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Resource planning and management are essential strategic practices in the supply chain. Resource allocation problems are becoming more complex due to the dynamic nature of these logistical systems. Since the increasing popularity of Deep Reinforcement Learning (DRL) algorithms in Gaming and Robotics, scholars have started investigating their potential for addressing supply chain concerns. The utilization of DRL-based approaches for addressing supply chain optimization problems remains largely unexplored. Therefore, we present a systematic literature analysis to investigate the adoption of DRL solution techniques for solving supply chain optimization problems. Afterward, we propose a novel method to address allocation problems in the supply chain founded on DRL-based algorithms, namely Asynchronous Actor critic (A3C) and Proximal Policy Optimization (PPO). A simulation model is developed to train and evaluate the application of these algorithms. We transform numerical data from the simulation into Gantt charts and pass them as observations. This formulation of the observation space is motivated by the fact that Deep Neural Networks (DNNs) are well-suited for image-based analysis. The computational results demonstrate that both algorithms successfully learn allocation policies using image-based observation considering multiple objective values. The results of the conducted experiments indicate that A3C achieves a more stable allocation policy than PPO in minimizing all considered objective values. [ABSTRACT FROM AUTHOR]

To promote a hydrogen economy in the near term, in this work, curtailed power from renewable generation in Northwest China is used as the energy source for electrolytic H 2. The feasibility of involving methanol as a liquid energy carrier is also explored considering the relatively high cost and safety risk of H 2 storage and transportation. Four technology routes including both centralized and distributed versions of pure hydrogen and methanol-mediated supply chains for local H 2 consumption at refueling stations are investigated with real-world scenarios in Lanzhou, China. The capacity of existing solar photovoltaics (PVs) is obtained from remote sensing images and a generic supply chain optimization model is developed to compute the minimum levelized cost of hydrogen (LCOH) for each route. Greenhouse gas (GHG) emissions and safety considerations are also assessed for each route. It was found that the green methanol route is the safest with GHG emissions being higher than expected. [Display omitted] • Four different technology routes for a localized H 2 supply chain are investigated. • An optimization model with operational flexibility and seasonal storage is developed. • The green methanol route is the safest with the GHG emissions higher than expected. • A pathway for green methanol reforming with CO 2 recycling is proposed and assessed. • Sensitivity analyses for curtailment rate, H 2 demand and distance are conducted. [ABSTRACT FROM AUTHOR]

To address the growing energy demand and environmental issues associated with fossil fuels, growing energy crops on marginal lands offers a sustainable solution to increase bioresources for bioenergy production while avoiding the conflict of food and fuel. Giant reed (Arundo donax L.), a non-food energy crop, is recognized for its substantial yield and adaptability to marginal lands, making it a promising candidate for bioenergy production and carbon sequestration. Despite its potential, the optimal supply chain configuration for giant reed (GR) based bioenergy production on China's marginal lands has not been established and evaluated. This study aims to fill this gap by developing and evaluating an integrated framework that includes the identification of available marginal lands, a region-specific growth model for GR biomass, and a Mixed-Integer Linear Programming (MILP) model for optimal design of spatial supply chain configuration. The growth model indicates an average yield potential of 24.5 t DM/ha annually. The optimization model provides multiple cost-effective solutions for the configurations of biomass production regions, storage depots, biorefineries, while considering transportation logistics across diverse geographic landscapes. Results at various replacement ratio of national energy consumption, demonstrate that large-scale GR plantation and utilization significantly increase the total cost but also contribute to substantial carbon sequestration, ranging from 23 Mt CO 2 eq at a 1% replacement ratio to 620 Mt CO 2 eq at 28% annually. This study not only provides a foundational framework for future research on the potential of GR in China's bioenergy development, but also highlights the feasibility of incorporating GR into China's bioenergy landscape. [Display omitted] • Giant reed (GR) emerges as a promising non-food energy crop for biofuel production in China. • Novel integrated framework for GR biomass supply chain simulation and optimization. • An APSIM-based model tailored for regional predictions of GR biomass yield. • Large scale MILP optimization addresses non-uniform biomass distribution across China's marginal lands. • Optimal GR supply chains demonstrate significant Carbon sink potential. [ABSTRACT FROM AUTHOR]

• A model is developed to represent the supply chain of renewable methanol in Mexico. • The model considers multiple potential residues to be used as raw materials. • The supply chain is optimized in terms of economic, environmental, and social objectives. • The use of captured carbon dioxide as raw material significantly reduces the environmental objective. Methanol is one of the most important chemical compounds, as it is the basis for producing a wide variety of derivatives. Its production through fossil sources such as natural gas in countries like Mexico is not entirely viable due to the fluctuations in the availability of this resource. The use of renewable sources to produce methanol represents an interesting area of opportunity to reduce the dependence on a single raw material. This work proposes the design of the methanol supply chain in Mexico using residual materials, finding a solution with the best compromise between profit, social impact, and CO 2 emissions. The solution with the best compromise corresponds to a profit of 7,334,100 USD/y, a marginalization index of 2592.536 and CO 2 emissions of -0.021 Mt/y. This solution has 8 different types of raw materials, 18 process plants and the use of three processing technologies: gasification, anaerobic digestion, and catalysis from CO 2. [ABSTRACT FROM AUTHOR]

Intelligent Decision Support Systems (DSS) have gained considerable interest within the field of construction engineering through the utilization of Artificial Intelligence (AI) and Machine Learning (ML) methodologies. These systems possess the capacity to enhance decision-making processes and optimize the results of projects. Nevertheless, it is imperative to conduct a thorough and inclusive examination that incorporates and evaluates the various methodologies employed within this particular discipline, thereby bridging the existing void in research. The current state of research in the field of AI and ML in construction engineering is characterized by a dearth of comprehensive reviews that encompass the wide array of methodologies utilized. Although certain studies have delved into specific facets of this subject, there remains a novelty and research gap in terms of a holistic examination of the topic. The objective of this review is to conduct a comprehensive analysis of AI and ML methodologies in the field of construction engineering, with a specific emphasis on their practical applications, advantages, and constraints. The study also ascertains potential areas for future research and improvement in the domain of Intelligent DSS. This study employed a rigorous approach that involved an extensive review of scholarly literature and empirical investigations from 2001 to June 2023. The findings of the analysis indicate a noticeable increase in the number of research papers focusing on Intelligent DSS within the field of construction engineering during the last twenty years. The field has witnessed the emergence of key research areas, namely the utilization of Internet of Things (IoT), explainable AI, supply chain optimization, sustainable construction practices, and human–machine collaboration. The results of this study hold significant implications for the construction industry. The incorporation of AI and ML techniques within Intelligent DSS can effectively tackle various issues such as cost overruns, project delays, and safety concerns. [ABSTRACT FROM AUTHOR]

