The energy recovery of agro-industrial residual biomass offers a pathway to reduce fossil fuel emissions in thermal processes while valorizing waste. In practice, however, the primary bottleneck is logistical: feedstocks are geographically dispersed, with low bulk density and high moisture content, driving up collection, pretreatment, and transport costs. This work combines geospatial processing with mathematical optimization to design a multi-stage logistics network. The model incorporates intermediate densification options and technology selection (chipping, pelletizing, or briquetting) to supply one or more final waste-to-energy plants. The case study focuses on Northeastern Uruguay, considering forestry residues, meat-processing waste, and rice husks. We formulate a multi-period Mixed-Integer Linear Programming (MILP) model aimed at minimizing the total annualized cost, encompassing transportation, logistical operations, capital investment, and plant O&M, subject to supply constrai... [more]

This paper presents a Europe-wide optimisation framework for designing and operating a multi-period Carbon Capture and Storage (CCS) supply chain across Europe. A MATLAB preprocessing pipeline constructs an auditable techno-economic dataset (emission nodes, ports, aquifers, candidate pipeline/shipping arcs and costs) and exports it to a GAMS optimisation model. The planning problem is formulated as a two-stage stochastic MILP, where scenario-independent first-stage decisions select discrete pipeline and shipping capacity bands and port operating modes, while scenario-dependent second-stage decisions allocate capture, transport and sequestration flows. Uncertainty is represented through correlated scenarios of capture unit costs for four capture technologies (CV=0.35, rho=0.8, Ns=20). To address the computational burden induced by inter-temporal binary investments and scenario replication, we apply a two-phase arc-screening heuristic: an LP relaxation on the full network identifies prom... [more]

Manufacturing disruptions can be a major driving factor in the wastage of resources and delays which result in spiralling costs and cancelled orders. Operational decision making should therefore consider the potential for disruptions from as many sources as possible, encouraging improvements to operational resilience and agility. Our work presents a scheduling and rescheduling framework formulated as a rolling horizon problem for the emulation of real time decision making within a dynamically changing scenario. The framework is applied to a complex multistage problem with parallel lines susceptible to disruptions as a result of process or equipment failures, or ineffective inventory management that results in material shortages. The framework is demonstrated for a simple example case which highlights the impact of disruptions on the time taken to complete orders and the associated costs. It is observed that the inclusion of disruptions can alter equipment congestion, shifting focus for... [more]

The pharmaceutical sector is essential for ensuring universal access to medicines, demanding ef-ficient supply chains that deliver drugs at optimal prices with minimal delays and shortages. Pharmaceutical supply chains (PSCs) face significant challenges, including strict quality controls, government regulations, drug perishability, high R&D costs, and complex transportation require-ments. The sector is undergoing a shift, driven by the rise of pharmaceutical components in emerging markets, unpredictable demand, and reduced R&D investments by major companies, which struggle to compete with generic pharmaceutical brands. Post-pandemic challenges and geopolitical risks have further exposed vulnerabilities in PSCs, leading to frequent supply disrup-tions, product shortages, and unreliable transportation. The increasing focus on regionalization highlights the need for more resilient supply chains to manage disruptions effectively. PSCs must incorporate robustness to address uncertainties an... [more]

Decisions taken throughout a bioprocess lifecycle are often guided by heuristic knowledge that is difficult to summarize and sort, scattered across heterogeneous tools and documents, and partly retained as tacit expert mental models alongside fragmented computational models. This fragmentation remains a central barrier to reproducibility, transparent provenance, and systematic reuse of prior learning across comparable development projects. In this paper, it is argued that a key missing link toward Bioprocessing 5.0 is the digitalization of FAIR knowledge through a Cognitive Digital Thread that couples semantic knowledge graphs with AI methods to connect experimental data, protocols, workflows, and decision rationale with mathematical models and digital twins in a machine-actionable and auditable manner. A digitalization roadmap is outlined as a sequence of capability stages-from local device and data integration, to reproducible workflow execution and metadata capture, to semantic know... [more]

Traditional centralised manufacturing offers efficient economies and broad market reach but faces increasing limitations with the rise of complex products requiring rapid localised delivery and greater supply chain resilience. The logistics demands of hospital-compounded therapies expose vulnerabilities in existing infrastructure, accentuating the need for rigorous evaluation of alternative paradigms. This study investigates the comparative performance of centralised and decentralised pharmaceutical manufacturing models, applying an agent-based simulation framework designed for specialised or time-sensitive drug product orders. The work implements an agent-based simulation to model both centralised and decentralised scenarios using key structural, resource, and demand parameters identified within the supply chain ecosystem. Comparison criteria include labour requirements, sustainability (as measured by environmental emissions and operational efficiency), and end-to-end supply chain lea... [more]

