Conference Agenda

Perishable consumer products, such as food, cosmetics, and household chemicals (e.g., pesticides and herbicides), present unique challenges their supply chain management due to their limited shelf life and uncertainties in demand and transportation. For instance, every year in the U.S., 38% of all food goes unsold or uneaten, which translates to almost 145 billion meals' worth of food, or roughly 1.8% of U.S. GDP. Inefficient warehousing and poor logistics are major factors contributing to product waste. Thus, in this work, we propose a robust optimization framework to jointly optimize facility allocation and inventory management for perishable product. The goal is to determine the optimal locations for product distribution centers, allocation of customers, and inventory policies that minimize the total costs associated with the life cycle involving food transportation, distribution, and storage, subject to uncertain demand conditions.

Specifically, we propose a two-stage mixed-integer linear programming (MILP) model that explicitly accounts for product perishability by enforcing a First-In-First-Out (FIFO) inventory policy, which reduces spoilage and ensures freshness upon delivery. To improve computational efficiency, we linearize the bilinear FIFO constraints and show that the linearization is exact. To ensure social equity and fairness in facility allocation, we define a fairness index (FI) and incorporate it in the optimization framework. Furthermore, we propose a robust optimization approach to address demand uncertainty by using affine demand functions to account for a range of potential scenarios and ensure resilience in the supply chain. To efficiently solve this joint optimization problem, we utilize a row and column generation technique within the robust optimization framework. This method enhances scalability and allows for efficient handling of large-scale problem instances, ensuring optimal or near-optimal solutions.

Overall, this robust optimization framework provides a comprehensive solution to the challenges of facility allocation and inventory management associated with perishable products, thereby reducing waste and carbon footprint and enhancing the supply chain’s robustness to uncertainty.

Data-driven decision-making is crucial in the transition to a low-carbon economy, especially as global industries strive to meet stringent sustainability goals. Traditional life cycle assessments (LCAs) often rely on static emission factors, overlooking the dynamic nature of the energy grid [1]. As renewable energy penetration increases, grid carbon intensity fluctuates significantly across time and regions, due to the inherent intermittency of renewable sources like wind and solar. This variability introduces discrepancies in emission estimations if time-averaged factors are applied, leading to sub-optimal process designs and
unintended environmental consequences.

To this end, we present a real-time emission-aware optimization framework, which is implemented through a mixed-integer linear programming (MILP) formulation to determine optimal design configurations and operation schedules while simultaneously mitigating emissions by utilizing electricity price forecasts, time-varying emission factors, sporadic weather data, and supply and demand variability. Furthermore, we emphasize the critical role of battery storage in mitigating the intermittency of renewables, enabling efficient energy storage of cleaner energy mix periods throughout the day. The optimization model integrates life cycle assessment criteria to evaluate various environmental inputs, categorized into direct on-site emissions (Scope 1), indirect emissions from energy use (Scope 2), and upstream, downstream, and construction-related emissions associated with the process system (Scope 3) [2, 3]. The framework, demonstrated through a detailed hydrogen production case study, yields a set of optimal solutions that balance the trade-offs between environmentally sustainable and economically competitive designs and operational strategies, all while complying with stringent carbon reduction targets.

References

[1] G. J. Miller, K. Novan, A. Jenn, Hourly accounting of carbon emissions from electricity consumption, Environmental Research Letters 17 (4) (2022) 044073. doi:10.1088/1748-9326/ac6147.URL https://dx.doi.org/10.1088/1748-9326/ac6147
[2] A. Hugo, E. Pistikopoulos, Environmental conscious long-range planning and design of supply chain networks, Journal of Cleaner Production 13 (2005) 1471–1491. doi:10.1016/j.jclepro.2005.04.011.
[3] E. G. Hertwich, R. Wood, The growing importance of scope 3 greenhouse gas emissions from industry, Environmental Research Letters 13 (10) (2018) 104013.

The ongoing energy transition involves decarbonization across many sectors. Amongst these, the transportation sector contributes significantly owing to its reliance on traditional fossil fuels as feedstock. Attaining decarbonization goals requires the adoption of novel sustainable technologies such as electric vehicles (EVs), hydrogen fuel cell vehicles (HFCVs), amongst others [1]. The feedstock transition towards electricity and dense energy carriers is challenged by the requirement for additional infrastructure to manage intermittency, power generation, and grid expansion which requires both materials and capital investment [2]. By evaluating and redirecting the role of carbon value vector from fossil fuel production towards the production of polymeric materials to empower the energy transition [3], we can optimize resource allocation and maintain economic viability, all while reducing environmental impact.

