In the current China railway system, the freight transportation service network is demand category oriented and is designed by using a hierarchical approach. This simplifies the design procedure, but reduces the flexi...
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In the current China railway system, the freight transportation service network is demand category oriented and is designed by using a hierarchical approach. This simplifies the design procedure, but reduces the flexibility and optimality of the plan. To make full use of the limited railway capacity and to improve the service flexibility and quality, this paper addresses the freight service network design problem with simultaneous consideration of mixed blocking policies and mixed service classes for heterogeneous demands. A mixed integer linear programming model is developed for the problem to maximize the operator's total transportation profit. Furthermore, a two-stage decomposition framework is proposed to solve the optimization model, where both exact and heuristic algorithms are employed. Extensive computational experiments on artificial and real-world instances indicate that the proposed solution approaches can generate high-quality solutions within reasonable time frame. Moreover, the mixed blocking policies could attract more freight demands and tend to result in higher profits, compared to the single blocking policy.
This paper introduces an on-demand sequencing and scheduling framework for Urban Air Mobility (UAM) with electric vertical takeoff and landing (eVTOL) aircraft. Safety and efficiency, considering factors such as batte...
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This paper introduces an on-demand sequencing and scheduling framework for Urban Air Mobility (UAM) with electric vertical takeoff and landing (eVTOL) aircraft. Safety and efficiency, considering factors such as battery state of charge and charging infrastructure, are critical factors for UAM operations. A new scheduling framework integrating considerations for battery consumption, parking and charging infrastructure, vertiport throughput, and fleet heterogeneity to maximize the operational efficiency of the eVTOL UAM fleet is proposed. A solution methodology utilizing a genetic algorithm and receding horizon scheduling achieves near-optimal solutions with an average optimality gap of 5.8% and a runtime of less than 1 min for dynamic scheduling. A case study based on the 2024 Paris Olympic air taxi operations demonstrates the efficacy of the proposed problem formulation and solution method.
This paper considers an N-pursuer-M-evader scenario involving L virtual targets. The virtual targets serve as an intermediary target for the pursuers, allowing the pursuers to delay their final assignment to the evade...
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This paper considers an N-pursuer-M-evader scenario involving L virtual targets. The virtual targets serve as an intermediary target for the pursuers, allowing the pursuers to delay their final assignment to the evaders. However, upon reaching the virtual target, the pursuers must decide which evader to capture. It is assumed that there are more pursuers than evaders and that the pursuers are faster than the evaders. The objective is two-part: first, assign each pursuer to a virtual target and evader such that the pursuer team's energy is minimized, and, second, choose the virtual targets' locations for this minimization problem. The approach taken is to consider the Apollonius geometry between each pursuer's virtual target location and each evader. Using the constructed Apollonius circles, the pursuer's travel distance and maneuver at a virtual target are obtained. These metrics serve as a gauge for the total energy required to capture a particular evader and are used to solve the joint virtual target selection and pursuer-evader assignment problem. This paper provides a mathematical definition of this problem, the solution approach taken, and an example. Following the example, a Monte Carlo analysis is performed, demonstrating the efficacy of the algorithm and its suitability for real-time applications.
Deep Neural Networks (DNNs) have found successful applications in various non-safety-critical domains. However, given the inherent lack of interpretability in DNNs, ensuring their prediction accuracy through robustnes...
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ISBN:
(纸本)9783031664557;9783031664564
Deep Neural Networks (DNNs) have found successful applications in various non-safety-critical domains. However, given the inherent lack of interpretability in DNNs, ensuring their prediction accuracy through robustness verification becomes imperative before deploying them in safety-critical applications. Neural Network Verification (NNV) approaches can broadly be categorized into exact and approximate solutions. Exact solutions are complete but time-consuming, making them unsuitable for large network architectures. In contrast, approximate solutions, aided by abstraction techniques, can handle larger networks, although they may be incomplete. This paper introduces AccMILP, an approach that leverages abstraction to transform NNV problems into mixed integer linear programming (MILP) problems. AccMILP considers the impact of individual neurons on target labels in DNNs and combines various relaxation methods to reduce the size of NNV models while ensuring verification accuracy. The experimental results indicate that AccMILP can reduce the size of the verification model by approximately 30% and decrease the solution time by at least 80% while maintaining performance equal to or greater than 60% of MIPVerify. In other words, AccMILP is well-suited for the verification of large-scale DNNs.