Resilience is becoming a top priority across industrial sectors, with increasing pressures to assess it systematically. In this work, we present an optimization-based framework for proactive design and planning under uncertainty of multi-product manufacturing networks, and testing of the reactive strategies available to withstand unforeseen disruptions. Specifically, the design problem is formulated as a two-stage stochastic optimization, integrating multi-period planning and scheduling, aimed towards mitigation against uncertainty. Designs are then fixed and tested through simulated outcomes from out-of-sample uncertainty distributions, with feasibility of operation monitored through the time-to-recover post disruption. Infeasibility triggers a scenario-update procedure via ??-means clustering, whereby critical uncertainty information based on simulated outcomes is integrated in the proactive planning step, including low-probability high-impact scenarios. Modular and non-modular desig... [more]

Stochastic programming (SP) is a powerful framework for decision-making under uncertainty, but its practical adoption in industry is often hindered by the difficulty in understanding the causal relationships that drive optimal solutions. In the two-stage SP, strategic first-stage decisions are coupled with operational second-stage recourse decisions. When the number of scenarios under consideration is large, understanding the direct link between the uncertainty realization and optimal recourse strategy becomes computationally and cognitively demanding. Common approaches to improve interpretability include trained classification trees or scenario reduction, replacing the large scenario set with a representative subset. This is often achieved through post-hoc clustering (e.g., k-means) based on uncertainty realizations or optimal recourse decisions. While useful, these methods only provide a statistical approximation of the solution space and may fail to reveal the underlying structural... [more]

Decarbonization of hydrogen-intensive industrial clusters is essential to meet the European Union's net-zero targets. Although hydrogen can replace fossil-based feedstocks and fuels in refineries and chemical industries, its production remains largely dependent on natural gas. Therefore, cost-effective and low-emission supply routes require a system-level approach that integrates regional resources, technologies, and industrial demand. This study applies a multi-objective optimization framework to design a low-carbon hydrogen supply system for Galicia (northwestern Spain), addressing two gaps in regional energy system modeling: model transferability across regions and integration of social criteria beyond techno-economic assessment. The model quantifies trade-offs between total system cost and greenhouse gas emissions, and an employment indicator is integrated via post-processing using TOPSIS. The results show that meeting 100% of the projected 2030 demand (105 kt H2/a) yields a single... [more]

When designing and planning Green Hydrogen Supply Chains (GHSCs), sustainability considerations are increasingly recognized as essential, particularly in light of decarbonization goals and climate policy targets. Existing research has largely focused on economic and environmental however, social sustainability aspects remain significantly underexplored. This work aims to develop a mathematical programming model to design a GHSC, considering simultaneously economic and social aspects. Solar PV, wind power, and PPA (wind) as energy sources are integrated, while transportation options include the construction of new pipelines, compared to the use of existing highways for trucks carrying liquefied or compressed hydrogen to deliver hydrogen to an oil refinery. The model is applied to a case study conducted in the Brazilian state of Bahia, where different social indicators will be explored, characterizing the case study context while allowing generalization to other contexts. Results allow u... [more]

The increasing popularity of electric vehicles (EVs) leads to an expected rise in the quantity of end-of-life lithium-ion batteries (LIBs) that require efficient management. This paper presents a State Task Network (STN) based optimization model to analyze and optimize the supply chain for LIBs, allowing for the selection of optimal processing routes, facility locations, capacities and reintegration of recovered materials, as well as analyzing the possible trade-offs between different end-of-life management strategies. Based on available data from the literature, the model is demonstrated with the LIB supply chain considering both primary production and different end-of-life strategies for spent LIBs (recycling and reuse). The case study reveals that mechanical pretreatment followed by hydrometallurgical recycling is the optimal pathway and it outperforms the linear supply chain in both costs and emissions. The cost optimal solution opts for more centralized collection and disassembly,... [more]

This document provides supplementary material supporting the Conference Paper “A Multi-Objective Optimisation and Superstructure-Based Decision-Support Tool for Regional Low-Carbon Hydrogen Roadmaps: Methodology and Application to a region of Spain”.

It includes additional methodological details, input data, model assumptions, and extended results that complement the analyses presented in the main manuscript.

