Review of Quantitative Methods for Supply Chain Resilience Analysis

Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested

Design of Recycle/Reuse Networks with Thermal Effects and Variable Sources

Recycle/reuse networks are commonly used in industrial facilities to conserve natural resources, reduce environmental impact, and improve process economics. The design of these networks is a challenging task because of the numerous possibilities of assigning stream (process sources) to units that may potentially employ them (process sinks). Additionally, several fresh streams with different qualities and costs may be used to supplement the recycle of process streams. The selection of the type and flow of these fresh resources is an important step in the design of the recycle/reuse networks. This work introduces systematic approaches to address two new categories in the design of recycle/reuse networks: (a) The incorporation of thermal effects in the network. Two new aspects are introduced: heat of mixing of process sources and temperature constraints imposed on the feed to the process sinks iv (b) Dealing with variation in process sources. Two types of source variability are addressed: flowrate and composition For networks with thermal effects, an assignment optimization formulation is developed. Depending on the functional form of the heat of mixing, the formulation may be a linear or a nonlinear program. The solution of this program provides optimum flowrates of the fresh streams as well as the segregation, mixing, and allocation of the process sources to sinks. For networks with variable sources, a computer code is developed to solve the problem. It is based on discretizing the search space and using the concept of "floating pinch" to insure solution feasibility and optimal targets. Case studies are solved to illustrate the applicability of the new approaches

Towards Analytical Approach to Effective Website Designs: A Framework for Modeling, Evaluation and Enhancement

Conference Theme: I.T. and Value CreationEffective website design is critical to the success of electronic commerce and digital government. Most prior website design research has taken a computational or cognitive/behavioral approach which may not yield optimal designs demanded by specific requirements. We consider website design as a structural problem which can be examined using analytical approach, such as mathematical optimization. Specifically, we propose a framework which classifies real-world design problems into generic website design categories and maps each resulting category into a graph model which can be analyzable or solved using appropriate analytical techniques. Our framework consists of generic designs and graph models, together with the necessary mapping. We classify the Web site applications and review their features proposed by previous research. We describe a generic website design category using its objective and key constraints that correspond to important design requirements. By modeling website design problems using well-defined structures and rigorous analysis methods, this framework is able to measure website accessibility in a systematic and quantifiable manner, arguably more desirable than existing qualitative ad-hoc practices. Overall, our framework can facilitate the website design process, enhance design quality, and increase ease of analysis, implementation and continuous improvement.link_to_subscribed_fulltex

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Uncertainty modeling in higher dimensions

Moderne Design Probleme stellen Ingenieure vor mehrere elementare Aufgaben. 1) Das Design muss die angestrebten Funktionalitäten aufweisen. 2) Es muss optimal sein in Hinsicht auf eine vorgegebene Zielfunktion. 3) Schließlich muss das Design abgesichert sein gegen Unsicherheiten, die nicht zu Versagen des Designs führen dürfen. All diese Aufgaben lassen sich unter dem Begriff der robusten Design Optimierung zusammenfassen und verlangen nach computergestützten Methoden, die Unsicherheitsmodellierung und Design Optimierung in sich vereinen. Unsicherheitsmodellierung enthält einige fundamentale Herausforderungen: Der Rechenaufwand darf gewisse Grenzen nicht überschreiten; unbegründete Annahmen müssen so weit wie möglich vermieden werden. Die beiden kritischsten Probleme betreffen allerdings den Umgang mit unvollständiger stochastischer Information und mit hoher Dimensionalität. Der niedrigdimensionale Fall ist gut erforscht, und es existieren diverse Methoden, auch unvollständige Informationen zu verarbeiten. In höheren Dimensionen hingegen ist die Anzahl der Möglichkeiten derzeit sehr begrenzt. Ungenauigkeit und Unvollständigkeit von Daten kann schwerwiegende Probleme verursachen - aber die Lage ist nicht hoffnungslos. In dieser Dissertation zeigen wir, wie man den hochdimensionalen Fall mit Hilfe von "Potential Clouds" in ein eindimensionales Problem übersetzt. Dieser Ansatz führt zu einer Unsicherheitsanalyse auf Konfidenzregionen relevanter Szenarien mittels einer Potential Funktion. Die Konfidenzregionen werden als Nebenbedingungen in einem Design Optimierungsproblem formuliert. Auf diese Weise verknüpfen wir Unsicherheitsmodellierung und Design Optimierung, wobei wir außerdem eine adaptive Aktualisierung der Unsicherheitsinformationen ermöglichen. Abschließend wenden wir unsere Methode in zwei Fallstudien an, in 24, bzw. in 34 Dimensionen.Modern design problems impose multiple major tasks an engineer has to accomplish. 1) The design should account for the designated functionalities. 2) It should be optimal with respect to a given design objective. 3) Ultimately the design must be safeguarded against uncertain perturbations which should not cause failure of the design. These tasks are united in the problem of robust design optimization giving rise to the development of computational methods for uncertainty modeling and design optimization, simultaneously. Methods for uncertainty modeling face some fundamental challenges: The computational effort should not exceed certain limitations; unjustified assumptions must be avoided as far as possible. However, the most critical issues concern the handling of incomplete information and of high dimensionality. While the low dimensional case is well studied and several methods exist to handle incomplete information, in higher dimensions there are only very few techniques. Imprecision and lack of sufficient information cause severe difficulties - but the situation is not hopeless. In this dissertation, it is shown how to transfer the high-dimensional to the one-dimensional case by means of the potential clouds formalism. Using a potential function, this enables a worst-case analysis on confidence regions of relevant scenarios. The confidence regions are weaved into an optimization problem formulation for robust design as safety constraints. Thus an interaction between optimization phase and worst-case analysis is modeled which permits a posteriori adaptive information updating. Finally, we apply our approach in two case studies in 24 and 34 dimensions, respectively

