This study focuses on optimizing production scheduling in multi-product plants with shared resources and costly changeover operations. Specifically, two main challenges are addressed, the unknown changeover behavior of new products and the need for rapid schedule generation after unforeseen events. An innovative framework integrating Machine Learning (ML) techniques with Mixed-Integer Linear Programming (MILP) is proposed for single-stage production processes. Initially, a regression model predicts unknown changeover times based on key product attributes. Then, a representation where distances correlate with changeover times is compiled through multidimensional scaling, allowing constrained clustering to group production orders according to available packing lines. Ultimately, the MILP model generates the production schedule within a constrained solution space, utilizing optimal product-to-line allocation from cluster segmentation. A case study inspired by a Greek construction material... [more]

Traditional scheduling approaches often rely on simplified process representations to reduce computational complexity, failing to capture the real-world dynamics where tasks often overlap, and their timing depends on finer operational steps. To address these limitations, this paper proposes a novel process representation that breaks down production tasks into smaller, more primitive steps called operations. Unlike traditional methods, this approach provides a more granular depiction of task timing and resource dependencies. Operations can define the start or end of other tasks, utilize shared resources, and incorporate recipe constraints that mandate task sequencing. The proposed representation is utilized to develop two MILP models to address the makespan and the cycle time minimization problems. Finally, the efficiency and practical applicability of the developed models are showcased with a help of a case study from the pharmaceutical industry.

The transition to a circular economy (CE) requires agents in circular supply chain (SC) networks to take a variety of different initiatives, many of which are dynamic in nature. We use a system dynamics (SD)-based approach to develop a generic framework for dynamic modeling of CE networks and propose a prototypical circular SC network by combining dynamic models for five actors: a manufacturer, consumer, material recovery facility (MRF), recycling facility, and the Earth. We apply this framework to the supply chain for Polyethylene Terephthalate (PET) plastic packaging by considering different scenarios over a 65-year time horizon in the US. We include both "slow-down-the-loop" initiatives (i.e., those that extend product use time through demand reduction or reuse) and "close-the-loop" initiatives (i.e., those that reintroduce product to the supply chain through recycling) by the consumer, as well as sorting and recycling capacity expansion. We find that, given the current recycling in... [more]

Emerging technologies require sophisticated design and optimization due to their rapid advancement and potential to alter material flows and life cycles. However, their future development remains uncertain due to sociotechnical factors such as regulations, infrastructure, and market dynamics. Multiple technologies are often considered simultaneously, but their interactions and synergies are not systematically evaluated. This study addresses pre-emptive life cycle design in social challenges by integrating emerging technologies into superstructures, which help visualize alternative candidates for design problems. Case studies on battery chemistry, plastics, and regional resource circulation demonstrate this approach. For battery technology, nickel-manganese-cobalt lithium batteries have dominated over lithium iron phosphate alternatives. Superstructures were developed to assess recycling technologies and were refined through communication with managers of Japanese national battery proje... [more]

Planning and scheduling are integral components of process supply chains. The presence of data correlation, particularly multivariate demand data dependency, can pose significant challenges to the decision-making process. This necessitates the consideration of dependency structures inherent in the underlying data to generate good-quality, feasible solutions to optimisation problems such as planning and scheduling. This work proposes a chance-constrained optimisation framework integrated with copulas, a non-parametric data estimation technique to forecast uncertain demand levels in accordance with specified risk thresholds in the context of a planning and scheduling problem. We focus on the integrated planning and scheduling problem following a bi-level optimisation formulation. The estimated demand forecasts are subsequently utilised within the Data-driven Optimisation of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework to solve the integrated optimisation problem, and deri... [more]

Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost−benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heur... [more]

As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate change, presenting three main pillars—economic, social, and environmental. The issue of how to reach sustainability goals within the sustainable manufacturing of pillars is a less-researched area. This paper’s main purpose and novelty is two-fold. First, it aims to provide a hierarchy of the green energy indicators and their measurements through a multi-criteria decision-making point of view to implement them as an alliance strategy towards sustainable manufacturing. Moreover, we aim to provide researchers and practitioners with a forecasting method to re-prioritize green energy indicators through a linearity factor model. The CODAS−Hamming−Mahalanobis method is used... [more]

Longitudinal cutting is the most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules. However, currently, the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low. In this study, we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication. The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques, Pearson correlation coefficient for feature selection, and the Random Forest Regression (RFR) for prediction. Compared with the other three Machine-Learning (ML) algorithms, the Random Forest algorithm has the best performance. The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy so it is suitable for man-hours prediction.

