Bioinspired Computational Framework for Enhancing Creativity, Optimality, and Robustness in Design

This paper presents results of transdisciplinary research on the development of a bioinspired computational framework for engineering design. This framework is intended to support design by addressing three critical design objectives, including novelty, optimality, and robustness. It provides several computational models and methods, which are inspired by fundamental processes occurring in nature, and discusses their potential for enhancing design. They include models and methods for evolutionary, developmental, and coevolutionary design. Their use is illustrated with examples from the area of steel structural design ranging from a simple cantilever beam design problem to a much more complex problem of designing wind bracings in tall buildings. The paper also shows how several methods and models can be integrated and form a coherent bioinspired computational framework for engineering design.     

Optimal Construction Site Layout Considering Safety and Environmental Aspects

A good site layout is vital to ensure the safety of the working environment and effective and efficient operations. Site layout planning has significant impacts on productivity, costs, and duration of construction. Construction site layout planning involves identifying, sizing, and positioning temporary and permanent facilities within the boundary of the construction site. Site layout planning can be viewed as a complex optimization problem. Although construction site layout planning is a critical process, systematical analysis of this problem is always difficult because of the existence of a vast number of trades and interrelated planning constraints. The problem has been solved using two distinct approaches: Optimization techniques and heuristics methods. Mathematical optimization procedures have been developed to produce optimal solutions, but they are only applicable for small-size problems. Artificial intelligent techniques have been used practically to handle real-life problems. On the other hand, heuristic methods have been used to produce good but not optimal solutions for large problems. In this paper, an optimization model has been developed for solving the site layout planning problem considering safety and environmental issues and actual distance between facilities. Genetic algorithms are used as an optimization bed for the developed model. In order to validate the performance of the developed model, a real-life construction project was tested. The obtained results proved that satisfactory solutions were obtained.     

Production Scheduling for Precast Plants using a Flow Shop Sequencing Model

There are two alternatives for production organization in precast factories—namely, the comprehensive method and the specialized method. Production scheduling under the specialized alternative has been found to be a difficult optimization problem if heterogeneous elements are involved. A flow shop sequencing model that incorporates actual constraints encountered in practice is proposed for this difficult case of precast production scheduling. The model is solved using a genetic algorithm (GA). The traditional minimize makespan and the more practical minimize tardiness penalty objective functions are optimized separately, as well as simultaneously using a normalized weighted GA. Comparisons between the GA and classical heuristic rules show that the GA can obtain good schedules for the model, giving a family of solutions that are at least as good as those produced by the use of heuristic rules.     

Sample Average Approximation Technique for Flexible Network Design Problem

Finding an optimal investment strategy to use scarce resources efficiently is challenging, since the transportation network parameters such as demand, capacity, and travel cost are uncertain. Sequencing investments over time can give flexibility to the planner so as to change, delay, or even abandon the future investment based on system realization. This paper presents a stochastic mathematical program with equilibrium constraints (STOCH-MPEC) formulation for a multistage network design problem, flexible network design problem (FNDP), accounting for demand stochasticity and demand elasticity. STOCH-MPEC problems can be computationally intractable, if the number of scenarios is large and/or the study network is large-scale. To reduce the associated complexity of FNDP, we develop a sample average approximate method (SAA) to efficiently solve the flexible network design problem. We implement the SAA on a test network and compare the performance of SAA with different sample sizes. We show that SAA can produce solutions that are close to the true solutions with considerably fewer scenarios and hence can be a viable computational technique for the stochastic network design problem.     

Optimal Layout of Sewer Systems: A Deterministic versus a Stochastic Model

The optimization of a new or partially existing urban drainage system may be modeled as a subproblems sequence of layout and optimal design within the discrete search space. The design optimization, incorporating the optimal selection of the pumping stations, intermediate manholes, pipe sections, and installation depths, for a general system fixed layout in plan, is a high level sequential decision problem which may be efficiently solved deterministically through a multilevel dynamic programming model. The optimal general layout may be selected in a deterministic way by means of a simple economical comparison of all plan solutions having optimized designs, for small to medium sized systems (if the specific restrictions of the applications are appropriately exploited) in practicable computer time. For larger dimension networks, where it is clearly impossible to achieve plan optimization with full enumeration (which is a NP complete), stochastic search models can be used. For the subproblem layout, an effective enumeration model is presented; the results of a stochastic model proposed previously, using simulated annealing for an application example, are compared and discussed in detail.     