Global supply chains are kept viable through the information shared through billions of electronic documents, many of which extensively use tables to display critical information. Making effective supply chain decisions requires the extraction of data from these tables which is hindered by the variations in layouts and styles of tables. In this paper, we propose Table Det: a deep learning based methodology to solve table detection and table image classification in data sheet images in a single inference as the first stage of the table text extraction pipeline. TableDet utilizes Cascade R-CNN with Complete IOU (CIOU) loss and a deformable convolution backbone as its underlying architecture to capture the variations in scales and orientations of tables. It also detects text and figures to enhance its table detection performance. We demonstrate the effectiveness of training TableDet with a dual-step transfer learning process and fine-tuning it with Table Aware Cutout (TAC) augmentation strategy. We achieved the highest F1 score for table detection against state-of-the-art solutions on ICDAR 2013 (complete set), ICDAR 2017 (test set) and ICDAR 2019 (test set) with 100%, 99.3% and 95.1% respectively. For the table image classification task we attained 100% recall and above 85% precision on three test sets. This classification capability ensures that all images with tables would be promoted to the next step in the table text extraction pipeline, with a small number of images without tables making it through. [ABSTRACT FROM AUTHOR]

Addressing the Water–Energy–Food Nexus has become the main point of many studies due to the necessity of finding a solution that fits the demands while considering the strong interdependence among the nexus elements and taking care of their sustainable security. However, managing the elements of the nexus is also a matter of making decisions in a multi-stakeholder environment, where the final solution should ensure the security of the nexus for all the participants. In this sense, we present a multi-stakeholder approach for the optimal management of resources and the mitigation of conflicts among stakeholders. The nexus security is modeled by sustainability indicators to measure the availability, accessibility and sufficiency of water, energy and food. Soft-clustering and multi-objective strategies, as well as the concept of ideological distance, are used for performing a similarity analysis in multi-criteria and multi-stakeholder environments to forecast alliances among participants and mitigate conflicts. For testing these ideas, the supply chain of water, energy and food utilities in a state in the northern part of Mexico, where there is strong competition for resources, is presented as a case study. This study indicates that it is possible to reduce the discussion among several stakeholders to a few groups, facilitating consensus. [Display omitted] • Water–Energy–Food nexus is addressed using a security approach. • Ideological distance is used to address similarities among the interests of stakeholders. • The Fuzzy C-means algorithm is used for defining potential alliances among stakeholders. • Trade-off solutions are computed for different alliances of stakeholders. [ABSTRACT FROM AUTHOR]

Hydrogen-specific policies are required to accelerate the adoption of low-carbon hydrogen. In this study, a range of economic instruments and regulations were incorporated explicitly to optimize a hydrogen supply chain. The effectivenes of policies aiming to enhance the financial viability of low-carbon hydrogen production was quantified. A spatially explicit, multi-period cost optimization model was developed for light duty hydrogen hydrogen vehicle deployment in British Columbia under three demand scenarios. Subsidies and regulations were coupled to current provincial policies (a carbon tax and a low carbon fuel standard). The results indicated that production tax credits and electricity incentives were up to 24 times more effective in facilitating low-carbon hydrogen production compared to capital subsidies, bans on steam methane reforming or the adoption of higher carbon taxes. The strategic deployment of policies over time was found to be more effective than cumulative subsidies. • Incentives were included explicitly in a hydrogen supply chain optimization model. • Effectiveness of subsidies was assessed with respect to cost and emissions reduction. • Production tax credit was 24x more effective for low-carbon hydrogen production. • Timely deployment was more effective than cumulative subsidies. • Hydrogen-specific policies can facilitate green hydrogen adoption. [ABSTRACT FROM AUTHOR]

Optimal clean process design requires strict constraints to enforce waste and byproduct management, all of which can be formulated in the language of mathematical programming. However, waste management and the utilization of by-products are often carried out in locations or periods other than the production process. The paper describes all modeling steps by P-graphs sufficient to represent raw material availability and production capacities in multiple time periods at multiple sites, as well as transportation and storage capacities of process materials and wastes. These steps are integrated into a single comprehensive model generation algorithm. For easier understanding, each model generation step is illustrated by a case study of planning a multi-site multi-period furniture production process alongside the recent challenges of energy supply and waste management. Finally, the case study of furniture production is analyzed under various circumstances to highlight the power of the proposed tools in daily production and transportation planning. Accordingly, the proposed method provides such alternative 5 best manufacturing and logistics plans that, in the event of a complete failure or overloading of one of the production capacities at either locations, there is still an alternative plan within a 3% profit decrease. • Optimization model is proposed for multi-site multi-period clean process design. • The mathematical model force waste and byproduct management. • Model generator algorithm is provided for the model generation. • Model generation is illustrated by a 3-day multi-site furniture manufacturing. • P-graph based solution procedure results in N-best alternative production plans. [Display omitted] [ABSTRACT FROM AUTHOR]

Highlights • Biogas production consists of four stages: hubs, reactors, condensers and demand points. • A mixed-integer nonlinear model is proposed to optimize biogas a plant location problem. • An algorithm finds a solution to locate hubs, reactors and condensers to minimize the total cost. • The approach advances the method to solve biogas plant location more economically. • The solution quality and computational performance outperformed other existing methods. Abstract Because of its sustainability, less environmental pollution and high profitability, rapidly growing number of the bio-methane gas (BMG) production systems implies successful commercialization in the new economic venture for renewable bioenergy. Covering from the collection and storage of feedstock (crops, grass, wood residue, and livestock waste) to the distribution of end product, this paper concerns with the optimization of supply chain cost for a BMG production system. The BMG production process is divided into four stages (collecting feedstock to the hubs, transporting feedstock from hubs to reactor(s), then transporting BMG from reactor(s) to condenser(s) and finally shipping the liquefied BMG from condensers to demand points. A mixed-integer mathematical model is proposed to optimally locate the hubs (to collect feedstock) and the BMG plants (reactors and condensers) so as to minimize the total cost of operating this supply chain system for renewable energy. The optimal solutions for the model cannot be readily obtained through convex optimization due to the complexity of the constrained objective function involving with mixed-integer variables. A genetic algorithm is devised to find the optimal solution and several numerical experiments are conducted to verify its performances. Large instances of the representative problems are solved with better quality solutions efficiently and the solution quality overpowers the existing solutions obtained by traditional Lingo and other comparable software. The impact of the model and solution approaches have both technological advancement in solving such problems and economic value to the biogas energy generation. [ABSTRACT FROM AUTHOR]

