Discrete cost optimization of composite floor system using social harmony search model

This paper presents a social harmony search algorithm model for the cost optimization of composite floor system with discrete variables. The total cost function includes the costs of concrete, steel beam and shear studs. The design is based on AISC load and resistance factor design specifications and plastic design concepts. Here, six decision variables are considered for the objective function. In order to demonstrate the capabilities of the proposed model in optimizing composite floor system designs, two design examples taken from the literature are studied. It is shown that use of the presented model results in significant cost saving. Hence, it can be of practical value to structural designers. Also the proposed model is compared to the original harmony search, its recently developed variants, and other meta-heuristic algorithms to illustrate the superiority of the present method in convergence and leading to better solutions. In order to investigate the effects of beam spans and loadings on the cost optimization of composite floor system a parametric study is also conducted.     

Design of reconfiguration mechanism for holonic manufacturing systems based on formal models

One of the key design issues of holonic manufacturing systems (HMS) is to effectively respond to resource failures based on the flexible holonic architecture. The objective of this paper is to propose a viable design methodology to implement reconfiguration mechanism in HMS. A reconfiguration mechanism is developed to accommodate changes based on collaboration of holons without leading to chaos at the shop floor. To deal with resource failures in HMS, an impact function is defined to characterize the impact of resource failures on different holons in a holarchy. A collaborative reconfiguration mechanism based on an impact function is proposed to effectively reconfigure the systems to achieve minimal cost solutions. The design and implementation methodology combines contract net protocol for negotiation of holons, Petri net for the representation of individual product holons and resource holons and FIPA-compliant agent platform for publication/discovery of holons. A simulation system is developed to verify the proposed reconfiguration mechanism.     

Optimal design and operation of a wastewater purification system

Due to the importance of coastal areas, is of the highest interest to implement purification systems that with minimum cost are able to assure water quality standards in agreement with the regional legislations. This work addresses the optimal design (outfall locations) and optimal operation (level of oxygen discharges) of a wastewater treatment system. This problem can be mathematically formulated as a two-objective mixed design and optimal control problem with constraints on the states and the design and control variables.     

High-throughput automation design considerations for biotechnology processes involving RNA purification protocols using multi-centrifuge bioseparation steps

Design of an automation line is a multi-objective optimization problem involving throughput, yield, floor space and cost constraints. The paper examines the feasibility of a computer-aided automation design for biotechnology applications using Arena? software. A generic case study chosen for this study involves a sequence of steps in a preparation process of RNA from tissue-cultured cells. These steps involve repetitive usage of centrifuging operations to perform separation of biochemical substances. A sample must be loaded onto a centrifuge by a pick-and-place device (typically a robot manipulator). Consecutive centrifuging steps may involve multiple centrifuges as well as robots, or some measure of equipment sharing. The paper proposes a unified simplified cost model for all design objectives (throughput, space utilization, process capital and operational cost) and a quantitative selection criterion to allow for an optimal automation design.     

A multi-objective problem based on fuzzy inference with application to parametric design of an electrophotographic system

This paper proposes a systematic approach to conduct system-level optimal parametric design for a class of electrophotographic systems. A conventional monochrome laser printer serves as the platform for verification of the proposed optimal design approach. Besides performance, we incorporate two other practical indices, i.e., cost and energy consumption, into design objectives and formulate a multi-objective optimization problem. A fuzzy inference system is established to provide the nonlinear or linguistic mapping between the decision variables and the design objectives. For comparative purpose, the problem is solved using both single-objective and multi-objective optimization algorithms. Note that the proposed approach is also applicable to other complex systems.     

An efficient framework for optimal robust stochastic system design using stochastic simulation

The knowledge about a planned system in engineering design applications is never complete. Often, a probabilistic quantification of the uncertainty arising from this missing information is warranted in order to efficiently incorporate our partial knowledge about the system and its environment into their respective models. This leads to a robust stochastic design framework where probabilistic models of excitation uncertainties and system modeling uncertainties can be introduced; the design objective is then typically related to the expected value of a system performance measure, such as reliability or expected life-cycle cost. For complex system models, this expected value can rarely be evaluated analytically and so it is often calculated using stochastic simulation techniques, which involve an estimation error and significant computational cost. An efficient framework, consisting of two stages, is presented here for the optimization in such robust stochastic design problems. The first stage implements a novel approach, called stochastic subset optimization (SSO), for iteratively identifying a subset of the original design space that has high plausibility of containing the optimal design variables. The second stage adopts some other stochastic optimization algorithm to pinpoint the optimal design variables within that subset. The focus is primarily on the theory and implementation issues for SSO but also on topics related to the combination of the two different stages for overall enhanced efficiency. An illustrative example is presented that shows the efficiency of the proposed methodology; it considers the optimization of the reliability of a base-isolated structure considering future near-fault ground motions.     