Integrated supply chain design using multi criteria mixed integer programming.

This research focuses on following key Supply Chain Design questions: determining supplier selection, production quantities, inventory locations and sizes, transportation option selection and transportation quantity in a multi stage, multi level supply chain. A Novel Integrated Supply Chain Design Framework that integrates Production Costs, Transportation Costs, First Time Quality and Supplier On-Time Delivery criteria has been proposed and implemented. Mixed Integer Linear Programming models were developed and four classes of problems were solved. Real world automotive industry data was used for testing and verifying these models. Key new knowledge, both data dependent and data independent, was gained in the course of this research. Data dependent insights include: (1) Recommendation for splitting the customer demand between two suppliers even in the absence of capacity constraints, and (2) Unit Production Cost, Unit Transportation Cost and FTQ were shown to be the most critical factors in the Total Global Supply Chain Costs. Data independent insights indicated that: (1) Supplier selection decisions at every stage and level should be made using a global integrated approach of considering both production and transportation costs across the complete supply chain avoiding the myopic approach of always looking for the cheapest part from the lowest bidding supplier, (2) Out-sourcing to a non-domestic, less expensive supplier is not always the best decision for every product when selecting suppliers, (3) The Total Global Supply Chain Costs, Production Costs and Transportation Costs all increase non-linearly with worsening FTQ of the Supply Chain links, and (4) Supplier FTQ has the most severe impact on the supply chain stage farthest from the Demand Consumption Stage with the impact severity being higher at lower FTQ rates. This research has clearly demonstrated the merits and benefits of taking an integrated decision making approach when selecting suppliers. A multi-criteria model that combines the cost of production, transportation, first-time quality and supplier on-time delivery has been proposed and tested. Significant savings can be achieved as a result of using the framework developed in this research. The savings in the total supply chain cost, in the automotive example used for illustration, were in excess of 15% which translates into several Million dollars over a period of 3 Years.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .M353. Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1110. Adviser: Hoda A. Elmaraghy. Thesis (Ph.D.)--University of Windsor (Canada), 2004

Development of sustainable groundwater management methodologies to control saltwater intrusion into coastal aquifers with application to a tropical Pacific island country

