210 research outputs found
Productivity Measure in Using Enterprise Resource Planning System in Selected Companies in Beijing, China
With the globalization of economic development and social development, the business environment of enterprises has changed. Only by continuously improving the digital level and management level of enterprises can they survive in the fierce global competition and develop. In this economic and social environment, enterprise managers need to implement Enterprise Resource Planning (ERP) system in order to better operate and manage enterprise business and improve enterprise operating profit. Its purpose is to standardize and restructure enterprise process, financial process, capital flow and information flow, and improve enterprise operation ability, profitability and growth ability. The implementation of ERP system will have an impact on the level of enterprise productivity. Therefore, taking manufacturing companies as the research object, it is of great significance to explore the impact of ERP system implementation on the level of enterprise productivity.Taking the manufacturing companies selected in Beijing, China as the research object, this paper uses the method of combining theoretical analysis and empirical research to study the impact of ERP system on enterprise productivity and enterprise performance, so as to improve the industry’s understanding of ERP system. This paper uses data statistics and empirical research methods to analyze the impact of ERP system on enterprise productivity and enterprise performance. Firstly, it introduces the background and significance of the research, and then reviews and combs the relevant literature at home and abroad on ERP system, enterprise performance and the impact of ERP system on enterprise performance; Based on management information system theory, business process reengineering theory and financial performance theory, financial performance is measured from three aspects: operation ability, profitability and growth ability. Reasonably select relevant indicators to build the index system. This paper selects the enterprise financial data of Beijing manufacturing company implementing ERP system from 2013 to 2015, and uses the data model to make an empirical analysis on the impact of manufacturing company implementing ERP system on enterprise productivity and enterprise performance. It is found that in China, the implementation of ERP system by Beijing manufacturing company will have a certain impact on enterprise productivity and enterprise performance Combined with the principle of ERP, this paper discusses the impact of ERP Implementation on enterprise performance. In the empirical research part, descriptive statistics and Wilcoxon paired rank sum test are used to verify the impact of ERP Implementation on enterprise performance. The results show that the implementation of ERP system will improve the operation ability, profitability and growth ability of enterprises. Therefore, when preparing for the ERP system, enterprises must do a full feasibility and demand analysis to ensure the smooth implementation of the ERP system. Finally, this paper gives some suggestions on the impact of ERP Implementation on enterprise performance
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search
(L2S) solver for routing problems. It learns to perform flexible k-opt
exchanges based on a tailored action factorization method and a customized
recurrent dual-stream decoder. As a pioneering work to circumvent the pure
feasibility masking scheme and enable the autonomous exploration of both
feasible and infeasible regions, we then propose the Guided Infeasible Region
Exploration (GIRE) scheme, which supplements the NeuOpt policy network with
feasibility-related features and leverages reward shaping to steer
reinforcement learning more effectively. Additionally, we equip NeuOpt with
Dynamic Data Augmentation (D2A) for more diverse searches during inference.
Extensive experiments on the Traveling Salesman Problem (TSP) and Capacitated
Vehicle Routing Problem (CVRP) demonstrate that our NeuOpt not only
significantly outstrips existing (masking-based) L2S solvers, but also
showcases superiority over the learning-to-construct (L2C) and
learning-to-predict (L2P) solvers. Notably, we offer fresh perspectives on how
neural solvers can handle VRP constraints. Our code is available:
https://github.com/yining043/NeuOpt.Comment: Accepted at NeurIPS 202
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
Learning heuristics for vehicle routing problems (VRPs) has gained much
attention due to the less reliance on hand-crafted rules. However, existing
methods are typically trained and tested on the same task with a fixed size and
distribution (of nodes), and hence suffer from limited generalization
performance. This paper studies a challenging yet realistic setting, which
considers generalization across both size and distribution in VRPs. We propose
a generic meta-learning framework, which enables effective training of an
initialized model with the capability of fast adaptation to new tasks during
inference. We further develop a simple yet efficient approximation method to
reduce the training overhead. Extensive experiments on both synthetic and
benchmark instances of the traveling salesman problem (TSP) and capacitated
vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The
code is available at: https://github.com/RoyalSkye/Omni-VRP.Comment: Accepted at ICML 202
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs).
