In wire-arc additive manufacturing, a wire is molten by an electrical or laser arc and deposited droplet-by-droplet to construct the desired workpiece, given as a set of two-dimensional layers. The weld source can mov...
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In wire-arc additive manufacturing, a wire is molten by an electrical or laser arc and deposited droplet-by-droplet to construct the desired workpiece, given as a set of two-dimensional layers. The weld source can move freely over a substrate plate, processing each layer, but there is also the possibility of moving without welding. A primary reason for stress inside the material is the large thermal gradient caused by the weld source, resulting in lower product quality. Thus, it is desirable to control the temperature of the workpiece during the process. One way of its optimization is the trajectory of the weld source. We consider the problem of finding a trajectory of the moving weld source for a single layer of an arbitrary workpiece that maximizes the quality of the part and derive a novel mixed-integer PDE-constrained model, including the calculation of a detailed temperature distribution measuring the overall quality. The resulting optimization problem is linearized and solved using the state-of-the-art numerical solver IBM CPLEX. Its performance is examined by several computational studies.
We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems,where task offloading decisions,transmit power,and computation resource allocation are join...
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We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems,where task offloading decisions,transmit power,and computation resource allocation are jointly *** considered energy consumption minimization problem is a non-convex mixed-integer nonlinear programming problem,which is challenging to ***,we develop a joint search and Successive Convex Approximation(SCA)scheme to optimize the non-integer variables and integer variables in the inner loop and outer loop,***,in the inner loop,we solve the optimization problem with fixed task offloading *** to the non-convex objective function and constraints,this optimization problem is still non-convex,and thus we employ the SCA method to obtain a solution satisfying the Karush-Kuhn-Tucker *** the outer loop,we optimize the offloading decisions through exhaustive ***,the computational complexity of the exhaustive search method is greatly *** reduce the complexity,a heuristic scheme is proposed to obtain a sub-optimal *** results demonstrate the effectiveness of the developed schemes.
mixedinteger optimization is very important and complicated task in the optimization field, which widely exists in the engineering problems. In order to improve the efficiency of derivative-free algorithm when solvin...
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mixedinteger optimization is very important and complicated task in the optimization field, which widely exists in the engineering problems. In order to improve the efficiency of derivative-free algorithm when solving the mixedinteger optimization problems, we propose an efficient derivative-free algorithm, which is based on the modified minimal positive base and the technique of search directions rotation. The method using the modified minimal positive base only needs at most function evaluations at every iteration, compared with the derivative-free algorithms based on the maximal 2n positive base, where n is the number of variables. Meantime, the technique of search directions rotation we proposed can overcome the disadvantage of the method based on the minimal positive base which can cause undesirable large angles between some positive base directions and large unexplored feasible domain. Accordingly the convergence to stationary points is proved. To evaluate the performance of our method, we compare it with two classical algorithms on 50 benchmark problems. The results of numerical experiments show that the method can reduce the number of function evaluations and improve the efficiency of the algorithm.
Faced with dynamic and increasingly diversified public transport requirements, bus operators are urged to propose operational innovations to sustain their competitiveness. In particular, ordinary bus operations are he...
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Faced with dynamic and increasingly diversified public transport requirements, bus operators are urged to propose operational innovations to sustain their competitiveness. In particular, ordinary bus operations are heavily constrained by well-established route options, and it is challenging to accommodate dynamic passenger flows effectively and with a good level of resource utilization performance. Inspired by the philosophy of sharing economy, many of the available transport resources on the road, such as minibuses and private vehicles, can offer opportunities for improvement if they can be effectively incorporated and exploited. In this regard, this paper proposes a metric learning-based prediction algorithm which can effectively capture the demand pattern and designs a route planning optimizer to help bus operators effectively deploy fixed routing and cooperative buses with traffic dynamics. Through extensive numerical studies, the performance of our proposed metric learning-based Generative Adversarial Network (GAN) prediction model outperforms existing ways. The effectiveness and robustness of the prediction-supported routing planner are well demonstrated for a real-time case. Further, managerial insights with regard to travel time, bus fleet size, and customer service levels are revealed by various sensitivity analysis.
Classification trees are one of the most common models in interpretable machine learning. Although such models are usually built with greedy strategies, in recent years, thanks to remarkable advances in mixed-integer ...
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Classification trees are one of the most common models in interpretable machine learning. Although such models are usually built with greedy strategies, in recent years, thanks to remarkable advances in mixed-integer programming (MIP) solvers, several exact formulations of the learning problem have been developed. In this paper, we argue that some of the most relevant ones among these training models can be encapsulated within a general framework, whose instances are shaped by the specification of loss functions and regularizers. Next, we introduce a novel realization of this framework: specifically, we consider the logistic loss, handled in the MIP setting by a piece-wise linear approximation, and couple it with & ell;1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1$$\end{document} -regularization terms. The resulting optimal logistic classification tree model numerically proves to be able to induce trees with enhanced interpretability properties and competitive generalization capabilities, compared to the state-of-the-art MIP-based approaches.
Facilities such as waste plants or wind turbines are often referred to as obnoxious facilities because they negatively affect their nearby environment, for example, through noise or pollution. In the obnoxious pmedian...
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Facilities such as waste plants or wind turbines are often referred to as obnoxious facilities because they negatively affect their nearby environment, for example, through noise or pollution. In the obnoxious pmedian problem, a set of clients and a set of potential locations for obnoxious facilities are given. From the set of potential locations, p facilities must be opened. The goal is to place the facilities far away from the clients to avoid high negative effects. Existing approaches for this planning problem are either not scalable to large instances or not flexible in considering practical constraints that often arise in real -world settings. To address these limitations, we propose a matheuristic for the obnoxious p -median problem. First, the matheuristic generates diverse initial solutions, allowing an effective exploration of the solution space. Second, iteratively improves these initial solutions using enhanced mathematical models. We additionally propose clustering -based scaling technique to tackle large instances. Thus, our matheuristic is scalable to instances involving thousands of clients and potential locations and is flexible to incorporate practical constraints. Our computational results show that our matheuristic outperforms the leading metaheuristics from the literature on large instances and is competitive with the leading metaheuristics on small and medium instances. The features of our matheuristic can be generalized and applied to related planning problems.
