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When leg/wheel mobile robots are controlled by model predictive control (MPC), the optimization is too complex for an embedded CPU. Then, we proposed a method which reduces computational complexity by sequential optimization for the models partitioned into each leg. However, robustness of the proposed method was not evaluated enough in the disturbing environment. In this paper, we conduct simulations on the inclined plane to evaluate the robustness. Through the simulations, the robustness of the proposed method is improved by changing the control cycle and the predictive length.

This paper considers the near-field source location problem for a nonuniform linear array (non-ULA) in the presence of sensor gain and phase errors. A sequential optimization calibration method is proposed to simultaneously estimate the gain and phase errors as well as the locations of calibration sources involving the ranges and the azimuths by exploiting some imprecise a-priori knowledge of calibration sources. At each iteration of the proposed method, the source locations, and the gain and phase errors are obtained iteratively. Finally, at the analysis stage, we evaluate the effectiveness of the proposed technique using some numerical simulations. Results show that the proposed algorithm shares the capability to jointly estimate the source locations and the errors.

Due to the presence of interactions in Multiple-Input Multiple-Output (MIMO) systems, the application of conventional Proportional-Integral-Derivative (PID) controllers for such systems becomes difficult in terms of tuning. The Genetic Algorithm has been extensively used in the literature in order to obtain the optimum multivariable PID tuning parameters based on simultaneous step changes in the set points of the system; however, they lead to sluggish responses when individual step changes are applied. This project investigates the application of sequential optimization procedure to obtain the tuning parameters for a multivariable PI controller applied to 2×2 MIMO transfer function that represents a binary distillation column. The top and bottom composition set points were individually step changed and the Genetic Algorithm was applied to minimize the Integral Averaged Error (IAE) until convergence was observed. The sequential optimization has shown to provide faster and less oscillatory response than the simultaneous procedure; however, the new methodology showed poor performance for disturbance rejection when simultaneous step changes were applied to the set points.

Multi-objective verification problems of parametric Markov decision processes under optimality criteria can be naturally expressed as nonlinear programs. We observe that many of these computationally demanding problems belong to the subclass of signomial programs. This insight allows for a sequential optimization algorithm to efficiently compute sound but possibly suboptimal solutions. Each stage of this algorithm solves a geometric programming problem. These geometric programs are obtained by convexifying the nonconvex constraints of the original problem. Direct applications of the encodings as nonlinear programs are model repair and parameter synthesis. We demonstrate the scalability and quality of our approach by well-known benchmarks.

Silica nanoparticles are increasingly investigated for use in drug delivery system, which requires sequential optimization of their properties to eliminate or ameliorate identified toxic characteristics. It has been discovered that cytotoxicity of silica nanoparticles is closely linked to many factors including their size, dose, cell type in the study, treatment time, surface area, and structural discrimination. This review article focuses on these complicating factors contributing to toxicity induced by silica nanoparticles and discusses potential mechanisms underlying the observed toxic effects. The article overviews and summarizes studies on several major affected tissues susceptible to silica nanoparticles and the possible main route of silica nanoparticles entering the central nervous system as well as mechanisms for silica nanoparticles–associated neurotoxicity. A series of in vitro and in vivo studies to show silica nanoparticles indeed have toxicity on neurons and central nervous system under certain conditions were provided as examples for further safety application and development.

The optimization of hyaluronic acid (HA) extraction from eggshell was studied by using a sequential experimentation approach. Using the results of factorial experiments, optimization experiments were carried out by response surface methodology. The experimental data as well as the validation tests showed the optimum performance when the extraction pH and time were bounded in the ranges of 3.4–3.5 and 3–3.4 days, respectively, at 9 °C. The purified HA was characterized by FTIR spectrum, harmonic mean size and average molecular weight. The study depicted the potential of eggshell as an alternative source for HA extraction to use in industry and medicine.

In this paper we study a production–inventory model for a single product where shortages are partially backlogged. Both the backorder cost and the lost sale cost depend on a fixed cost and a cost proportional to the shortage time. We assume a mixture between the dispatching policies known as LIFO (last in, first out) and FIFO (first in, first out) in the discipline of service to fill the backlogged demand. By using a sequential optimization procedure, we determine in a closed-form the optimal production policies and the optimal profit for all the possible cases obtained from the developed model. We show how this new model generalizes several inventory models proposed in the literature. Finally, numerical results are presented to analyze the sensitivity of the optimal policies with respect to changes in some parameters of the system.

