Advances in Generalized Disjunctive and Mixed-Integer Nonlinear Programming Algorithms and Software for Superstructure Optimization.

The development of new mixed-integer nonlinear programming (MINLP) algorithms, coupled with advances in computers and software, is opening promising possibilities to rigorously model, optimize, and automate the synthesis of engineering systems. A general overview of the MINLP approach and algorithms will be presented in this paper with the aim of gaining a basic understanding of these techniques. Strengths and weaknesses will be discussed, as well as difficulties and challenges which still need to be overcome. In particular, it will be shown how proper problem representations, effective modeling schemes, and solution strategies can play a crucial role in the successful application of these techniques. The application of MINLP algorithms in synthesis will be illustrated with several examples.

Mechanical Bus for Modular Product Design

A non-linear mathematical farm business optimisation model, that is set within a spatial economic framework, has been developed. The model incorporates factors such as location, spatial market orientation and technology use, and identifies the business strategy that is optimal in different market and policy environments. Farm household time-use is incorporated centrally within the model, enabling it to examine how on-farm and off-farm activities compete for limited farm household human resources. The model is applied to a beef and sheep farm that can choose between selling livestock to meat processors or processing on-farm and selling direct to consumers. Model simulations reveal when it is optimal for the farm business to innovate in this way and how this decision is affected by changes in key parameters. The farm business model is solved using the GAMS/LINDOGlobal mathematical programming software package. While traditional nonlinear programming and mixed-integer nonlinear programming algorithms are guaranteed to converge only under certain convexity assumptions, GAMS/LINDOGlobal finds guaranteed globally optimal solutions to general nonlinear problems. The model and model results are discussed within the context of theoretical underpinnings, model tractability, and potential applications.

This paper investigates the oversimplification of the inherently complex systems engineering process in space life support. The standard systems engineering process steps are described. The International Space Station (ISS) life support system is explained with its goals and performance criteria. Although it is not usually emphasized, the essential function of developing a hierarchy of systems and subsystems is to simplify the design process. The System Complexity Metric (SCM) shows how this divide‐and‐conquer approach also reduces the system complexity. The complete systems engineering process has many detailed steps. It is often simplified because of the effort required and the human limitations on working memory and decision span. Systems analysis demands slow, logical, and focused thinking but is often bypassed in favor of quick, intuitive, subconscious “gut feel.” A study of 100 system designs found examples of 12 specific mental mistakes, such as ignoring stakeholder needs, and these mistakes are essentially oversimplifications of the systems engineering process. An analysis of space life support goals, options, criteria, and processes found 11 examples of oversimplifications in systems engineering, such as neglecting safety and cost. All these 11 oversimplifications could be traced to one or more of the 12 previously identified mental mistakes or other well‐known ones, such as ignoring sunk costs. Oversimplification of the systems engineering process is rarely noticed but is a common and harmful problem. A study of failures in 50 different space systems found that problems in systems engineering caused failures and often led to errors in design, development, and test that further contributed to failure. It seems that more diligent systems engineering could prevent many project problems and failures, but projects seem to be more guided by “gut feel” based on tradition, authority, and consensus than on the logical, rational systems engineering approach.

Effective resource management plays a pivotal role in wireless networks, which, unfortunately, typically results in challenging mixed-integer nonlinear programming (MINLP) problems. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as the task mismatch problem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). In contrast to the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely, imitation learning . To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL , which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples. Numerical simulations demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while providing significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.

Automated design of synthetic biocircuits in the stochastic regime

This paper describes and expands upon the proposed System of Systems Engineering (SoSE) Process Framework for the Air Force Materiel Command first outlined in IEEE SoSE 2011. Through repeated uses, it became quickly understood that systems-of-systems processes were foundationally similar to but different from basic systems engineering processes. The SoSE Process Framework, supporting processes, and products provide a context to integrate across modern complex systems-of-systems. In defining the SoSE Process Framework we tailor existing systems engineering processes and add a minimal amount of SoS-level engineering to it. The four core SoSE processes, Architecture and Analysis, Test and Evaluation, Management and Governance together can help to successfully deliver integrated capabilities at the SoS and Enterprise level within the U.S. Air Force. The key concept introduced is the alignment of analysis and engineering efforts across the Enterprise System Engineering and Architecting realm, and the synchronization of decision making within the SoSE components. The paper concludes by discussing implications of the SoSE Process Framework, as well as recommendations for actions, products, key stakeholder decisions and future challenges.

