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Introduction In line with the recent developments in OR/MS Education, a survey (available at https://ec.europa.eu/eusurvey/runner/ORMSeducation) was conducted amongst European universities and other higher education institutions (HEIs) from June to October 2015. The survey and related activities correspond to the first phase of the European Study on OR/MS Education. The purpose of the study is to obtain detailed insight into the current state of this field in Europe. The study was supported by the Association of European Operational Research Societies (EURO). It should be noted that EURO currently includes 31 member countries. Most of these member societies are from Europe but three countries, outside of the European continent, namely Israel, South Africa and Tunisia are also EURO member societies. A few of the survey respondents identified their affiliation in European countries that are not represented in EURO (e.g., Ukraine), or stated that they are visiting fellows from abroad (e.g., India); in this regard the terms Europe and European, as used in this article, shall include these respondents., This study was made possible through the kind support of EURO, a significant number of OR societies in EURO, collaborating colleagues, the EU-Survey services from the European Commission, and Springer Verlag. We acknowledge and thank them for that.

Introduction Education in Operational Research / Management Science (OR/MS) has shown significant progress, but further developments that may help to identify key factors related to OR/MS programmes at Higher Education Institutions (HEI) are still required. With the support of partner institutions of the Association of European Operational Research Societies (EURO), a study on OR/MS education in European countries was initiated. The first phase focused on the following main aspects namely: enrolment of students to OR/MS courses, first year OR/MS students’ failure rates, continuous promotion of the subject area, OR/MS courses, and the transition of graduates into the labour market. With this objective, the working group prepared a questionnaire aimed mainly at the OR/MS academic community, in order to gain a better understanding of how HEI - universities, colleges, polytechnics and research centers - are addressing OR/MS education topics in Europe., This study was made possible through the kind support of EURO, a significant number of OR societies in EURO, collaborating colleagues, the EU-Survey services from the European Commission, and Springer Verlag. We acknowledge and thank them for that.

OBJECTIVE: To evaluate discrepancies between flow cytometry (FCM) and image cytometry (ICM), ploidy incidence and relation between DNA ploidies and survival in distal bile duct carcinomas (DBDCs).STUDY DESIGN: Forty-four archival tumor samples from patients with DBDC who underwent subtotal pancreatoduodenectomy from 1985 to 1996 were examined for DNA ploidy using FCM and ICM.RESULTS: Overall, 59% (26/44) of the tumors were aneuploid by at least one of the two techniques. We detected more cases of aneuploidy with ICM than FCM in formalin-fixed, paraffin-embedded DBDCs, 62% (21/34) versus 33% (13/40), respectively. When results could be compared, moderate strength of agreement (kappa = .45) was demonstrated. No correlation was found between DNA ploidy by FCM, ICM or combined FCM-ICM and survival time (P = .80, P = .35, and P = .54, respectively).CONCLUSION: Approximately 59% of DNA histograms contained aneuploid cell populations. Although ICM, as compared to FCM, is more sensitive in assessing the ploidy status of DBDC, both methods were complementary. Most discrepancies between FCM and ICM were due to the dilution of aneuploid populations by non-neoplastic diploid cells. DNA ploidy assessment in DBDC did not offer the possibility of improving the ability to predict survival.

This article describes the set up of a rule-based expert system for histologic typing and grading of invasive breast cancer, which is designed to be a user-friendly tool that may be helpful for teaching and to support diagnosis making. The system raises questions and offers fixed choices to the user (usually yes/no) until a histologic diagnosis can be made with reasonable probability or enough data are available to assign a grade. As to histologic typing, the expert system is able to make the following diagnoses: ductal carcinoma, lobular carcinoma, medullary carcinoma, colloidal carcinoma, tubular carcinoma, and invasive cribriform carcinoma. If the diagnosis "ductal carcinoma" is arrived, the system offers the option to assign a histologic grade to the lesion. A first evaluation of the system in 30 cases (five each of the different subtypes) with unequivocal diagnoses by two human experts showed that the system classified 29 of the tumours in the same way as the human experts. The discrepancy case was solved after adding one rule to the system. Ten cases where a discrepancy existed between the original diagnosis of a referring centre and a reviewing human expert were all classified by the expert system in the same way as the human expert. The expert system thus seems to perform well. Further plans for evaluating, modifying and expanding the system are disclosed.