A double stage sequential optimization algorithm for finding the optimal fiber content and its distribution in solid composites, considering uncertain design parameters, is presented. In the first stage, the optimal amount of fiber in a Fiber Reinforced Composite (FRC) structure with uniformly distributed fibers is conducted in the framework of a Reliability Based Design Optimization (RBDO) problem. In the second stage, the fiber distribution optimization having the aim to more increase in structural reliability is performed by defining a fiber distribution function through a Non-Uniform Rational B-Spline (NURBS) surface. The output of stage 1(optimal fiber content for homogeneously distributed fibers) is considered as the input of stage 2. The output of stage 2 is Reliability Index (RI) of the structure with optimal fiber content and optimal fiber distribution. First order reliability method in order to approximate the limit state function and a homogenization approach, based on the assumption of random orientation of fibers in the matrix, are implemented. The proposed combined model is able to capture the role of available uncertainties in FRC structures through a computationally efficient algorithm using all sequential, NURBS and sensitivity based techniques. Performed case studies show as an increase in model uncertainties yields to structural unreliability. Moreover, when system unreliability increases fiber distribution optimization becomes more influential.

In this paper, the constraints of the sequential optimization of the lexicographic approach are relaxed in order to obtain a simple alternative approach to solve multiobjective problems. In other words, we allow the decision-maker to deviate from the optimal solution of each iteration if he/she prefers. Some properties of the obtained solutions are studied and the relationship between these solutions and those of the weighted sum scalarization and the ϵ-constraint scalarization and also the elastic ϵ-constraint scalarization are investigated. Finally, some examples are provided to show more details.

In this paper, the "Reliability-Based Design Optimization (RBDO)" is used to solve an uncertain design optimization problem with insufficient experimental data. For modeling the uncertainty in these cases, the possibility theory is applied and a new method in the "Possibility-Based Design Optimization (PBDO)" strategy is introduced. The efficiency of the proposed approach is compared with the basic PBDO method (PMA) using two engineering examples. The two facts that in such cases "the PBDO could provide conservative solutions" and "satisfy system level reliability" but RBDO couldn't, are studied too.

Background Quality control of herbal medicines based on characteristic components is an important trend. Although the plant metabolomics provide a powerful tool for species classification, the discovered marker is usually limited in practical application. For rapid discovery of efficient marker combination, we proposed a strategy integrating targeted metabolite profiling and sequential optimization method. Methods This strategy included: (1) directional enrichment and chemical profiling of targeted metabolites by matrix solid phase dispersion (MSPD) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS). (2) Partial least squares discrimination analysis (PLS-DA)-based sequential screening of efficient marker combination was constructed for various species predictions. Five Lonicera species and their characteristic metabolites, sponins, were taken as a case study. Results A total of 19 saponins were identified, and 12 major and available saponins were enriched based on MSPD and quantified by LC-MS/MS in 5 Lonicera species flower buds. Followed by 3 runs of PLS-DA-based screening, a combination consisting of macranthoidin B, dipsacoside B and α-hederin was discovered as the effective chemical marker for 5 analogous Lonicera flower classification. Conclusion Our study provides an effective and applicable approach to select the practical marker combination for the assessment of analogical herb medicines.

A step length adjustment (SLA) iterative algorithm was proposed for locating the minimum performance target point (MPTP) in the inverse reliability analysis. This paper elaborates SLA and two deliberately designed numerical examples are used to compare SLA with other algorithms appearing in recent literatures for locating MPTP. The results show that SLA is much more robust and efficient. Then SLA and sequential optimization and reliability assessment (SORA) are combined to solve reliability-based design optimization (RBDO) problems. In the reliability assessment of SORA, with the design obtained from the previous cycle, SLA is used to locate MPTP. Then in the deterministic optimization, the boundaries of violated constraints are shifted to the feasible direction according to the MPTP obtained in the reliability assessment. Several examples frequently cited in similar studies are used to compare SORA-SLA with other RBDO algorithms. The results indicate the effectiveness and robustness of SORA-SLA.

This paper presents a method to dampen the variations in the output of aggregated wind power through geographic allocation of wind power generation sites. The method, which is based on the sequential optimization of site localization, is applied to the Nordic countries and Germany, using meteorologic wind speed data as the input. The results show that the variability in aggregated wind power output mitigates by applying sequential optimization. For the data used in this work, the coefficient of variation (standard deviation/mean) was 0.54 for the optimized aggregation of sites, as compared with 0.91 for the present day installation. An optimal allocation of wind power generation site reduces the need for dispatch and other measures to deal with the intermittent nature of wind power.