Particle Swarm Optimization for Construction Site Unequal-Area Layout

Layout of temporary facilities on a construction site is essential to enhancing productivity and safety, and is a complex issue due to the unique nature of construction. This paper proposes a particle swarm optimization (PSO)-based methodology to solve the construction site unequal-area facility layout problem. A priority-based particle representation of the candidate solutions to the layout problem is proposed. The particle-represented solution in terms of priorities should be transformed to the specific layout plan with consideration of nonoverlap and geometric constraints. In addition, a modified solution space boundary handling approach is proposed for controlling particle updating with regard to the priority value range. Computational experiments are carried out to justify the efficiency of the proposed method and investigate its underlying performances. This study aims at providing an alternative and effective means for solving the construction site unequal-area layout problem by utilizing the PSO algorithm.     

Layout Design of Irrigation Networks in Highly Parcelled Territories Using Geographical Information System

Traditional irrigation zones in the east of Spain have been denoted by the high level of parcellation. The layout of the irrigation network design in highly parcelled territories presents an important degree of difficulty, the previous experience of the designer in this task being crucial in the final result. In this work, a new heuristic algorithm for layout of the irrigation network design is presented. We start from a classical graph theory algorithm (Dijkstra’s algorithm) used for solving the shortest path spanning tree problem. This algorithm is modified to assign weights to the arcs and plot limits are used as if they were the arcs of a graph. The algorithm is implemented on a geographical information system, thus creating an application that automatically generates the layout of the irrigation network design. The only necessary initial data are the origin of the network (supply point) and the hydrants (delivery points). The advantage of this heuristic is that the subjectivity introduced for the designer is removed. Moreover, it allows for solving complex problems, and therefore it is applicable to highly parcelled zones, where the number of vertices and edges is so high that it would inhibit calculating capacity of any optimization process. A practical example is presented, in which the layout design obtained by applying the heuristic is compared with the original existing layout.     

Evolution Program for Layout Geometry of Rectilinear Looped Networks

This paper presents a new algorithm for the design of layout geometry of looped water distribution networks based on rectilinear grids. The algorithm is an evolution program based on genetic algorithms. It incorporates new methods for generating the initial population and performing the operations of crossover and mutation. The new methods overcome the problem of generating infeasible solutions that result when the commonly used genetic algorithm methods operate on solutions using the chosen coding scheme. This paper includes the results of tests that measure the effectiveness and computational effort of the new methods and a demonstration of the algorithm through application to an example problem.     

Dynamic Layout Planning Using a Hybrid Incremental Solution Method

Efficiently using site space to accommodate resources throughout the duration of a construction project is a critical problem. It is termed the “dynamic layout planning” problem. Solving it involves creating a sequence of layouts that span the entire project duration, given resources, the timing of their presence on site, their changing demand for space over time, constraints on their location, and costs for their relocation. A dynamic layout construction procedure is presented here. Construction resources, represented as rectangles, are subjected to two-dimensional geometric constraints on relative locations. The objective is to allow site space to all resources so that no spatial conflicts arise, while keeping distance-based adjacency and relocation costs minimal. The solution is constructed stepwise for consecutive time frames. For each resource, selected heuristically one at a time, constraint satisfaction is used to compute sets of feasible positions. Subsequently, a linear program is solved to find the optimal position for each resource so as to minimize all costs. The resulting sequence of layouts is suboptimal in terms of the stated global objective, but the algorithm helps the layout planner explore better alternative solutions.     

单一尺寸圆坯的无缝钢管坯料设计模型与算法

无缝钢管坯料设计是在满足生产工艺要求下,将客户订单钢管合理地分配到生产原料圆坯的过程.实际生产中的批量原则使得每个钢管订单在圆坯中有最小分配重量要求;由于无缝钢管分配支数必须取整,导致钢管订单在圆坯中的分配重量并非连续取值.因此,比起相关的板坯设计问题和装箱问题,无缝钢管坯料设计的求解更为复杂.本文给出了无缝钢管坯料设计问题的一般性描述,并建立了混合整数规划模型.针对库存中只有单一尺寸圆坯的情况,简化了问题模型并且求得了问题的下界.结合问题特点,提出了基于贪婪策略的两阶段启发式算法,并用实际生产数据和仿真数据验证了算法求解此类问题具有很好的有效性和稳定性.      