Waste management or rather materials management is an important policy subject. Residents, legislators and planners are confronted with ever increasing targets for reused or recycled material flows. In this thesis, the focus is on the design of collection schemes for recyclable municipal solid waste. The toolbox offered by the field of combinatorial optimization is used to find optimal or near-optimal solutions for these waste collection problems. After a thorough review on the literature on strategic network optimization models in waste reverse supply chains, two real-life waste collection problems are considered. The first one is about the collection of recyclables from households on a tactical level. The second one concerns the collection of containers from civic amenity sites and is situated on a tactical-operational level. The models aim at minimizing the total cost, which consists of vehicle and personnel costs. Both problems prove hard to solve. Since a general purpose solver could not find satisfactory results, alternative solution approaches, based on a problem decomposition, are proposed. A mixed integer linear programming based heuristic, combined with a column generation lower bound procedure gives good results for the first problem. For the container collection problem however, two alternative approaches are developed. A first one is a cutting plane heuristic based on combinatorial Benders decomposition. A second one applies a route generation and route selection scheme. At least for the problem instances considered in this thesis, the latter outperforms the former. Therefore, the route generation and selection solution process is embedded in an optimization-simulation model. This model incorporates, next to cost, also environmental impacts in its objective function. Additionally, the solutions are simulated to evaluate their behavior under real-life conditions. status: published

The study of pedestrian walking behaviour and crowd dynamics is an important topic with many applications. Many computationally efficient but less accurate macroscopic models (where the crowd is described as a whole using average quantities such as density and velocity at a certain location and time) and computationally less efficient but more accurate microscopic models (where each pedestrian is described as a separate entity) have been developed to describe the walking behaviour of individual pedestrians and large crowds. Moreover, many optimisation models have been proposed to solve problems involving pedestrians. Most of these models have hitherto focused on evacuation problems, where for a given building and an initial distribution of pedestrians, the optimal evacuation plan for each pedestrian is computed. A few researchers have also focused on design problems to find the optimal layout of a pedestrian facility or bottleneck passages. In this thesis, we consider the link between timetabling problems and crowd flow optimisation. Indeed, the assignment of events to timeslots and rooms in a timetable has an impact on the resulting people flows. For example, in a university course timetable, at the end of each timeslot, students have to switch rooms to go to their next class. This can cause congestion problems in the halls and stairwells in universities where the class rooms are concentrated in one or a few buildings. Furthermore, if the building needs to be evacuated at a certain time, this evacuation process is also influenced by the university course timetable. After all, the timetable determines how many people are present in the building in each timeslot and in which rooms. University course timetables are not the only example. The timetable at large conferences, music festivals, cultural events, or sports events also determines the people flows during the event. This thesis consists of four main parts. Chapter 2 presents a review of optimisation models for pedestrian evacuation and design problems. Relevant empirical research and descriptive (mathematical) models of pedestrian walking behaviour are also discussed. This review shows that most models include the inverse relationship between density and walking speed, but that calibration and implementation of the proposed models are lacking. Chapter 3 focuses on the university course timetabling problem (UCTP) at KU Leuven Campus Brussels. A two-stage mixed-integer programming (MIP) approach is developed to build a timetable that maximises the scheduling preferences and minimises the travel times of students between lectures in consecutive timeslots. Pedestrians are modelled using a macroscopic network model with a density-dependent arc traverse time. The model is extended to optimise evacuations time of students in the event of an emergency. Computational results show that the model succeeds in constructing timetables with reduced travel or egress times. However, the model fails to solve large real-life instances, such as the KU Leuven instance. Therefore, a heuristic approach is developed to solve the problem. In contrast to the two-stage MIP approach, the heuristic is able to find good quality solutions for the KU Leuven instance. Moreover, it succeeds in improving upon the solutions found by the two-stage MIP approach for all other test instances. In Chapter 4, we develop a generic, flexible model for timetabling incorporating resulting people flows. To keep the model generic and tractable, only the assignment of events to rooms is optimised, while the assignment of events to timeslots is considered given. The pedestrian walking behaviour and crowd dynamics are described using the microscopic pedestrian simulator Menge developed by Curtis et al. (2016). A surrogate-based tabu search heuristic is proposed to solve the problem. The surrogate model is used to speed up the search by filtering the number of candidate solutions that are evaluated using the expensive Menge simulator. The performance of different surrogate models is evaluated. The model is used to solve two applications, one where we minimise evacuation times and one where we minimise travel times between events in consecutive timeslots. The results show that for both applications the model succeeds in building timetables with significantly reduced travel or egress times. Finally, the model is implemented in a scheduling tool with graphical user interface and applied to the room assignment problem at KU Leuven Campus Brussels. It shows that our model is able to tackle real-life problem instances with large numbers of pedestrians. Finally, Chapter 5 compares the network model of Chapter 3 and the microscopic Menge simulator of Chapter 4. Using exhaustive search on small problem instances, the quality assigned to each solution in the solution space by the different models is compared in detail. Moreover, the modelling power and the robustness of the models with respect to the calibration of their parameters is discussed. ispartof: pages:529-530 nrpages: 2 status: published