Reliability based design optimization (RBDO) has been widely implemented in engineering practices for high safety and reliability. It is an important challenge to improve computational efficiency. Sequential optimization and reliability assessment (SORA) has made great efforts to improve computational efficiency by decoupling a RBDO problem into sequential deterministic optimization and reliability analysis as a single-loop method. In this paper, in order to further improve computational efficiency and extend the application of the current SORA method, an enhanced SORA (ESORA) is proposed by considering constant and varying variances of random design variables while keeping the sequential framework. Some mathematical examples and an engineering case are given to illustrate the proposed method and validate the efficiency.

In this paper, a closed-loop logistics system with manufacturing and remanufacturing cycle is analyzed using three optimization methods. They are: (i) sequential optimization, (ii) centralized optimization without benefit sharing, and (iii) centralized optimization with benefit sharing. In the first method, the decisions of sequential optimization are made initially by the retailers, and then by the manufacturer. In the second method, the decision of the centralized optimization is made simultaneously by the whole logistics system. The second method is not favorable to the retailers because the retailers' profit will become smaller as a result of collaboration. Therefore a third method with benefit sharing is investigated. The logistics system includes a manufacturer and multi-retailer considering price-sensitive demand and deterioration. Industrial examples of such products are IC chips, computers and mobile phones that usually outdate or decrease in value due to technological innovation. The decrease in value is considered as a form of deterioration. In order to add value, these outdated products can be remanufactured and resold to market. Four key effects are investigated in this study: (i) recycling in supply chain, (ii) deterioration, (iii) collaboration effect between manufacturer and retailers using three optimization methods, and (iv) price sensitive demand.

In order to improve the efficiency and robustness of reliability-based multidisciplinary design optimization (RBMDO), a new collaborative strategy (named C-RBMDO) which integrates performance measure approach (PMA) and concurrent subspace optimization strategy (CSSO) is proposed. Both the mathematical model and optimization procedure are put forward. The traditional triple-level nested flowchart of RBMDO is decoupled with the sequential optimization and reliability assessment (SORA). The deterministic multidisciplinary design optimization and the multidisciplinary reliability analysis are executed by CSSO and PMA respectively. Finally, the proposed method is verified through the design example of gear transmission.

A sequential optimization strategy, based on statistical experimental designs, was used to enhance the production of riboflavin by recombinant Bacillus subtilis X42. The two-level Plackett–Burman (PB) design was implemented to screen medium components that significantly influence riboflavin production. Among the 11 variables tested, glucose, yeast powder, MgSO4•7H2O, urea, CuCl2, MnCl2 were identified as the most significant factors (confidence levels above 95%) for riboflavin production. The selected significant parameters such as yeast powder, urea and CuCl2 were further optimized using a central composite design. The optimum medium was (in g/L): yeast powder, 20.8; urea, 4.8; CuCl2, 0.024; glucose, 100; MgSO4•7H2O, 0.5; K2HPO4, 0.5; KH2PO4, 1; FeCl2, 0.02. The optimum medium led to a maximum riboflavin concentration of 7.9 g/L in shake flasks, which was 11.2% higher than the control medium.

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: i which rules are better from the point of view of classification --exact or approximate; and ii which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization length+coverage or coverage+length is better than the ordinary optimization length or coverage.

Fomitopsis palustris, screened from 11 wood rotting fungi, was optimized with a sequential optimization strategy to produce the largest amount of cellulase, and the efficiency of the enzyme was evaluated. Based on the Plackett–Burman and Box–Behnken designs, the most significant variables, time, urea, and Tween 80 were varied for optimizing cellulase production. An optimized result for FPase activity with 130.45 FPU/mL was achieved for an 8-day culture containing 4.46 g/L of urea and 27.83 μL/L of Tween 80, which experimentally matched well with the predicted value from the model. The obtained crude cellulase was subsequently employed in the saccharification of the poplar wood, Populus tomentosa, which was pretreated with liquid hot water (LHW) at different temperatures. A maximum release of 25.15% of reducing sugars was observed after a 72-h enzymatic hydrolysis of the 180 °C-LHW-pretreated poplar wood, which is 1.72 times higher than that from untreated wood (14.66%), indicating that F. palustris has a potential to produce cellulase for woody biomass hydrolysis.

