Aircraft stands and runways at airports are critical airport resources for aircraft scheduling and parking. Making use of limited apron and runway resources to improve airport efficiency is becoming increasingly impor...
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Aircraft stands and runways at airports are critical airport resources for aircraft scheduling and parking. Making use of limited apron and runway resources to improve airport efficiency is becoming increasingly important. In this paper, we study a realistic Aircraft Scheduling and Parking Problem (ASPP) with the goal of simultaneously determining the takeoff and landing time of each aircraft with consideration for wake vortex effect constraints and parking positions in the limited parking apron at a target airport. The objective of the ASPP is to minimise the total service time for aircraft. We developed a mixed-integer linear programme formulation for the ASPP. A novel improved bottom-left/right strategy is applied to construct solutions and a Hybrid Simulated Annealing and Reduced Variable Neighborhood Search (HSARVNS) is proposed to identify near-optimal solutions. Numerical experiments on randomly generated ASPP instances and on a large set of benchmarks for a reduced version of the ASPP (i.e. the classical Two-Dimensional Strip-Packing Problem (2D-SPP)) demonstrate the effectiveness and efficiency of the proposed approach. For the ASPP, HSARVNS can find optimal solutions for small instances in a fraction of a second and can find high-quality solutions for instances with up to 250 aircraft within a reasonable timeframe. For the 2D-SPP, the HSARVNS can find optimal solutions for 32 of 38 tested benchmarks within 90 s on average.
A lower bound for a finite-scenario-based chance-constrained program is the quantile value corresponding to the sorted optimal objective values of scenario sub-problems. This quantile bound can be improved by grouping...
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A lower bound for a finite-scenario-based chance-constrained program is the quantile value corresponding to the sorted optimal objective values of scenario sub-problems. This quantile bound can be improved by grouping subsets of scenarios at the expense of solving larger subproblems. The quality of the bound depends on how the scenarios are grouped. In this paper, we formulate a mixed-integer bilevel program that optimally groups scenarios to tighten the quantile bounds. For general chance-constrained programs, we propose a branch-and-cut algorithm to optimize the bilevel program, and for chance-constrained linear programs, a mixed-integer linear-programming reformulation is derived. We also propose several heuristics for grouping similar or dissimilar scenarios. Our computational results demonstrate that optimal grouping bounds are much tighter than heuristic bounds, resulting in smaller root-node gaps and better performance of scenario decomposition for solving chance-constrained 0-1 programs. Also, the optimal grouping bounds can be greatly strengthened using larger group size. Summary of Contribution: Chance-constrained programs are in general NP-hard but widely used in practice for lowering the risk of undesirable outcomes during decision making under uncertainty. Assuming finite scenarios of uncertain parameter, chance-constrained programs can be reformulated as mixed-integer linear programs with binary variables representing whether or not the constraints are satisfied in corresponding scenarios. A useful quantile bound for solving chance-constrained programs can be improved by grouping subsets of scenarios at the expense of solving larger subproblems. In this paper, we develop algorithms for optimally and heuristically grouping scenarios to tighten the quantile bounds. We aim to improve both the computation and solution quality of a variety of chance-constrained programs formulated for different Operations Research problems.
This paper addresses the problem of disassembly lot-sizing for the single-product type. Due to some specific characteristics of disassembly systems, surplus inventory can be generated while satisfying the demand for t...
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This paper addresses the problem of disassembly lot-sizing for the single-product type. Due to some specific characteristics of disassembly systems, surplus inventory can be generated while satisfying the demand for the components. Disposal decisions are considered here to avoid inventory accumulations throughout the planning horizon. Three new mixed-integer programming (MIP) formulations are proposed to model the problem. The formulations differ from each other concerning the quality of the lower bound provided by their linear relaxation, which is an important issue in MIP resolution methods. Two efficient heuristics are also investigated for real-case applications when MIP algorithms are not relevant. The three formulations and the performance of the heuristics are compared based on new randomly generated instances for disassembly lot-sizing problems. As a managerial insight, the disposal decisions in disassembly lot-sizing models are relevant to save inventory costs.
