This paper deals with the problem of the design and configuration of a distribution system that supports the delivery of goods to customers using various commercial channels. In this sense, a modelled distribution sys...
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This paper deals with the problem of the design and configuration of a distribution system that supports the delivery of goods to customers using various commercial channels. In this sense, a modelled distribution system, called an omni-channel distribution system, should ensure the delivery of the same products in different ways while guaranteeing its highest efficiency. The authors propose a single-criterion mixed-integer programming model for the configuration of a complex distribution system of goods in an omni-channel environment. A minimized cost function, composed of key logistics elements including transport, warehouse location, and manipulation processes, is the objective function. The model was experimentally verified, and as a result, potential savings of around 5-8% of the omni-channel design distribution system costs versus the multi-channel distribution system costs were identified. In the paper, an extended sensitivity analysis is also performed to show how specific parameters of the model can change the final recommendation.
Boolean quadratic optimization problems occur in a number of applications. Their mixedinteger-continuous nature is challenging, since it is inherently NP-hard. For this motivation, semidefinite programming relaxation...
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Boolean quadratic optimization problems occur in a number of applications. Their mixedinteger-continuous nature is challenging, since it is inherently NP-hard. For this motivation, semidefinite programming relaxations (SDR's) are proposed in the literature to approximate the solution, which recasts the problem into convex optimization. Nevertheless, SDR's do not guarantee the extraction of the correct binary minimizer. In this paper, we present a novel approach to enhance the binary solution recovery. The key of the proposed method is the exploitation of known information on the eigenvalues of the desired solution. As the proposed approach yields a non-convex program, we develop and analyze an iterative descent strategy, whose practical effectiveness is shown via numerical results. Copyright (C) 2020 The Authors.
We present optimization methods for predictive maintenance scheduling of building heating, ventilation, and air conditioning (HVAC) systems via mixed-integer programming. The optimization framework we introduce is com...
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We present optimization methods for predictive maintenance scheduling of building heating, ventilation, and air conditioning (HVAC) systems via mixed-integer programming. The optimization framework we introduce is composed of optimization models and parameter generation methods. Optimization models with time discretized into daily time periods are developed to enable the consideration of slow operation-dependent degradation during long horizons while maintaining computational efficiency. To improve the quality of the obtained maintenance schedules, system operation is simultaneously optimized. Parameter generation methods are introduced to provide parameters for constructing operation-related constraints. The proposed optimization framework can account for various HVAC systems with complex configurations. We show the quality of the generated operation-related parameters, and we provide medium-horizon case studies of central plants to show the model performance. (C) 2020 Elsevier B.V. All rights reserved.
This paper provides a solution to conflict resolutions between Autonomous Vehicles (AV) crossing an urban intersection. The conflict resolution problem is formulated as an optimal control problem, where the objective ...
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This paper provides a solution to conflict resolutions between Autonomous Vehicles (AV) crossing an urban intersection. The conflict resolution problem is formulated as an optimal control problem, where the objective is to minimize the energy consumption of all the vehicles, while avoiding collisions. Since the problem has a combinatorial nature, it is tackled though a sequential mixed-integer quadratically constrained programming approach. Simulation results show that since the AVs do not need to follow specific driving rules, the intersection crossing order is chosen to optimize the overall energy consumption. The research outcome underlines the benefits of moving towards fully autonomous systems which will allow for higher traffic throughput. Furthermore, the proposed formulation is the starting point for future explorations towards real-time implementation. Copyright (C) 2020 The Authors.
This paper studies the strategic problem of finding a cost optimal fleet of vessels to support maintenance operations at offshore wind farms. A dual-level stochastic model is formulated, taking into account both long-...
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This paper studies the strategic problem of finding a cost optimal fleet of vessels to support maintenance operations at offshore wind farms. A dual-level stochastic model is formulated, taking into account both long-term strategic uncertainty and short-term operational uncertainty in a single optimization model. The model supports wind farm owners in making strategic decisions regarding the number, placement, charter length, and types of vessels to charter, to meet maintenance demands throughout the lifetime of a wind farm. To evaluate the quality of strategic fleet size and mix decisions, the model also considers the operational decisions of how to utilize the fleet to support maintenance operations. The model accounts for strategic uncertainties that have not been considered in previously developed optimization models for offshore wind, such as uncertainty related to long-term trends in electricity prices and subsidy levels, the stepwise development of wind farms, and technology development in the vessel industry. To solve the proposed stochastic programming model we have developed an ad hoc integer L-shaped method, with customized optimality cuts. The computational experiments show that the proposed method outperforms solving the deterministic equivalent using a commercial MIP solver.
In this paper we consider multi-hop wireless sensor networks using time division multiple access to radio channel. It is assumed that packets, periodically generated at the sensors at the beginning of each consecutive...