Saltwater intrusion due to the over-exploitation of groundwater in coastal aquifers is a critical challenge facing groundwater-dependent coastal communities throughout the world. Sustainable management of coastal aquifers for maintaining abstracted groundwater quality within permissible salinity limits is regarded as an important groundwater management problem necessitating urgent reliable and optimal management methodologies. This study focuses on the development and evaluation of groundwater salinity prediction tools, coastal aquifer multi-objective management strategies, and adaptive management strategies using new prediction models, coupled simulation-optimization (S/O) models, and monitoring network design, respectively. Predicting the extent of saltwater intrusion into coastal aquifers in response to existing and changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMR), an innovative artificial intelligence-based machine learning algorithm, to predict salinity at monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purposes, the prediction results of SVMR are compared with well-established genetic programming (GP) based surrogate models. The prediction capabilities of the two learning machines are evaluated using several measures to ensure their practicality and generalisation ability. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. The performance evaluations suggest that the predictive capability of SVMR is superior to that of GP models. The sensitivity analysis identifies a subset of the most influential pumping rates, which is used to construct new SVMR surrogate models with improved predictive capabilities. The improved predictive capability and generalisation ability of SVMR models, together with the ability to improve the accuracy of prediction by refining the dataset used for training, make the use of SVMR models more attractive. Coupled S/O models are efficient tools that are used for designing multi-objective coastal aquifer management strategies. This study applies a regional-scale coupled S/O methodology with a Pareto front clustering technique to prescribe optimal groundwater withdrawal patterns from the Bonriki aquifer in the Pacific Island of Kiribati. A numerical simulation model is developed, calibrated and validated using field data from the Bonriki aquifer. For computational feasibility, SVMR surrogate models are trained and tested utilizing input-output datasets generated using the flow and transport numerical simulation model. The developed surrogate models were externally coupled with a multi-objective genetic algorithm optimization (MOGA) model, as a substitute for the numerical model. The study area consisted of freshwater pumping wells for extracting groundwater. Pumping from barrier wells installed along the coastlines is also considered as a management option to hydraulically control saltwater intrusion. The objective of the multi-objective management model was to maximise pumping from production wells and minimize pumping from barrier wells (which provide a hydraulic barrier) to ensure that the water quality at different monitoring locations remains within pre-specified limits. The executed multi-objective coupled S/O model generated 700 Pareto-optimal solutions. Analysing a large set of Pareto-optimal solution is a challenging task for the decision-makers. Hence, the k-means clustering technique was utilized to reduce the large Pareto-optimal solution set and help solve the large-scale saltwater intrusion problem in the Bonriki aquifer. The S/O-based management models have delivered optimal saltwater intrusion management strategies. However, at times, uncertainties in the numerical simulation model due to uncertain aquifer parameters are not incorporated into the management models. The present study explicitly incorporates aquifer parameter uncertainty into a multi-objective management model for the optimal design of groundwater pumping strategies from the unconfined Bonriki aquifer. To achieve computational efficiency and feasibility of the management model, the calibrated numerical simulation model in the S/O model was is replaced with ensembles of SVMR surrogate models. Each SVMR standalone surrogate model in the ensemble is constructed using datasets from different numerical simulation models with different hydraulic conductivity and porosity values. These ensemble SVMR models were coupled to the MOGA model to solve the Bonriki aquifer management problem for ensuring sustainable withdrawal rates that maintain specified salinity limits. The executed optimization model presented a Pareto-front with 600 non-dominated optimal trade-off pumping solutions. The reliability of the management model, established after validation of the optimal solution results, suggests that the implemented constraints of the optimization problem were satisfied; i.e., the salinities at monitoring locations remained within the pre-specified limits. The correct implementation of a prescribed optimal management strategy based on the coupled S/O model is always a concern for decision-makers. The management strategy actually implemented in the field sometimes deviates from the recommended optimal strategy, resulting in field-level deviations. Monitoring such field-level deviations during actual implementation of the recommended optimal management strategy and sequentially updating the strategy using feedback information is an important step towards adaptive management of coastal groundwater resources. In this study, a three-phase adaptive management framework for a coastal aquifer subjected to saltwater intrusion is applied and evaluated for a regional-scale coastal aquifer study area. The methodology adopted includes three sequential components. First, an optimal management strategy (consisting of groundwater extraction from production and barrier wells) is derived and implemented for the optimal management of the aquifer. The implemented management strategy is obtained by solving a homogeneous ensemble-based coupled S/O model. Second, a regional-scale optimal monitoring network is designed for the aquifer system, which considers possible user noncompliance of a recommended management strategy and uncertainty in aquifer parameter estimates. A new monitoring network design is formulated to ensure