In this paper, we address a simultaneous optimization framework for product family configuration and supply chain planning. The customer determines the product family configuration and the manufacturer determines the supply chain planning. A conceptual model is developed for the simultaneous optimization of product family configuration and supply chain planning incorporating customer's behavior. The effectiveness of the proposed model is investigated. [ABSTRACT FROM AUTHOR]

Virtualization of a supply chain is a basis for constructing a Cyber-Physical System (CPS) for a smart supply chain. Total optimization and furthermore autonomous optimization of the supply chain will become possible using this CPS system. Models of component enterprises are divided into three types: manufacturer model, part/material supplier model and retailer model, and models are cataloging based on these types. Each enterprise is modeled in terms of its behavior. Each component enterprise agent which shows each enterprise's behavior, is implemented from selected enterprise models in catalogues. An enterprise model is constructed as integrated model of data model and mathematical model. A virtual supply chain is structured as a multi agent system based on balanced cooperation of enterprise agents. A virtual supply chain which is automatically structured using enterprise catalogue, could be used to simulate the various phases including negotiation between enterprises. [ABSTRACT FROM AUTHOR]

Abstract The environment depletion caused by the intensive use of fossil fuels to produce electricity to satisfy the increasing demands has motivated the searching of alternative energy sources. In this context, residual biomass from different crops has been considered as alternative. However, there are significant issues associated to this energy source, which are mainly related to the low density and high transportation costs. Therefore, in this paper is presented an optimization approach for the strategic planning for the use of waste biomass pellets in power plants for electric energy production. The proposed approach considers all the activities included in the supply chain since the collection of residues until the combustion of pellets in power plants. Particularly, the location and design of the pelleting plants in a macroscopic region, as well as the selection of biomass and transportation routes are considered to satisfy the needs of the power plants at the minimum cost and minimum environmental impact. A case study for the national case of Mexico is presented, where the results show that is possible to satisfy 50% of the national demand of the power plants through the use of residual biomass, and simultaneously the associated CO 2 emissions can be reduced 25%. Graphical abstract Image Highlights • Pellets from biomass residues can be used in power plants. • Bioenergy can be integrated in the electric energy production. • The supply chain must be optimized in the integral system. • The optimal configuration includes an environmental impact reduction. [ABSTRACT FROM AUTHOR]

Highlights • Carbon capture utilization storage (CCUS) supply chain problem is studied. • All stages for carbon capture, transport and storage are taken into account. • The model is applied to a realistic case study of Northeast China. • Scenarios assessing toward different CO 2 reduction level are implemented. • The intermediate sites for CCUS supply chain lessen transportation and total costs. Abstract In recent years, several strategies have been developed and adopted in a bid to diminish the carbon dioxide (CO 2) released into the atmosphere. Carbon capture, utilization and storage (CCUS) system is one of the options. In this paper, we develop a CCUS supply chain superstructure by introducing more comprehensive transportation routes as well as the resultant system deployment schemes. A mixed integer linear programming (MILP) model is proposed to optimize the strategic CCUS deployment in Northeast China by making simultaneous selection of emission sources, capture facilitates, CO 2 pipeline, intermediate transportation sites, utilization and storage sites. The CCUS cost includes the cost of flue gas dehydration, CO 2 capture, transportation and injection, and revenue from CO 2 utilization through enhanced oil recovery (CO 2 -EOR). The overall network is economically optimized over a 20 years' life span to provide the geographic distribution and scale of capture, utilization and sequestration sites as well as the transportation routes for different scenarios. The results suggest that it is economic feasible to reduce 50% of the current CO 2 emissions from the stationary sources at a total annual cost $2.30 billion accompanied with $0.77 billion of revenue generated annually through CO 2 -EOR. Overall, the optimal CCUS supply chain network correspond to a net cost of $23.53 per ton of CO 2. The results are compared with source-sink model and it can be observed that the total annualized net cost is reduced from $1.62 billion to $1.53 billion and the transportation cost are reduced from $0.27 billion to $0.19 billion. [ABSTRACT FROM AUTHOR]

This paper presents a deep learning-based model predictive control (MPC) method for operational supply chain optimization in real time. The method follows an offline-online procedure. In the offline phase, the state-space model of a supply chain system is developed and the MPC problem for supply chain operation is formulated. Then, the MPC problem is solved for a set of initial states to obtain the corresponding optimal inputs. A deep neural network (DNN) is built and trained by using the optimal state-input pairs as training data to approximate the optimal MPC law. In the online phase, the DNN controller is employed to provide real-time decisions. In this paper, the MPC problem for supply chain operation is formulated as a mixed-integer linear program. A deep learning-based MPC method is proposed to accommodate time delays in the system. Moreover, a heuristic method is proposed for feasibility recovery with the binary decision variables taken into account. The training set for the DNN controller contains two subsets, one formed from MPC solutions corresponding to random initial states, and the other formed from optimal state-input pairs in closed-loop simulations. The deep learning-based MPC is validated via two case studies through closed-loop simulation. The first case study involves a linear MPC, and the second case study involves a more complicated mixed-integer linear MPC. Results show that deep learning-based MPC can achieve a high accuracy in approximating the MPC decisions and a significant reduction in the online computation time. Compared with MPC, the average performance loss of using deep learning-based MPC in the two cases is 0.43% and 1.8%, respectively. • Application of deep learning-based model predictive control (MPC) to supply chains. • A method to accommodate time delays in the system. • A feasibility recovery approach for deep learning-based mixed-integer MPC. • A data generation method that combines random and closed-loop simulation samples. [ABSTRACT FROM AUTHOR]