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ISBN:
(纸本)9781728163000
In this paper we consider multi-hop wireless sensor networks using time division multiple access to radio channel. It is assumed that packets, periodically generated at the sensors at the beginning of each consecutive time frame, are to be delivered to (Internet) gateways. Although in the nominal network condition it is sufficient when the packet streams produced by each sensor reach one of the gateways, from resilience viewpoint it would be advantageous to deliver them to multiple gateways, preferably using the broadcast capability of wireless nodes. Clearly, under the assumption of periodic packet traffic, a longer frame must be used when packets are to be delivered (without losses) to multiple gateways rather to single gateways. Since traffic throughput (one packet per stream per frame) is inversely proportional to the frame length, a clear tradeoff between resilience and traffic throughput arises. In the paper we present a numerical study, using a modification of our previous optimization model for frame length minimization in the considered kind of networks, that illustrates the relation between the frame length and the number of gateways each packet stream is supposed to reach. A general conclusion is that the frame length increase is non-negligible when multiple gateways are assumed, and depends heavily on the way the resulting multicast traffic is handled and how the particular subsets of gateways are selected for particular streams.
In hyperthermia treatments, cancer tissue is heated to enhance the desired effects of radio- and chemotherapies. A powerful technology for noninvasive feedback-controlled hyperthermia is magnetic-resonance-guided high...
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In hyperthermia treatments, cancer tissue is heated to enhance the desired effects of radio- and chemotherapies. A powerful technology for noninvasive feedback-controlled hyperthermia is magnetic-resonance-guided high-intensity focused ultrasound (MR-HIFU), which enables fast and millimeter-accurate heating inside the body. Electronic beam steering allows for volumetric heating, but due to its limited steering range can only be used to treat small tumors. For the treatment of larger tumors, the transducer itself must be mechanically relocated as well. Due to system limitations, however, the admissible transducer positions must be restricted to a finite set that is chosen a priori. Moreover, non-negligible time is needed for transducer relocation, during which no heating is possible. In this paper, we present a mixed-integer model predictive controller that simultaneously optimizes over the power deposition by electronic beam steering - a continuous subproblem - as well as the mechanical transducer motions - a discrete subproblem. By incorporating model knowledge of the tissue's thermal response and of the transducer carrier motion system into the predictive algorithm, the controller optimizes treatment temperature while respecting temperature and actuation constraints. The performance of the proposed feedback control setup is demonstrated by means of simulation. Copyright (C) 2020 The Authors.
The dynamic facility layout problem (DFLP) is to decide the locations of the departments in a facility over multiple planning periods. The main challenge in the DFLP is that there are two conflicting objectives of the...
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The dynamic facility layout problem (DFLP) is to decide the locations of the departments in a facility over multiple planning periods. The main challenge in the DFLP is that there are two conflicting objectives of the problem: minimizing the material handling cost and the rearrangement cost. The cost of changing the structure of the layout over the planning horizon is also considered. Therefore, we model and solve the unequal area zone-based DFLP (ZDFLP) where the dimensions of the departments are decision variables and the departments are assigned to flexible zones with a pre-structured positioning. A zone-based block layout inherently includes possible aisle structures which can easily be adapted to the material handling system in use. This is particularly important in the DFLP because the changes in a block layout may require structural modifications in the material handling system, which in turn may be very costly. The proposed approach also considers determining relative department locations, their dimensions, as well as input/output (I/O) points concurrently for the first time in the literature. A new matheuristic, which combines concepts from simulated annealing, variable neighbourhood search, and mixed-integer programming, is used to solve the ZDFLP with promising results.
We address the problem of scheduling tasks characterized by dynamic completion time behavior. Existing scheduling methodologies reduce problems to using constant task durations and are therefore not suitable for probl...
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We address the problem of scheduling tasks characterized by dynamic completion time behavior. Existing scheduling methodologies reduce problems to using constant task durations and are therefore not suitable for problems with more complex completion time behavior. One such problem is scheduling applications on computer networks with time-varying communication channel capacities. We present a methodology that can be used to solve such problems. The specific contributions of this work are: (a) a novel representation for the completion time of tasks in scheduling problems, (b) a method for approximating that representation efficiently using an affine over-approximation, and (c) a mixed-integer programming-based formulation to solve a variety of scheduling problems using this representation and approximation. The resulting framework can be used to solve scheduling problems with time-varying task durations, and thus solve problems with time-varying communication links or resources. A case study is provided, in which we devise a scheduling scheme for computation on a computer network composed of processors onboard a satellite constellation. This example is an instance of scheduling applications on computer networks with known time-varying communication channel capacities and the results of the case study demonstrate the improvement of using the approximation method over existing methods.
Shorter product life cycles in the chemical industry have led to uncertain demand in terms of volume, location and type. Modular production concepts can be used to serve such volatile customer demand more efficiently....
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Shorter product life cycles in the chemical industry have led to uncertain demand in terms of volume, location and type. Modular production concepts can be used to serve such volatile customer demand more efficiently. Modular plants increase the tactical flexibility of the supply chain substantially compared to centralized production with large-scale plants. Furthermore, production facilities can be flexibly opened, deactivated and reactivated. Modular plants can be relocated in the medium term, and their production capabilities can be reconfigured via process module changes. This work proposes new mixed-integer programming formulations for the tactical planning of production networks in the chemical industry;these formulations prescribe the volume, location, and process of modular plants in the production network over a multi-period planning horizon. The economic value of modular flexibility is investigated in a case study from the chemical industry under the consideration of diverse demand structures. The results have several managerial implications for the technical implementation and development of modular production technology in the chemical industry. (C) 2019 Elsevier B.V. All rights reserved.
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