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Scheduling is a major component for the efficient operation of the process industries. Especially in the current competitive globalized market, scheduling is of vital importance to most industries, since profit margins are miniscule. Prof. Sargent was one of the first to acknowledge this. His breakthrough contributions paved the way to other researchers to develop optimization-based methods that can address a plethora of process scheduling problems. Despite the plethora of works published by the scientific community, the practical implementation of optimization-based scheduling in industrial real-life applications is limited. In most industries, the optimization of production scheduling is seen as an extremely complex task and most schedulers prefer the use of a simulation-based software or manual decision, which result to suboptimal solutions. This work presents a comprehensive review of the theoretical concepts that emerged in the last 30 years. Moreover, an overview of the contributions that address real-life industrial case studies of process scheduling is illustrated. Finally, the major reasons that impede the application of optimization-based scheduling are critically analyzed and possible remedies are discussed.

In recent years, the ever-increasing research interest in various aspects of the electricity pool-based markets has generated a plethora of complementarity-based approaches to determine participating agents’ optimal offering/bidding strategies and model players’ interactions. In particular, the integration of multiple and diversified market agents, such as conventional generation companies, renewable energy sources, electricity storage facilities and agents with a mixed generation portfolio has instigated significant competition, as each player attempts to establish their market dominance and realize substantial financial benefits. The employment of complementarity modelling approaches can also prove beneficial for the optimal coordination of the electricity and natural gas market coupling. Linear and nonlinear programming as well as complementarity modelling, mainly in the form of mathematical programs with equilibrium constraints (MPECs), equilibrium programs with equilibrium constraints (EPECs) and conjectural variations models (CV) have been widely employed to provide effective market clearing mechanisms, enhance agents’ decision-making process and allow them to exert market power, under perfect and imperfect competition and various market settlements. This work first introduces the theoretical concepts that regulate the majority of contemporary competitive electricity markets. It then presents a comprehensive review of recent advances related to complementarity-based modelling methodologies and their implementation in current competitive electricity pool-based markets applications.

Environmental policies and intermittent renewable energy (RE) drive large-scale hydrogen production towards hybrid supply configurations, combining collocated RE units and the electricity market (EM). This links the power and hydrogen sectors through EM/hydrogen prices, dispatch, and hydrogen demand profiles. In a hybrid configuration, the strategic role of RE in the EM enhances these links by creating profit opportunities. This work develops a bi-level model, optimizing electrolyzer size and location, operational decisions and RES bidding strategies, while explicitly modeling EM clearing. In the upper-level, an EM player, owning strategically bidding RE assets, evaluates expanding into the use of electrolyzers that act as price-takers. The lower-level problem clears the EM. The proposed framework is applied to an IEEE 24-node test system. The results show how EM conditions determine investments for different hydrogen price cases. It is revealed that differentiated electricity sourcing across electrolyzers and efficiency-preserving dispatch impact operational decisions, leading to revenue improvements. Moreover, renewable capacity withholding is used to avoid zero EM prices and mitigate the economic impact of unmet hydrogen demand when RE availability is limited and electrolyzer participation in the EM is restricted. Time-window-constrained hydrogen demand mitigates unutilized RE by 39% compared to that for hourly demand.

•Optimal inventory profile and flow decisions for the COVID-19 Vaccine Supply Chain.•Optimization of daily vaccination plans in the clinics.•Development of a novel mixed-integer linear programming model.•A decomposition algorithm to successfully address large-scale problems.•Reactive planning of vaccinations through a rolling-horizon technique. This work presents a novel framework to simultaneously address the optimal planning of COVID-19 vaccine supply chains and the optimal planning of daily vaccinations in the available vaccination centres. A new mixed integer linear programming (MILP) model is developed to generate optimal decisions regarding the transferred quantities between locations, the inventory profiles of central hubs and vaccination centres and the daily vaccination plans in the vaccination centres of the supply chain network. Specific COVID-19 characteristics, such as special cold storage technologies, limited shelf-life of mRNA vaccines in refrigerated conditions and demanding vaccination targets under extreme time pressure, are aptly modelled. The goal of the model is the minimization of total costs, including storage and transportation costs, costs related to fleet and staff requirements, as well as, indirect costs imposed by wasted doses. A two-step decomposition strategy based on a divide-and-conquer and an aggregation approach is proposed for the solution of large-scale problems. The applicability and efficiency of the proposed optimization-based framework is illustrated on a study case that simulates the Greek nationwide vaccination program. Finally, a rolling horizon technique is employed to reactively deal with possible disturbances in the vaccination plans. The proposed mathematical framework facilitates the decision-making process in COVID-19 vaccine supply chains into minimizing the underlying costs and the number of doses lost. As a result, the efficiency of the distribution network is improved, thus assisting the mass vaccination campaigns against COVID-19.