Container technology has gained a widespread application in cloud computing environments due to its low resource overhead and high flexibility. However, as the number of containers grows, it becomes increasingly challenging to achieve the rapid and coordinated optimization of multiple objectives for container scheduling, while maintaining system stability and security. This paper aims to overcome these challenges and provides the optimal allocation for a large number of containers. First, a large-scale multi-objective container scheduling optimization model is constructed, which involves the task completion time, resource cost, and load balancing. Second, a novel optimization algorithm called LSMOF-AD (large-scale multi-objective optimization framework with muti-stage and adaptive differential strategies) is proposed to effectively handle large-scale container scheduling problems. The experimental results show that the proposed algorithm has a better performance in multiple benchmark p... [more]

Due to the extensive use of fossil fuels, energy conservation and sustainable transportation have become hot topics. Electric vehicles (EVs), renowned for their clean and eco-friendly attributes, have garnered considerable global attention and are progressively being embraced worldwide. However, disorganized EV charging not only reduces charging station efficiency but also threatens power grid stability. In this low-carbon era, photovoltaic storage charging stations offer a solution that accommodates future EV growth. However, due to the significant instability in both the charging load and photovoltaic power generation within charging stations, it is critical to maximize local photovoltaic power consumption and minimize the impact of disorganized EV charging on the power grid. This paper formulates an intelligent scheduling strategy for electric heavy trucks within charging stations based on typical photovoltaic output data. The study focuses on a photovoltaic storage charging station... [more]

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mech... [more]

Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and... [more]

Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for the semiconductor industry can be solved by constraint answer set programming (CASP) and its solver called clingcon. A successful solution to this problem is achieved, and we found that for all tested problems, CASP is faster and obtains smaller makespan values for seven of the eight problems tested than the solutions based on mixed integer linear programming and constraint paradigms. The considered scheduling problem includes a robot for lot transfers between baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, constraint processing, and Satisfiability Modulo Theories. For a long time, exact methods su... [more]

The hot rolled strip is a basic industrial product whose surface quality is of utmost importance. The condition of hot rolling work rolls that have been worn for a long time is the key factor. However, the traditional scheduling method controls risks to the surface quality by setting fixed rolling length limits and penalty scores, ignoring the wear condition differences caused by various products. This paper addresses this limitation by reconstructing a hot rolling-scheduling model, after developing a model for pre-assessment of the risk to surface quality based on the Weibull failure function, the deformation resistance formula, and real production data from a rolling plant. Additionally, Ant Colony Optimization (referred to as ACO) is employed to implement the scheduling model. The simulation results of the experiments demonstrate that, compared to the original scheduling method, the proposed one significantly reduces the cumulative risk of surface defects on products. This highlight... [more]

Sand produced along with well-production fluids accumulates in the surface facilities over time, taking valuable space, while the sand carried with the fluids damages downstream equipment. Thus, sand is separated from the fluid in the sand traps and separators and removed during periodic clean-ups. But at high sand productions, the probability of unscheduled facilities shutdowns increases. Such extreme production conditions can be handled by strategic planning and optimal design of the separator network to enable maximum sand separation at minimal equipment cost while ensuring the accumulation extent is within tolerable limits. This paper develops a mathematical model to optimize the separator network design to maximize sand separation while the sand accumulation extent and total equipment cost are minimal. The optimization model is formulated using multi-objective mixed-integer nonlinear programming (MINLP). The capabilities of the developed model to assist sand management in the sepa... [more]

This paper considers the optimal incorporation of renewable ammonia production facilities into existing supply chain networks which import ammonia from conventional producers while accounting for uncertainty in this conventional ammonia price. We model the supply chain transition problem as a two-stage stochastic optimization problem which is formulated as a Mixed Integer Linear Programming problem. We apply the proposed approach to a case study on Minnesota's ammonia supply chain. We find that accounting for conventional price uncertainty leads to earlier incorporation of in-state renewable production sites in the supply chain network and a reduction in the quantity and cost of conventional ammonia imported over the supply chain transition horizon. These results show that local renewable ammonia production can act as a hedge against the volatility of the conventional ammonia market.

Modern mechanical recycling infrastructure for plastic is capable of processing only a small subset of waste plastics, reinforcing the need for parallel disposal methods such as landfilling and incineration. Emerging pyrolysis-based chemical technologies can “upcycle” plastic waste into high-value polymer and chemical products and process a broader range of waste plastics. In this work, we study the economic and environmental benefits of deploying an upcycling infrastructure in the continental United States for producing low-density polyethylene (LDPE) and polypropylene (PP) from post-consumer mixed plastic waste. Our analysis aims to determine the market size that the infrastructure can create, the degree of circularity that it can achieve, the prices for waste and derived products it can propagate, and the environmental benefits of diverting plastic waste from landfill and incineration facilities it can produce. We apply a computational framework that integrates techno-economic analy... [more]

To mitigate the effects of catastrophic failure while maintaining resource and production efficiencies, energy systems need to be designed for resilience and sustainability. Conventional approaches such as redundancies through backup processes or inventory stockpiles demand high capital investment and resource allocation. In addition, responding to unexpected “black swan” events requires that systems have the agility to transform and adapt rapidly. To develop targeted solutions that protect the system efficiently, the supply chain network needs to be considered as an integrated multi-scale system incorporating every component from individual process units all the way to the whole network. This approach can be readily integrated with analogous multiscale approaches for sustainability, safety, and intensification. In this work, we bring together classical supply chain resilience with process systems engineering to leverage the multi-scale nature of energy systems for developing resilienc... [more]