The United States (US) military plans to acquire drop-in biofuels (renewable diesel and biojet fuel) to reduce carbon emissions and diversify military energy portfolio. To expedite this endeavor, the military provided direct financial incentives to offset investment costs of selected drop-in biofuel demonstration facilities. It is not known if investment incentives alone will stimulate the creation of a full-scale advanced biofuel supply chain capable of meeting US military demands, given limited availability of low-cost sustainable biomass feedstocks in some areas and considering the uncertainty in global oil prices. The objective of this work is to determine 1) whether a state in the US can meet its share of military biofuel targets from local biomass resources, and 2) if direct financial incentives can expedite the development of the military biofuel supply chain, under two different oil price scenarios. The Biofuel supply chain GeoSpatial and Temporal Optimizer (BioGeSTO), was developed for that purpose and applied to the state of California, USA from 2020 to 2040. The BioGeSTO model determined that biomass resources in California can meet 12–19% of its annual military targets between 2020 and 2040 of renewable diesel and biojet fuel using the Fischer-Tropsch (FT) and Hydro-Treatment of Esters and Fatty Acids (HEFA) conversion technologies. However, under the reference oil price scenario, only HEFA conversion facilities introduced at 2027 in Kings County were found feasible. Under the high oil price scenario, both the HEFA and FT technologies were financially feasible and the supply chain production approaches the theoretical production limit by 2032. In both scenarios, providing investment incentives has a modest impact on expediting the supply chain, as facilities are introduced only 1–3 years earlier when receiving direct investment incentives. Sensitivity analysis shows that biomass availability has the greatest impact on the supply chain performance such that a 50% increase in the baseline amount of biomass feedstock results in a 150% surge in the total cumulative production. In conclusion, the reference oil price scenario drastically limits the ability of California to meet its military drop-in biofuel targets. Assuming a high oil price scenario, the state may be able to meet military biofuel targets by subsidizing local biomass production and importing the rest of the biomass required for this purpose from the northwestern states of Washington and Oregon. [ABSTRACT FROM AUTHOR]

The transport sector involves high consumption of fossil fuels being a cornerstone sector to foster energy reduction. This goal can be achieved through the introduction of alternative and sustainable energy sources such bio-electricity and biofuels. To attain a viable and sustainable introduction of these energy sources into the market, the whole supply chain must be evaluated. This paper presents an extension of a multi-period and spatially explicit features, which are embodied in a mixed integer linear programming framework, in order to optimize a multi-echelon supply chain simultaneously in terms of economic (net present value) and environmental performance (greenhouse gases emissions), considering biomass cultivation, transport, conversion into bioethanol or bioelectricity, distribution and final usage in alternative vehicles. Bioethanol and bioelectricity supply chains are assessed considering corn, stover, Arundo donax , Miscanthus , poplar and wood residues as possible biomass feedstock, for multiple first and second generation conversion technologies. Furthermore, a sensitivity analysis is performed to assess the effect of uncertainty associated with some economic and environmental parameters. Results are also compared with those from previous studies. Results show the effectiveness of the model at providing decision makers with a quantitative analysis to optimize the economic and environmental performance of different design configurations. [ABSTRACT FROM AUTHOR]

Using forest-based biomass to produce bio-energy and bio-fuels could provide economic, environmental, and social benefits for communities. However, variability in biomass availability and high cost of delivered feedstock impact the profitability of the supply chain. Therefore, optimization models were developed in previous studies to design cost effective and profitable biomass-based supply chains at strategic level. Medium term variations in biomass supply and demand are not usually accounted for in strategic models. This may affect the feasibility of strategic plans prescribed by the optimization model at tactical level. To solve this issue, an integrated model is developed in this paper that includes strategic and tactical decisions simultaneously in order to optimize forest-based biomass supply chains. In addition to yearly variations in biomass supply, which can occur due to changes in harvest level, monthly variations in biomass availability, bioenergy/biofuels demand, and losses during preprocessing and storage of biomass are incorporated in the model. Other unique features of this model compared to the few integrated models developed in previous studies are as follows. (1) Decision regarding opening a new conversion facility is made yearly, not just at the beginning of the planning horizon. (2) A multi-product (heat, electricity, bio-oil and pellets) supply chain is considered. (3) The impact of fossil-based energy prices on bio-conversion investment decisions are accounted for in the model. (4) The optimization problem is modeled in a way that the global optimum solution is obtained within a reasonable time. Using a case study in Interior British Columbia, it is shown in the paper that the capacity of conversion technologies and the amount of procured biomass prescribed by the strategic model would not be sufficient to meet the monthly demand of bioenergy. Moreover, the net present value of the strategic model is overestimated due to underestimating the demand and procurement cost, and ignoring storage costs. It is shown that these issues are resolved using the integrated model. [ABSTRACT FROM AUTHOR]

We present a comprehensive supply chain optimization model to determine optimal shale oil and gas infrastructure investments in the United States. The model encompasses multiple shale plays, commodities, plant locations, conversion technologies, transportation modes and both local and foreign markets. The dynamic evolution of supply, demand and price parameters and the uncertainty in parameter realizations are fully taken into account. Imposing two different scenario sets over a time horizon of twenty-five years, the model maximizes the expected net present value of the entire undertaking. We analyze the features of the optimal infrastructure investments and associated operating decisions, perform case studies which highlight the importance of incorporating uncertainty into the model and analyze the stability of the stochastic solutions as the degree of uncertainty changes. The overall opportunity set of investments is sparse, and there is a tendency for over-investment in new liquefied natural gas capacity when the uncertainties in future oil prices are not taken fully into account. [ABSTRACT FROM AUTHOR]

To establish a systematic and efficient forest bioenergy supply chain management system, most previous related studies did not investigate a system that involves generating two or more types of energy simultaneously, and did not consider the carbon emissions caused by electricity power generation of forest biomass combustion. Consequently, this study addressed the problems regarding the tactical supply chain optimization of a forest biomass electricity and second-generation bioethanol coproduction plant. A mixed-integer linear programming model was employed to determine the optimal production strategies to maximize the profit and to minimize the carbon emissions cost. The advantages of coproduction include enabling equipment and facility sharing, reducing investment costs, and dispersing the risks that involve raw materials, leading to synergy. Empirical simulation results indicated that the coproduction model yielded more profit than the independently operated model. When electricity demand is reduced, the coproduction model mitigates its profit losses through an increase in its bioethanol production. However, when faced with an increase in carbon emission costs, the coproduction model loses its advantages. According to the sensitivity analysis, electricity prices are the most vital parameter to the profit of the model, followed sequentially by the higher heating value of raw materials and carbon emission costs. [ABSTRACT FROM AUTHOR]

