Purpose The purpose of this study is to provide conflict-free operations in terminal manoeuvre areas (TMA) using the point merge system (PMS), airspeed reduction (ASR) and ground holding (GH) techniques. The objective...
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Purpose The purpose of this study is to provide conflict-free operations in terminal manoeuvre areas (TMA) using the point merge system (PMS), airspeed reduction (ASR) and ground holding (GH) techniques. The objective is to minimize both total aircraft delay (TD) and the total number of the conflict resolution manoeuvres (CRM). Design/methodology/approach The mixedintegerlinearprogramming (MILP) is used for both single and multi-objective optimization approaches to solve aircraft sequencing and scheduling problem (ASSP). Compromise programming and epsilon-constraint methods were included in the methodology. The results of the single objective optimization approach results were compared with baseline results, which were obtained using the first come first serve approach, in terms of the total number of the CRM, TD, the number of aircraft using PMS manoeuvres, ASR manoeuvres, GH manoeuvres, departure time updates and on-time performance. Findings The proposed single-objective optimization approach reduced both the CRM and TD considerably. For the traffic flow rates of 15, 20 and 25 aircraft, the improvement of CRM was 53.08%, 41.12% and 32.6%, the enhancement of TD was 54.2%, 48.8% and 31.06% and the average number of Pareto-optimal solutions were 1.26, 2.22 and 3.87, respectively. The multi-objective optimization approach also exposed the relationship between the TD and the total number of CRM. Practical implications The proposed mathematical model can be implemented considering the objectives of air traffic controllers (ATCOs) and airlines operators. Also, the mathematical model is able to create conflict-free TMA operations and, therefore, it brings an opportunity for ATCOs to reduce frequency occupancy time. Originality/value The mathematical model presents the total number of CRM as an objective function in the ASSP using the MILP approach. The mathematical model integrates ATCOs' and airline operators' perspective together with new objective functions.
The utilization of renewable energy to run desalination plants has enormously expanded in the last two decades. In this study, a grid-connected hybrid solar-wind system is proposed to power a small-scale Reverse Osmos...
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The utilization of renewable energy to run desalination plants has enormously expanded in the last two decades. In this study, a grid-connected hybrid solar-wind system is proposed to power a small-scale Reverse Osmosis (RO) desalination unit. In a case study, the system's performance has been analyzed under the weather conditions of the Eastern Province, Saudi Arabia. A numerical model has been developed based on a mixed-integer linear programming (MILP) approach to design and size the proposed system. The developed model is solved on an hourly basis to capture hourly variations of weather conditions with the aim to obtain an efficient design to operate the RO plant and supply freshwater to a small community living in a remote area at minimum cost. The developed model allows finding the optimal number of wind turbines, the number of photovoltaic (PV) modules, and the energy purchased from the national grid. Since the desalination energy consumption depends on the feed water conditions, two energy consumption rates are considered, namely, 2 and 4 kWh/m(3). The results show that brackish water can be purified for the two different energy requirements at a cost varying between 1.72 and 1.84 $/m(3), respectively. (C) 2021 Elsevier Ltd. All rights reserved.
We consider two mathematical problems that are connected and occur in the layer-wise production process of a workpiece using wire-arc additive manufacturing. As the first task, we consider the automatic construction o...
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We consider two mathematical problems that are connected and occur in the layer-wise production process of a workpiece using wire-arc additive manufacturing. As the first task, we consider the automatic construction of a honeycomb structure, given the boundary of a shape of interest. In doing this, we employ Lloyd's algorithm in two different realizations. For computing the incorporated Voronoi tesselation we consider the use of a Delaunay triangulation or alternatively, the eikonal equation. We compare and modify these approaches with the aim of combining their respective advantages. Then in the second task, to find an optimal tool path guaranteeing minimal production time and high quality of the workpiece, a mixed-integer linear programming problem is derived. The model takes thermal conduction and radiation during the process into account and aims to minimize temperature gradients inside the material. Its solvability for standard mixed-integer solvers is demonstrated on several test-instances. The results are compared with manufactured workpieces.
