Iftekhar A. Karimi

Agent-based supply chain management*/1: framework

Abstract In the face of highly competitive markets and constant pressure to reduce lead times, enterprises today consider supply chain management to be the key area where improvements can significantly impact the bottom line. More enterprises now consider the entire supply chain structure while taking business decisions. They try to identify and manage all critical relationships both upstream and downstream in their supply chains. Some impediments to this are that the necessary information usually resides across a multitude of resources, is ever changing, and is present in multiple formats. Most supply chain decision support systems (DSSs) are specific to an enterprise and its supply chain, and cannot be easily modified to assist other similar enterprises and industries. In this two-part paper, we propose a unified framework for modeling, monitoring and management of supply chains. The first part of the paper describes the framework while the second part illustrates its application to a refinery supply chain. The framework integrates the various elements of the supply chain such as enterprises, their production processes, the associated business data and knowledge and represents them in a unified, intelligent and object-oriented fashion. Supply chain elements are classified as entities, flows and relationships. Software agents are used to emulate the entities i.e. various enterprises and their internal departments. Flows—material and information—are modeled as objects. The framework helps to analyze the business policies with respect to different situations arising in the supply chain. We illustrate the framework by means of two case studies. A DSS for petrochemical cluster management is described together with a prototype DSS for crude procurement in a refinery.

Agent-based supply chain management—2: a refinery application

Abstract The refinery business involves tasks that span several departments and process large amount of data. Among others, these include crude procurement, logistics and scheduling (storage, distillation units, etc.). Current refinery decision support systems (DSSs) fail to integrate all the decision-making processes of a refinery, to interface with other systems in place, to incorporate dynamic data from various sources and to assist different departments concurrently. In part 1 of this two-part paper, we proposed an agent-based framework for supply chain DSSs. Here, we demonstrate its application through a prototype DSS, called petroleum refinery integrated supply chain modeler and simulator or PRISMS, for crude procurement. PRISMS serves as a central DSS through which all processes of a refinery can be studied and enables integrated decisions with respect to the overall refinery business. In particular, PRISMS can be used to study the effects of internal policies of the refinery and its various departments. We illustrate this through three detailed ‘what-if’ studies that provide an insight into how the business responds to changes in policies, exogenous events and plant modifications.

Decision support for integrated refinery supply chains: Part 1. Dynamic simulation

Supply chain studies are increasingly given top priority in enterprise-wide management. Present-day supply chains involve numerous, heterogeneous, geographically distributed entities with varying dynamics, uncertainties, and complexity. The performance of a supply chain relies on the quality of a multitude of design and operational decisions made by the various entities. In this two-part paper, we demonstrate that a dynamic model of an integrated supply chain can serve as a valuable quantitative tool that aids in such decision-making. In this Part 1, we present a dynamic model of an integrated refinery supply chain. The model explicitly considers the various supply chain activities such as crude oil supply and transportation, along with intra-refinery supply chain activities such as procurement planning, scheduling, and operations management. Discrete supply chain activities are integrated along with continuous production through bridging procurement, production, and demand management activities. Stochastic variations in transportation, yields, prices, and operational problems are considered in the proposed model. The economics of the refinery supply chain includes consideration of different crude slates, product prices, operation costs, transportation, etc. The proposed model has been implemented as a dynamic simulator, called Integrated Refinery In-Silico (IRIS). IRIS allows the user the flexibility to modify not only parameters, but also replace different policies and decision-making algorithms in a plug-and-play manner. It thus allows the user to simulate and analyze different policies, configurations, uncertainties, etc., through an easy-to-use graphical interface. The capabilities of IRIS for strategic and tactical decision support are illustrated using several case studies.

A model-based rescheduling framework for managing abnormal supply chain events

Enterprises today have realized the importance of supply chain management to achieve operational efficiency, cut costs, and maintain quality. Uncertainties in supply, demand, transportation, market conditions, and many other factors can interrupt supply chain operations, causing significant adverse effects. These uncertainties motivate the development of decision support systems for managing disruptions in the supply chain. In this paper, we propose a model-based framework for rescheduling operations in the face of supply chain disruptions. A causal model, called the composite-operations graph, captures the cause-and-effect among all the variables in supply chain operation. Its subgraph, called scheduled-operations graph, captures the causal relationships in a schedule and is used for identifying the consequences of a disruption. Rescheduling is done by searching a rectifications-graph, which captures all possible options to overcome the disruption effects, based on a user-specified utility function. In contrast to heuristic approaches, the main advantages of the proposed model-based rescheduling method are the completeness of solution search and flexibility of the utility function. The proposed framework is illustrated using a refinery supply chain example.

Genome‐scale modeling and in silico analysis of ethanologenic bacteria Zymomonas mobilis

Bioethanol has been recognized as a potential alternative energy source. Among various ethanol‐producing microbes, Zymomonas mobilis has acquired special attention due to its higher ethanol yield and tolerance. However, cellular metabolism in Z. mobilis remains unclear, hindering its practical application for bioethanol production. To elucidate such physiological characteristics, we reconstructed and validated a genome‐scale metabolic network (iZM363) of Z. mobilis ATCC31821 (ZM4) based on its annotated genome and biochemical information. The phenotypic behaviors and metabolic states predicted by our genome‐scale model were highly consistent with the experimental observations of Z. mobilis ZM4 strain growing on glucose as well as NMR‐measured intracellular fluxes of an engineered strain utilizing glucose, fructose, and xylose. Subsequent comparative analysis with Escherichia coli and Saccharomyces cerevisiae as well as gene essentiality and flux coupling analyses have also confirmed the functional role of pdc and adh genes in the ethanologenic activity of Z. mobilis, thus leading to better understanding of this natural ethanol producer. In future, the current model could be employed to identify potential cell engineering targets, thereby enhancing the productivity of ethanol in Z. mobilis. Biotechnol. Bioeng. 2011; 108:655–665.

