arXiv subjects

Feng Chu

Publications and source records attributed to Feng Chu.

6 recordsLinked to original sources

arXiv2026

Sequential Service Region Design with Capacity-Constrained Investment and Spillover Effect

Service region design determines the geographic coverage of service networks, shaping long-term operational performance. Capital and operational constraints preclude simultaneous large-scale deployment, requiring expansion to proceed sequentially. The resulting challenge is to determine when and where to invest under demand uncertainty, balancing intertemporal trade-offs between early and delayed investment and accounting for network effects whereby each deployment reshapes future demand through inter-regional connectivity. This study addresses a sequential service region design (SSRD) problem incorporating two practical yet underexplored factors: a $k$-region constraint that limits the number of regions investable per period and a stochastic spillover effect linking investment decisions to demand evolution. The resulting problem requires sequencing regional portfolios under uncertainty, leading to a combinatorial explosion in feasible investment sequences. To address this challenge, we propose a solution framework that integrates real options analysis (ROA) with a Transformer-based Proximal Policy Optimization (TPPO) algorithm. ROA evaluates the intertemporal option value of investment sequences, while TPPO learns sequential policies that directly generate high option-value sequences without exhaustive enumeration. Numerical experiments on realistic multi-region settings demonstrate that TPPO converges faster than benchmark DRL methods and consistently identifies sequences with superior option value. Case studies and sensitivity analyses further confirm robustness and provide insights on investment concurrency, regional prioritization, and the increasing benefits of adaptive expansion via our approach under stronger spillovers and dynamic market conditions.

cs.LG↗

arXiv2024

Physics-informed Meta-instrument for eXperiments (PiMiX) with applications to fusion energy

Data-driven methods (DDMs), such as deep neural networks, offer a generic approach to integrated data analysis (IDA), integrated diagnostic-to-control (IDC) workflows through data fusion (DF), which includes multi-instrument data fusion (MIDF), multi-experiment data fusion (MXDF), and simulation-experiment data fusion (SXDF). These features make DDMs attractive to nuclear fusion energy and power plant applications, leveraging accelerated workflows through machine learning and artificial intelligence. Here we describe Physics-informed Meta-instrument for eXperiments (PiMiX) that integrates X-ray (including high-energy photons such as $\gamma$-rays from nuclear fusion), neutron and others (such as proton radiography) measurements for nuclear fusion. PiMiX solves multi-domain high-dimensional optimization problems and integrates multi-modal measurements with multiphysics modeling through neural networks. Super-resolution for neutron detection and energy resolved X-ray detection have been demonstrated. Multi-modal measurements through MIDF can extract more information than individual or uni-modal measurements alone. Further optimization schemes through DF are possible towards empirical fusion scaling laws discovery and new fusion reactor designs.

physics.data-an↗

arXiv2023

Sustainable Collaborative Strategy in Pharmaceutical Refrigerated Logistics Routing Problem

The rapid growth of pharmaceutical refrigerated logistics poses sustainability challenges, including elevated costs, energy consumption, and resource inefficiency. Collaborating multiple depots can enhance logistics efficiency when standalone distribution centers have limited transport resources, i.e., refrigerated vehicles. However, the sustainable benefits and performance across different strategies remain unexplored. This study fills this research gap by addressing a refrigerated pharmaceutical routing problem. While many collaborative strategies prioritize economic and environmental benefits, our approach highlights a vital social indicator: maintaining vehicle flow equilibrium at each depot during collaboration. This ensures the stability of transport resources for all stakeholders, promoting sustainable collaborative logistics. The problem is formulated as a multi-depot vehicle routing problem with time windows (MDVRPTW). Three collaborative strategies using Clustering VRP (CLUVRP) and improved Open VRP (OVRP) are proposed and compared. We develop two approaches to address traditional OVRP limitations in ensuring vehicle flow equilibrium at each depot. Our models consider perishable pharmaceuticals and time-dependent travel speeds. Three hybrid heuristics based on Simulated Annealing and Variable Neighborhood Search (SAVNS) are proposed and evaluated for efficacy. Computational experiments and a case study demonstrate distinct sustainable benefits across various strategies, offering valuable insights for decision-makers in the refrigerated logistics market.

stat.AP↗

arXiv2023

A 1.431-Competitive Algorithm for Combinatorial Group Testing

In the context of fault-detection problems, the objective is to identify all defective items among a set of $n$ binary-state items using the minimum number of tests. The {group testing} paradigm, which allows testing a subset of items in a single test, serves as a fundamental technique for efficiently classifying large populations. We study a central problem in the combinatorial group testing model where the number $d$ of defective items is unknown in advance. Let $M_\alpha(d|n)$ denote the maximum number of tests required by an algorithm $\alpha$ for this problem, and $M(d,n)$ denote the minimum number of tests required in the worst case when $d$ is known in advance. An algorithm $\alpha$ is called a $c$-\emph{competitive algorithm} if there exist constants $c$ and $a$ such that, for $0\le d < n$, $M_{\alpha}(d|n)\le cM(d,n)+a$. We design a new adaptive algorithm with a competitive constant $c \le 1.431$, thus pushing the competitive ratio below the best-known one of $1.452$. To achieve this, we propose a novel solution framework based on an unexplored up-zig-zag strategy and a studied strongly competitive algorithm.

math.CO↗

arXiv2022

An investigation of shock formation versus shock mitigation of colliding plasma jets

This work studies the interaction between colliding plasma jets to understand regimes in which jet merging results in shock formation versus regimes in which the shock formation is mitigated due to the collisionless interpenetration of the jets. A kinetic model is required for this study because fluid models will always produce a shock upon the collision of plasma jets. The continuum-kinetic, Vlasov-Maxwell-Dougherty model with one velocity dimension is used to accurately capture shock heating, along with a novel coupling with a moment equation to evolve perpendicular temperature for computational efficiency. As a result, this relatively inexpensive simulation can be used for detailed scans of the parameter space towards predictions of shocked versus shock-mitigated regimes, which is of interest for several fusion concepts such as plasma-jet-driven magneto-inertial fusion (PJMIF), high-energy-density plasmas, astrophysical phenomena, and other laboratory plasmas. The initial results obtained using this approach are in agreement with the preliminary outcomes of the Plasma Liner Experiment (PLX).

physics.plasm-ph↗

arXiv2019

Mars' plasma system. Scientific potential of coordinated multi-point missions: "The next generation" (A White Paper submitted to ESA's Voyage 2050 Call)

The objective of this White Paper submitted to ESA's Voyage 2050 call is to get a more holistic knowledge of the dynamics of the Martian plasma system from its surface up to the undisturbed solar wind outside of the induced magnetosphere. This can only be achieved with coordinated multi-point observations with high temporal resolution as they have the scientific potential to track the whole dynamics of the system (from small to large scales), and they constitute the next generation of Mars' exploration as it happened at Earth few decades ago. This White Paper discusses the key science questions that are still open at Mars and how they could be addressed with coordinated multipoint missions. The main science questions are: (i) How does solar wind driving impact on magnetospheric and ionospheric dynamics? (ii) What is the structure and nature of the tail of Mars' magnetosphere at all scales? (iii) How does the lower atmosphere couple to the upper atmosphere? (iv) Why should we have a permanent in-situ Space Weather monitor at Mars? Each science question is devoted to a specific plasma region, and includes several specific scientific objectives to study in the coming decades. In addition, two mission concepts are also proposed based on coordinated multi-point science from a constellation of orbiting and ground-based platforms, which focus on understanding and solving the current science gaps.

physics.space-ph↗