Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. However, engineering design poses a fundamentally different challenge for large language models (LLMs) compared with traditional textbook-style problem solving or factual question answering. Although existing benchmarks have driven progress in areas such as language understanding, code synthesis, and scientific problem solving, real-world engineering design demands the synthesis of domain knowledge, navigation of complex trade-offs, and management of the tedious processes that consume much of practicing engineers' time. Despite these shared challenges across engineering disciplines, no benchmark currently captures the unique demands of engineering design work. In this work, we introduce EngDesign, an Engineering Design benchmark that evaluates LLMs' abilities to perform practical design tasks across nine engineering domains. Unlike existing benchmarks that focus on factual recall or question answering, EngDesign uniquely emphasizes LLMs' ability to synthesize domain knowledge, reason under constraints, and generate functional, objective-oriented engineering designs. Each task in EngDesign represents a real-world engineering design problem, accompanied by a detailed task description specifying design goals, constraints, and performance requirements. EngDesign pioneers a simulation-based evaluation paradigm that moves beyond textbook knowledge to assess genuine engineering design capabilities and shifts evaluation from static answer checking to dynamic, simulation-driven functional verification, marking a crucial step toward realizing the vision of engineering Artificial General Intelligence (AGI).

Rapid development of electric vehicles holds the promise to significantly mitigate greenhouse gas emissions. However, the lack of pervasive en-route charging infrastructure on regional highway networks hinders the use of electric vehicles for long-distance intercity trips. Traditionally, transportation and power systems are often studied separately, each by itself as a complex system. This paper proposes a system of systems modeling framework for planning charging infrastructure deployment in intercity transportation networks while considering users’ travel behavior and coupling relationships between transportation and power systems. The aim is at facilitating long-distance electric vehicle travels and providing an effective and comprehensive tool to evaluate the total emissions from both the transportation and power sectors. We look at a somewhat “forward-looking” problem in which the charging loads of widely-adopted electric vehicles induce interdependence between the power and the transportation sectors. A general equilibrium modeling framework is developed to capture the interdependencies among charging infrastructure design, users’ travel behaviors, and power sector operations. An iterative solution approach is proposed to solve the overall equilibrium between the transportation and power sectors, and a heuristic algorithm is developed to solve the bi-level subproblem for the charging infrastructure deployment. Numerical experiments based on a semi-realistic case study are performed to demonstrate the applicability of the proposed modeling and solution approach.

This paper proposes a reliable hypercube queuing problem where servers (e.g., vehicles) can be dispatched either individually or jointly as cooperative units, and these servers are subjected to probabilistic disruptions. The probability of a server disruption depends on the amount of resources (e.g., firefighter staffing number) allocated to this server. If any server in a cooperation unit is disrupted, a less preferred (e.g., based on capabilities and source station) but functioning and available unit will be dispatched instead. This paper derives formulas for steady-state system performance metrics for this new hypercube queuing system. The queuing model is embedded into a mixed-integer non-linear program to optimize the allocation of resources so as to minimize the total costs under server disruptions. An empirical case study is used to compare the system performance measures under a range of implementation scenarios, and sensitivity analyses are conducted to reveal insights.

Waste management agencies need to plan their waste collection activities in an efficient way such that they not only provide high-quality and timely service to customers but also maximize their net profit from recyclable waste, i.e., the total recycling revenue minus total operating costs. This paper proposes a mixed-integer linear program model for the waste collection problem in urban areas while considering the recycling operation at the sorting facility. A hybrid solution approach with both discrete and continuous optimization components is developed to solve the problem. The continuous component builds upon asymptotic routing cost formulas from the continuous approximation literature, while it is integrated into the discrete optimization component via an iterative stochastic approximation procedure. A series of numerical experiments are conducted, and results show that the proposed model and hybrid solution approach significantly outperform state of the art benchmarks from the literature.

It is sometimes challenging to plan winter maintenance operations in advance because snow storms are stochastic with respect to, e.g., start time, duration, impact area, and severity. In addition, maintenance trucks may not be readily available at all times due to stochastic service disruptions. A stochastic dynamic fleet management model is developed to assign available trucks to cover uncertain snow plowing demand. The objective is to simultaneously minimize the cost for truck deadheading and repositioning, as well as to maximize the benefits (i.e., level of service) of plowing. The problem is formulated into a dynamic programming model and solved using an approximate dynamic programming algorithm. Piecewise linear functional approximations are used to estimate the value function of system states (i.e., snow plow trucks location over time). We apply our model and solution approach to a snow plow operation scenario for Lake County, Illinois. Numerical results show that the proposed algorithm can solve the problem effectively and outperforms a rolling-horizon heuristic solution.