Mohammad Marufuzzaman

Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment

This paper presents a two-stage stochastic programming model used to design and manage biodiesel supply chains. This is a mixed-integer linear program and an extension of the classical two-stage stochastic location-transportation model. The proposed model optimizes not only costs but also emissions in the supply chain. The model captures the impact of biomass supply and technology uncertainty on supply chain-related decisions; the tradeoffs that exist between location and transportation decisions; and the tradeoffs between costs and emissions in the supply chain. The objective function and model constraints reflect the impact of different carbon regulatory policies, such as carbon cap, carbon tax, carbon cap-and-trade, and carbon offset mechanisms on supply chain decisions. We solve this problem using algorithms that combine Lagrangian relaxation and L-shaped solution methods, and we develop a case study using data from the state of Mississippi. The results from the computational analysis point to important observations about the impacts of carbon regulatory mechanisms as well as the uncertainties on the performance of biocrude supply chains.

Environmentally Friendly Supply Chain Planning and Design for Biodiesel Production via Wastewater Sludge

This study presents mathematical models that capture the impact of different carbon-emission-related policies on the design of the biodiesel supply chain. These mathematical models identify locations and production capacities for biocrude production plants by exploring the trade-offs that exist between transportation costs, facility investments costs, and emissions. The mathematical models capture the dynamics of biomass supply and transportation costs during a predefined time horizon. We analyze the behavior of the chain under different regulatory policies such as carbon cap, carbon tax, carbon cap and trade, and carbon offset mechanisms. A number of observations are made about the impact of each policy on the supply chain performance. The models we developed are solved by using a commercial software GAMS/CPLEX. We use the state of Mississippi as the testing grounds for these models, and employ ArcGIS to visualize and validate the results from the optimization models.

Designing a reliable bio-fuel supply chain network considering link failure probabilities

Presents a pre-disaster planning model to strengthen the links between the multi-modal facilities.Failure probability of links between the multi-modal facilities is estimated using a spatial static model, which developed from real world data.Conduct a case study of biofuel supply chain with data from Mississippi and Alabama.The model saves

Designing a Reliable and Dynamic Multimodal Transportation Network for Biofuel Supply Chains

0.27/gallon when a disaster happens. This study presents a pre-disaster planning model that seeks to strengthen a bio-fuel supply chain systems multi-modal facility links while accounting for limited budget availability. The model presented here determines which set of facilities and links to select that will maximize post-disaster connectivity and minimize bio-fuel supply chain related costs. The failure probability of the links between the multi-modal facilities is estimated using a spatial statistic model, which is developed from real world data. This paper develops a generalized Benders decomposition algorithm to solve this challenging NP -hard problem. The proposed algorithm is validated via a real-world case study with data from Mississippi and Alabama. Computational results show that the proposed solution approach is capable of solving the problem efficiently. Several experiments are conducted to demonstrate the applicability of this model by testing various model parameters on bio-fuel supply chain network performance, including reliability improvement cost, availability of budget, biomass supply changes, and the risk averseness degree for decision makers. Numerical analysis indicates that, under normal conditions, the minimum cost model determines a unit bio-fuel delivery cost of

Managing congestion in a multi-modal transportation network under biomass supply uncertainty

3.56/gallon. However, in case of a disaster, the unit bio-fuel delivery cost provided by the minimum cost model increases to

Botnet detection using graph-based feature clustering

3.96/gallon, compared to

In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes

3.69/gallon provided by the reliable model solution.

A Benders based rolling horizon algorithm for a dynamic facility location problem

This paper presents a cost-efficient and reliable supply chain network design model for biomass to be delivered to biofuel plants. Biomass is bulky, so transportation modes such as rail and barge can be used to deliver this product. For this reason, this study focuses on multimodal supply chain designs for biofuel. Biomass supply is highly seasonal, but the high production seasons for biomass in the Southeast United States often coincide with or are followed by hurricanes, and drought seasons, both of which impact transportation. The dynamic multimodel transportation network design model this paper presents enables this supply chain to cope with biomass supply fluctuations and to hedge against natural disasters. The mixed-integer nonlinear programming model proposed is an 𝒩𝒫-hard problem, and we develop an accelerated Benders decomposition algorithm and a hybrid rolling horizon algorithm to solve this problem. We tested the performance of the algorithm on a case study using data from the Southeast United ...

Supply Chain Network Model for Biodiesel Production via Wastewaters from Paper and Pulp Companies

This research presents a two-stage stochastic programming model that is used to design and manage a biomass co-firing supply chain network under feedstock supply uncertainty. The model we propose extends current models by taking congestion effects into account. A non-linear cost term is added in the objective function representing the congestion factor which increases exponentially as flow of biomass approaches the capacity of multi-modal facility. We first linearize the model and then use a nested decomposition algorithm to obtain a feasible solution in a reasonable amount of time. The nested decomposition algorithm that we propose combine constraint Generation algorithm with a sample average approximation and Progressive Hedging (PH) algorithm. We apply some heuristics such as rolling horizon algorithm and variable fixing technique to enhance the performance of the PH algorithm. We develop a case study using data from the states of Mississippi and Alabama and use those regions to test and validate the performance of the proposed algorithm. Numerical experiments show that the proposed algorithm can solve large-scale problems with a larger number of scenarios and time periods to a near optimal solution in a reasonable amount of time. Results obtained from the experiments reveal that the delivery cost increases and less hubs with higher capacity are selected if we take congestion cost into account.

Sustainable Design of On-Demand Supply Chain Network for Additive Manufacturing

Detecting botnets in a network is crucial because bots impact numerous areas such as cyber security, finance, health care, law enforcement, and more. Botnets are becoming more sophisticated and dangerous day-by-day, and most of the existing rule based and flow based detection methods may not be capable of detecting bot activities in an efficient and effective manner. Hence, designing a robust and fast botnet detection method is of high significance. In this study, we propose a novel botnet detection methodology based on topological features of nodes within a graph: in degree, out degree, in degree weight, out degree weight, clustering coefficient, node betweenness, and eigenvector centrality. A self-organizing map clustering method is applied to establish clusters of nodes in the network based on these features. Our method is capable of isolating bots in clusters of small sizes while containing the majority of normal nodes in the same big cluster. Thus, bots can be detected by searching a limited number of nodes. A filtering procedure is also developed to further enhance the algorithm efficiency by removing inactive nodes from consideration. The methodology is verified using the CTU-13 datasets, and benchmarked against a classification-based detection method. The results show that our proposed method can efficiently detect the bots despite their varying behaviors.