Supply chain superstructure optimization model for export of either wood chips or compressed hydrogen from Norway to Germany

Emerging sectors within the biopharmaceutical industry are undergoing rapid scale-up due to the market boom of gene therapies and vaccine platform technologies. Manufacturers are pressured to orchestrate resources and plan investments under future demand uncertainty and, critically, an early-stage process uncertainty for platforms still under development. In this work, a multi-product multi-stage stochastic optimization problem integrating demand uncertainty is presented and augmented with a worst-case optimization approach with respect to process uncertainty. Results focus on a comparison between fixed equipment facilities and modular technologies, highlighting an inherent flexibility of the latter option due to shorter recourse actions for capacity scale-out. The impact of process uncertainty integration is quantified. With more conservative decisions taken in first-stages of the time horizon, expected costs result lower for modular single-use equipment. This suggests that capacity a... [more]

One of the proposed strategies to reach net-zero goals is the diversification of a country’s energy mix and transition to technologies that favour the mitigation of greenhouse gas emissions, while decreasing dependency on conventional fuels. This work presents a mathematical model that describes key production routes for two proposed energy transition vectors, biomethane and hydrogen, expressed as a Mixed-Integer Linear Problem (MILP). The supply chain is optimized with the objective of maximizing the profits from the global supply chain. The problem is formulated as an allocation problem, with production distributed between biomethane and hydrogen markets. The case study focuses on a region in Mexico where second-generation biomass for biogas production is abundant, while hydrogen is produced from biomethane using steam methane reforming. The results highlight the importance of balancing resource allocation in shared supply chains. With a production ratio of 60% biomethane and 40% hyd... [more]

In this work, we propose a mixed-integer linear programming (MILP) model that optimizes economic and environmental objectives by retrofitting fuel stations for the case study of Spain. The model contains set-covering constraints that ensure that there is at least one retrofitted fuel station within a radius of 20 kilometers of each retrofitted fuel station. The results indicate that by retrofitting fuel stations to allow for electric vehicles, both economic and environmental objectives improve, while showing which power plants would be tasked with the increase in electricity production to satisfy the increased electric demand.

Uncertainty plays a crucial role in strategic supply chain design. In this study, we explore robust approaches to model uncertainty when the non-deterministic parameters are placed in the independent term, on the right-hand side (RHS) of the constraints. We consider the "disjunctive adjustable column-wise robust optimization" (DACWRO), a disjunctive formulation introduced previously in our group, and compare it with the adjustable column-wise robust optimization (ACWRO) formulation, a specific technique for solving robust optimization problems when the original robust optimization approach may assume too-conservative results. Given that the proposed method is based on the generalized disjunctive programming (GDP) technique, it is a higher lever modelling approach that represents the discrete nature of the decision process. In addition, it provides alternative MILP representations that can be further tested and compared. The analysis assesses the computational performance and reformulat... [more]

The battle against climate change and the search for innovative solutions to mitigate its effects have become the focus of the researchers’ attention. One potential approach to reduce the impacts of the global warming could be the design of a Carbon Capture and Storage Supply Chain (CCS SC). However, the high complexity of the model requires exploring alternative ways to optimise it. In this work, a CCS multi-period supply chain for Europe is designed. Data on CO2 emissions have been sourced from the EDGAR database, which includes information spanning the last 50 years. Since this problem involves optimising the cost and operation decisions over a 10-year time horizon, it would be advisable to forecast carbon dioxide emissions to enhance the reliability of the data used. For this purpose, a neural network-based model is implemented for forecasting N-Beats. Furthermore, a temporal decomposition scheme is used to address the intractability issues of the model. The selected method is Lag... [more]

Perishable consumer products like food, cosmetics, and household chemicals face challenges in supply chain management due to limited shelf life and uncertainties in demand and transportation. To address some of these issues, this work proposes a robust optimization framework for jointly optimizing facility allocation and inventory management. The framework determines optimal locations for distribution centers and their assigned customers, as well as inventory policies that minimize the total costs related to transportation, distribution, and storage under uncertain demand in a robust setting. Specifically, we develop a two-stage mixed-integer linear programming (MILP) model is that incorporates First-In-First-Out (FIFO) inventory policy to reduce spoilage. The bilinear FIFO constraints are linearized to improve computational efficiency. Social equity is integrated by defining a fairness index and incorporating it in facility allocation. Demand uncertainty is tackled using a robust opti... [more]