We propose a method for designing policies for convex stochastic control problems characterized by random linear dynamics and convex stage cost. We consider policies that employ quadratic approximate value functions as a substitute for the true value function. Evaluating the associated control policy involves solving a convex problem, typically a quadratic program, which can be carried out reliably in real-time. Such policies often perform well even when the approximate value function is not a particularly good approximation of the true value function. We propose value-gradient iteration, which fits the gradient of value function, with regularization that can include constraints reflecting known bounds on the true value function. Our value-gradient iteration method can yield a good approximate value function with few samples, and little hyperparameter tuning. We find that the method can find a good policy with computational effort comparable to that required to just evaluate a control policy via simulation. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) capture, utilization, and storage (CCUS) by mineralization has been shown to reduce greenhouse gas (GHG) emissions not only in stand-alone plants but also in large-scale climate-optimal supply chains. Yet, implementing the large-scale supply chain for CCUS by mineralization requires a substantial financial investment and, thus, a deep understanding of its economics. The current literature estimates the economics of CO 2 mineralization for stand-alone plants. While CO 2 mineralization plants have their specific a) CO 2 supply, b) solid feedstock supply, c) energy supply, and d) product market, the plant-level cost estimation does not account for a large and potentially shared supply chain. In our study, we assess the economics of mineralization by designing and analyzing cost-optimal supply chains for CCUS by mineralization in Europe. Our results show that the CO 2e abatement costs of individual mineralization plants in a supply chain range from 110 to 312 €/ton CO 2e avoided. The proposed supply chains for CCUS by mineralization can avoid 60 Mt CO 2e /year in Europe at CO 2e abatement costs comparable to CO 2 capture and geological storage. Furthermore, we identify five locations that could offer a robust business case for CO 2 mineralization. The analysis thus shows pathways on how to add CO 2 mineralization to the GHG mitigation portfolio of Europe. • Designing cost-optimal supply chains for CO 2 mineralization in Europe. • CO 2e abatement costs of CO 2 mineralization range from 110 to 312 €/ton CO 2e avoided. • Avoiding 11 % of industrial emissions in Europe at competitive costs. • Robust business case for CO 2 mineralization in five locations. [ABSTRACT FROM AUTHOR]

While Stackelberg leader–follower games and bilevel programming have become increasingly prevalent in game-theoretic modeling and optimization of decentralized supply chains, existing models can only handle linear programming or quadratic programming followers’ problems. When discrete decisions are involved in the follower's problem, the resulting lower-level mixed-integer program prohibits direct transformation of the bilevel program into a single-level mathematical program using the KKT conditions. To address this challenge, we propose a mixed-integer bilevel programming (MIBP) modeling framework and solution algorithm for optimal supply chain design and operations, where the follower is allowed to have discrete decisions, e.g., facility location, technology selection, and opening/shutting-down of production lines. A reformulation-and-decomposition algorithm is developed for global optimization of the MIBP problems. A case study on an integrated forestry and biofuel supply chain is presented to demonstrate the application, along with comparisons to conventional centralized modeling and optimization methods. [ABSTRACT FROM AUTHOR]

We address the effects of the exchanges of leader-follower relationship to the multi-period supply chain planning under demand uncertainty. Most of conventional works assume that the leader-follower relations for multiple companies are treated as constant in all time periods. However, in order to accommodate changes caused by uncertainties, it is necessary to develop reconfigurable supply chain that can flexibly change its structure of supply chain according to the dynamical changes of manufacturing environment. In this paper, a multi-period supply chain planning model that can change the structure of supply chain over time periods under uncertainty is developed. We provide a novel production planning model that can effectively determine the optimal configuration of leader-follower relationship in the bilevel supply chain planning problem. Then, the total profit of the supply chain is analyzed by a game theoretical analysis. The effects on the change of configurations are investigated from computational experiments. [ABSTRACT FROM AUTHOR]

Expected changes in European biofuel regulations lead to uncertainty in supply chain planning for second generation biofuel industries. Previous studies used optimization to design biofuels' supply chains and a few of them analyzed the impact of selected biofuel policy instruments on sustainability targets. This study presents a mixed integer programming model for the optimal design of second generation biofuel supply chains considering all policy instruments of European biofuel regulations. The model maximizes the net present value of second generation biofuel supply chains considering time-dependent quotas for biofuel market shares and greenhouse gas emission savings. The model includes multiple counting of second generation biofuel towards meeting biofuel market share targets and captures all greenhouse gas emissions of the second generation biofuel supply chain including land use change emissions. We demonstrated the use of the model through the case study of second generation biodiesel in a German region, and evaluated the sensitivity of the optimal solution to model parameters and policy instruments' levels. A set of remarks were made regarding the impact of policy instruments on the performance of second generation biodiesel supply chain under study. Future work entails the application of the analysis to the aggregate level in the European Union. [ABSTRACT FROM AUTHOR]

The fossil fuel consumption is still a global concern that has received great deal of attention from Chemical Engineering and Operation Research fields. In this sense, this paper presents a SCND (Supply Chain Network Design) for second-generation biofuel production. For this aim, we proposed a mathematical model that includes, simultaneously, economic and environmental issues. The model feasibility has been tested in Colombia. In this case study, the bioethanol production from Coffee Cut Stem (Coffee-CS) is examined considering the three main echelons of the supply chain. Experimental results showed that Coffee-CS is a profitable and sustainable feedstock for biofuel production. [ABSTRACT FROM AUTHOR]

Electricity generated from forest-based biomass is an attractive source of renewable energy. However, the cost of generating heat and/or electricity from it is relatively high due to the low energy density of wood, high moisture content and variations in its quality and availability. Models have been developed to optimize the supply chain and reduce the cost per kilowatt hour generated. This paper focuses on incorporating uncertainty in the supply chain of such a model. The model considers the tactical supply chain planning of a power plant over a one-year time horizon with monthly time steps. Uncertain parameters which impact the net profit of the power plant include ‘biomass quality,’ namely moisture content and higher heating value, and ‘monthly available biomass’ from different suppliers. Robust optimization is used to model uncertainty in the quality of biomass. Then a hybrid, multi-stage, ‘stochastic programming-robust optimization’ model is presented in order to simultaneously include uncertainty in biomass quality and biomass availability. It is demonstrated that the hybrid model takes advantage of both modelling approaches to balance the profit estimates and the tractability to various circumstances. The model provides solution considering all instances of the uncertain parameters within the defined sets and scenario tree. The results revealed a major trade-off between profit and range of biomass quality. Profit decreased by up to 23% when there was ±13% variation in moisture content and ±5% change in higher heating value. The model achieved a biomass purchase cost that was lower than the current commercial costs at the power plant. Implementing the model could prevent production curtailment and undesirable fluctuation in storage levels which occurred in the past due to variations in biomass availability and quality. [ABSTRACT FROM AUTHOR]

