Problem definition: Distributionally robust optimization (DRO) is ubiquitous to address uncertainties inherent in operations management (OM) problems. Recently, an alternative goal-driven framework, robust satisficing (RS), is proposed. RS aims to attain a prescribed target, such as avoiding overshooting the cost budget, as much as possible under uncertainty. The goal-driven modeling philosophy fits many OM problems, yet there is a lack of direct comparisons between DRO and RS. In this paper, we uncover connections between DRO and RS. Methodology/results: Suppose both models are based on the Wasserstein metric and consider a risk-aware convex objective function affected by uncertain parameters. We demonstrate that they share the same solution family. We establish the correspondence between the radius parameter in DRO and the target parameter in RS such that the optimal solutions to the two models coincide. Inspired by the globalized distributionally robust counterpart (GDRC), we extend the analysis to GDRC and the globalized robust satisficing (GRS). We reveal that GDRC and GRS have the same solution families as DRO and RS, respectively. More importantly, we establish novel results on the equivalence of DRO, GDRC, RS, and GRS models under previously stated conditions. Managerial implications: The equivalence results help unify performance bounds of DRO and RS models. Specifically, each model now has an additional set of theoretical guarantees from the other model, and any bounds derived for one model automatically apply to other equivalent models via some parameter mapping. Despite the theoretical equivalence result, the performance of the DRO and RS models can vary depending on how the model parameters are selected. The experimental findings show how these differences emerge when transitioning from theory to practice. Additionally, the experiments provide insights for practitioners, such as how the use of cross-validation can help reflect the true model preferences, particularly when only a few validation points are set.

Product image displayed in advertising significantly influences consumers' cognition and behavior. The display of the last stage of a product before its consumption (LSC), which is defined as a state in which a product is to be used or consumed (for example, the skincare product with its lid opened, dumplings picked up by chopsticks, etc.) is commonly adopted in marketing practices. However, the research on the effectiveness of LSC display is still insufficient. To reveal the effect of LSC display on purchase intention and the underlying mechanism, this paper conducted five experimental studies with 1184 Chinese participants involved. Generally, results indicated that (i) displaying the LSC (vs. complete product image) in advertisements fosters purchase intention; (ii) the mental simulation of product consumption plays a mediating role in the positive effect of the LSC display; (iii) this effect only exists in the context of hedonic products purchase (vs. functional products). This research puts forward evidence to show the positive effects of LSC display, the mechanism of mental simulation and the boundary conditions of product type for the existence of the LSC effects.

This paper studies one risk-neutral supplier and one loss-averse retailer supply chain with reference dependence under carbon emissions tax regulation, where stochastic demand is sensitive to carbon emissions. The optimal decision-making for both supplier and retailer in the supply chain are derived. Specifically, the loss-averse retailer's optimal ordering policy is obscure, and affected by the loss aversion, carbon tax, contract parameters, random demand and yield. It is shown that the optimal ordering quantity of the retailer decreases in the carbon emissions tax and loss aversion level. Further, The risk-neutral supplier's optimal carbon reduction technology investment level is affected by costs for carbon emission reduction, cost-sharing ratios, carbon emissions tax, contract parameters and random demand. It is demonstrated that the higher the retailer's sharing ratio of carbon emission reduction costs, the higher the supplier's optimal carbon reduction technology investment level. In addition, this paper shows that the call option contract improves the supply chain's the performance and decreases invalid carbon emissions. Finally, it is shown that the joint carbon emission reduction and coordination of the supply chain need to meet two conditions, which related to the demand distribution and carbon reduction technology investment level. © 2025 Technical Committee on Control Theory, Chinese Association of Automation.

This paper considers a multiperiod emergency medical services (EMS) location problem and introduces two two-stage stochastic programming formulations that account for uncertainty about emergency demand. Whereas the first model considers both a constraint on the probability of covering the realized emergency demand and minimizing the expected cost of doing so, the second one employs probabilistic envelope constraints that allow us to control the degradation of coverage under the more severe scenarios. These models give rise to large mixed-integer programs, which can be tackled directly or by using a conservative approximation scheme. For the former, we implement the branch-and-Benders-cut method, which improves significantly the solution time when compared with using both a recently proposed state-of-the art branch-and-bound algorithm and the CPLEX solver. Finally, a practical study is conducted using historical data from the Northern Ireland Ambulance Service and sheds some light on optimal EMS location configuration for this region and on necessary trade-offs that must be made between emergency demand coverage and expected cost. These insights are confirmed through an out-of-sample performance analysis.

针对用户需求的随机性和光伏发电的不确定性对电动汽车光伏充电站运营带来的挑战，建立了考虑需求时空分布不均衡的随机鲁棒分时定价模型。研究了光伏充电站网络的联合定价策略，以解决充电需求和光伏发电量在时空分布上的不匹配问题。使用多项Logit模型刻画用户充电行为，采用分布鲁棒优化方法刻画了光伏发电不确定性。在将该随机鲁棒定价模型等价转化为二阶锥规划模型的基础上，提出了精确算法和两阶段启发式算法对其进行求解。利用现实数据验证了算法有效性，同时验证分时定价策略相比使用固定定价策略在平衡充电供需、引导错峰充电、提高光伏发电利用率和充电网络收益等方面的优势。

本文考虑了联合充换电操作的新能源汽车换电站的选址定容问题.换电站内部充换电操作是连接换电需求与换电站选址定容决策的关键一环，但以往研究都忽略了对该过程的详细刻画.本文将换电站内部充换电操作建模为多周期优化问题，并给出了该问题最优解的结构性质.在此基础上考虑换电需求的不确定性， 将换电站内部运营与换电站选址定容决策结合起来，构建了分布式鲁棒优化模型和目标鲁棒式优化模型.针对模型中难求解的多阶段问题，本文利用线性决策准则近似求解两模型，并将融合辅助变量的升维技术扩展到考虑情景的多阶段鲁棒优化模型中，从理论上给出了升维前后模型之间的关系.最后进行数值实验， 验证了本文所提模型和升维技术的有效性.