Following the long-term goal of substituting conventional power generation with cleaner energy will lead to an integration of a large share of small energy generation units imposing large problem sizes for coordination. The expected huge number of entities leads to a need for new techniques reducing the computational effort for coordination. Predictive scheduling is a frequent task in energy grid control. For a number of energy resources, schedules have to be found that fulfill several objectives at the same time. Considering day-ahead scenarios with 96-dimensional schedules imposes additional challenges to this already hard combinatorial problem. We explore the effects of reducing complexity by partitioning the data domain of the optimization problem for a sequential approach that integrates energy models for constraint handling directly into the optimization process. We explore the effects of different partitioning schemes and evaluate the trade-off between accuracy and effort with several simulation studies.

A framework is introduced for solving a sequence of slowly changing optimization problems, including those arising in regression and classification applications, using optimization algorithms such as stochastic gradient descent (SGD). The optimization problems change slowly in the sense that the minimizers change at either a fixed or bounded rate. A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples needed from the distributions underlying each problem in order to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. Experiments with synthetic and real data are used to confirm that this approach performs well., Comment: submitted to ICASSP 2016, extended version

We propose a new qualitative economic model based optimization approach to compose an optimal set of infrastructure service requests over a long-term period. The economic model is represented as a temporal CP-Net to capture the provider's dynamic business strategies in qualitative service provisions. The multidimensional qualitative preferences are indexed in a k-d tree to compute the preference ranking of a set of incoming requests. We propose a heuristic based sequential optimization process to select the most preferred composition without the knowledge of historical request patterns. Experimental results prove the feasibility of the proposed approach. Springer International Publishing Switzerland 2016. Scopus

This chapter presents the design optimization methods for electrical machines in terms of different optimization situations, including low- and high-dimensional, single- and multi- objectives and disciplines. Firstly, the traditional design optimization methods are briefly reviewed, and the challenges presented. Then, five new types of design optimization methods are presented to improve the optimization efficiency of electrical machines, particularly those complex structured permanent magnet machines, in terms of different optimization situations. They are (a) a sequential optimization method for design optimization of low-dimensional problems of electromagnetic devices including electrical machines, (b) a multi-objective sequential optimization method for engineering multi-objective problems, (c) a multi-level design optimization method (or sequential subspace optimization method) for high dimensional problems, (d) a multi-level genetic algorithm for high dimensional optimization problems as well, and (e) the multi-disciplinary design optimization method. Design examples with detailed experimental and optimization results are illustrated for each optimization method.

Reliability-based design optimization (RBDO) has been receiving increasing attention for achieving high safety and reliability in engi- neering design. Sequential optimization and reliability assessment (SORA), as one of the efficient single-loop methods, decouples an RBDO problem into sequential deterministic optimization and reliability analysis. An enhanced SORA (ESORA) method is proposed with the aim of further improving the computational efficiency for RBDO, considering both cases of constant and varying variances of random design inputs while keeping the single-loop framework. Vehicle side impact example is used to test and compare the efficiency of the proposed method with existing approaches.

PurposeThe purpose of this paper is to study joint inventory and pricing strategy for a continuous inventory review system. While dynamic pricing decisions are often studied in the literature along with inventory management, the authors' aim in this study is to obtain a single long‐run optimal price; also to gain insight about how to obtain the optimal price and inventory control variables simultaneously and then the benefits of joint optimization of the inventory and pricing decisions over the sequential optimization policy often followed in practice.Design/methodology/approachA general (R;Q) policy system with fixed cost of ordering is modelled and then the case where unsatisfied demand is lost is studied. General forms of both the additive and multiplicative demand models are used to obtain structural results.FindingsBy showing optimality conditions on the price and inventory decision variables, two algorithms on how to obtain optimal decision variables, one for additive and another for multiplicative demand‐price model are provided. Through extensive numerical analyses, the potential profit increases are reported if the price and inventory problem are solved simultaneously instead of sequentially. In addition, the sensitivities of optimal decision variables to system parameters are revealed.Practical implicationsAlthough there are several studies in the literature investigating emergency price change models, they use arbitrary exogenous prices menus. However, the value of a price change can be better appreciated if the long‐run price is optimal for the system.Originality/valueVery few researchers have investigated constant price and inventory optimization, and while there are several past studies demonstrating the benefits of dynamic pricing over a static one, there still are not many findings on the benefit of joint price and inventory optimization.