Diagnostic Imaging Information Systems: DREAM. Reporting management

From the clinical standpoint, the report represents the synthesis of the diagnostic process and it is the central activity of radiology. However, reporting cannot be separated from the entire organizational cycle inside the Diagnostic Imaging Department as well as within the structure as a whole. The effectiveness of the contribution an information system can give to the radiologist is related to the information and functional integration of the various applications operating in the hospital and over the territory. Based on these considerations, the main types of support the information tool can offer, are reported together with the primary objectives which were basic to the development of the reporting module within the DREAM system and the major solutions adopted to facilitate the attainment of these objectives.     

Multiple Scales Solution for a Beam with a Small Bending Stiffness

This paper considers the problem of a beam with a small bending stiffness, within the framework of a nonlinear beam model that includes both the classical cable and the linear beam as limiting cases. This problem, treated as a perturbation of the catenary solution, is solved with the multiple scales method. The resulting expressions of the beam deflection and of the internal forces, as well as those obtained with the more commonly applied matched asymptotics method, are compared with numerical results. This comparison indicates that a better accuracy can be achieved with the multiple scales approach, for a similar computational effort. These results also suggest that application of the multiple scales method to the solution of beam problems involving boundary layers extend the range of values of the small parameter, for which accurate analytical solutions can be obtained by a perturbation technique.     

Self-Adaptive Fitness Formulation for Evolutionary Constrained Optimization of Water Systems

In design of water distribution networks, there are several constraints that need to be satisfied; supplying water at an adequate pressure being the main one. In this paper, a self-adaptive fitness formulation is presented for solving constrained optimization of water distribution networks. The method has been formulated to ensure that slightly infeasible solutions with a low objective function value remain fit. This is seen as a benefit in solving highly constrained problems that have solutions on one or more of the constraint bounds. In contrast, solutions well outside the constraint bounds are seen as containing little genetic information that is of use and are therefore penalized. In this method, the dimensionality of the problem is reduced by representing the constraint violations by a single infeasibility measure. The infeasibility measure is used to form a two-stage penalty that is applied to infeasible solutions. The performance of the method has been examined by its application to two water distribution networks from literature. The results have been compared with previously published results. It is shown that the method is able to find optimum solutions with less computational effort. The proposed method is easy to implement, requires no parameter tuning, and can be used as a fitness evaluator with any evolutionary algorithm. The approach is also robust in its handling of both linear and nonlinear equality and inequality constraint functions. Furthermore, the method does not require an initial feasible solution, this being an advantage in real-world applications having many optimization variables.     

Minimum Weight of a Sandwich Panel

The least‐weight problem of a sandwich panel with a truncated, hollow, hexagonal core, and subjected to a given bending moment along each edge is analyzed in this paper. In order to meet the practical manufacturing requirements and be within allowable stress limits, constraints are placed on the geometrical dimensions of the structural parts of the sandwich panel as well as on the physical strength, such as the allowable stresses. Upper and lower limiting values are assigned for each of the design variables. Through the use of the penalty function, the minimization problem subjected to a set of twenty inequality constraints is changed to a sequence of unconstrained ones. The modified Fletcher‐Powell method is used by a proper choice of the penalty parameter and the reduction factor. The methodology presented here can be extended to include multiple loading conditions, bending rigidity, and shear rigidity requirements, which present no additional difficulties except to increase the number of constraints.     

Intelligent Agent Optimization of Urban Bus Transit System Design

The transit route network design (TRND) problem seeks a set of bus routes and schedules that is optimal in the sense that it maximizes the utility of an urban bus system for passengers while minimizing operator cost. Because of the computational intractability of the problem, finding an optimal solution for most systems is not possible. Instead, a wide variety of heuristic and meta-heuristic approaches have been applied to the problem to attempt to find near-optimal solutions. This paper presents an optimization system that synthesizes aspects of previous approaches into a scalable, flexible, intelligent agent architecture. This architecture has successfully been applied to other transportation and logistics problems in both research studies and commercial applications. This study shows that this intelligent agent system outperforms previous solutions for both a benchmark Swiss bus network system and the very large bus system in Delhi, India. Moreover, the system produces in a single run a set of Pareto equivalent solutions that allow a transit operator to evaluate the trade-offs between operator costs and passenger costs.     