Transport and logistics networks are more complex than ever before. Complex networks and limitation of available capacities make flow processing schedule (FPS) a challenging problem to ensure customer satisfaction and...
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Transport and logistics networks are more complex than ever before. Complex networks and limitation of available capacities make flow processing schedule (FPS) a challenging problem to ensure customer satisfaction and profitability. This paper addresses an integrated hub location and flow processing schedule problem (HLFPSP) to determine hub locations, the allocated traffic flows and the optimal FPS at hubs under capacity constraints. Specifically, in an air transport network, the optimal FPS leads to optimal flight scheduling. The problem is formulated as a mixed-integer linear programming (MILP) model to minimize total tardiness costs (at operational level) and hub construction costs (at strategic level), simultaneously. The developed model is utilized to solve small-size hub location-scheduling problems. To provide a good solution in a reasonable time, the Lagrangian relaxation algorithm is employed. Based on the data from 80 airports in Turkey, the application of problem in real world is showed and the efficiency of the proposed solution methods is evaluated. Finally, a number of sensitivity analyses and managerial insights are provided to enrich the computational results.
Planning of distribution processes has been represented by vehicle routing problems (VRP), where objective functions are commonly related to the cost of routes and delivery times involved in those processes. However, ...
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Planning of distribution processes has been represented by vehicle routing problems (VRP), where objective functions are commonly related to the cost of routes and delivery times involved in those processes. However, other objectives, such as equity of demand satisfaction, are of particular interest in social or non-profit distribution systems. This study addresses a VRP focused on an egalitarian distribution among all demand centroids considering a heterogeneous fleet. In particular, our goal is to maximize the minimum fraction of fulfilled demand to foster the equity of demand satisfaction while minimizing the delivery times as a secondary objective. We formulate our optimization problem as a mixed-integerlinear program that is intractable by using a commercial solver for large instances. Therefore, we design three variants of the biased random-key genetic algorithm, and one of them can obtain near-optimal solutions for instances up to 120 demand points and 15 routes in less than one minute of computational time. Finally, we present an analysis of the trade-off between egalitarian demand satisfaction and total delivery time for a scenario based on an economically deprived region in Mexico.
Tailored mixed-integer Optimal Control policies for real-world applications usually have to avoid very short successive changes of the active integer control. Minimum dwell time (MDT) constraints express this requirem...
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Tailored mixed-integer Optimal Control policies for real-world applications usually have to avoid very short successive changes of the active integer control. Minimum dwell time (MDT) constraints express this requirement and can be included into the combinatorial integral approximation decomposition, which solves mixed-integer optimal control problems (MIOCPs) to epsilon-optimality by solving one continuous nonlinear program and one mixed-integerlinear program (MILP). Within this work, we analyze the integrality gap of MIOCPs under MDT constraints by providing tight upper bounds on the MILP subproblem. We suggest different rounding schemes for constructing MDT feasible control solutions, e.g., we propose a modification of Sum Up Rounding. A numerical study supplements the theoretical results and compares objective values of integer feasible and relaxed solutions.
There are currently more than 500 million people without access to electricity on the African continent alone. With an expected strong population growth and the global goals for reducing carbon emissions, the challeng...
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There are currently more than 500 million people without access to electricity on the African continent alone. With an expected strong population growth and the global goals for reducing carbon emissions, the challenge of providing electricity for all is tremendous. We present an approach that tackles electrification bottom-up in a decentral approach from so-called electrification seeds: Business owners or public institutions that invest in an electricity system to ensure reliable electricity supply for themselves may serve as a seed by also selling elec-tricity in their surroundings via mini-or microgrids. This approach will allow for cheaper solutions due to economies of scale. While private households can also be addressed by simple solutions e.g. solar home systems, the power of the "electrification seed" approach is that it can also provide enough secure power for small and medium enterprises (SME) and thus drive economic development. It is important to address the individual environment of the respective electrification seed, since there is no standard solution. To do so, the method includes an entire toolchain from estimating demands and structures with satellite data and Geographic Information System (GIS) software over employing an energy system model for finding the optimal technological design. As a last step, the viability of the electrification seed concept is verified with an exemplary business plan. The results show positive business cases for the electrification seed and a reduction of electricity costs for end-customers by 11.3 %. Altogether, this gives a very optimistic outlook of the suggested approach to support the great challenge of sustainable electrification for economic growth in developing countries. (c) 2021 International Energy Initiative. Published by Elsevier Inc. All rights reserved.