The coupling of finite element simulations to mathematical optimization techniques has contributed significantly to product improvements and cost reductions in the metal forming industries. The next challenge is to bridge the gap between deterministic optimization techniques and the industrial need for robustness. This paper introduces a generally applicable strategy for modeling and efficiently solving robust optimization problems based on time consuming simulations. Noise variables and their effect on the responses are taken into account explicitly. The robust optimization strategy consists of four main stages: modeling, sensitivity analysis, robust optimization and sequential robust optimization. Use is made of a metamodel-based optimization approach to couple the computationally expensive finite element simulations with the robust optimization procedure. The initial metamodel approximation will only serve to find a first estimate of the robust optimum. Sequential optimization steps are subsequently applied to efficiently increase the accuracy of the response prediction at regions of interest containing the optimal robust design. The applicability of the proposed robust optimization strategy is demonstrated by the sequential robust optimization of an analytical test function and an industrial V-bending process. For the industrial application, several production trial runs have been performed to investigate and validate the robustness of the production process. For both applications, it is shown that the robust optimization strategy accounts for the effect of different sources of uncertainty onto the process responses in a very efficient manner. Moreover, application of the methodology to the industrial V-bending process results in valuable process insights and an improved robust process design.

The paper considers the properties of feasible and optimal scheduling of jobs on one machine under constraints on the terms of the beginning and completion of jobs and on partial sequences of job performance. The established properties and the lower-bound estimates of the length of the optimal schedule are used to develop methods for the exact and approximate solutions of the formulated problem by sequential optimization algorithms. The proposed algorithms are illustrated by numerical examples and can be successfully applied to solve these problems in the absence of constraints.

A new implementation of the Nudged Elastic Band (NEB) optimization method is presented. This approach uses a global procedure that yields the whole reaction path, and thus it provides an alternative to the sequential optimization of the transition state and consequent calculation of the minimum energy path. Furthermore the algorithm is very useful when one is not sure if a saddle point exists, because it can be used to eliminate the possibility of a saddle point when one does not exist. Three different versions of the NEB algorithm have been implemented. The influences of various parameters and methodological choices on the performance of the method have been studied, and the quality of the results is assessed by comparison with the saddle point and minimum energy path calculations sequential method. Recommendations are made for algorithmic choices and default parameters.

This paper presents a two-steps optimization procedure of a polygeneration unit. The unit simultaneously provides power, heat, cooling and fresh water to a Spanish tourist resort (450 rooms). The first step consist on the synthesis and design of the polygeneration scheme: a “superstructure” was constructed to allow the selection of the appropriate choice and size of the plant components, from both economic and environmental considerations. At that first step, only monthly averaged requirements are considered. The second step includes hourly data and analysis as well as energy storage systems. A detailed modelling of pre-selected devices is then required to also fulfil economic and environmental constraints. As a result, a better performance is obtained compared to the first step. Thus, the two-steps procedure explained here permits the complete design and operation of a decentralized plant producing simultaneously energy (power, heat and cooling) but also desalted water (that is, trigeneration + desalination). Remarkable benefits for the analyzed case study are found: a Net Present Value of almost 300,000 €, a primary energy saving ratio of about 18% and more than 850 ton per year of avoided CO2 emissions.

When a human needs to generate a large force, they will try to apply an impulsive force with dynamic cooperation of the whole body. In this paper we first discuss impact dynamics of humanoid robots and then propose a way to generate impact motions for a humanoid robot to exert a large force while keeping a balance. In the impact motion generation, Sequential Quadratic Programming (SQP) is used to solve a non-linear programming problem in which an objective function and constraints may be non-linear functions of the motion parameters. Impact motions are generated using SQP so that the impact force is maximized while the angular momentum is minimized. Breaking wooden boards with a Karate chop is taken as a case study because it is a typical example of tasks that utilize impulsive force. A humanoid robot motion for the Karate chop is generated by the proposed method. In order to validate the designed motion, experiments are carried out using a small humanoid robot Fujitsu HOAP-2. The Karate-chop motion generated by the proposed method is compared with the motion designed by a human. The results of breaking the wooden boards experiments clearly show the effectiveness of the proposed method.