In this paper, we show how four recently developed modeling and solution methods can be integrated to address mixed integer programs for the scheduling of large-scale chemical production systems. The first method uses multiple discrete time grids. The second adds tightening constraints that lower bound the total production and number of batches for each task and material based on the customer demand, while the third generates upper bounding constraints based on inventory and resource availability. The final method is a reformulation that introduces a new integer variable representing the total number of batches of a task. We apply the aforementioned methods to large-scale problems with a variety of processing features, including variable conversion coefficients, changeovers, various storage policies, continuous processing tasks, setups, and utilities, using a discrete-time model. We illustrate how these methods lead to significant improvements in computational performance. [ABSTRACT FROM AUTHOR]

[Display omitted] • Levelised cost of hydrogen for residential heating and mobility was minimised. • European Union targets on greenhouse gas emission reduction were included. • The needed amount of solar photovoltaics and wind turbines was estimated. • Integrating energy service demand patterns decreases system cost. • Wind energy is favourable because of both cost and avoided greenhouse gas emission. Decentralised renewable energy production in the form of fuels or electricity can have large scale deployment in future energy systems, but the feasibility needs to be assessed. The novelty of this paper is in the design and implementation of a mixed integer linear programming optimisation model to minimise the net present cost of decentralised hydrogen production for different energy demands on neighbourhood urban scale, while simultaneously adhering to European Union targets on greenhouse gas emission reductions. The energy system configurations optimised were assumed to possibly consist of a variable number or size of wind turbines, solar photovoltaics, grey grid electricity usage, battery storage, electrolyser, and hydrogen storage. The demands served are hydrogen for heating and mobility, and electricity for the households. A hydrogen residential heating project currently being developed in Hoogeveen, The Netherlands, served as a case study. Six scenarios were compared, each taking one or multiple energy demand services into question. For each scenario the levelised cost of hydrogen was calculated. The lowest levelised cost of hydrogen was found for the combined heating and mobility scenario: 8.36 € kg−1 for heating and 9.83 € kg−1 for mobility. The results support potential cost reductions of combined demand patterns of different energy services. A sensitivity analysis showed a strong influence of electrolyser efficiency, wind turbine parameters, and emission reduction factor on levelised cost. Wind energy was strongly preferred because of the lower cost and the low greenhouse gas emissions, compared to solar photovoltaics and grid electricity. Increasing electrolyser efficiency and greenhouse gas emission reduction of the used technologies deserve further research. [ABSTRACT FROM AUTHOR]

CO 2 mineralization not only captures and stores CO 2 permanently but also yields value-added products utilized in, for example, the cement industry. CO 2 mineralization has been shown to potentially substantially reduce greenhouse gas (GHG) emissions. Realizing CO 2 mineralization's potential on a large scale requires a) solid feedstock, b) CO 2 sources, c) low-carbon energy, and d) markets for mineralization products. In general, these four requirements of CO 2 mineralization are not satisfied at the same location. Thus, the assessment of CO 2 mineralization's large-scale potential necessitates the full supply chain considering all requirements for CO 2 mineralization simultaneously. At present, neither the potential of CO 2 mineralization for GHG emissions reduction on a large scale nor the required supply chain to achieve the potential are fully understood. In our study, we design a climate-optimal supply chain for CO 2 capture, utilization, and storage (CCUS) by CO 2 mineralization to quantify the large-scale potential of CO 2 mineralization in Europe. Our results show that a climate-optimal CCUS by CO 2 mineralization could avoid up to 130 Mt CO 2e /year of the industrial emissions in Europe even with the current energy supply system. By 2040, CCUS by CO 2 mineralization could provide negative emissions of up to 136 Mt CO 2e /year. The required energy and CO 2 for the CCUS supply chain can be provided either by expanding the current infrastructure by about 5 % or, even more climate efficiently, by building new infrastructure. The critical steps toward achieving the large potential of CO 2 mineralization in Europe are 1) scaling up the CO 2 mineralization technology to the industrial level and 2) exploiting large-scale mineral deposits. [Display omitted] • Supply chain optimization for CO 2 mineralization in Europe. • 130 Mt CO 2e annual reduction by CO 2 mineralization with current energy mix. • 5 % expansion of current infrastructure is needed for energy and CO 2 supply. • Potential for 136 Mt CO 2e /year negative emissions by 2040. • Critical steps: scaling up the technology & exploiting large-scale mineral deposits. [ABSTRACT FROM AUTHOR]

In many urbanized areas the roadside and nature conservation management offers a biomass-for-bioenergy resource potential which is barely valorized, because of the fragmented biomass production sites and the scarcity of accurate data on the spatial availability of the biomass. In this study, a GIS based assessment was performed to determine the regional non-woody biomass-for-bioenergy potential for biogas from conservation areas and roadsides in Flanders, Belgium. These systems, with an area of 31,055 ha, have an annual herbaceous biomass production of 203 kton dry matter. The full associated biomass-to-bioenergy supply chain was optimized in four scenarios to maximize the net energy output and the profit. The scenario analysis was performed with OPTIMASS, a recently developed GIS based strategic decision support system. The analysis showed that the energetic valorization of conservation and roadside biomass through anaerobic digestion had a positive net energy balance, although there is still much room for improvements. Economically, however, it is a less interesting biomass resource. Most likely, the economic picture would change when other ecosystem services delivered by the protected biodiversity would be taken into account. Future technical advances and governmental incentives, like green energy certificates, will be necessary to incorporate the biomass into the energy chain. By tackling the existing barriers and providing a detailed methodology for biomass potential assessments, this study tries to facilitate the valorization of conservation and roadside biomass in order to reconcile biodiversity and road safety goals with renewable energy targets. [ABSTRACT FROM AUTHOR]

The core driver of this study is to deal with the design of anaerobic digestion based biomass to energy supply chains in a cost effective manner. In this concern, a decision model is developed. The model is based on fuzzy multi objective decision making in order to simultaneously optimize multiple economic objectives and tackle the inherent uncertainties in the parameters and decision makers’ aspiration levels for the goals. The viability of the decision model is explored with computational experiments on a real-world biomass to energy supply chain and further analyses are performed to observe the effects of different conditions. To this aim, scenario analyses are conducted to investigate the effects of energy crop utilization and operational costs on supply chain structure and performance measures. [ABSTRACT FROM AUTHOR]