Completing its final development and early deployment on the Navy's multi-role aircraft, the F/A-18 E/F Super Hornet, the SHAred Reconnaissance Pod (SHARP) provides the war fighter with the latest digital tactical reconnaissance (TAC Recce) Electro-Optical/Infrared (EO/IR) sensor system. The SHARP program is an evolutionary acquisition that used a spiral development process across a prototype development phase tightly coupled into overlapping Engineering and Manufacturing Development (EMD) and Low Rate Initial Production (LRIP) phases. Under a tight budget environment with a highly compressed schedule, SHARP challenged traditional acquisition strategies and systems engineering (SE) processes. Adopting tailored state-of-the-art systems engineering process models allowd the SHARP program to overcome the technical knowledge transition challenges imposed by a compressed program schedule. The program's original goal was the deployment of digital TAC Recce mission capabilities to the fleet customer by summer of 2003. Hardware and software integration technical challenges resulted from requirements definition and analysis activities performed across a government-industry led Integrated Product Team (IPT) involving Navy engineering and test sites, Boeing, and RTSC-EPS (with its subcontracted hardware and government furnished equipment vendors). Requirements development from a bottoms-up approach was adopted using an electronic requirements capture environment to clarify and establish the SHARP EMD product baseline specifications as relevant technical data became available. Applying Earned-Value Management (EVM) against an Integrated Master Schedule (IMS) resulted in efficiently managing SE task assignments and product deliveries in a dynamically evolving customer requirements environment. Application of Six Sigma improvement methodologies resulted in the uncovering of root causes of errors in wiring interconnectivity drawings, pod manufacturing processes, and avionics requirements specifications. Utilizing the draft NAVAIR SE guideline handbook and the ANSI/EIA-632 standard: Processes for Engineering a System, a systems engineering tailored process approach was adopted for the accelerated SHARP EMD prgram. Tailoring SE processes in this accelerated product delivery environment provided unique opportunities to be technically creative in the establishment of a product performance baseline. This paper provides an historical overview of the systems engineering activities spanning the prototype phase through the EMD SHARP program phase, the performance requirement capture activities and refinement process challenges, and what SE process improvements can be applied to future SHARP-like programs adopting a compressed, evolutionary spiral development acquisition paradigm.

This is an era which the ELT telescopes define 'the new science' for astronomy. Thus, to make optimal contribution (i.e. valuable scientific output in an efficient, sustainable manner), a mid class telescope such as DAG 4m, must have a carefully designed Systems Architecture, which, in our opinion, could only be possible by means of a proper Systems Engineering (SE) process. From a Platonic perspective, the SE work must be started well before (at least conceptually), even the 'idea' of erecting an observatory. As can be seen from the experiences, this, however, never happens, and also it did not happen in our case. Regarding the facts given above, a SE process for DAG observatory has been initiated soon. As a bunch of constraints had already been formed, the work primarily focuses on the scientific instrumentation. This paper demonstrates various phases of the SE process carried until now. We will also discuss the methodologies/techniques that are planned to be used in the future.

PurposeWhile the beneficial impact of supply chain integration (SCI) and modular product design are generally acknowledged, few empirical studies have examined how an organization can achieve better performance through SCI with modular product design. The purpose of this paper is to examine the relationship between SCI and modular product design, as well as their impact on product performance.Design/methodology/approachBy surveying 251 manufacturers in Hong Kong, structural equation modelling is used to test the research constructs and the hypothesized model.FindingsThe results confirm that information sharing, product co‐development and organizational coordination are crucial organizational processes within SCI. Companies that have high levels of product modularity appear to be good at product co‐development and organizational coordination directly and at information sharing indirectly. Furthermore, companies that have high levels of product co‐development or product modularity appear to have better product performance.Research limitations/implicationsThis paper theoretically and empirically identifies three specific organizational processes within SCI (information sharing, product co‐development and organizational coordination), which affect modular product design and product performance. These more specific findings were previously absent from the literature. However, the study is limited to the cross‐sectional nature of a survey study, the operationalization of SCI and product modularity, and the nature of the product types.Originality/valueThis paper empirically examines the relationships between SCI and product modularity, which has seldom been attempted in previous research. It clearly identifies exactly which processes within SCI are directly and indirectly related to product modularity.

