Johann L. Hurink

Management and Control of Domestic Smart Grid Technology

Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.

Tabu search for the job-shop scheduling problem with multi-purpose machines

In this paper we study the following generalization of the job-shop scheduling problem. Each operation can be performed by one machine out of a set of machines given for this operation. The processing time does not depend on the machine which has been chosen for processing the operation. This problem arises in the area of flexible manufacturing. As a generalization of the jobshop problem it belongs to the hardest problems in combinatorial optimization. We show that an application of tabu search techniques to this problem yields excellent results for benchmark problems.ZusammenfassungIn dieser Arbeit behandeln wir die folgende Verallgemeinerung des Job-Shop Scheduling Problems. Jede Operation kann auf einer beliebigen Maschine aus einer Menge von Maschinen, die für diese Operation gegeben ist, bearbeitet werden. Die Bearbeitungszeit hängt dabei nicht von der gewählten Maschine ab. Das in dieser Arbeit behandelte Problem tritt im Bereich der flexiblen Fertigung auf. Als Verallgemeinerung des klassischen Job-Shop Problems gehört es zu den schwierigsten Problemen aus dem Bereich der kombinatorischen Optimierung. Wir zeigen, daß eine Anwendung der Tabu-Search Metaheuristik hervorragende Ergebnisse für die von uns untersuchten Testprobleme liefert.

A master surgical scheduling approach for cyclic scheduling in operating room departments

This paper addresses the problem of operating room (OR) scheduling at the tactical level of hospital planning and control. Hospitals repetitively construct operating room schedules, which is a time-consuming, tedious, and complex task. The stochasticity of the durations of surgical procedures complicates the construction of operating room schedules. In addition, unbalanced scheduling of the operating room department often causes demand fluctuation in other departments such as surgical wards and intensive care units. We propose cyclic operating room schedules, so-called master surgical schedules (MSSs) to deal with this problem. In an MSS, frequently performed elective surgical procedure types are planned in a cyclic manner. To deal with the uncertain duration of procedures we use planned slack. The problem of constructing MSSs is modeled as a mathematical program containing probabilistic constraints. Since the resulting mathematical program is computationally intractable we propose a column generation approach that maximizes the operation room utilization and levels the requirements for subsequent hospital beds such as wards and intensive care units in two subsequent phases. We tested the solution approach with data from the Erasmus Medical Center. Computational experiments show that the proposed solution approach works well for both the OR utilization and the leveling of requirements of subsequent hospital beds.

An Exact Approach for Relating Recovering Surgical Patient Workload to the Master Surgical Schedule

No other department influences the workload of a hospital more than the Department of Surgery and in particular, the activities in the operating room. These activities are governed by the master surgical schedule (MSS), which states which patient types receive surgery on which day. In this paper, we describe an analytical approach to project the workload for downstream departments based on this MSS. Specifically, the ward occupancy distributions, patient admission/discharge distributions and the distributions for ongoing interventions/treatments are computed. Recovering after surgery requires the support of multiple departments, such as nursing, physiotherapy, rehabilitation and long-term care. With our model, managers from these departments can determine their workload by aggregating tasks associated with recovering surgical patients. The model, which supported the development of a new MSS at the Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital, provides the foundation for a decision support tool to relate downstream hospital departments to the operating room.

Domestic energy management methodology for optimizing efficiency in Smart Grids

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of domestic technologies have been developed to improve this efficiency. These technologies on their own already improve the efficiency, but more can be gained by a combined management. Multiple optimization objectives can be used to improve the efficiency, from peak shaving and Virtual Power Plant (VPP) to adapting to fluctuating generation of wind turbines. In this paper a generic management methology is proposed applicable for most domestic technologies, scenarios and optimization objectives. Both local scale optimization objectives (a single house) and global scale optimization objectives (multiple houses) can be used. Simulations of different scenarios show that both local and global objectives can be reached.

A branch & bound algorithm for the open-shop problem

A fast branch & bound method for the open-shop problem based on a disjunctive graph formulation of the problem is developed. Computational results show that the method yields excellent results. Some benchmark problems from the literature were solved to optimality for the first time.

Improving local search heuristics for some scheduling problems. Part II

Local search techniques like simulated annealing and tabu search are based on a neighborhood structure defined on a set of feasible solutions of a discrete optimization problem. For the scheduling problems

Makespan minimization for flow-shop problems with transportation times and a single robot

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Run-time spatial mapping of streaming applications to a heterogeneous multi-processor system-on-chip (MPSoC)

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A three-step methodology to improve domestic energy efficiency

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