Nowadays, energy-efficient scheduling has assumed a key role in ensuring the sustainability of manufacturing processes. In this context, we focus on the bi-objective problem of scheduling a set of jobs on identical pa...
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Nowadays, energy-efficient scheduling has assumed a key role in ensuring the sustainability of manufacturing processes. In this context, we focus on the bi-objective problem of scheduling a set of jobs on identical parallel machines to simultaneously minimize the maximum completion time and the total energy consumption over a time horizon partitioned into a set of discrete slots. The energy costs are determined by a time-of-use pricing scheme, which plays a crucial role in regulating energy demand and flattening its peaks. First, we uncover a symmetry-breaking property that characterizes the structure of the solution space of the problem. As a consequence, we provide a novel, compact mixed-integer linear programming formulation at the core of an efficient exact solution algorithm. A thorough experimental campaign shows that the use of the novel mathematical programming formulation enables the solution of larger-scale instances and entails a reduction in the computational times as compared to the formulation already available in the literature. Furthermore, we propose a new heuristic that improves the state-of-the-art in terms of required computational effort and quality of solutions. Such a heuristic outperforms the existing heuristics for the problem and is also capable of speeding up the exact solution algorithm when used for its initialization. Finally, we introduce a novel dynamic programming algorithm that is able to compute the optimal timing of the jobs scheduled on each machine to further improve the performance of the new heuristic.& COPY;2023 Elsevier B.V. All rights reserved.
Unit Commitment (UC) and Optimal Power Flow (OPF) are two fundamental problems in short-term electric power systems planning that are traditionally solved sequentially. The state-of-the-art mostly uses a direct curren...
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Unit Commitment (UC) and Optimal Power Flow (OPF) are two fundamental problems in short-term electric power systems planning that are traditionally solved sequentially. The state-of-the-art mostly uses a direct current (DC) approximation of the power flow equations. However, utilizing the DC approach in the UC-level may lead to infeasible or suboptimal generator commitment schedules for the OPF problem. In this paper, we aim to simultaneously solve the UC Problem with alternating current (AC) power flow equations, which combines the challenging nature of both UC and OPF Problems. Due to the highly nonconvex nature of the AC flow equations, we utilize the mixed-integer second-order cone programming (MISOCP) relaxation of the UC Problem as the basis of our solution approach. The MISOCP relaxation is utilized for finding both a lower bound and a candidate generator commitment schedule. Once this schedule is obtained, we solve a multi-period OPF problem to obtain feasible solutions for the UC problem with AC power flows. For smaller instances, we develop two different algorithms that exploit the recent advances in the OPF literature and obtain high-quality feasible solutions with provably small optimality gaps. For solving larger instances, we develop a Lagrangian decomposition based approach that yields promising results.
In the evolving landscape of logistics, drone technology presents a solution to the challenges posed by traditional ground-based deliveries, such as traffic congestion and unforeseen road closures. This research addre...
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In the evolving landscape of logistics, drone technology presents a solution to the challenges posed by traditional ground-based deliveries, such as traffic congestion and unforeseen road closures. This research addresses the Truck-Drone Delivery Problem (TDDP), wherein a truck collaborates with a drone, acting as a mobile charging and storage unit. Although the Traveling Salesman Problem (TSP) can represent the TDDP, it becomes computationally burdensome when nodes are dynamically altered. Motivated by this limitation, our study's primary objective is to devise a model that ensures swift execution without compromising the solution quality. We introduce two meta-heuristics: the Strawberry Plant, which refines the initial truck schedule, and Genetic Algorithms, which optimize the combined truck-drone schedule. Using "Dataset 1" and comparing with the Multi-Start Tabu Search (MSTS) algorithm, our model targeted costs to remain within 10% of the optimum and aimed for a 73% reduction in the execution time. Of the 45 evaluations, 37 met these cost parameters, with our model surpassing MSTS in eight scenarios. In contrast, using "Dataset 2" against the CPLEX solver, our model optimally addressed all 810 experiments, while CPLEX managed only 90 within the prescribed time. For 20-customer scenarios and more, CPLEX encountered memory limitations. Notably, when both methods achieved optimal outcomes, our model's computational efficiency exceeded CPLEX by a significant margin. As the customer count increased, so did computational challenges, indicating the importance of refining our model's strategies. Overall, these findings underscore our model's superiority over established solvers like CPLEX and the economic advantages of drone-assisted delivery systems.
The planning of efficient shift schedules is a key challenge for many service companies whose economic success heavily relies on the efficient employment of personnel. In spite of the recent advances in autonomous dri...
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The planning of efficient shift schedules is a key challenge for many service companies whose economic success heavily relies on the efficient employment of personnel. In spite of the recent advances in autonomous driving, mobility services, such as ride pooling, still heavily rely on the use of human drivers and will presumably remain in this category in the near to midterm. As a consequence, shift scheduling of drivers is one of the key success factors in the current industry environment. Determining appropriate shifts that minimize an under- and oversupply of vehicles for all planning periods is a challenging task, since demand can vary heavily over time and the assignment flexibilities are limited due to driver preferences and regulations. In this work, we present a shift scheduling model for ridepooling services. Moreover, we introduce a data generator for instances with realistic properties of a ridepooling service. Using it, we study the effect of different kinds of flexibilities on solution quality.
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