Traditionally, customizing ACO for a specific problem requires the expert
design of knowledge-driven heuristics. In this paper, we propose DeepACO, a
generic framework that leverages deep reinforcement learning to automate
heuristic designs. DeepACO serves to strengthen the heuristic measures of
existing ACO algorithms and dispense with laborious manual design in future ACO
applications. As a neural-enhanced meta-heuristic, DeepACO consistently
outperforms its ACO counterparts on eight COPs using a single neural model and
a single set of hyperparameters. As a Neural Combinatorial Optimization method,
DeepACO performs better than or on par with problem-specific methods on
canonical routing problems. Our code is publicly available at
https://github.com/henry-yeh/DeepACO.Comment: Accepted at NeurIPS 202
Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method
The stochastic shortest path problem is of crucial importance for the development of sustainable transportation systems. Existing methods based on the probability tail model seek for the path that maximizes the probability of arriving at the destination before a deadline. However, they suffer from low accuracy and/or high computational cost. We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy. By further adopting dynamic neural networks to learn the value function, our method can scale well to large road networks with arbitrary deadlines. Experimental results on real road networks demonstrate the significant advantages of our method over other counterparts
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
Recently, neural heuristics based on deep reinforcement learning have
exhibited promise in solving multi-objective combinatorial optimization
problems (MOCOPs). However, they are still struggling to achieve high learning
efficiency and solution quality. To tackle this issue, we propose an efficient
meta neural heuristic (EMNH), in which a meta-model is first trained and then
fine-tuned with a few steps to solve corresponding single-objective
subproblems. Specifically, for the training process, a (partial)
architecture-shared multi-task model is leveraged to achieve parallel learning
for the meta-model, so as to speed up the training; meanwhile, a scaled
symmetric sampling method with respect to the weight vectors is designed to
stabilize the training. For the fine-tuning process, an efficient hierarchical
method is proposed to systematically tackle all the subproblems. Experimental
results on the multi-objective traveling salesman problem (MOTSP),
multi-objective capacitated vehicle routing problem (MOCVRP), and
multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform
the state-of-the-art neural heuristics in terms of solution quality and
learning efficiency, and yield competitive solutions to the strong traditional
heuristics while consuming much shorter time.Comment: Accepted at NeurIPS 202
Dynamic modeling and optimal control of a positive buoyancy diving autonomous vehicle
The positive buoyancy diving autonomous vehicle combines the features of an Unmanned Surface Vessel (USV) and an Autonomous Underwater Vehicle (AUV) for marine measurement and monitoring. It can also be used to study reasonable and efficient positive buoyancy diving techniques for underwater robots. In order to study the optimization of low power consumption and high efficiency cruise motion of the positive buoyancy diving vehicle, its dynamic modeling has been established. The optimal cruising speed for low energy consumption of the positive buoyancy diving vehicle is determined by numerical simulation. The Linear Quadratic Regulator (LQR) controller is designed to optimize the dynamic error and the actuator energy consumption of the vehicle in order to achieve the optimal fixed depth tracking control of the positive buoyancy diving vehicle. The results demonstrate that the LQR controller has better performance than PID, and the system adjustment time of the LQR controller is reduced by approximately 56% relative to PID. The motion optimization control method proposed can improve the endurance of the positive buoyancy diving vehicle, and has a certain application value
Learning to Search for Job Shop Scheduling via Deep Reinforcement Learning
Recent studies in using deep reinforcement learning (DRL) to solve Job-shop
scheduling problems (JSSP) focus on construction heuristics. However, their
performance is still far from optimality, mainly because the underlying graph
representation scheme is unsuitable for modeling partial solutions at each
construction step. This paper proposes a novel DRL-based method to learn
improvement heuristics for JSSP, where graph representation is employed to
encode complete solutions. We design a Graph Neural Network based
representation scheme, consisting of two modules to effectively capture the
information of dynamic topology and different types of nodes in graphs
encountered during the improvement process. To speed up solution evaluation
during improvement, we design a novel message-passing mechanism that can
evaluate multiple solutions simultaneously. Extensive experiments on classic
benchmarks show that the improvement policy learned by our method outperforms
state-of-the-art DRL-based methods by a large margin
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