Integrated supply chain planning for multinational pharmaceutical enterprises

Abstract The management of global supply chains is highly complex and vital for multinational pharmaceutical enterprises. Global integrated planning in multi-site, multi-echelon network of a multinational company has attracted some academic interest. However, the focus has largely been on efficient solution strategies for large problems. In this work, we develop simple yet powerful MILP model for multi-period enterprise-wide planning. We represent the entire enterprise in a seamless fashion with a granularity of individual task campaigns on each production line. Our model integrates procurement, production, and distribution along with the effects of international tax differentials, inventory holding costs, material shelf-lives, waste treatment / disposal, and other real-life factors on the after-tax profit of the company. To demonstrate the performance of our model, we solve two example problems of planning multinational pharmaceutical enterprise. For our evaluation, we consider an industrial scale planning problem for a supply chain network consisting of 34 different entities and producing 9 different products, for a period of 5 years.

Scheduling multistage batch plants with parallel units and no interstage storage

Scheduling production optimally in multistage multiproduct plants with parallel units is a very difficult but routine problem. While most batch plants avoid interstage storage due to minimize contamination, cleaning, waste, and so forth, the scheduling of plants with no interstage storage and mixed wait policies have received little attention compared to those with unlimited storage/wait. In this paper, we develop and evaluate several different mixed-integer linear programming formulations for scheduling plants with identical and nonidentical parallel processing units and unlimited and zero-wait interstage policies. Because the best approach for handling identical parallel units seems to be sequence-based and that for handling nonidentical units seems to be slot-based, we employ judicious mixes of these approaches to address real plants with mixes of stages with identical and nonidentical units. Our models also allow mixes of unlimited and zero interstage wait policies and scheduling objectives of makespan, tardiness, earliness, and weighted just-in-time. The weighted just-in-time scheduling seems to be more difficult than even the makespan scheduling, and more importantly, a modeling approach that does well for the former does not necessarily suit the latter. While the models presented in this paper do address successfully the scheduling needs of realistic batch plants, considerable future work is warranted for models that can solve large scheduling problems of this type.

Decision support for integrated refinery supply chains: Part 2. Design and operation

Abstract Supply chain management has continually attracted much attention as companies are constantly looking into areas where they can cut costs and improve profit margin while maintaining customer satisfaction. Optimizing design and operation of the supply chain is vital for this purpose. Simulation models that capture the dynamics and uncertainties of the supply chain can be used to effectively conduct design and operation optimization studies. In Part 1 of this two-part paper, we proposed an integrated refinery supply chain dynamic simulator called Integrated Refinery In-Silico (IRIS). Here, we demonstrate the application of IRIS to provide decision support for optimal refinery supply chain design and operation based on a simulation–optimization framework. Three case studies are presented: identifying the optimal strategy to deal with supply disruptions, optimization of design decisions regarding additional capacity investments, and optimization of policies’ parameters. These decisions are optimized for two objectives: profit margin and customer satisfaction. The framework consists of a linkage between IRIS and a non-dominated sorting genetic algorithm, implemented in a parallel computing environment for computational efficiency. Results indicate that the proposed framework works well for supporting policy and investment decisions in the integrated refinery supply chain.

Improving the logistics of multi-compartment chemical tankers

Ocean transportation is the workhorse for logistics in global chemical supply chains. Often, logistics cost can be as high as 20% of the purchasing cost. Efficient routing and scheduling of multi-parcel chemical tankers to reduce logistics expenditure is important for both chemical and shipping industry. We consider the maximum-profit scheduling of a fleet of multi-parcel tankers engaged in shipping bulk liquid chemicals. For this, we present a mixed-integer linear programming (MILP) formulation using variable-length slots and propose a heuristic decomposition algorithm that obtains the fleet schedule by repeatedly solving the base formulation for a single ship. The formulation is generally applicable to all kinds of carriers engaged in the transportation of multiple commodities, and to transportation systems where frequent schedule updates or a short-term planning horizon is required. We illustrate our approach on a real industrial case study involving 10 tankers, 36 ports and 79 cargos. Our approach showed an increase of 32.7% in profit as compared to the plan actually used by a major chemical shipping company.

Characterizing Escherichia coli DH5α growth and metabolism in a complex medium using genome‐scale flux analysis

Genome‐scale flux analysis of Escherichia coli DH5α growth in a complex medium was performed to investigate the relationship between the uptake of various nutrients and their metabolic outcomes. During the exponential growth phase, we observed a sequential consumption order of serine, aspartate and glutamate in the complex medium as well as the complete consumption of key carbohydrate nutrients, glucose and trehalose. Based on the consumption and production rates of the measured metabolites, constraints‐based flux analysis of a genome‐scale E. coli model was then conducted to elucidate their utilization in the metabolism. The in silico analysis revealed that the cell exploited biosynthetic precursors taken up directly from the complex medium, through growth‐related anabolic pathways. This suggests that the cell could be functioning in an energetically more efficient manner by reducing the energy needed to produce amino acids. The in silico simulation also allowed us to explain the observed rapid consumption of serine: excessively consumed external serine from the complex medium was mainly converted into pyruvate and glycine, which in turn, led to the acetate accumulation. The present work demonstrates the application of an in silico modeling approach to characterizing microbial metabolism under complex medium condition. This work further illustrates the use of in silico genome‐scale analysis for developing better strategies related to improving microbial growth and enhancing the productivity of desirable metabolites. Biotechnol. Bioeng. 2009; 102: 923–934.