that candidate monitoring wells are placed at high risk (highly contaminated) locations. In addition, a k-means clustering methodology is utilized to select candidate monitoring wells in areas representative of the entire model domain. Finally, feedback information in the form of salinity measurements at monitoring wells is used to sequentially modify pumping strategies for future time periods in the management horizon. The developed adaptive management framework is evaluated by applying it to the Bonriki aquifer system. Overall, the results of this study suggest that the implemented adaptive management strategy has the potential to address practical implementation issues arising due to user noncompliance, as well as deviations between predicted and actual consequences of implementing a management strategy, and uncertainty in aquifer parameters. The use of ensemble prediction models is known to be more accurate standalone prediction models. The present study develops and utilises homogeneous and heterogeneous ensemble models based on several standalone evolutionary algorithms, including artificial neural networks (ANN), GP, SVMR and Gaussian process regression (GPR). These models are used to predict groundwater salinity in the Bonriki aquifer. Standalone and ensemble prediction models are trained and validated using identical pumping and salinity concentration datasets generated by solving numerical 3D transient density-dependent coastal aquifer flow and transport numerical simulation models. After validation, the ensemble models are used to predict salinity concentration at selected monitoring wells in the modelled aquifer under variable groundwater pumping conditions. The predictive capabilities of the developed ensemble models are quantified using standard statistical procedures. The performance evaluation results suggest that the predictive capabilities of the standalone prediction models (ANN, GP, SVMR and GPR) are comparable to those of the groundwater variable-density flow and salt transport numerical simulation model. However, GPR standalone models had better predictive capabilities than the other standalone models. Also, SVMR and GPR standalone models were more efficient (in terms of computational training time) than other standalone models. In terms of ensemble models, the performance of the homogeneous GPR ensemble model was found to be superior to that of the other homogeneous and heterogeneous ensemble models. Employing data-driven predictive models as replacements for complex groundwater flow and transport models enables the prediction of future scenarios and also helps save computational time, effort and requirements when developing optimal coastal aquifer management strategies based on coupled S/O models. In this study, a new data-driven model, namely Group method for data handling (GMDH) approach is developed and utilized to predict salinity concentration in a coastal aquifer and, simultaneously, determine the most influential input predictor variables (pumping rates) that had the most impact onto the outcomes (salinity at monitoring locations). To confirm the importance of variables, three tests are conducted, in which new GMDH models are constructed using subsets of the original datasets. In TEST 1, new GMDH models are constructed using a set of most influential variables only. In TEST 2, a subset of 20 variables (10 most and 10 least influential variables) are used to develop new GMDH models. In TEST 3, a subset of the least influential variables is used to develop GMDH models. A performance evaluation demonstrates that the GMDH models developed using the entire dataset have reasonable predictive accuracy and efficiency. A comparison of the performance evaluations of the three tests highlights the importance of appropriately selecting input pumping rates when developing predictive models. These results suggest that incorporating the least influential variables decreases model accuracy; thus, only considering the most influential variables in salinity prediction models is beneficial and appropriate. This study also investigated the efficiency and viability of using artificial freshwater recharge (AFR) to increase fresh groundwater pumping rates from production wells. First, the effect of AFR on the inland encroachment of saline water is quantified for existing scenarios. Specifically, groundwater head and salinity differences at monitoring locations before and after artificial recharge are presented. Second, a multi-objective management model incorporating groundwater pumping and AFR is implemented to control groundwater salinization in an illustrative coastal aquifer system. A coupled SVMR-MOGA model is developed for prescribing optimal management strategies that incorporate AFR and groundwater pumping wells. The Pareto-optimal front obtained from the SVMR-MOGA optimization model presents a set of optimal solutions for the sustainable management of the coastal aquifer. The pumping strategies obtained as Pareto-optimal solutions with and without freshwater recharge shows that saltwater intrusion is sensitive to AFR. Also, the hydraulic head lenses created by AFR can be used as one practical option to control saltwater intrusion. The developed 3D saltwater intrusion model, the predictive capabilities of the developed SVMR models, and the feasibility of using the proposed coupled multi-objective SVMR-MOGA optimization model make the proposed methodology potentially suitable for solving large-scale regional saltwater intrusion management problems. Overall, the development and evaluation of various groundwater numerical simulation models, predictive models, multi-objective management strategies and adaptive methodologies will provide decision-makers with tools for the sustainable management of coastal aquifers. It is envisioned that the outcomes of this research will provide useful information to groundwater managers and stakeholders, and offer potential resolutions to policy-makers regarding the sustainable management of groundwater resources. The real-life case study of the Bonriki aquifer presented in this study provides the scientific community with a broader understanding of groundwater resource issues in coastal aquifers and establishes the practical utility of the developed management strategies