The sharing of electric vehicles and the Internet of Vehicles both positively impact societal benefits. However, the complexity, uncertainty and multi-directionality of transport–energy coupled systems greatly increase the difficulty of integrated management. This paper proposes a multi-objective robust optimization framework to solve the integrated management issue of electric vehicle sharing system operations and Internet of Vehicles energy scheduling under uncertain information, in which components such as renewable energy, microgrids, autonomous driving and their impacts are fully considered. To enhance the capability of integrated management and the synergy between supply and demand, a charging and discharging scheduling strategy that considers a converged demand response policy is proposed. Numerical analyses using real data provide valuable validation and insights. The proposed method and scheduling strategy enable the implementation of integrated management in photovoltaic microgrids and significantly improve economic and environmental benefits. Meanwhile, the microgrid system can operate in an "off-grid mode", which enables a multi-energy complementary mechanism. Additionally, electric vehicles release a great deal of scheduling flexibility through demand-side management, which combined with electric energy storage, can efficiently local consumption of photovoltaic power. © 2024 Elsevier Ltd

This paper addresses the location and capacity planning of battery swapping stations of electric vehicles, combining the charging and swapping operations in the stations. The charging and swapping operations within the swapping station are a crucial link connecting the swapping demand with the decisions on station location and capacity planning. However, previous research has overlooked providing a detailed characterization of this process. This study models the internal operations of the swapping station as a multi-period optimization problem and provides insights into the structural properties of the optimal solution to this problem. Building upon this foundation, considering the uncertainty in swapping demands, we integrate the internal operational aspects with the station location and capacity planning to construct a distributionally robust optimization model and a robust satisficing model. To deal with the hard multistage problem in the model, we utilize the linear decision rule to approximately solve the two models and extend the lifting technique by incorporating auxiliary variables into multistage scenario-wise robust optimization models. The theoretical analysis establishes the relationship between the models before and after lifting. Finally, numerical experiments are conducted to validate the effectiveness of the proposed model and lifting techniques. © 2024 Systems Engineering Society of China. All rights reserved.

Enhancing energy security and cutting carbon emissions have rapidly boosted heavy-duty hydrogen fuel cell truck (HFCT) industry. Heavy-duty HFCT penetration involves four key players: consumers, refueling station operators, heavy-duty HFCT manufacturers, and hydrogen suppliers. Clear comprehension of government subsidy impacts on these agents can boost penetration. We used a system dynamics (SD) model to analyze single-stage and two-stage subsidy policies' impact on four agents, employing Shanghai, China as a case study. Results indicate that (1) Acquisition subsidies are most impactful, followed by refueling station construction subsidies, and R&D subsidies are least efficient, whether two-stage or single-stage. (2) For two-stage subsidies, higher and longer acquisition subsidies in the later stage are preferable, early stage benefits from higher, extended refueling station construction subsidies., the effect of the two-stage subsidy policy for heavy-duty HFCT R&D subsidies is not much different, and the hydrogen R&D subsidy should be lower and longer in the early stage. (3) Lower and longer subsidies for hydrogen R&D in the early stage will have the best subsidy effect. (4) The penetration reacts more to oil price shifts than operational cost changes. Finally, specific policy implications are offered to enhance heavy-duty HFCT adoption. © 2023 Elsevier Ltd

The combination of low-carbon electricity and electric vehicles brings considerable economic and environmental benefits but also introduces challenges due to the complexity and uncertainty of system synergies in smart cities. To fully exploit the advantages of photovoltaic power generation and electric vehicles and to release the potential of electric vehicles as distributed energy storage facilities, this paper develops a multi-objective robust optimization framework that accounts for the benefits of multiple parties of smart charging and discharging systems and depicts a bounded uncertainty set based on partial statistical information from real data. The original model is scalarized and linearized using efficient methods such as max-ordering scalarization and the robust augmented weighted Tchebycheff to facilitate the solution. Moreover, a smart charging and discharging scheduling strategy based on a convergent demand response strategy is proposed to achieve better demand-side management. A case study, based on real data from Car2go and the solar radiation intensity in Portland, Oregon, shows that (1) The methodological framework effectively addresses the smart scheduling issue that considers the interests of multiple parties in complex systems with uncertainty, in contrast to conventional methods. (2) The designed convergent demand response strategy promotes photovoltaic power generation and charging demand synergy and achieves regulation effects such as peak shaving and valley filling. (3) The proposed method and strategy yield good results in multiple aspects, such as charging costs, load regulation, and clean energy utilization, while enhancing the economic and environmental benefits. © 2023 Elsevier Ltd