This dissertation analyzes the stochastic multi-item inventory problem from the horizontal collaboration point of view. In Chapter 2 of this dissertation, we cover the general features of the Joint replenishment problem (JRP), and we classify the existing JRP models according to the demand characteristics. The resulting categorizations provide a useful overview for researchers studying inventory replenishment problems, by providing detailed information on the inventory system characteristics and the employed solution approaches, highlighting articles that involve stochastic variables (e.g., demand arrivals or lead times) and present exact solution approaches. Further, a description of the most commonly used allocation mechanisms is presented together with their formulation. In Chapter 3, we present a Continuous-Time Markov Chain (CTMC) model to compute the steady-state distribution of the possible state transitions of the two-company setting when orders are synchronized using the can-order policy. The CTMC model allows us to quantify the exact inventory and transportation cost performance of each company in the collaboration, and to optimize the optimal control parameters of the can-order policy that maximizes the coalition gains, i.e., minimizes the total joint logistics costs. Once the collaboration costs are calculated, two decisions have to be made: (i) which set of costs will be redistributed, and (ii) how those costs will be allocated. We consider different agreements to distribute the costs (or gains) of the collaboration, ranging from no cost redistribution at all, sharing the transportation costs (or its gains) only, to sharing the total logistics costs (or its gains) that are impacted by the collaboration. Also, we study four different allocation mechanisms to redistribute the collaboration costs/gains. Each of these allocation mechanisms can be applied to each of the four sets of costs. We quantify the individual cost performance of transportation and inventory holdings, and we investigate which cost-sharing agreement enables a long-term partnership where each company obtains individual gains when they set up a collaborative shipping agreement. While Chapter 3 studies the performance of the collaboration under the can-order policy and the long-term stability per type of cost-sharing agreement, Chapter 4 focusses on the non-cooperative framework of the information game. We investigate the incentives for information distortion and whether/when this may pose a threat to the stability (and thus the long-term viability) of the collaboration. Specifically, we investigate the best reporting strategy for each company, assuming that each company works out by reasoning. That is, a company may misreport its demand in order to maximize its realized individual gains on the assumption that the other company behaves in equal manner. Of course, the resulting realized individual gains depend on the specific set of costs to be redistributed and on the allocation mechanism selected. Further, we investigate two types of contract to allocate the costs among the companies. We employ the concept of dominance, i.e., by iteratively eliminating dominated strategies, to find the best reporting strategy under each cost-sharing agreement and to analyze under which settings truth-telling reporting may be an equilibrium. In the inventory management literature, the lot size of the replenishment orders is usually determined by the retailer to balance the fixed cost per order against the holding costs, whereby the transportation utilization rate or the CO2 implications are not considered. In Chapter 5 we revise the stand-alone and collaborative inventory models taking sustainability concerns into account. We thus reformulate for both models the classical single objective optimization approach as a multi-objective inventory problem. Conceptually, we investigate the individual costs and the total carbon emissions generated as the result of the storage of each inventory item, and the frequencies of individual (and joint) orders placed. We make use of a non-dominated sorting procedure to eliminate all possible dominated combinations of control parameters of the can-order policy, i.e., combinations that result in a higher carbon emission for a given cost, in order to identify the set of efficient solutions (Pareto optimal solutions). These results are used to provide some insights about the effectiveness of different ordering policies to control carbon emissions. The results of the multi-criteria decision analysis indicates that a slight percentage cost increase results in a significant reduction of carbon emissions. Finally, Chapter 6 summarizes the conclusions, addresses the limitations of this dissertation, and suggests avenues for further research by highlighting both trends and gaps in the research field. status: published