As industries come under growing pressure to minimize carbon emissions without compromising the efficiency of operations, the integration of energy-aware production scheduling with emerging energy markets, renewable energy, and policy mechanisms is critical. This paper identifies critical shortcomings in current academic and industrial approaches—namely, an excessive reliance on deterministic assumptions, a limited focus on dynamic operational realities, and the underutilization of regulatory mechanisms such as carbon trading. We advocate for a paradigm shift to more robust, adaptable, and policy-compliant scheduling systems that provide space for on-site renewable generation, battery energy storage systems (BESSs), demand-response measures, and real-time electricity pricing schemes like time-of-use (TOU) and real-time pricing (RTP). By integrating recent advances and their critical analysis of limitations, we map out a future research agenda for the integration of uncertainty modeling, machine learning, and multi-level optimization with policy compliance. In this paper, we propose the need for joint efforts from researchers, industries, and policymakers to collectively develop industrial scheduling systems that are both technically efficient and adherent to sustainability and regulatory requirements.

Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins-key molecules that regulate cell cycle transition-have been utilized. Herein, a novel integrated experimental-modelling platform is presented whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cycle-specific production rates. The solution method delivered fast computational time that renders the model's use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies.

This work focuses on the design and techno-economic evaluation of an industrial facility for the production of malonic acid. The raw material utilized is commercial glucose syrup with a concentration of 95%. Based on a patent of Lygos, Inc., an innovative biotechnology research company, this study presents a comprehensive synthesis, design, and simulation framework for the production of malonic acid through oligosaccharide fermentation. An integrated process flowsheet is proposed and simulated using SuperPro Designer™. The analysis indicates that for an installation capacity of about 8000 MT/yr of the final product with a purity of 99.5%, the production cost is estimated at USD 7.92/kg. A comprehensive study of the capacity’s impact on economics reveals that this cost could decrease to as low as USD 6.05/kg. A parametric analysis and optimization conducted at the flowsheet level identifies opportunities for further reducing production costs, laying the groundwork for a potential decrease in the product’s selling price.

This work addresses the lot-sizing and production scheduling problem of multi-stage multi-product food industrial facilities. More specifically, the production scheduling problem of the semi-continuous yogurt production process, for two large-scale Greek dairy industries, is considered. Production scheduling decisions are made using two approaches: (i) an optimization approach and (ii) a rule-based approach, which are followed by a comparative study. An MILP model is applied for the optimization of short-term production scheduling of the two industries. Then, the same problems are solved using the commercial scheduling tool ScheduleProTM, which derives scheduling decisions using simulation-based techniques and empirical rules. It is concluded that both methods, despite having their advantages and disadvantages, are suitable for addressing complex food industrial scheduling problems. The optimization-based approach leads to better results in terms of operating cost reduction. On the other hand, the complexity of the problem and the experience of production engineers and plant operators can significantly impact the quality of the obtained solutions for the rule-based approach.

The conversion of CO2 into valuable chemicals such as formic acid offers a promising approach to reducing CO2 emissions. This study presents a techno-economic assessment of two continuous catalytic processes for formic acid production via carbon dioxide (CO2) hydrogenation. The processes differ in the type of nitrogenous base used, operating under either homogeneous or heterogeneous catalytic conditions. Process simulations and techno-economic evaluations were performed in SuperPro DesignerTM for a medium-scale facility with an annual CO2 processing capacity of around 14 kMT. The homogeneous catalysis pathway demonstrated superior plant performance, producing 13.03 kMT of formic acid per year at 99.78% purity. In contrast, the heterogeneous pathway required higher capital investment and exhibited lower overall efficiency. The techno-economic analysis confirmed the economic viability of the homogeneous process, with a production cost of $1.18/kg and favorable investment indicators, whereas the heterogeneous route proved economically unattractive under the evaluated assumptions. Sensitivity analysis identified the selling price of formic acid as the most critical profitability parameter, with the homogeneous process remaining robust across varying conditions. Overall, homogeneous catalytic CO2 hydrogenation demonstrates a technically efficient and economically promising process for the chemical transformation of CO2, contributing to carbon management.

This work explores five falling film evaporator (FFE) simulation approaches combined with energy consumption minimization strategies, namely Mechanical Vapor Recompression and Thermal Vapor Recompression (MVR and TVR, respectively). Global system analysis and advanced dynamic optimization strategies are then investigated to minimize steam consumption, the cost of steam, and the total annualized cost and to maximize product yield. The results indicate that higher TVR discharge pressures, or MVR compression ratios, along with higher feed temperatures, enhance evaporation but increase operational costs. The most economical option includes three evaporator effects with TVR to achieve 50% product dry mass content. However, for a 35% dry mass content, MVR becomes cost-effective with an 11% reduction in unit electricity prices or a simultaneous 7% drop in electricity prices and a 5% increase in gas-based steam prices. Furthermore, switching from milk powder production to milk concentrates leads to an annual cost reduction ranging from 10.8 to 44%. Additionally, a forecasted 20% (or more) reduction in biomass-based steam cost can lead to lower annual expenditure compared with the nominal NG-based steam case. Regarding the total annualized cost, for a new plant design, optimization strategies lead to a 9–45% reduction in the total cost depending on the case under consideration.