Grid-Based Heuristic Method for Multifactor Landfill Siting

Siting a landfill requires the processing of a large amount of spatial data. However, the manual processing of spatial data is tedious. A geographical information system (GIS), although capable of handling spatial data in siting analyses, generally lacks an optimization function. Optimization models are available for use with a GIS, but they usually have difficulties finding the optimal site from a large area within an acceptable computational time, and not easily directly available with a raster-based GIS. To overcome this difficulty, this study developed a two stage heuristic method. Multiple factors for landfill siting are considered and a weighted sum is computed for evaluating the suitability of a candidate site. The method first finds areas with significantly high potentialities and then applies a previously developed mixed-integer programming model to locate the optimal site within the potential areas, and can significantly reduce the computational time required for resolving a large siting problem. A case study was implemented to demonstrate the effectiveness of the proposed method, and a comparison with the previously developed model was provided and discussed.     

A Potts Neuron Approach to Communication Routing

A feedback neural network approach to communication routing problems is developed, with emphasis on multiple shortest path problems, with several requests for transmissions between distinct start and end nodes. The basic ingredients are a set of Potts neurons for each request, with interactions designed to minimize path lengths and prevent overloading of network arcs. The topological nature of the problem is conveniently handled using a propagator matrix approach. Although the constraints are global, the algorithmic steps are based entirely on local information, facilitating distributed implementations. In the polynomially solvable single-request case, the approach reduces to a fuzzy version of the Bellman-Ford algorithm. The method is evaluated for synthetic problems of varying sizes and load levels, by comparing to exact solutions from a branch-and-bound method, or to approximate solutions from a simple heuristic. With very few exceptions, the Potts approach gives high-quality legal solutions. The computational demand scales merely as the product of the numbers of requests, nodes, and arcs.     

Optimal Design with Probabilistic Objective and Constraints

Significant challenges are associated with solving optimal structural design problems involving the failure probability in the objective and constraint functions. In this paper, we develop gradient-based optimization algorithms for estimating the solution of three classes of such problems in the case of continuous design variables. Our approach is based on a sequence of approximating design problems, which is constructed and then solved by a semiinfinite optimization algorithm. The construction consists of two steps: First, the failure probability terms in the objective function are replaced by auxiliary variables resulting in a simplified objective function. The auxiliary variables are determined automatically by the optimization algorithm. Second, the failure probability constraints are replaced by a parametrized first-order approximation. The parameter values are determined in an adaptive manner based on separate estimations of the failure probability. Any computational reliability method, including first-order reliability and second-order reliability methods and Monte Carlo simulation, can be used for this purpose. After repeatedly solving the approximating problem, an approximate solution of the original design problem is found, which satisfies the failure probability constraints at a precision level corresponding to the selected reliability method. The approach is illustrated by a series of examples involving optimal design and maintenance planning of a reinforced concrete bridge girder.     

Global Optimization of Combined Region Aggregation and Leveling Model

The advent and widespread use of innovative spatial analysis technologies, such as geographic information systems (GIS), computer aided design (CAD) systems, and global positioning systems (GPS), have prompted great interest in spatial optimization. The tasks of selecting an optimal subregion from a larger region—region aggregation—and determining an optimal strategy for cutting and filling that subregion to a uniform elevation—land leveling—are examples of spatial analyses that can benefit from these powerful computer technologies. The combined region aggregation and leveling problem is a complex spatial problem that often involves the comprehensive consideration of multiple, incommensurate, and often conflicting objectives, while at the same time satisfying a set of prespecified physical and logical constraints. Traditionally, these two problems are solved separately, often precluding the identification of global optima. Through this research, a multiobjective integer programming model that considers these problems simultaneously is formulated, a computational algorithm for solving the model is presented, and numerical results that demonstrate the efficiency and effectiveness of this procedure are discussed. Computational experiments report polynomial complexity of the heuristic procedure against exponential worst-case complexity of traditional enumerative methods.