On the prediction of topology and local properties for optimal trussed structures

A new formulation is presented for mathematical modelling to predict the distribution of material, material properties, and topology for the optimal design of trussed structures. The design problem is cast in a form to minimize a measure ofgeneralized compliance, which is calculated as a sum over the structure of weighted displacement. Member stiffnesses appear as design variables and, starting with a given ground structure, the solution predicts the optimal layout and distribution of stiffness. The isoperimetric constraint in the reformulated problem measures totalcost in generalized form, based on independently specified unit relative cost factors for each truss element. One or another form of optimal design is generated via a process where designated elements in the unit relative cost field are adjusted systematically at each cycle. The generalized cost feature provides as well for the introduction of certain technical constraints into the design problem, e.g. the facility to design around obstacles. Results for each cycle of an algorithm for computational treatment are identified as the solution to a properly posed optimization problem. Computational procedures are demonstrated by the prediction of optimal designs for a variety of truss problems in 2D.     

Floor shape optimization for green building design

Shape is an important consideration in green building design due to its significant impact on energy performance and construction costs. This paper presents a methodology to optimize building shapes in plan using the genetic algorithm. The building footprint is represented by a multi-sided polygon. Different geometrical representations for a polygon are considered and evaluated in terms of their potential problems such as epistasis, which occurs when one gene pair masks or modifies the expression of other gene pairs, and encoding isomorphism, which occurs when chromosomes with different binary strings map to the same solution in the design space. Two alternative representations are compared in terms of their impact on computational effectiveness and efficiency. An optimization model is established considering the shape-related variables and several other envelope-related design variables such as window ratios and overhangs. Life-cycle cost and life-cycle environmental impact are the two objective functions used to evaluate the performance of a green building design. A case study is presented where the shape of a typical floor of an office building defined by a pentagon is optimized with a multi-objective genetic algorithm.     

Soft computing based multi-objective optimization of steam cycle power plant using NSGA-II and ANN

In this paper a steam turbine power plant is thermo-economically modeled and optimized. For this purpose, the data for actual running power plant are used for modeling, verifying the results and optimization. Turbine inlet temperature, boiler pressure, turbines extraction pressures, turbines and pumps isentropic efficiency, reheat pressure as well as condenser pressure are selected as fifteen design variables. Then, the fast and elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) is applied to maximize the thermal efficiency and minimize the total cost rate (sum of investment cost, fuel cost, and maintenance cost) simultaneously. The results of the optimal design are a set of multiple optimum solutions, called ‘Pareto optimal solutions’. The optimization results in some points show 3.76% increase in efficiency and 3.84% decrease in total cost rate simultaneously, when it compared with the actual data of the running power plant. Finally as a short cut to choose the system optimal design parameters a correlation between two objectives and fifteen decision variables with acceptable precision are presented using Artificial Neural Network (ANN).     

Reliability-based optimization of design variance to identify critical tolerances

Reliability-based design optimization (RBDO) is a topic of interest for research in both academia and industry. RBDO typically involves adjusting the mean values of the design variables while fixing the spread parameters, often measured as variance, in order to accomplish a given objective within the stated constraints. This paper proposes an alternate way to meet given design criteria by fixing the mean values of the statistical inputs and allowing the spread parameters to become design variables. To do this, product cost models are proposed in terms of statistical variables. By performing this type of optimization, the design changes are kept to a minimum, and the focus is instead shifted to variance control. An initial study is performed on a three-bar truss subject to static loading with material variability. A more complex example is performed involving the cost minimization of an unmanned undersea vehicle subjected to hydrostatic buckling.     

Optimal number of hosts in a distributed system based on cost criteria

Redundant or distributed systems are increasingly used in system design so that the required reliability and availability can be easily achieved. However, such an approach requires additional resources that can be very costly. Hence, how to design and test such a system in the most cost-effective way is of concern to the developers. A general cost model and a solution algorithm are presented for the determination of the optimal number of hosts and optimal system debugging time that minimize the total cost while achieving a certain performance objective. During testing, software faults are corrected and the reliability shows an increasing trend, and hence system reliability increases. A general system model is constructed based on a Markov process with software reliability and availability obtained from software reliability growth models. The optimization problem is formulated based on the cost criteria and the solution procedure is described. An application example is presented.     

Robustness-based design optimization under data uncertainty

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point data and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.     