We present a new formulation for the facility layout problem based on the sequence-pair representation, which is used successfully in VLSI design. By tightening the structure of the problem with this formulation, we h...
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We present a new formulation for the facility layout problem based on the sequence-pair representation, which is used successfully in VLSI design. By tightening the structure of the problem with this formulation, we have extended the solvable solution space from problems with nine departments to problems with eleven departments. (C) 2006 Elsevier B.V. All rights reserved.
Energy consumption in commercial buildings accounts for a significant proportion of worldwide energy consumption. Any increase in the energy efficiency of the energy systems for commercial buildings would lead to sign...
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Energy consumption in commercial buildings accounts for a significant proportion of worldwide energy consumption. Any increase in the energy efficiency of the energy systems for commercial buildings would lead to significant energy savings and emissions reductions. In this work, we introduce an energy systems engineering framework towards the optimal design of such energy systems with improved energy efficiency and environmental performance. The framework features a superstructure representation of the various energy technology alternatives, a mixed-integer optimization formulation of the energy systems design problem, and a multi-objective design optimization solution strategy, where economic and environmental criteria are simultaneously considered and properly traded off. A case study of a supermarket energy systems design is presented to illustrate the key steps and potential of the proposed energy systems engineering approach. (c) 2010 Elsevier Ltd. All rights reserved.
This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as mixed logical dynamical (MLD) systems. These are descr...
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This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as mixed logical dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD systems include linear hybrid systems, finite state machines, some classes of discrete event systems, constrained linear systems, and nonlinear systems which can be approximated by piecewise linear functions. A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting on-line optimization procedures are solved through mixedinteger quadratic programming (MIQP), for which efficient solvers have been recently developed. Some examples and a simulation case study on a complex gas supply system are reported. (C) 1999 Elsevier Science Ltd. All rights reserved.
Design and management of complex systems with both integer and continuous decision variables can be guided using mixed-integer optimization models and analysis. We propose a new mixed-integer black-box optimization (M...
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Design and management of complex systems with both integer and continuous decision variables can be guided using mixed-integer optimization models and analysis. We propose a new mixed-integer black-box optimization (MIBO) method, subspace dynamic-simplex linear interpolation search (SD-SLIS), for decision making problems in which system performance can only be evaluated with a computer black-box model. Through a sequence of gradient-type local searches in subspaces of solution space, SD-SLIS is particularly efficient for such MIBO problems with scaling issues. We discuss the convergence conditions and properties of SD-SLIS algorithms for a class of MIBO problems. Under mild conditions, SD-SLIS is proved to converge to a stationary solution asymptotically. We apply SD-SLIS to six example problems including two MIBO problems associated with petroleum field development projects. The algorithm performance of SD-SLIS is compared with that of a state-of-the-art direct-search method, NOMAD, and that of a full space simplex interpolation search, Full-SLIS. The numerical results suggest that SD-SLIS solves the example problems efficiently and outperforms the compared methods for most of the example cases. (c) 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 305-322, 2017
Current increases in the demand for electricity require sustainable energy management measures and have promoted the adoption of clean and renewable sources, particularly at the residential building level. Active dema...