Sequential optimization strategy based on statistical experimental design including one-factor-at-a-time (OFAT) method was used to enhance the production of lipase by Candida cylindracea ATCC 14830 using palm oil mill effluent as a basal medium in shake flask cultures. The two-level Plackett–Burman (PB) design was implemented to screen the medium components that significantly influence the production. Following the OFAT method, three significant components influencing lipase production were identified as peptone, Tween-80 and inoculum. The optimum values of these three components were determined by response surface methodology (RSM) based on the face-centered central composite design (FCCCD). The validity of the model developed was verified, and the optimum medium containing 0.45% (w/v) peptone, 0.65% (v/v) Tween-80 and 2.2% (v/v) inoculum led to amaximumlipase production of 20.26 U/ml, which was 5.19-fold higher than the unoptimized medium. The analysis of variance indicated that the established model was significant (P < 0.05). This study innovatively developed a fermentation medium utilizing renewable palm oil mill effluent (POME) to produce lipase at considerable level.

This paper is devoted to the consideration of an algorithm for sequential optimization of paths in directed graphs relative to di_erent cost functions. The considered algorithm is based on an extension of dynamic programming which allows to represent the initial set of paths and the set of optimal paths after each application of optimization procedure in the form of a directed acyclic graph.

Several medium formulations were screened for the production of a thermostable and organic solvent tolerant lipase by a recombinant Escherichia coli BL21. The highest lipase production (28.9 ± 4.1 IU/mL) was obtained in Luria Bertani medium with the addition of 1% (w/v) glucose. The medium formulation and fermentation conditions were then subjected to sequential optimization. Using a Plackett-Burman design, glucose, NaCl, temperature and induction time were found to be the most significant variables affecting lipase production, and these were then optimized using response surface methodology (RSM). The large value of R2 (0.979) showed that the quadratic model used for the prediction is highly significant. The optimum levels of these four significant variables (glucose, NaCl, temperature and induction time) as predicted by RSM were 32.4 g/L, 5 g/L, 31.7°C and 2.1 h, respectively. The amount of lipase activity (50.2 ± 4.5 IU/mL) produced under these optimal conditions fitted well to the value (48.9 IU/mL) predicted by RSM. Production of lipase in optimized fermentation was about 2.5-fold higher than in non-optimized fermentation.

This paper studies a buy-back contract between a buyer and a backup supplier when the buyer’s main supplier experiences disruptions. The expected profit functions and the optimal decisions of the contract players are derived through a sequential optimization process. The common properties of the contract as well as the differences under the demand uncertainty and the main supplier’s recurrent supply uncertainty are explored through comparative studies and numerical examples. The study contributes to the literature by providing a better understanding of the impacts of demand and supply uncertainties and by shedding insights on the value of a backup supply.

Products such as IC chips, computers and mobile phones can become out of date due to technological innovation. However, these outdated products can be remanufactured and resold to market. In this paper, a closed-loop supply chain inventory system with multi-manufacturing cycles and multi-remanufacturing cycles is analyzed using sequential and global optimization. In the case of sequential optimization, the decision is made initially by the down-stream player, then by the up-stream player. In the case of global optimization, the decision is made jointly by all the players. The supply chain includes a manufacturer, a retailer, a collector and a material supplier. This paper considers out of date as a type of deterioration and generalizes the following three cases: (i) single manufacturing cycle and single remanufacturing cycle, (ii) single manufacturing cycle and multi-remanufacturing cycles, and (iii) multi-manufacturing cycles and single remanufacturing cycle. The analytical results of this study show that a significant increase in the joint profit will result when the integrated policy is adopted.

The existing work on via allocation in 3D ICs ignores power/ground vias’ ability to simultaneously reduce voltage bounce and remove heat. This paper develops the first in-depth study on the allocation of power/ground vias in 3D ICs with simultaneous consideration of power and thermal integrity. By identifying principal ports and parameters, effective electrical and thermal macromodels are employed to provide dynamic power and thermal integrity as well as sensitivity with respect to via density. With the use of sensitivity, an efficient via allocation simultaneously driven by power and thermal integrity is developed. Experiments show that compared to sequential power and thermal optimization using static integrity, sequential optimization using the dynamic integrity reduces non-signal vias by up to 18%, and simultaneous optimization using dynamic integrity further reduces non-signal vias by up to 45.5%. Accepted version

The paper provides comparison of three different approaches to on-line tuning of generalized adaptive notch filters (GANFs) - the algorithms used for identification/tracking of quasi-periodically varying dynamic systems. Tuning is needed to adjust adaptation gains, which control tracking performance of GANF algorithms, to the unknown and/or time time-varying rate of system nonstationarity. Two out of three compared approaches are classical solutions - the first one incorporates sequential optimization of adaptation gains while the second one is based on the concept of parallel estimation. The main contribution of the paper is that it suggests the third way - it shows that the best results can be achieved when both approaches mentioned above are combined in a judicious way. Such joint sequential/parallel optimization preserves advantages of both treatments: adaptiveness (sequential approach) and robustness to abrupt changes (parallel approach). Additionally the paper shows how, using the concept of surrogate outputs, one can extend the proposed single-frequency algorithm to the multiple frequencies case, without falling into the complexity trap known as the ''curse of dimensionality''.