This study aims to develop an optimization-based approach to design the carbon capture, utilization, and storage (CCUS) supply chain and analyze the optimal configuration and investment strategies. To achieve this goal, we develop an optimization model that determines the logistic decision-making to maximize the net present value (NPV) and minimize the net CO2 emissions (NCE) of the strategies of the CCUS supply chain under logical and practical constraints. We estimate the technical (production scale and energy consumption), economic (capital and operating expenditure), and carbon-related (CO2 emissions) parameters based on the literature. By adjusting major cost drivers and economic bottlenecks, we determined major decision-making problems in the CCUS framework, such as sequestration vs. utilization. As a real case study, the future CCUS system of South Korea was evaluated, which includes three major CO2 emitting industries in South Korea (power plants, steel, and chemicals), as well... [more]

The urgent need to mitigate climate change and reduce reliance on fossil fuels highlights green hydrogen as a key component of the global energy transition. This study assesses the feasibility of producing hydrogen offshore using wind energy, focusing on economic and environmental aspects. Offshore wind energy offers several advantages: access to water for electrolysis, potentially lower hydrogen export costs compared to electricity, and storage systems that stabilize wind energy output. However, significant challenges remain, including the high costs of storage solutions, capital expenditures (CAPEX), and operational costs (OPEX). A Mixed-Integer Linear Programming (MILP) model optimizes the production units, storage, and distribution processes. A case study in southern France examines hydrogen production from a 150 MW floating wind farm. While hydrogen produced from offshore wind ranks among the most environmentally friendly, its costs remain high, and production volumes fall short o... [more]

Commodity chemicals and energy supply chains are an essential part of the hydrocarbon industry in several countries. As these supply chains are susceptible to disruptions caused by various risks, the economies of countries that depend on the hydrocarbon sector as a major source of income might be negatively affected. One major risk is the price fluctuations of the resources used in the multiple stages of the supply chains. Investment decisions in this sector aim to secure the investment portfolio's financial returns against the risk of price fluctuations. This work introduces an adaptation of a portfolio optimization technique, the modern portfolio theory (MPT) to the case of commodity chemicals and energy supply chain investments by considering all supply chain stages in formulating the MPT framework. A case study considering four chemical commodities and three potential importing countries is presented with a sensitivity analysis that studies the impact of changing the costs associat... [more]

This study introduces a mathematical programming approach to optimize biomass-to-hydrocarbon supply chain design and planning, aiming to balance economic and environmental outcomes. The model incorporates a range of residual biomass types from forestry, sawmills, and the pulp and paper industry, with the option to establish various processing facilities and technologies over a multi-period planning horizon. The analysis involves selecting forest areas, identifying biomass sources, and determining the optimal locations, technologies, and capacities for facilities converting wood-based residues into methanol and pyrolysis oil, which can be further refined into biodiesel and drop-in fuels. Using Life Cycle Assessment (LCA) in a gate-to-gate analysis, forest supply chain carbon emissions are estimated and integrated into the optimization model, extending previous research. A multi-objective framework is employed to minimize CO2-equivalent emissions while minimizing present costs, with effi... [more]

The growing size and complexity of energy system optimization models, driven by high-resolution spatial data, pose significant computational challenges. This study introduces methods to reduce model’s size and improve computational efficiency while preserving solution accuracy. First, a composite-curve-based approach is proposed to aggregate granular data into larger resolutions without averaging out specific properties. Second, a general clustering method groups geographically proximate fields, replacing multiple transportation arcs with a single arc to reduce transportation-related variables. Lastly, a two-step algorithm that decomposes the sup-ply chain design problems into two smaller, more manageable subproblems is introduced. These methods are applied to a case study of switchgrass-to-biofuels network design in eight U.S. Midwest states, demonstrating their effectiveness with realistic and detailed spatial data.

Most studies in process systems engineering are applying incomplete methods when incorporating sustainability. Including sustainability is a laudable goal, and practitioners are encouraged to develop systems that promote economic, environmental, and social aspects. Ten methods that are often overlooked in performing sustainable process systems engineering are listed in this effort and discussed in detail. Practitioners are encouraged to create designs that are inherently safer, to be more complete in their identification of process chemicals used and released, to be complete in their definitions of supply chains, and to apply additional environmental impact categories. Other methods point to items that are factors in process systems engineering such as disruptive recycling, robust superstructures for optimizations, and employing complete sets of objectives. Finally, users should be aware that sustainability tools are available, which might have been outside of their awareness.