Enhancing transportation network capacity by congestion pricing with simultaneous toll location and toll level optimization

In previous studies of congestion pricing, the objective was to minimize total travel time or maximize total social welfare of all travellers in transportation networks. In this article, a new objective function of maximizing the reserve capacity of networks is proposed, and a new bi-level model is formulated for the implementation of congestion pricing, where either link tolls or path tolls are charged. Since the bi-level model is neither convex nor differentiable, the traditional gradient based methods cannot solve the problem for a global optimum. To circumvent the difficulty of computing, the congestion pricing problem of simultaneous toll link and toll level optimization is formulated as a single-level optimization program with equilibrium constraints. Then the equilibrium constraints, the travel time functions, and toll location constraints are all linearized by introducing mixed integer variables. As a result, the overall problem is formulated into a mixed-integer linear program, which can determine the global optimum. Numerical results show that this approach is effective and efficient.     

A robust optimisation model for production planning and pricing under demand uncertainty

The profitability of every manufacturing plant is dependent on its pricing strategy and a production plan to support the customers’ demand. In this paper, a new robust multi-product and multi-period model for planning and pricing is proposed. The demand is considered to be uncertain and price-dependent. Thus, for each price, a range of demands is possible. The unsatisfied demand is considered to be lost and hence, no backlogging is allowed. The objective is to maximise the profit over the planning horizon, which consists of a finite number of periods. To solve the proposed model, a modified unconscious search (US) algorithm is introduced. Several artificial test problems along with a real case implementation of the model in a textile manufacturing plant are used to show the applicability of the model and effectiveness of the US for tackling this problem. The results show that the proposed model can improve the profitability of the plant and the US is able to find high quality solutions in a very short time compared to exact methods.     

A surrogate assisted parallel multiobjective evolutionary algorithm for robust engineering design

A number of multi-objective evolutionary algorithms have been proposed in recent years and many of them have been used to solve engineering design optimization problems. However, designs need to be robust for real-life implementation, i.e. performance should not degrade substantially under expected variations in the variable values or operating conditions. Solutions of constrained robust design optimization problems should not be too close to the constraint boundaries so that they remain feasible under expected variations. A robust design optimization problem is far more computationally expensive than a design optimization problem as neighbourhood assessments of every solution are required to compute the performance variance and to ensure neighbourhood feasibility. A framework for robust design optimization using a surrogate model for neighbourhood assessments is introduced in this article. The robust design optimization problem is modelled as a multi-objective optimization problem with the aim of simultaneously maximizing performance and minimizing performance variance. A modified constraint-handling scheme is implemented to deal with neighbourhood feasibility. A radial basis function (RBF) network is used as a surrogate model and the accuracy of this model is maintained via periodic retraining. In addition to using surrogates to reduce computational time, the algorithm has been implemented on multiple processors using a master–slave topology. The preliminary results of two constrained robust design optimization problems indicate that substantial savings in the actual number of function evaluations are possible while maintaining an acceptable level of solution quality.     

Distributed polygeneration using local resources for an Indian village: multiobjective optimization using metaheuristic algorithm

Clean Technologies and Environmental Policy - Introduction of renewable energy systems is an imperative need at present. Hybridization of locally available different renewable resources is required...     

Single- and multiobjective optimization problems in robust parameter design

This paper reviews the evolution of off-line quality engineering methods with respect to one or more quality criteria, and presents some recent results. The fundamental premises that justify the use of robust product/process design are established with an illustrative example. The use of designed experiments to model quality criteria and their optimization is briefly reviewed. The fact that most design-for-quality problems involve multiple quality criteria motivates the development of multiobjective optimization techniques for robust parameter design. Two situations are considered: one in which response surface models for the quality characteristics can be obtained using regression and considered over a continuous factor space, and one in which the problem scenario and the experiment permit only discrete parameter settings for the design factors. In the former scenario, a multiobjective optimization technique based on the reference-point method is presented; this technique also incorporates an inference mechanism to deal with uncertainty in the response surface models caused by finite, noisy data. In the discrete-factors scenario, an efficient method to reduce computational complexity for a class of models is presented.     