In this paper, the authors focus on examining the coordination mechanisms for a two-stage supply chain comprising one supplier and one retailer. The authors consider such a channel relationship that the transaction quantity between the two members is sensitive to the supplier’s inventory level and that the supplier’s unit inventory holding cost has a linear stepwise structure. They devise a coordinated revenue-sharing contract with bargaining so that each party’s respective profit is better than that resulted from the simple sequential optimization mechanism. The key contract parameters, namely the supplier’s inventory level and the retailer’s revenue-sharing fraction, are obtained and analyzed. Numerical illustrations of the contracts are given and shed lights on how the supply chain should coordinate in order to gain better performance.

In this paper, two approaches of production-inventory control are considered: the future requirements-based approach and the inventory consumption-based approach. We focus on a single-stage and single-item inventory system under non-stationary demand, information about which is provided by means of forecasts. Forecasts and forecast uncertainties are assumed to be exogenous data known in advance at each period over a forecasting horizon. Three forecast-based dynamic inventory control policies are analysed. These dynamic policies are extensions of the standard static re-order point and order-up-to-level policies that have been developed under the stationary demand assumption. The policies’ parameters are derived by using a sequential optimization procedure. A numerical investigation is conducted based on theoretical generated data; the results demonstrate the benefit of using the dynamic policies when compared with the static ones. Connections between the analysed policies are also highlighted.

In this paper, mathematical programming coupled with superstructure is utilized to optimize regeneration recycling water networks at the stage of grass-roots design. The optimization is virtually a multi-objective problem with several important parameters on regeneration recycling involved. Considering the relative importance of these parameters, sequential optimization is adopted to solve this multi-objective problem. Freshwater consumption, regenerated water flowrate and contaminant regeneration load, are in turn minimized, which correspond to three mathematical models. By solving these models step by step, a regeneration recycling water network can be constructed, which is economically favorable in terms of qualitative analysis. The complexity of water network configuration is also taken into account by minimizing the number of interconnections among processes. Additionally several examples are used to demonstrate the application of the proposed methodology. In comparison with the available technologies, the proposed methodology excels in simplicity and adaptability for grass-roots design by exempting from complicated and variable economic parameters.

Microbial fermentations involving two or more kinds of competing cells and operating under realistic conditions are difficult to monitor, model and optimize by model-based methods. They deviate from ideal behavior in two significant aspects: incomplete dispersion in the broth and the influx of disturbances. The approach here has been to optimize the filtered noise and dispersion on-line through neural networks. This method has been applied to the fed-batch production of streptokinase (SK). The culture has two kinds of cells — active (or productive) and inactive — and their growth is inhibited by the substrate and the primary metabolite (lactic acid). Using simulated data, the fermentation was optimized by a system of three neural networks, updated continually during successive time intervals. Such sequential optimization with dynamic filtering of inflow noise generated better cell growth and SK activity than static optimization and even an ideal fermentation. Les fermentations microbiennes faisant intervenir deux ou plusieurs sortes de cellules en competition et se deroulant dans des conditions reelles, sont difficiles a surveiller, a modeliser et a optimiser par des methodes basees sur des modeles. De telles fermentations s'ecartent du comportement ideal dans deux voies importantes : la dispersion incomplete dans le bouillon et la venue de perturbations. Notre approche consiste ici a optimiser le bruit filtre et la dispersion en continu par des reseaux neuronaux. Cette methode a ete appliquee a la production a alimentation discontinue de streptokinase (SK). La culture comporte deux sortes de cellules — actives (ou productives) ou inactives — et leur croissance est inhibee par le substrat et la metabolite primaire (acide lactique). A l'aide de donnees simulees, la fermentation a ete optimisee par un systeme de trois reseaux neuronaux, qui ont ete mis a jour continuellement a des intervalles de temps successifs. Une telle optimisation sequentielle avec filtrage dynamique du bruit genere une meilleure croissance des cellules et activite du SK que l'optimisation statique et meme la fermentation ideale.