This study develops a multi-echelon closed-loop supply chain (CLSC) optimization model for durable products by considering refurbishment, recycling processes, and carbon regulation. A mixed integer linear programming ...
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This study develops a multi-echelon closed-loop supply chain (CLSC) optimization model for durable products by considering refurbishment, recycling processes, and carbon regulation. A mixed integer linear programming (MILP) is developed for CLSC network design involving suppliers, manufacturers, refurbishment centers, recycling centers, disposal centers, assembly centers, collection centers, and retailers. A carbon cap-and-trade policy is adopted to lessen the emissions emitted from some activities in CLSC. The objectives of the study are to determine the optimal allocations of echelons and to investigate the influences of collection rate, refurbishment rate, recycling rate and carbon policy on CLSC. A numerical example is presented to illustrate the application of the proposed model, and a sensitivity analysis is provided to investigate the effect of key parameters on the model's behavior and system performance. The results show that the changes in collection, refurbishment, and recycling rates significantly influence the optimal allocation decisions. The results also show that by adopting a carbon cap-and-trade policy, CLSC can benefit both economically and environmentally. However, the level of benefits obtained will depend significantly on the size of the carbon cap and the selling or buying price of carbon in the market.
The article studies a variant of the facility location problem applied for electric vehicle charging station infrastructure in Romania. The best places are chosen from the locations without a charging station to minim...
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Several factors affect the flexibility and the complexity of the project selection and the contractor selection problems. Project portfolio managers are expected to select the best combination of projects and contract...
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Several factors affect the flexibility and the complexity of the project selection and the contractor selection problems. Project portfolio managers are expected to select the best combination of projects and contractors considering multiple conflicting objectives in a multi-period planning horizon. In this paper, we propose an integrated project portfolio optimization and contractor selection problem. The problem is modeled through a multi-objective mixed integer linear programming (MILP) model. Three solution approaches including Goal programming (GP), Fuzzy Goal programming (FGP), and fuzzy goal programming considering a fuzzy preference relationship are proposed. All solution approaches have been applied to a real case. The computational results show the out performance of FGP considering fuzzy relations. The time complexity of the proposed models in the sense of the relation of CPU time and the number constraints and variables of the models were discussed.
In response to the highly competitive market and the demand for a low-carbon economy, logistics enterprises have paid special attention not only to cost management but also to environmental sustainability for better d...
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The fourth industrial revolution (Industry 4.0) has enabled rapid product variations and technological developments such as reconfigurable manufacturing systems (RMSs). Planning and scheduling in RMS differ from tradi...
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The fourth industrial revolution (Industry 4.0) has enabled rapid product variations and technological developments such as reconfigurable manufacturing systems (RMSs). Planning and scheduling in RMS differ from traditional systems;therefore, the manufacturing industry has faced implementation barriers. This research proposes a new mixed-integerlinearprogramming (MILP) formulation for process planning and scheduling in RMSs. The formulation, considers new aspects such as number of products, their quantity and complexity, calibration rate, work-in-process (WIP), and inventory management. Moreover, this research promotes RMS effectiveness for business and provides new insights on the effects of different factors on the overall performance. The applicability of the proposed formulation is illustrated with a case study. The results showed that RMS outperforms a traditional system with 30% savings in cost and up to a 25% increase in demand fulfillment. Sensitivity analyses are conducted to investigate the effect of different parameters on the cost-effectiveness of RMS compared to traditional manufacturing systems. Analysis of variance (ANOVA) highlighted the importance of the reconfigurability of the machines compared to other settings
This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, ...
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This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, which are affected by a marketing strategy. Customer demands need to be satisfied by the supply chain through production, transportation and distribution. The optimization problem for coordination of production, transportation and distribution is first formulated as a linearprogramming with demands as input parameters in the constraint. Our objective is to maximize the expectation of the optimal profit of the supply chain given random demands by selecting an optimal marketing strategy. A simulation optimization technique is proposed to control the generation of random demands and solve the linearprogramming for efficiently learning the optimal marketing strategy. Numerical results show that our method can significantly improve the expected profit of the supply chain and reduce the computational burden of solving linearprogramming for achieving a given level of probability of correct selection of the optimal marketing strategy. Furthermore, we extend the optimization problem to a mixedintegerprogramming and also demonstrate the computational efficiency of our proposed method.
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