There is increasing interest in utility-scale solar power plants with storage which can flexibly dispatch renewable energy to the grid. However, plant design possesses many degrees of freedom and non-obvious trade-off...
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There is increasing interest in utility-scale solar power plants with storage which can flexibly dispatch renewable energy to the grid. However, plant design possesses many degrees of freedom and non-obvious trade-offs in performance. Software tools can estimate or optimize the performance of a specific plant configuration under market and weather conditions of interest;the associated cost parameters and operating assumptions strongly influence estimates of plant performance and decisions regarding optimal sizing. We employ the National Renewable Energy Laboratory's Hybrid Optimization and Performance Platform, which incorporates optimal dispatch when evaluating plant performance, and investigate the sensitivity to weather and market conditions, operating limitations, and the presence of a capacity-based incentive. We demonstrate changes in plant performance and optimal sizing with respect to these inputs and discuss implications. Results show that PV-with-battery designs are more profitable under our assumptions, but that designs including a concentrated solar power (CSP) system produce significantly greater annual energy;and that CSP-with-thermal energy storage designs maximizing the benefit-to-cost ratio have an input-dependent linear relationship between the CSP field solar multiple and the hours of storage as the project budget varies.
With the continuous expansion of shipping demand, the problem of environmental pollution is becoming increasingly severe. An effective strategy for mitigating carbon emissions is the implementation of well-designed sh...
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ISBN:
(纸本)9781510674479
With the continuous expansion of shipping demand, the problem of environmental pollution is becoming increasingly severe. An effective strategy for mitigating carbon emissions is the implementation of well-designed shipping routes. To address this concern, we construct a branch transportation network under the hub-and-spoke route structure and then formulate a mixed- integerprogramming model with the aim of minimizing overall operational costs. A genetic algorithm is designed to solve the model and determine the optimal combination of liner routes. Taking the main ports in the Bohai Rim region as an example, a series of numerical experiments are carried out. The results demonstrate that the deceleration of navigation is a feasible approach for carbon emission reduction.
In many real-world mixed-integer optimization problems from engineering, the side constraints can be subdivided into two categories: constraints which describe a certain logic to model a feasible allocation of resourc...
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In many real-world mixed-integer optimization problems from engineering, the side constraints can be subdivided into two categories: constraints which describe a certain logic to model a feasible allocation of resources (such as a maximal number of available assets, working time requirements, maintenance requirements, contractual obligations, etc.), and constraints which model physical processes and the related quantities (such as current, pressure, temperature, etc.). While the first type of constraints can often easily be stated in terms of a mixed-integer program (MIP), the second part may involve the incorporation of complex non-linearities, partial differential equations or even a black-box simulation of the involved physical process. In this work, we propose the integration of a trained tree-based classifier-a decision-tree or a random forest, into a mixed-integer optimization model as a possible remedy. We assume that the classifier has been trained on data points produced by a detailed simulation of a given complex process to represent the functional relationship between the involved physical quantities. We then derive MIP-representable reformulations of the trained classifier such that the resulting model can be solved using state-of-the-art solvers. At the hand of several use cases in terms of possible optimization goals, we show the broad applicability of our framework that is easily extendable to other tasks beyond engineering. In a detailed real-world computational study for the design of stable direct-current power networks, we demonstrate that our approach yields high-quality solutions in reasonable computation times.
In this paper, we investigate an energy-aware flow shop scheduling problem under sequence-dependent setup times, group scheduling, and renewable energy constraints. We aim to minimize the total energy cost dependent o...
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In this paper, we investigate an energy-aware flow shop scheduling problem under sequence-dependent setup times, group scheduling, and renewable energy constraints. We aim to minimize the total energy cost dependent on time-of-use electricity tariffs. To this end, we develop two mixed-integer linear programming models, including a time-unit index model and a time-interval index model. Besides, we develop a decomposition-based heuristic algorithm to solve efficiently medium-size instances. Using extensive computational experiments, we show that the heuristic algorithm outperforms both developed models, and the time-interval index model indicates superior performance than the time-unit index model. Finally, we provide a set of sensitivity analyses and evaluation of economic performance.& COPY;2022 Elsevier B.V. All rights reserved.
Hematopoietic stem cell transplantation is a usual treatment used against cancers and blood diseases. To diminish the incidence and severity of graft-versus-host disease, donors and recipients should be sufficiently m...
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Hematopoietic stem cell transplantation is a usual treatment used against cancers and blood diseases. To diminish the incidence and severity of graft-versus-host disease, donors and recipients should be sufficiently matched in terms of human leukocyte antigen. Iranians' stem cell bank was established to help patients search for their compatible donors. However, in many cases, matched donors are not found in the domestic bank. The low efficiency of the national adult stem cell bank and the expensive cost of adding donors are the main challenges of this study, driving us to design the stem cell donor network for the first time. Initially, using the common weights data envelopment analysis approach, we identified the most efficient donation zones. Afterward, we proposed a mixed-integer programming model in order to design the donor network. Finally, to show the applicability of the model, we provided a real case study of Iran. The results of this study shed light on three important questions: Where should the stem cell donors be attracted from? How many donors does the stem cell bank need to increase the odds of finding a matched donor within the country? And what would be the optimal location and allocation of the donor network facilities?
PurposeMaintenance stands are the most valuable maintenance resources and provide the necessary maintenance space and maintenance facilities for aircraft maintenance. To expand the maintenance market, maintenance, rep...
