Strategic Technology Maturation and Insertion (STMI): a requirements guided, technology development optimization process

This research presents a Decision Support System (DSS) process solution to a problem faced by Program Managers (PMs) early in a system lifecycle, when potential technologies are evaluated for placement within a system design. The proposed process for evaluation and selection of technologies incorporates computer based Operational Research techniques which automate and optimize key portions of the decision process. This computerized process allows the PM to rapidly form the basis of a Strategic Technology Plan (STP) designed to manage, mature and insert the technologies into the system design baseline and identify potential follow-on incremental system improvements. This process is designated Strategic Technology Maturation and Insertion (STMI). Traditionally, to build this STP, the PM must juggle system performance, schedule, and cost issues and strike a balance of new and old technologies that can be fielded to meet the requirements of the customer. To complicate this juggling skill, the PM is typically confronted with a short time frame to evaluate hundreds of potential technology solutions with thousands of potential interacting combinations within the system design. Picking the best combination of new and established technologies, plus selecting the critical technologies needing maturation investment is a significant challenge. These early lifecycle decisions drive the entire system design, cost and schedule well into production The STMI process explores a formalized and repeatable DSS to allow PMs to systematically tackle the problems with technology evaluation, selection and maturation. It gives PMs a tool to compare and evaluate the entire design space of candidate technology performance, incorporate lifecycle costs as an optimizer for a best value system design, and generate input for a strategic plan to mature critical technologies. Four enabling concepts are described and brought together to form the basis of STMI: Requirements Engineering (RE), Value Engineering (VE), system optimization and Strategic Technology Planning (STP). STMI is then executed in three distinct stages: Pre-process preparation, process operation and optimization, and post-process analysis. A demonstration case study prepares and implements the proposed STMI process in a multi-system (macro) concept down select and a specific (micro) single system design that ties into the macro design level decision

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

A three-phase approach for robust project scheduling: an application for R&D project scheduling

During project execution, especially in a multi-project environment unforeseen events arise that disrupt the project process resulting in deviations of project plans and budgets due to missed due dates and deadlines, resource idleness, higher work-in-process inventory and increased system nervousness. In this thesis, we consider the preemptive resource constrained multi-project scheduling problem with generalized precedence relations in a stochastic and dynamic environment and develop a three-phase model incorporating data mining and project scheduling techniques to schedule the R&D projects of a leading home appliances company in Turkey. In Phase I, models classifying the projects with respect to their resource usage deviation levels and an activity deviation assignment procedure are developed using data mining techniques. Phase II, proactive project scheduling phase, proposes two scheduling approaches using a bi-objective genetic algorithm (GA). The objectives of the bi-objective GA are the minimization of the overall completion time of projects and the minimization of the total sum of absolute deviations for starting times for possible realizations leading to solution robust baseline schedules. Phase II uses the output of the first phase to generate a set of non-dominated solutions. Phase III, called the reactive phase, revises the baseline schedule when a disruptive event occurs and enables the project managers to make “what-if analysis” and thus to generate a set of contingency plans for better preparation

Operations Management

Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies

Technology and Management Applied in Construction Engineering Projects

This book focuses on fundamental and applied research on construction project management. It presents research papers and practice-oriented papers. The execution of construction projects is specific and particularly difficult because each implementation is a unique, complex, and dynamic process that consists of several or more subprocesses that are related to each other, in which various aspects of the investment process participate. Therefore, there is still a vital need to study, research, and conclude the engineering technology and management applied in construction projects. This book present unanimous research approach is a result of many years of studies, conducted by 35 well experienced authors. The common subject of research concerns the development of methods and tools for modeling multi-criteria processes in construction engineering

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Improving Stochastic Simulation-based Optimization for Selecting Construction Method of Precast Box Girder Bridges

A large amount of reconstruction work is expected on existing highways due to the fact that highway infrastructures in North America are approaching or have surpassed their service life. The literature of construction engineering and management suggest that urban highway construction projects often overrun in budget and time. Bridges are crucial elements of urban highways, therefore, efficient planning of the construction of bridges is deemed necessary. Bridge construction operations are characterized as equipment-intensive, repetitive, have cyclic nature and involve high uncertainties. Without selecting the best construction method and the optimum number of equipment and crews, projects will take longer and cost more than necessary. The main objectives of this research are to: (1) develop a quantitative method that is capable of obtaining near optimum construction scenarios for bridge construction projects; and (2) obtain these optimum scenarios with an accurate estimate of their objective functions, a high confidence in their optimality and within a short period of time. The ability of stochastic simulation-based optimization to find near optimum solutions is affected mainly by: (1) the number of candidate solutions generated by the optimization algorithm; and (2) the number of simulation replications required for each candidate solution to achieve a desirable statistical estimate. As a result, a compromise between the accuracy of the estimate of the performance measure index of a candidate solution and the optimality of that candidate solution must be made. Moreover, comparing the performance of different candidate solutions based on the mean values is not accurate because the means of the two objectives (i.e., cost and time) are not always the means of the joint distribution of the two objectives. Finally, the resulting near optimum solutions are not necessarily achievable. In order to achieve the abovementioned objectives, the following research developments were made: (1) a stochastic simulation-based multi-objective optimization model; (2) a method for incorporating variance reduction techniques into the proposed model; (3) a method to execute the proposed model in parallel computing environment on a single multi-core processor; and (4) a method to apply joint probability to the outcome of the proposed model. The proposed methods showed an average of 84% reduction in the computation time and an average of 18% improvement in the hypervolume indicator over the traditional method when variance reduction techniques are used. Combining variance reduction computing with parallel computing resulted in a time saving of 90%. The use of the joint probability method showed an improvement over the traditional method in the accuracy of selecting the project duration (D) and cost (C) combination that satisfies a certain joint probability. For simulation models with high correlation between the outputs, ΔD and ΔC are not as large as in simulation models with moderate or low correlation, which indicates the existence of a negative relationship between correlation and ΔD and ΔC. In addition, the existence of high correlation permits the reduction of the number of simulation replications required to get a sound estimation of a project, which also indicates the existence of a negative relationship between correlation and the number of replications required