Plastics are widely used for their affordability and versatility across many consumer and industrial applications. However, the end-of-life (EoL) management stage can often lead to releasing hazardous chemical additives and degradation products into the environment. The increasing demand for plastics is expected to increase the frequency of material releases throughout the plastic EoL management activities, creating a challenge for policymakers, including ensuring proper material segregation and disposal management and increasing recycling efficiency and material reuse. This research designed a Python-based EoL plastic management tool to support decision-makers in analyzing the holistic impacts of potential plastic waste management policies. The constructed tool was developed to reduce the complexity of material flow analysis calculations, estimating material releases, and environmental impacts. The utility of the tool was tested through the hypothetical nationwide adoption of an exten... [more]

Sustainable and economically viable plastic recycling methodologies are vital for addressing the increasing environmental consequences of single-use plastics. In this study, we evaluate the plastic waste management value for the state of Georgia, US and investigate the potential of introducing novel depolymerization methods within the network. An equation-based formulation is developed to identify the optimum supply-chain design given the geographic location of existing facilities. Chemical recycling technologies that have received increasing attention are evaluated as candidate technologies to be integrated within the network. The optimum supply-chain design is selected based on environmental and economic objectives. The designed network of pathways uses a mix of different technologies (chemical and mechanical recycling) in a way that are both economically environmentally sound.

Circular systems design is an emerging approach for promoting sustainable development. Despite its perceived advantages, the characterization of circular systems remains loosely defined and ambiguous. This work proposes a network optimization framework that evaluates three objective functions related to economic and environmental domains and employs a Pareto analysis to illuminate the trade-offs between objectives. The US polyethylene terephthalate (PET) value chain is selected as a case study and represented via a superstructure containing various recycling pathways. The superstructure optimization problems are modeled as a mixed integer linear program (MILP) and linear programs (LPs), implemented in Pyomo, and solved with CPLEX for a one-year assessment horizon. Solutions to the circular economy models are then compared to the corresponding solutions of linear economy models. Preliminary results show that the optimal circular network is advantageous over the optimal linear network fo... [more]

An important measure to achieve global reduction in CO2 emissions is CO2 capture, transport, and storage. The deployment of CO2 capture requires the development of a shared CO2 transport infrastructure, where CO2 can be transported with different transport modes. Furthermore, the cost of CO2 transport can be subject to significant economies of scale effects with respect to the amount of CO2 transported, also mentioned as clustering effects. Therefore, optimizing the shared infrastructure of multiple CO2 sources can lead to significant reductions in infrastructure costs. This paper presents a novel formulation of the clustered CO2 transport network. The Markov Decision Process formulation defined here allows for more detailed modeling of non-linear, discrete transport costs and increased geographical resolution. The clustering effects are modeled through cooperative multi-agent interactions. A multi-agent, reinforcement learning-based algorithm is proposed to optimize the shared transpo... [more]

With growing concern over the effects of green-house gas emissions, there has been an increase in emission-reducing policies by governments around the world, with over 70 countries having set net-zero emission goals by 2050-2060. These are ambitious goals that will require large investments into the expansion of renewable and low-carbon technologies. The decisions about which technologies should be invested in can be difficult to make since they are based on information about the future, which is uncertain. When considering emerging technologies, a source of uncertainty to consider is how the costs will develop over time. Learning curves are used to model the decrease in cost as the total installed capacity of a technology increases. However, the extent to which the cost decreases is uncertain. To address the uncertainty present in multiple aspects of the energy sector, multistage stochastic programming is employed considering both exogenous and endogenous uncertainties. It is observed... [more]

Increasing wind and solar electricity generation in power systems increases temporal variability in electricity prices which incentivizes the development of flexible processes for electricity generation and electricity-based fuels/chemicals production. Here, we develop a computational framework for the integrated design and optimization of multi-product processes interacting with the grid under time-varying electricity prices. Our analysis focuses on the case study of nuclear-based hydrogen (H2) and electricity generation, involving nuclear power plants (NPP) producing high temperature heat and electricity coupled with a high temperature steam electrolyzers (HTSE) for H2 production. The ability to co-produce H2 along with nuclear is widely seen as critical to improving the economics of nuclear energy technologies. To that end, our model focuses on evaluating the least-cost design and operations of the NPP-HTSE system while accounting for: a) power consumption variation with current den... [more]

This research is dedicated to designing and economically evaluating the green ammonia supply chain, considering the fluctuating nature of renewable energy sources and energy demand across both hourly and seasonal variations. It also explores the impact of economies of scale and the delays associated with long-distance shipping to meet energy demands in a timely manner. These considerations require the formulation of a Mixed-Integer Nonlinear Programming model, further complicated by the necessity for a two-stage stochastic programming approach. We introduce a hierarchical optimization framework that utilizes a decomposition method to differentiate between one-time design decisions and subsequent operational choices. At the upper level, potential design solutions are identified through the Bayesian Optimization and Hyperband algorithm, which effectively navigates the non-linear challenges posed by economies of scale. The lower level then addresses a Mixed-Integer Linear Programming prob... [more]