Food losses represent a significant issue affecting food supply chains. The possibility of recovering such products can be seen as an effective way to reduce such a phenomenon, improve supply chain performances and ameliorate the conditions of undernourished people. The topic has been already investigated by a previous paper enforcing the hypothesis of deterministic and constant Shelf Life (SL) of products. However, such a model cannot be properly extended to products affected by uncertainties of the SL as it does not take into account the deterioration costs and loss of profits due to the overcoming of the SL within the cycle time. Thus the present paper presents an extension of the previous one under stochastic conditions of the food quality. Differently from the previous publication, this work represents a general model applicable to all supply chains, especially to those managing fresh products characterized by uncertain SL such as fruits and vegetables. The deterioration costs and loss of profits are included in the model and the optimal time at which to withdraw the products from the shelves as well as the quantities to be shipped at each alternative destination have been determined. A comparison of the proposed model with that reported in the previous publication has been carried out in order to underline the impact of the SL variability on the optimality conditions. The results show that the food recovery strategy in the presence of uncertainty of the food quality is rewarding, even if the optimal profit is lower than that of the deterministic case. [ABSTRACT FROM AUTHOR]

The European Commission recently proposed to review the existing 2009/28/EC Directive on biofuels, in order to increase their environmental sustainability and to decrease the recourse to alimentary biomasses. The paper evaluates the impact of this proposal on the emerging bioethanol production system in northern Italy and compares it with the current European Directive by analyzing the results of the supply chain economic optimization based on a mixed integer linear programming model. The impact of the proposed policy is also assessed in terms of greenhouse gas emissions and fuel security. Eventually, the effects on fuel consumers and taxpayers are taken into account. [ABSTRACT FROM AUTHOR]

Uncertainty in biomass supply is a critical issue that needs to be considered in the production planning of bioenergy plants. Incorporating uncertainty in supply chain planning models provides improved and stable solutions. In this paper, we first reformulate a previously developed non-linear programming model for optimization of a forest biomass power plant supply chain into a linear programming model. The developed model is a multi-period tactical-level production planning problem and considers the supply and storage of forest biomass as well as the production of electricity. It has a one-year planning horizon with monthly time steps. Next, in order to incorporate uncertainty in monthly available biomass into the planning, we develop a two-stage stochastic programming model. Finally, to balance the risk and profit, we propose a bi-objective model. The results show that uncertainty in availability of biomass has an additional cost of $0.4 million for the power plant. Using the proposed stochastic optimization model could reduce this cost by half. [ABSTRACT FROM AUTHOR]

We propose a bilevel mixed-integer nonlinear programming (MINLP) model for the optimal design and planning of non-cooperative supply chains from the manufacturer's perspective. Interactions among the supply chain participants are captured through a single-leader–multiple-follower Stackelberg game under the generalized Nash equilibrium assumption. Given a three-echelon superstructure, the lead manufacturer in the middle echelon first optimizes its design and operational decisions, including facility location, sizing, and technology selection, material input/output and price setting. The following suppliers and customers in the upstream and downstream then optimize their transactions with the manufacturer to maximize their individual profits. By replacing the lower level linear programs with their KKT conditions, we transform the bilevel MINLP into a single-level nonconvex MINLP, which is further globally optimized using an improved branch-and-refine algorithm. We also present two case studies, including a county-level biofuel supply chain in Illinois, to illustrate the application of the proposed modeling and solution methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a two-stage mixed integer programming (MIP) interdiction model in which an interdictor chooses a limited amount of elements to attack first on a given network, and then an operator dispatches trains through the residual network. Our MIP model explicitly incorporates discrete unit flows of trains on the rail network with time-variant capacities. A real coal rail transportation network is used in order to generate scenarios to provide tactical and operational level vulnerability assessment analysis including rerouting decisions, travel and delay costs analysis, and the frequency of interdictions of facilities for the dynamic rail system. [ABSTRACT FROM AUTHOR]

Highlights: [•] The food recovery is seen as suitable way to manage food near to its expiry date. [•] The food losses in the retailing stage is an alarming phenomenon. [•] The paper addresses the mathematic modeling of the profit related to food recovery. [•] The optimal time to withdraw the products is determinant for food recovery. [Copyright &y& Elsevier]

Highlights: [•] We implement economic MPC for supply chain systems after a short review of the economic MPC closed-loop stability theory. [•] We propose a novel multiobjective cost function that considers both economic cost and risk (modeled as penalty for deviations from safety stock). [•] We provide an example of integrating control theory and scheduling method by identifying terminal constraints that ensure recursive feasibility for a scheduling problem. [ABSTRACT FROM AUTHOR]

Highlights: [•] Economic feasibility and the optimal production planning for commodity chemicals. [•] Trade-off between economic and environmental metrics for chemicals is analyzed. [•] Biomass availability and facility capital costs are the most important factors. [ABSTRACT FROM AUTHOR]

Highlights: [•] Optimization-based decision support tool for supply chain optimization under uncertainty. [•] Robust MPC strategy applied for the control of a hybrid supply chain system. [•] Conservativeness of open-loop robust MPC reduced through two-stage stochastic programming. [•] Simulation results indicate robust formulations provide reduction in back orders. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we consider integrated planning of the supply chain at a multi-site pulp company. In addition to the traditional focus on pulp products, also energy aspects are considered, both in the form of raw material and as revenue generating products. The idea is that energy intense raw materials not only gives fiber to the pulp process but also generates an energy surplus that can be used in different ways to create additional value or revenues. The planning horizon is 1year and monthly time periods are considered. Decisions included in the planning are; purchase and transportation of raw materials from harvest areas to pulp mills, production allocation by dividing the production among the pulp mills, energy mix by choosing the energy input at the pulp mills, and distribution of products from mills to customer. An MILP model for the entire supply chain is proposed. A number of different scenarios including real data from the case company are analyzed and evaluated. The aim of the study is thus to investigate the effects on profitability while taking energy issues into consideration. [Copyright &y& Elsevier]