An artificial immune network for multiobjective optimization problems

Multiobjective optimization is an important problem of great complexity and evolutionary algorithms have been established as a dominant approach in the field. This article suggests a method for approximating the Pareto front of a given function based on artificial immune networks. The proposed method uses cloning and mutation on a population of antibodies to create local subsets of the Pareto front. Elements of these local fronts are combined, in a way that maximizes diversity, to form the complete Pareto front of the function. The method is tested on a number of well-known benchmark problems, as well as an engineering problem. Its performance is compared against state-of-the-art algorithms, yielding promising results.     

A novel surrogate-based approach for optimal design of electromagnetic-based circuits

A new geometric design centring approach for optimal design of central processing unit-intensive electromagnetic (EM)-based circuits is introduced. The approach uses norms related to the probability distribution of the circuit parameters to find distances from a point to the feasible region boundaries by solving nonlinear optimization problems. Based on these normed distances, the design centring problem is formulated as a max–min optimization problem. A convergent iterative boundary search technique is exploited to find the normed distances. To alleviate the computation cost associated with the EM-based circuits design cycle, space-mapping (SM) surrogates are used to create a sequence of iteratively updated feasible region approximations. In each SM feasible region approximation, the centring process using normed distances is implemented, leading to a better centre point. The process is repeated until a final design centre is attained. Practical examples are given to show the effectiveness of the new design centring method for EM-based circuits.     

A surrogate-based cooperative optimization framework for computationally expensive black-box problems

Optimization and Engineering - Most parallel surrogate-based optimization algorithms focus only on the mechanisms for generating multiple updating points in each cycle, and rather less attention...     

Simplified robust and multiobjective optimization of piezoelectric energy harvesters with uncertain parameters

International Journal of Mechanics and Materials in Design - Harvesting energy from mechanical vibrations using piezoelectric materials presents itself as an interesting alternative energy source,...     

A supportive pricing model for suppliers with purchase order financing

Traditionally, lowering the purchase price is welcomed by a business buyer since people believe it can reduce the costs. However, it can be another story if her supplier is short of money and applies for purchase order financing to fund the production. This paper identifies the conditions under which a buyer has the motivation of allowing higher purchase price to facilitate the supplier’s loan application and consequently improve the supply availability. We use a Stackelberg model to describe the interaction between the supplier’s plan (deciding the loan amount and production input simultaneously) and the buyer’s order design (deciding the purchase price and order size jointly). We show the buyer’s optimal order design has four kinds of closed-form solutions as the supplier’s operational state follows a two-point distribution. The design choice should depend on the item value and the supplier’s reliability. In short, this research shows when to increase the purchase price and how to coordinate the order size with it. Generally speaking, if the item value is high and the supplier’s reliability is medium, a buyer allowing a premium price can ensure the supply and actually maximise her expected profit.     