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PurposeMaintenance stands are the most valuable maintenance resources and provide the necessary maintenance space and maintenance facilities for aircraft maintenance. To expand the maintenance market, maintenance, repair and overhaul (MRO) urgently need to achieve a reasonable schedule between aircraft maintenance requirements and maintenance stand capability to improve aircraft maintenance continuity and reduce the risk of scratching due to aircraft movement. This study aims to design a maintenance stand scheduling (MSS) model based on spatiotemporal constraints to solve the problem of maintenance stand schedules. Design/methodology/approachTo address the problem of maintenance stand schedules, this study introduces mixed-integer programming algorithm to design the MSS model on the basis of classical hybrid flow shop structure. When designing the optimization objective function of MSS modeling, the spatiotemporal constraints are mainly considered. Specifically, first, the spatial constraints between maintenance stands are fully considered so that more aircraft can be parked in the workshop. Second, the optimization objective is designed to minimize the number of aircraft movements by defining multiple maintenance capabilities of the stand. Finally, a solution based on spatiotemporal constraints is proposed in the solving process. FindingsA set of MRO production data from Guangzhou is used as a test data set to demonstrate the effectiveness of the proposed MSS model. Originality/valueThe types of maintenance stands are defined and divided into four categories: fixed stand, temporary stand, half-body stand and engine ground test stand, which facilitates optimal modeling;a new scheduling model is designed considering both temporal constraints and spatial constraints, which can improve both the utilization of maintenance stand and safety (reduce the risk of scratching between aircraft).
In considering route optimization from multiple distribution centers called depots via some intermediate facilities called satellites to final customers with multiple commodities request, we introduce the Multi-Commod...
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In considering route optimization from multiple distribution centers called depots via some intermediate facilities called satellites to final customers with multiple commodities request, we introduce the Multi-Commodity Two-Echelon Vehicle Routing Problem with Satellite Synchronization (MC-2E-VRPSS). The MC-2E-VRPSS involves the transportation from multiple depots to satellites on the first echelon and the deliveries from satellites to final customers on the second echelon. The MC-2E-VRPSS integrates satellite synchronization constraints and time window constraints for satellites on the two-echelon network and aims to determine cost-minimizing routes for the two echelons. The satellite synchronization constraints which trucks from the multiple depots to some satellites need to be coordinated guarantee the efficiency of the second echelon network. In this study, we develop a mixedintegerprogramming model for the MC-2E-VRPSS. For validating the model formulation, we conduct the computational experiments on a set of small-scale instances using GUROBI and an adaptive large neighborhood search (ALNS) heuristic which we develop for the problem. Furthermore, the computation experiments for evaluating the applicability of the ALNS heuristic compared with large neighborhood search (LNS) on a set of large-scale instances are also conducted, which proved the effectiveness of the ALNS.
Due to enacting laws and increasing awareness of environmental issues, the design of a Closed-Loop Supply Chain network (CLSC) has received attention. The design of CLSC is a strategic issue with long-term effects and...
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Due to enacting laws and increasing awareness of environmental issues, the design of a Closed-Loop Supply Chain network (CLSC) has received attention. The design of CLSC is a strategic issue with long-term effects and faces uncertainty in the real world, which affects its performance. In the studies on CLSC, robust optimization, cognitive uncertainty, and soft constraints are not assessed simultaneously in modeling and this area is deficient. So, in this investigation, mixed-robust-possibilistic-flexible programming is proposed. This research develops CLSC problem-solving approaches under conditions of cognitive uncertainty and soft constraints and leads to the presentation of operation engineering and optimization in CLSC. The Decision Maker's (DM) risk level is measured flexibly using a credibility criterion. Also, deviation of possibilistic and constraint violations are controlled in the proposed approach. To evaluate the presented approach, a study is executed to design a paper supply chain with economic and environmental objectives. The results show that it is possible to determine the number, place of facilities, and optimal flow of products and materials between different centers. The proposed approach and multi-objective model solution method are capable of providing realistic and flexible solutions based on the trade-off between other objectives and DMs' preferences. The performance of the proposed approach was analyzed and results confirmed the developed approach compared to similar approaches for the design of CLSC.
This letter investigates a coordinated multi-agent path planning and tracking method. The solution of a pre-processed dynamic scheduling problem performs target assignment and provides optimal starting times and paths...
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This letter investigates a coordinated multi-agent path planning and tracking method. The solution of a pre-processed dynamic scheduling problem performs target assignment and provides optimal starting times and paths for each agent. Afterwards, a linear model predictive controller ensures robust and fast path tracking while preventing agents from collisions. This task is formulated as a discretized quadratic programming (QP) problem and is solved using an in-house developed semi-smooth Newton method. Numerical experiments have demonstrated the efficiency of the approach.
This research considers the problem of scheduling jobs on unrelated parallel machines with inserted idle times to minimize the earliness and tardiness. The aims at investigating how particular objective value can be i...
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ISBN:
(纸本)9783037858967
This research considers the problem of scheduling jobs on unrelated parallel machines with inserted idle times to minimize the earliness and tardiness. The aims at investigating how particular objective value can be improved by allowing machine idle time and how quality solutions can be more effectively obtained. Two mixed-integer programming formulations combining with three dispatching rules are developed to solve such scheduling problems. They can easy provide the optimal solution to problem involving about nine jobs and four machines. From the results of experiments, it is found that: (1) the inserted idle times decreases objective values more effectively;(2) three dispatching rules are very competitive in terms of efficiency and quality of solutions.
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