• A three-stage supply chain optimization model has been developed. • The RJF supply chain relies on winter carinata crops as a reliable biomass feedstock. • Three direct monetary incentives are evaluated under an optimal supply chain. • The RJF supply chain profitability is highly sensitive to changes in PCP and BCAP. • When RJF price increases by 50%, the RJF supply chain becomes profitable. Currently, the global aviation industry uses around 341 billion liters of jet fuel per year, with demand predicted to grow by 50% by the end of 2050. Renewable jet fuel (RJF) may cut greenhouse gas emissions (GHG), increase fuel diversity for the aviation industry, and promote rural economies. The commercialization of RJF has been delayed due to a shortage of sustainable biomass resources. This study recommends using winter carinata crops as a reliable biomass feedstock in the southeastern US states, where the availability of resources will be investigated in each agricultural zone. RJF production is more expensive than traditional jet fuel production. Investors and legislators need to learn more about prospective federal financial initiatives, such as subsidies and grants, to help with RJF supply chain implementation. In this paper, using a mathematical programming approach, we designed an RJF supply chain and then investigated the effects of three direct monetary incentive programs, including producer credit program (PCP), biomass crop assistance program (BCAP), and biorefinery assistance program (BAP), to accelerate the commercialization of RJF manufacturing. According to the findings, the amount of incentives through PCP needed to fulfill 50% of the RJF demand was assessed to cover 16.70% of the total costs, while the BCAP could reach the commercialization threshold by receiving incentives for 22.84% of the biomass purchasing cost. Furthermore, having the BAP covering 89.39% of the annual capital and operating costs could help commercialize RJF production. This study also evaluated the effects of changes in renewable fuel prices, demand fulfillment rates, biomass yield rates, and the price of biomass feedstock and its resulting meal on the profitability of the supply chain. The study's findings will advise policymakers and investors on developing the RJF supply chain given various financial assistance programs and subsidies. [ABSTRACT FROM AUTHOR]

• Novel power-to-liquid (PtL) supply chain optimization approach. • In Germany, PtL production is favored in the greater North Sea coast area. • To achieve 2 % PtL in aviation, an investment of 1.6 B€ is required for Germany. • PtL production and supply costs range from 2052 to 2258 € t−1. • A PtL quota in aviation is recommended at national level and not as per airport. Electricity-based jet fuel from renewables, also labeled sustainable aviation fuel (SAF), is expected to play a key role in the defossilization of aviation. The realization of first plants for the production of such power-to-liquid (PtL) fuels is imminent. This raises the challenge of an effective PtL supply chain design, which is addressed in this work. A novel optimization approach is introduced that enables an integrated selection of sites and technologies. Economically favorable supply chain designs are presented for the case study of Germany, with production primarily located in the wind power rich greater North Sea coast area. The Heide Refinery and Emsland Lingen Refinery sites are particularly suited for fuel synthesis, while cement and iron & steel plants are preferred as CO 2 point sources. This study shows that at an electricity price of 0.05 € kWh−1, the costs for SAF production and supply range from 2052 to 2258 € t−1. The minimum is obtained at a production volume equivalent to 4 % of the national jet fuel demand. To achieve the envisioned 2030 target of 2 % PtL SAF, an investment volume of 1.6 B€ is expected. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose to use distributed model predictive control for supply chain optimization. In particular, we focus on inventory management in supply chains. We use cooperative model predictive control, in which each agent makes their local decisions by optimizing the overall supply chain objective. Motivated by recent results in Stewart, Wright, and Rawlings (2011), we develop a new cooperative MPC algorithm that is applicable to any stabilizable system, and in particular to supply chain models. We illustrate cooperative MPC for a two node supply chain example and compare its performance and properties with other classical distributed operating policies. [Copyright &y& Elsevier]

Abstract: Enterprise-wide Optimization (EWO) has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the supply, manufacturing and distribution activities of a company to reduce costs, inventories and environmental impact, and to maximize profits and responsiveness. Major operational items include planning, scheduling, real-time optimization and control. We provide an overview of EWO in terms of a mathematical programming framework. We first provide a brief overview of mathematical programming techniques (mixed-integer linear and nonlinear optimization methods), as well as decomposition methods, stochastic programming and modeling systems. We then address some of the major issues involved in the modeling and solution of these problems. Finally, based on the EWO program at the Center of Advanced Process Decision-making at Carnegie Mellon, we describe several applications to show the potential of this area. [Copyright &y& Elsevier]

Abstract: There has been a growing interest in liquid biofuels during the recent years due to concerns about security of energy supply and global climate change. Targets have been set throughout the globe for blending biofuels into conventional petroleum fuels. In this paper, an optimisation framework for the strategic design of a hybrid first/second generation ethanol supply chain is presented. The model seeks to address sustainability issues such as the use of food crops, land use requirements of second generation crops and competition for biomass with other sectors. The applicability of the model is demonstrated with a case study of ethanol production in the UK. [Copyright &y& Elsevier]

In this paper, we address the strategic planning of integrated bioethanol-sugar supply chains (SC) under uncertainty in the demand. The design task is formulated as a multi-scenario mixed-integer linear programming (MILP) problem that decides on the capacity expansions of the production and storage facilities of the network over time along with the associated planning decisions (i.e., production rates, sales, etc.). The MILP model seeks to optimize the expected performance of the SC under several financial risk mitigation options. This consideration gives a rise to a multi-objective formulation, whose solution is given by a set of network designs that respond in different ways to the actual realization of the demand (the uncertain parameter). The capabilities of our approach are demonstrated through a case study based on the Argentinean sugarcane industry. Results include the investment strategy for the optimal SC configuration along with an analysis of the effect of demand uncertainty on the economic performance of several biofuels SC structures. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a game theoretic model in which one manufacturer and its suppliers are coordinated. Quality levels of products from suppliers are considered. Due to fluctuation of demand resulting from varying quality levels, manufacturers are subject to demand-side risks. The objective of the research is to help decision making between manufacturers and suppliers in order to improve level of quality. Stackelberg game is applied to coordinate the manufacturer and suppliers Then, Pareto efficient solutions are utilized to generate supplier''s optimal response functions. Computational experiments are conducted to demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: Supply chain management (SCM) involves the management of material and information flow among the members of chain such as vendors, manufacturing plants and distribution centers. The main processes in supply chain are production planning, control, distribution and logistics. Effective management of all these processes provides the manufacturing companies great advantages in time based competition. In order to gain a competitive edge, firms must be able to deliver their products fast and on-time. This can be managed by better production planning, scheduling and due-date setting. This paper suggests a hybrid approach for setting realistic order due dates for a produce-to-order manufacturing company operating within a supply chain environment. The proposed hybrid approach combines mathematical and simulation modeling to bring together the advantages of both approaches. [Copyright &y& Elsevier]