A robust adaptive model reduction method for damage simulations

In our opinion, many of complex numerical models in materials science can be reduced without losing their physical sense. Due to solution bifurcation and strain localization of continuum damage problems, damage predictions are very sensitive to any model modification. Most of the robust numerical algorithms intend to forecast one approximate solution of the continuous model despite there are multiple solutions. Some model perturbations can possibly be added to the finite element model to guide the simulation toward one of the solutions. Doing a model reduction of a finite element damage model is a kind of model perturbation. If no quality control is performed the prediction of the reduced-order model (ROM) can really differ from the prediction of the full finite element model. This can happen using the snapshot Proper Orthogonal Decomposition (POD) model reduction method. Therefore, if the expected purpose of the reduced approximation is to estimate the solution that the finite element simulation should give, an adaptive reduced-order modeling is required when reducing finite element damage models.We propose an adaptive reduced-order modeling method that enables to estimate the effect of loading modifications. The Rousselier continuum damage model is considered. The differences between the finite element prediction and the one provided by the adapted reduced-order model (ROM) remain stable although various loading perturbations are introduced. The adaptive algorithm is based on the APHR (A Priori Hyper Reduction) method. This is an incremental scheme using a ROM to forecast an initial guess solution to the finite element equations. If, at the end of a time increment, this initial prediction is not accurate enough, a finite element correction is added to the ROM prediction. The proposed algorithm can be viewed as a two step Newton–Raphson algorithm. During the first step the prediction belongs to the functional space related to the ROM and during the second step the correction belongs to the classical FE functional space. Moreover the corrections of the ROM predictions enable to expand the basis related to the ROM. Therefore the ROM basis can be improved at each increment of the simulation. The efficiency of the adaptive algorithm is checked comparing the amount of global linear solutions involved in the proposed scheme versus the amount of global linear solutions involved in the classical incremental Newton–Raphson scheme. The quality of the proposed approximation is compared to the one provided by the classical snapshot Proper Orthogonal Decomposition (POD) method.     

A simulation based approach for supply network control

We focus on the problem of strategic network design in the context of multi-site enterprises. We develop a simulation model for various supply network designs evaluation. The suggested model is generic in the sense that only input data modifications are needed to adapt the model to a given supply network structure, while the model's procedural logic stays unchanged. The model reproduces the dynamics of the totality of the supply chain activities. It takes into account control policies related to the inventory management and to the scheduling of production activities. It also carries out local optimisations by dynamically allocating the customers’ demands to the appropriate distribution site and the replenishment orders expressed by the distribution and production sites of the enterprise so as to minimise a global cost function. Simulation execution permits to compute various performance measures. These indicators inform the decision-maker about the profit of the enterprise and about its customer's service level. In this paper, we propose to detail the simulation model. Then, for a fictitious problem of supply network design based on two real life applications, we highlight the influence of the control policies on the performance of the studied enterprise.     

A model for process-based crash simulation

Manufacturing of a bumper system from aluminium extrusions often involves series of forming operations performed in the soft W-temper condition, and then artificially age-hardening of the components to the material's peak hardness T6 condition. It is probable that proper finite element (FE) modelling of the crash performance of the resulting systems must rely upon a geometry obtained from an FE model following the process route, i.e., including simulation of all major forming operations. The forming operations also result in an inhomogeneous evolution of some internal variables (among others the effective plastic strain) within the shaped components. Results from tensile tests reveal that plastic straining in W-temper leads to a significant change of the T6 work-hardening curves. In addition, the tests show that the plastic pre-deformation causes a reduction of the elongation of the T6 specimens. In the present work, these process effects have been included in a user-defined elastoplastic constitutive model in LS-DYNA incorporating a state-of-the-art anisotropic yield criterion, the associated flow rule and a non-linear isotropic work hardening rule as well as some ductile fracture criteria. A first demonstration and assessment of the modelling methodology is shown by ‘through-process analysis’ of two uniaxial tensile test series. The industrial use and relevance of the modelling technique is subsequently demonstrated by a case study on an industrial bumper beam system.     

A minimal network model for creep

We present a minimal rheological network model for the creep function in plastic flow. The model has a very small number of elements—a maximum of two linear springs, two Newtonian dashpots and a frictional mass. No non-Newtonian dashpots or other nonlinear elements are invoked. A wide variety of creep-curve shapes including those with inflection points across which the curvature changes sign are shown to be reproduced by the model and its sub-cases.     

A robust methodology for the simulation of postbuckling response of composite plates

The application of a robust numerical method, namely the differential quadrature method (DQM) for the analysis of buckling and postbuckling of laminated composite plates is introduced. The method is combined with an arc-length strategy to solve the resulting system of nonlinear equations. The treatment accounts for the effect of large deformation by including the von Karman strains. Imperfections in the form of global deflection, conforming to the preferred buckling modes are introduced throughout the plate. Case studies are used to evaluate the geometrically nonlinear response of composite plates under the set conditions, and the results are compared with those obtained by finite element solution and the results obtained from a published literature.     