To achieve this, we propose modeling the nexus between energy and materials within a circular economy. The multiscale modeling and optimization framework utilizes the resource-task-network (RTN) methodology and a life cycle assessment (LCA) approach, integrating simultaneous design and scheduling, considering future material demand, availability, and production capacities. The framework’s capabilities are demonstrated through a case study on the transition from gasoline-fueled vehicles to EVs, analyzing 1) the role of carbon value vectors in resources and materials production, and 2) electricity generation, storage, and dispatch using intermittent renewables. The study reveals the interactions between energy, material, and mobility value chains and provides configurations where such synergies can be exploited. Moreover, the sensitivity to considered parameters as well as the trade-offs between objectives are highlighted.

Keywords— Energy transition, Material transition, Carbon value vectors

Topics— T2: Sustainable Product Development and Process Design, T3: Large Scale Design and Planning/Scheduling

References

[1] H. C. Lau, S. Ramakrishna, K. Zhang, A. V. Radhamani, The role of carbon capture and storage in the energy transition, Energy & Fuels 35 (9) (2021) 7364–7386.

[2] J. Holechek, H. Geli, M. Sawalhah, R. Valdez, A. G. Assessment, Can renewable energy replace fossil fuels by 2050? sustainability 14 (2022) 4792.

[3] R. Kakodkar, B. Flores, M. Sousa, Y. Lin, E. Pistikopoulos, Towards energy and material transition integration-a systematic multi-scale modeling and optimization framework, 2024, pp. 461–468. doi:10.69997/sct.171988.

Biomass and waste-derived plastics are being promoted as sustainable carbon-based materials to replace petroleum-based plastics. However, their environmental advantages are not guaranteed because of the uncertain environmental impact in the biomass supply processes (Lam et al., 2019). In addition, numerous processes in the resin production and recycling are under development, or only operating at small scales; the lack of knowledge organization leads to incomplete understanding among stakeholders. Therefore, it is difficult to conduct a comprehensive assessment and system design of the plastic life cycle. It is essential to examine the ‘true’ societal ripple effects of biomass-based plastics in the context of sustainable development while discussing optimal combinations of resources and technologies.

This study aims to elucidate the carbon flow within the life cycle of biomass/recycled-derived plastics, and assess the renewability of carbon sources and their environmental impacts. Furthermore, it discusses the potential for biomass and waste as carbon sources and their appropriate introduction pathways. The research progresses through the development of assessment methods, including the metrics and system boundaries, model construction, flow analysis, interpretation of results, and validation of findings. Greenhouse gas (GHG) emissions were chosen as the indicator for environmental impact assessment, meanwhile a novel Carbon Circularity Indicator (CCI) was developed to assess the renewability of carbon sources. CCI extends the Material Circularity Indicator (Ellen MacArthur Foundation, 2019), which measures the recyclability of petroleum-based plastics, to cover the entire life cycle, including raw material supply stages. Technologies related to plastics were gathered based on keywords extracted through bibliometric analysis. A superstructure was visualized to represent the carbon flow in the life cycle of plastics. A carbon flow analysis method was developed using this superstructure as the system boundary. The inventory data and GHG emissions for each process necessary for flow analysis were primarily based on existing research and literature, supplemented by process simulations where needed.

Our preliminary assessment of the superstructure and system highlighted that the selection of raw materials, resin applications, and recycling technologies are the hotspots in life cycle design in terms of environmental impact and carbon renewability. The flow analysis revealed the impact of introducing biomass and waste as carbon sources on the overall plastic life cycle considering constraints, such as the availability of biomass feedstocks and the capacity for recycling. We also identified the processes, resins, and applications that should be prioritized for substitution with biomass-derived plastics.

To enhance the interpretability of the model, we address uncertainties in the biomass cultivation process, including regional boundary differences. In addition, we are working on the model towards incorporating variations in end-of-life flows based on different applications, and scenario analysis incorporating temporal factors, such as future technological developments.

W. Y. Lam, M. Kulak, S. Sim, H. King, M. A. J. Huijbregts, R. Chaplin-Kramer, 2019, Greenhouse gas footprints of palm oil production in Indonesia over space and time, Sci. Total Environ., 688, 827-837
Ellen MacArthur Foundation, 2019, Circularity Indicators -An Approach to Measuring Circularity-