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Current increases in the demand for electricity require sustainable energy management measures and have promoted the adoption of clean and renewable sources, particularly at the residential building level. Active demand management is usually carried out through load shifting based on specific techniques, such as optimisation, heuristics, model-based predictive control and machine learning methodologies. This work addresses the problem of residential load scheduling via optimisation techniques. A compressive receding horizon strategy is proposed for week-ahead load shifting, and the selection is driven by traditional receding horizon and day-ahead allocation strategy misalignment, with weekly household appliance usage patterns. The proposed approach is compared with receding horizon and day-ahead scheduling techniques over 30 different weeks for a prototypical smart home with non-controllable demand, which is representative of a four-resident family and includes micro power generation and battery storage. The simulation results confirm the validity of the proposed strategy in the context of household appliance scheduling problems and show competitive electricity costs and resident discomfort performance compared to state-of-the-art approaches. Furthermore, the proposed compressive receding horizon strategy fully exploits weather and photovoltaic generation forecasts to promote self-consumption and grid demand stress reduction while providing environmental gains and financial benefits to the utility service and consumers, particularly in the case of simultaneously scheduling a huge number of households.
作者：
Erhard, MelanieUniv Augsburg
Chair Hlth Care Operat Hlth Informat Management Fac Business & Econ Univ Str 16 D-86159 Augsburg Germany Klinikum Augsburg UNIKA T
Univ Ctr Hlth Sci Neusasser Str 47 D-86156 Augsburg Germany
In Germany, around 40% of the hospitals do not generate an annual surplus. This leads to an increasing pressure on hospitals' management to reorganize and restructure their processes and resources to decrease the ...
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In Germany, around 40% of the hospitals do not generate an annual surplus. This leads to an increasing pressure on hospitals' management to reorganize and restructure their processes and resources to decrease the upcoming costs and become profitable. Since personnel, especially physicians, generates a major part of the arising costs, assigning staff efficiently provides an opportunity to decrease associated costs. Up to now, experienced physicians create rosters manually which is cost and time intense due to the problem's complexity and especially the fluctuation in demand. To circumvent this difficulty, it is our main aim to create a new mathematical modeling approach to implement additional flexibility in the rostering process to better match supply and demand. Therefore, we formulate the problem as mixed-integer programming model with the objective to minimize occurring labor costs of physicians over the considered planning horizon subject to coverage of demand to make flexibility monetarily evaluable. In our approach, full flexibility in terms of patterns of working days, shift types, and the placement of the break is provided. To solve the problem under consideration, a column generation heuristic is presented. In our experimental study, the performance of the provided solution approach as well as the effect of additional flexibility in the rostering process are evaluated using real life data. Results indicate the significant impact of implementing flexibility in the scheduling process on the salary costs of the number of required physicians and evidence the superior quality of our solution approach.
Inland vessels are often used to transport containers between large seaports and the hinterland. Each time a vessel arrives in such a port, it typically visits several terminals to load and unload containers. In the P...
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Inland vessels are often used to transport containers between large seaports and the hinterland. Each time a vessel arrives in such a port, it typically visits several terminals to load and unload containers. In the Port of Rotterdam, the largest port in Europe, there are 77,000 inland vessels that have moored in the port in 2014 for transporting cargo. With the significant growth of containerized cargo transportation over the last decade, large seaports are under pressure to ensure high handling efficiency. Due to this development and the limited capacity at terminals, the inland vessels usually spend longer time in the port that originally planned. This leads to low utilization of terminal resources and congestion in the port. This paper proposes a novel two-phase planning approach that could improve this, taking into account several practical constraints. Specifically, we take into account the restricted opening times of terminals, the priority of sea-going vessels, and the different terminal capacities and sizes. In addition, we also consider the option for inland vessels to carry out additional inter-terminal transport tasks. Our approach is based on the integration of mixed-integer programming (MIP) and constraint programming (CP) to generate rotation plans for inland vessels. In the first phase, a single vessel optimization problem is solved using MIP. In the second phase, a multiple vessel coordination problem is formulated using CP;three large neighborhood search (LNS)-based heuristics are proposed to solve the problem. Simulation experiments show that the proposed INS-based heuristic outperforms the performance obtained with a state-of-the-art commercial CP solvers both regarding the solution quality and the computation time. Moreover, the simulation results indicate significant improvements with shorter departure times, sojourn times and waiting times. (C) 2017 Elsevier Ltd. All rights reserved.
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