A bi-objective model for robust yard allocation scheduling for outbound containers

This article examines the yard allocation problem for outbound containers, with consideration of uncertainty factors, mainly including the arrival and operation time of calling vessels. Based on the time buffer inserting method, a bi-objective model is constructed to minimize the total operational cost and to maximize the robustness of fighting against the uncertainty. Due to the NP-hardness of the constructed model, a two-stage heuristic is developed to solve the problem. In the first stage, initial solutions are obtained by a greedy algorithm that looks n-steps ahead with the uncertainty factors set as their respective expected values; in the second stage, based on the solutions obtained in the first stage and with consideration of uncertainty factors, a neighbourhood search heuristic is employed to generate robust solutions that can fight better against the fluctuation of uncertainty factors. Finally, extensive numerical experiments are conducted to test the performance of the proposed method.     

A model for the thermo-oxidative degradation of polyimides

Polyimides, due to their superior mechanical behavior at high temperatures, are used in a variety of applications that include aerospace, automobile and electronic packaging industries, as matrices for composites, as adhesives etc. In this paper, we extend our previous model in S. Karra and K. Rajagopal (Mech. Mater. 43(1):54?C61, 2011), to include thermo-oxidative degradation of these high temperature polyimides. Appropriate forms for the Helmholtz potential and the rate of dissipation are chosen to describe the degradation. The results for a specific boundary value problem, using our model, compares well with the experimental creep data for PMR-15 resin that is aged in air.     

A bi-objective robust model for emergency resource allocation under uncertainty

Emergency resource allocation constitutes one of the most critical elements of response operations in the field of emergency management. This paper addresses an emergency resource allocation problem which involves multiple competing affected areas and one relief resource centre under supply shortage and uncertainty in the post-disaster phase. In humanitarian situations, both the efficiency and fairness of an allocation policy have a considerable influence on the effectiveness of emergency response operations. Thus, we formulate a bi-objective robust emergency resource allocation (BRERA) model which tries to maximise efficiency as well as fairness under different sources of uncertainties. To obtain decision-makers’ most preferred allocation policy, we propose a novel emergency resource allocation decision method which consists of three steps: (1) develop a bi-objective heuristic particle swarm optimisation algorithm to search the Pareto frontier of the BRERA model; (2) select a coefficient to measure fairness; and (3) establish a decision method based on decision-makers’ preference restricted by the fairness coefficient. Finally, a real case study taken from the 5 December 2008 Wenchuan Earthquake demonstrates the effectiveness of the proposed method through numerical results. The solution and model robustness are also analysed.     

一种考虑概率分布的鲁棒优化模型

文章以随机规划中的机会约束思想为指导,根据随机参数的概率分布情况,提出了两种鲁棒性条件约束,并在此基础上建立了一种新的鲁棒优化模型,使模型的可行解控制在一定的鲁棒性指标的范围内。该模型不但可处理约束两端同时含有随机参数的情况,还可以方便地推广到非线性模型中。仿真实例说明了模型的有效性。     

马场坪收费站汽车爆炸破坏的数值模拟分析

应用LS-DYNA有限元程序建立马场坪收费站在汽车爆炸作用下的数值计算模型,对空间网架结构在爆炸作用下的冲击波超压、破坏过程和破坏模式进行了数值模拟计算。提取网架结构内表面有限测点的爆炸冲击波超压并将其进行本征正交分解（POD）,得到冲击波压力场的显性表达和主本征模态。对模拟计算结果与收费站实际破坏情况进行比较分析,揭示了空间网架结构的爆炸破坏机理和原因。数值结果表明,LS-DYNA非线性有限元模型与POD法的结合,能够准确地模拟空间网架结构在爆炸作用下的破坏过程和模式,充分泄爆可以防止空间网架结构在爆炸作用下的破坏倒塌,避免人员损伤。提出在空间网架结构的抗爆和防爆设计中,应采取“强结构弱围护”的设计原则。