A stochastic process model based on an initial space-filling design is fitted to a sequential optimization approach for complex computer models (Jones et al., 1998) and sequentially refined by the expected improvement criterion. This criterion balances..

With higher reliability and safety requirements, reliability-based design has been increasingly applied in multidisciplinary design optimization (MDO). A direct integration of reliability-based design and MDO may present tremendous implementation and numerical difficulties. In this work, a methodology of sequential optimization and reliability assessment for MDO is proposed to improve the efficiency of reliability-based MDO. The central idea is to decouple the reliability analysis from MDO with sequential cycles of reliability analysis and deterministic MDO. The reliability analysis is based on the first-order reliability method (FORM). In the proposed method, the reliability analysis and the deterministic MDO use two MDO strategies, the multidisciplinary feasible approach and the individual disciplinary feasible approach. The effectiveness of the proposed method is illustrated with two example problems.

A sequential optimization strategy with the aid of statistical design of experiments was used to enhance the lipase (triacylglycerol acylhydrolases, EC 3.1.1.3) production by Bacillus sphaericus in submerged cultivation. A Plackett–Burman experimental design was used to evaluate the twelve medium components. Various vegetable oil inducers were tested for lipase production in the second step and the third step was to identify the optimal values of the significant medium components with sesame oil as the inducer using response surface methodology. A predictive model of the combined effects of the independent variables using response surface methodology and an artificial neural network was proposed. Unstructured kinetic models, a logistic model and a Luedeking–Piret model, were used to describe the cell mass and lipase production respectively. The significant variables affecting lipase production were found to be glucose, olive oil, peptone, NaCl and MnSO4.H2O. Sesame oil was found to be the best inducer for lipase production by Bacillus sphaericus. The maximum lipase activity of 4.45 U mL−1, which was 1.5 times the maximum activity obtained in the Plackett–Burman experimental trials, was obtained at the optimum combination of medium constituents containing 12.695 g L−1 glucose, 13.161 mL L−1 sesame oil, 9.947 g L−1 peptone, 3.25 g L−1 NaCl, 0.5917 g L−1 MnSO4.H2O and other insignificant components at the fixed level. The statistical design of experiments offers an efficient methodology to identify the significant variables and to optimize the factors with a minimum number of experiments for lipase production by Bacillus sphaericus. Copyright © 2007 Society of Chemical Industry

In this paper, graphical method is employed to optimize single-contaminant regeneration reuse water systems. On the concentration-mass load diagram, three categories of water-using systems with regeneration reuse are analyzed in terms of the geometric features of limiting composite curve. Total regeneration and partial regeneration are identified first. Then based on sequential optimization and at a specified post-regeneration concentration, the optimal water supply lines for regeneration reuse systems can be constructed. The optimal water supply line corresponds to minimum freshwater consumption, minimum regenerated water flowrate and minimum contaminant regeneration load. Formulas for calculating these targets are summarized and interactions of these parameters are discussed. The concepts of limiting points for regeneration reuse, which are the counterpart of pinch for direct reuse, are proposed to indicate the bottlenecks of a water system with regeneration reuse. Different locations of the limiting points for different systems underlie that the optimal regeneration concentration can be greater than, equal to or smaller than the pinch concentration of the system.

Self-optimization of chemical reactions enables faster optimization of reaction conditions or discovery of molecules with required target properties. The technology of self-optimization has been expanded to discovery of new process recipes for manufacture of complex functional products. A new machine-learning algorithm, specifically designed for multiobjective target optimization with an explicit aim to minimize the number of “expensive” experiments, guides the discovery process. This “black-box” approach assumes no a priori knowledge of chemical system and hence particularly suited to rapid development of processes to manufacture specialist low-volume, high-value products. The approach was demonstrated in discovery of process recipes for a semibatch emulsion copolymerization, targeting a specific particle size and full conversion.

With the rapid development of living standard, the demand for housing is rising. Prefabricated affordable housing (PAH) is an important way to solve the problem. In this paper, the factors of sequential PAH demand and the future development trend in Shenyang is analyzed by regression equation. By mathematical programming theory, multi-period dynamic sequential optimization model for PAH production scale is established. The minimum difference between demand and supply is the objective function. It gives full consideration of the biggest supply capacity of PAH, the total investment in fixed assets restrictions, government-mandated minimum land area which must be used for PAH. For government departments, the model can provide quantitative decision-making optimization method for multi-period dynamic sequential PAH production scale.