This paper is focused on solving an industrially-motivated, rich routing variant of the so-called full truckload pickup and delivery problem. It addresses a setting where the distributor has to transport full truckloa...
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This paper is focused on solving an industrially-motivated, rich routing variant of the so-called full truckload pickup and delivery problem. It addresses a setting where the distributor has to transport full truckload shipments between distribution centers and customer locations, yet the distributor's owned fleet is inadequate to perform the totality of the required deliveries and thus a subset of the deliveries has to be outsourced to third-party carriers. In this work, we propose a novel mixed-integer linear programming formulation to model this problem. Using datasets inspired from industrial practice, we evaluate the computational tractability of this model and demonstrate its potential to serve as a decision-support system for real-life operations. Furthermore,we hypothesize that the distributor may realize cost savings when the later portion of the distribution period is utilized to pre-load cargo for delivery during the following period. To that end, we augment the original model to allow for such cargo pre-loading, and we conduct a rolling horizon-based simulation study to quantify its overall economic effect.
Research has shown the effective use of storage technologies to support the operation of a network with a large share of renewable sources of energy. This is done by storing excess energy when generation exceeds deman...
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Research has shown the effective use of storage technologies to support the operation of a network with a large share of renewable sources of energy. This is done by storing excess energy when generation exceeds demand and delivering the extra energy stored when demand exceeds generation. This ensures that renewable resources are used even when renewable generation is not sufficient to satisfy the demand. To maximise the benefit of storage units in the network, their size and location within the network need to be optimised to the characteristics of the network, the demand and generation profiles. The optimisation is done to minimise network congestion and losses while maximising the benefits of the storage elements in the network. Several optimisation processes have been employed to perform this task but consideration of the storage technology and storage utilisation were not sufficiently explored. The proposed method considered optimising the size and location of distributed energy storage resources in a radial distribution network taking into consideration the effect of storage technology. The results showed that the location and sizes of distributed energy storage depend not only on the aggregated size of the technology but also on the technology types. The results also show that the size of the aggregated storage influences the overall network losses. Generally, the larger the aggregated size of distributed storage, the more the storage units are distributed resulting in a greater reduction of network losses. Among the storage technologies considered, Lithium-Ion batteries presented the most improvement in network losses due to their higher-rated power delivery and charging capacity relative to the energy storage size.
Machine Learning models are increasingly used for decision making, in particular in high-stakes applications such as credit scoring, medicine or recidivism prediction. However, there are growing concerns about these m...
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ISBN:
(纸本)9798350397444
Machine Learning models are increasingly used for decision making, in particular in high-stakes applications such as credit scoring, medicine or recidivism prediction. However, there are growing concerns about these models with respect to their lack of interpretability and the undesirable biases they can generate or reproduce. While the concepts of interpretability and fairness have been extensively studied by the scientific community in recent years, few works have tackled the general multi-class classification problem under fairness constraints, and none of them proposes to generate fair and interpretable models for multi-class classification. In this paper, we use mixed-integer linear programming (MILP) techniques to produce inherently interpretable scoring systems under sparsity and fairness constraints, for the general multi-class classification setup. Our work generalizes the SLIM (Supersparse linearinteger Models) framework that was proposed by Rudin and Ustun to learn optimal scoring systems for binary classification. The use of MILP techniques allows for an easy integration of diverse operational constraints (such as, but not restricted to, fairness or sparsity), but also for the building of certifiably optimal models (or sub-optimal models with bounded optimality gap).
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