Robust optimal parametric LQ control with a guaranteed cost bound and applications

For an optimal parametric linear quadratic (LQ) control problem, a design objective is to determine a controller of constrained structure such that the closed-loop system is asymptotically stable and an associated performance measure is optimized. In the presence of system uncertainty, the system via a parametric LQ design is further required to be robust in terms of maintaining the closed-loop stability with a guaranteed cost bound. This problem is referred to as ‘robust optimal parametric LQ control with a guaranteed cost bound’ and is addressed in this work. A new design method is proposed to find an optimal controller for simultaneously guaranteeing robust stability and performance over a specified range of parameter variations. The results presented generalize some previous work in this area. A versatile numerical algorithm is also given for computing the robust optimal gains. The usefulness of the design method is demonstrated by numerical examples and a design of the robust control of a VTOL helicopter.     

Pheromone-based coordination for manufacturing system control

A pheromone-based coordination approach, which comes from the collective behavior of ant colonies for food foraging, is applied to control manufacturing system in this paper, aiming at handling dynamic changes and disturbances. The pheromone quantum of manufacturing cell is calculated inversely proportional to the cost, which can guarantee a minimal cost to process the orders. This approach has the capacity for optimization model to automatically find efficient routing paths for processing orders and to reduce communication overhead which exists in contract net protocol in shop floor control system. A prototype system is developed, and experiments confirm that pheromone-based coordination approach has excellent control performance and adaptability to disturbances in shop floor.     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

Estimation and optimal control design of a biological wastewater treatment process

This paper deals with the on-line estimation and optimal control of a biological wastewater treatment process. The objective of the control is to force the residual substrate and the dissolved oxygen concentrations to track a given reference model despite the disturbances and system parameter uncertainties. The control law is based on one step ahead prediction of the controlled variables and minimization of an appropriate quadratic cost function. The technique is based on direct exploitation of the nonlinear model representing the wastewater treatment process and is coupled with an asymptotic estimator for on-line tracking of simultaneously unavailable states and time varying parameters. The estimated variables are used in the explicit design of the control algorithm according to certainty equivalence principle. A simulation study subject to measurement noise and abrupt jumps in the kinetic parameters shows the feasibility and robustness of the control strategy.     

An application of geometric programming to heat exchanger design

This paper presents a geometric programming model for optimal design of refrigeration heat exchangers. A dual method is used to solve the design problem to obtain the optimum values of the design variables. Analyses are performed to determine the changes in the optimum values of the design variables with respect to the changes in the unit costs of components, material specifications, and tube sizes.     

Design optimization of microprocessor-based remote multiplexing systems

A physical model of a microprocessor-based remote multiplexing system is constructed, based on the physical layout of the plant and the distribution of input/output variables. A cost index is generated based on the above model, which when maximized, yields a maximum solution that defines the cost optimal number, location and load of the remote systems. The optimal solution is determined by simulation in a medium-size digital computer.     

Integrated Structural–Architectural Design for Interactive Planning

Traditionally, building floor plans are designed by architects with their usability, functionality and architectural aesthetics in mind; however, the structural properties of the distribution of load‐bearing walls and columns are usually not taken into account at this stage. In this paper, we propose a novel approach for the design of architectural floor plans by integrating structural layout analysis directly into the planning process. In order to achieve this, we introduce a planning tool which interactively enforces checks for structural stability of the current design, and which on demand proposes how to stabilize it if necessary. Technically, our solution contains an interactive architectural modelling framework as well as a constrained optimization module where both are based on respective architectural rules. Using our tool, an architect can predict already in a very early planning stage whose designs are structurally sound such that later changes due to stability reasons can be prevented. We compare manually computed solutions with optimal results of our proposed automated design process in order to show how much our proposed system can help architects to improve the process of laying out structural models optimally.     

Accessibility and ergonomic analysis of assembly product and jig designs

In the aircraft industry, the design of floor assembly jigs (FAJs) is an important activity that directly affects productivity. It involves tool frame generation and locator and clamp placement to ensure that the assembly components are held properly with respect to each other to meet the required tolerances. The tool designer also has to analyze the design to ensure that the assembly process does not pose accessibility and ergonomics related problems. The current approach is dependent on the experience of the tool designer and the limited visualization possible on commercial CAD systems. This leads to extensive redesign when accessibility and ergonomic related problems are detected on the physical prototype. In this research, an integrated Virtual Reality-based environment is being developed for the analysis of assembly product and jig designs. CAD models of the assembly product and jig are imported into a Virtual Reality (VR)-based visualization system for accessibility analysis. A motion tracking system is integrated to allow ergonomic posture analysis. The combined VR and motion tracking system allows evaluation of alternate assembly sequences and the jig design. In this paper, the theoretical basis for the analysis environment is presented along with details of the prototype implementation of this system.

Relevance to industry

Floor assembly jigs are used extensively by the aircraft industry. Improvement of their design process will lead to savings in better design and reduced development time and cost. Better designs will require fewer changes after the jigs have been fabricated. The overall result will be a reduction in product realization time and cost and improved product quality.