are compared. Computational results show that a structured energy procurement concept has a high potential to significantly reduce energy costs if SMEs are willing to take over volume and price risk.

When faced with a medical problem, patients contact their primary care physician (PCP) first. Here mainly two types of patient requests occur: non-scheduled patients who are walk-ins without an appointment and scheduled patients with an appointment. Number and position of the scheduled appointments influence waiting times for patients, capacity for treatment and the utilization of PCPs. As the number of patient requests differs significantly between weekdays, the challenge is to match capacity with patient requests and provide as few appointment slots as necessary. In this way, capacity for walk-ins is maximized while overall capacity restrictions are met. Decisions as to the optimal appointment capacity per day on a tactical decision level has gained little attention in the literature. A mixed integer linear model is developed, where the minimum number of appointments scheduled for a weekly profile is determined. We are thus able to give the answer as to how many appointments to offer on each day in a week in order to create a schedule that takes patient preferences as well as PCP preferences into account. Appointment schedules are often influenced by uncertain demands due to the number of urgent patients, interarrivals and service times. Based on an exemplary case study, the advantages of the optimal appointment schedule on different performance criteria are shown by detailed stochastic simulations.

Information systems and the information enclosed are of significant value and it is indispensable for organizations to ensure their protection. To achieve high security, existing knowledge is available and provides recommendations and guidelines to follow. Due to the large amount of data and the complex dependencies within their structure, it is often challenging to make informed design decisions. This paper proposes a quantitative model that is tailored to the optimal selection of security safeguards from an existing security knowledge base. The input data are extracted from the extensive IT baseline protection catalogues of the German Federal Office for Information Security (BSI). The total amount of data include more than 500 threats and 1200 safeguard options. In an application example, we illustrate that an optimal decision can reduce the number of required safeguards substantially while still maintaining a high security level.

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.

Providing high quality emergency medical services (EMS) and ensuring accessibility to these services for the general public is a key task for health care systems. Given a limited budget available resources, e.g. ambulances, have to be used economically in order to ensure a high quality coverage. Emergency vehicles have to be positioned and repositioned such that emergencies can be reached within a legal time frame. Empirical studies have shown temporal and spatial variations of emergency demand as well as variations of travel times during a day. The numbers of emergency calls within 24 h differ significantly between night and day and show peaks especially during rush hours. We provide a data driven model considering time and spatial dependent degrees of coverage. This allows a simultaneous optimization of empirically required coverage with minimal number of ambulances, respectively costs. Therefore utilization and quality criteria are to be implemented. An integer linear program is formulated using time periods in order to model time-dependent demand and time-dependent travel times. It is shown on large empirical data records that the presented dynamic model outperforms existing static models with respect to coverage and utilization of resources.

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.

German electricity prices are highly influenced by the volatile and stochastic residual power load due to renewable energies. This constitutes a major challenge for energy providers, especially for municipal supply companies which provide their customers with district heat as well. Efficient and flexible combined heat and power (CHP) plants are used to fulfill the unsteady loads for heat and residual power. A heat storage offers the possibility to decouple generation from demand with respect to time. This leads to additional flexibility as it allows a power price-oriented operation of the CHP plant in order to realize profits by trading electricity on the spot market. In order to support the investment decision of a municipal energy provider, we quantify the influence of the value drivers for a heat storage which can be determined by specific demand patterns and price developments in day-to-day operations. Integrating a well-known linear model we optimize the different plant operations and power trade in a generation portfolio with and without heat storage. Results quantify the value of the storage depending on the extent and duration of fluctuations in the feed-in of renewable energies and corresponding prices.

Current results of revenue management and pricing policy research enable organizations to identify and realize optimal sales strategies and decisions. In response, enterprises which have to deal with the results of pricing policies are compelled to optimize their purchase. New developments in the aviation sector and airline industry along with a highly competitive flight ticket market and the importance of strategic purchase reveal the necessity of creating new solutions based on quantitative models. A decision-making process is outlined in order to support travel contract negotiations, an optimal supplier selection and allocation of flight tickets considering traditional airlines trade off against low-cost carriers, which provide less routes but usually lower costs.

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 is still growing. As a consequence, methods and tools which can support the R&D planning and decision process also become more numerous and sophisticated. This main chapter gives an overview over important methods and reviews the literature in this area. Special attention is given to the modeling of different kinds of uncertainty using fuzzy logic which is particularly important in R&D planning. A broad spectrum of fuzzy approaches is used to support in research and development decision making.

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.

Fuzzy Set Theory as proposed by L. Zadeh in 1965 is adaptable to different problem structures and well suited to model human evaluation and decision making processes. If uncertainty enters into models, it is classically of the stochastic kind which can properly be modelled by using probability theory. There are other fields, however, in which the description of problems contains uncertainties, in which, however, they are of a different kind than randomness. One of the most important areas of this kind is probably that of human problem solving and decision making: here the human factor enters with all its vaguenesses of perception, of subjectivity, of attitudes of goals and conceptions. To use a modeling language which is dichotomous in this area seems far from adequate.