A new, R-graph based, superstructure and corresponding MINLP model for designing conventional distillation columns are presented. A GDP representation (GR) of the superstructure is first constructed, then it is transformed to MINLP representation to which, in turn, additional trivial improvements are added. The new model has been tested on binary mixture examples, and the obtained results are compared to the results of an MINLP model which developed according to the GDP model of Yeomans and Grossmann. 9 The new model yields shorter computation time and provides better local optima. Additionally, the new model has been used for optimizing a complex multicomponent separation system consisting of several distillation columns. In order to handle such a complex system with a huge number of nonlinear equations, the outer approximation algorithm is modified to provide good initial values for the NLP subproblems. Distillation is one of the most widespread processes applied for separating multicomponent liquid mixtures. It is used for working up large volume or stream, and it requires high investment and operation outlays. The significance of the design of economically optimal distillation processes is of no question. Enormous interest has also been addressed to the area of designing optimal heat integrated distillation columns and distillation sequences. Minimizing the cost of a distillation process implies finding the optimal configuration of each individual column, as well. In the present article we consider staged column models only. The number of stages, and the stage numbers of the feed and side-stream points, are discrete decision variables. The total cost of a column may be modeled as the sum of the fix (capital) cost, depending on the number of stages and on the column diameter, and the variable (operation) cost related to the utility consumption. The objective of the design procedure is to find the optimal configuration of the column, which has the minimum total (annualized) cost. In order to model these processes, discrete decisions are required for calculating the number of stages. Optimizing single columns is a well-known procedure; all the basic textbooks outline how to do it in an easy manner. 1 After approximately determining the minimum and the estimated optimal number of theoretical stages, optimizing over the continuous variables is performed at varied values of the discrete variables. This is a 2-dimension discrete array of continuous optimization tasks in the case of a single-feed, two-product column, because there are merely two column sections in this case. This task becomes much more difficult if several feeds and side-products are to be taken into account. The real problem, however, is synthesizing a distillation sequence, or a system of advanced distillation columns, or a complex flowsheet containing distillation units when the number of distillation columns and their connections are not known in advance. In order to solve the complex synthesis problem outlined above, a proper superstructure for a single column, together with a proper generalized disjunctive programming (GDP) model or a proper mixed-integer nonlinear programming (MINLP) model, is a minimum requirement. Once such a model works well for a single column, the problem of more complex flowsheets may also be addressed. Mixed-integer nonlinear programming (MINLP) and generalized disjunctive programming (GDP) are the two exact methodologies commonly applied for solving process engineering problems with discrete decisions. The former includes algebraic equations describing the process, and binary variables related to discrete decisions. The latter method uses logic variables and expressions to represent the problem. 2 Both formulations have been successfully applied to rigorous models of distillation columns. Both methods apply a fixed maximum number of stages, and the actually used stages are selected from this set. Mathematical formulations that represent rigorous models of distillation column configurations fall into two categories: (i) one task-one equipment (OTOE) representations and (ii) variable task-equipment (VTE) representations. 3

Due to their many advantages, modular structures commonly exist in artificial and natural systems, and the concept of modular product design has recently received extensive attention from the engineering research community. Although some work has been done on modularity, most of it is qualitative and exploratory in nature, and little is quantitative. One reason for this gap is the lack of a clear definition of modularity. This thesis begins with a detailed discussion on the concepts of “modularity” and “module.” Based on the background presented here, a mutual information-based method is proposed to quantify modularity. The method is based on the view that coupling is information flow instead of real physical interactions. Information flow can be quantified by mutual information, which is based on randomness (or uncertainty). Since most engineering products can be modeled as stochastic systems and therefore have randomness, the mutual information-based method can be applied in very general cases, and it is shown that the commonly existing linkage counting modularity measure is a special case of the mutual information-based modularity measure. The mutual information-based method is applicable to final design products. But at the early stage of the engineering design process, there are generally only function diagrams. To exploit the benefits of modularity as early as possible, a minimal description length principle-based modularity measure is proposed to determine the modularity of graph structures, which can represent function diagrams. The method is used as criteria to hierarchically decompose abstract graph structures and the real function structure of an HP printer by evolutionary computation. Due to the specialty of genome representations in evolutionary computation, new genetic operators are developed to determine optimal hierarchical decompositions. This quantitative modularity measure has been developed to synthesize modular engineering products, especially by evolutionary design. There are many factors affecting evolving modular structures, such as genome representation, fitness function, learning, and task structure. The thesis preliminarily studies the effects of the modularity of tasks on the modularity of products in evolutionary computation. Using feed-forward neural networks as examples, the results show that the effects are task-dependent and rely on the amount of resources available for the tasks.

Globally convergent exact and inexact parametric algorithms for solving large-scale mixed-integer fractional programs and applications in process systems engineering

Challenges in Replacing Heuristics-Based Trialand-Error Procedures by Mathematical Optimization for Basic Equipment Design

Researchers have expanded the definition of product modularity from function-based modularity to life-cycle process-based modularity. In parallel, measures of product modularity have been developed as well as corresponding modular product design methods. However, a correct modularity measure and modular design method are not enough to realize modular product design. To apply the measure and design method correctly, product representation becomes an important aspect of modular design and imperative for realizing the promised cost savings of modularity. In this paper, a representation for retirement process-based modular design has been developed. Built upon previous representations for assembly and manufacturing-based product design, the representation includes a process similarity matrix and a process dependency matrix. The retirement process-based similarity is based on the similarity in components’ post-life intents (recycling, reuse, disposal), and either the degree of their material compatibility if the components will be recycled, or their disassembly direction or disassembly tools if they need to be disassembled from each other for retirement. Process similarity within a module leads to increased process efficiency (the elimination of non-value added tasks) from the sharing of tooling/equipment. Retirement process-based dependency is developed based on disassembly difficulty, one aspect of the physical interactions between components. Retiring components together as a module to eliminate disassembly and differential processing and reducing the disassembly difficulty between the modules can increase the efficiency of the retirement process. We have first presented which process elements we should consider for defining retirement process similarity and dependency, and then constructed the respective similarity and dependency factors tables. These tables include similarity and dependency factors, which, along with their quantifications, are used to determine a product’s modular architecture to facilitate the retirement process. Finally, a fishing reel is used to illustrate how to apply these factors tables to generate the similarity and dependency matrices that represent a product for retirement-process based modular design. Using these representations as input to the DSM-based modular design methods, we can achieve a design with a modular architecture that improves the retirement process efficiency and reduces retirement costs.