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This paper presents a new step-wise sequential solution strategy for the synthesis of heat integrated water networks (HIWNs) comprising of two mathematical models. The first model minimizes water and energy costs. The optimal freshwater and water flow rates within the HIWN are determined from this model. Using these optimal flow rates, different configurations of HIWN are evaluated for the purpose of heat integration. The second model is the stage-wise heat exchanger network (Yee and Grossmann, 1990), which is solved for each of the evaluated network configuration in a sequential manner. Using this proposed strategy, a set of locally optimal HIWNs are produced and the best one is chosen based on the minimum total annual cost (TAC). The proposed sequential strategy is applied to small and medium sized examples in the literature. The results show that the proposed new sequential solution strategy can be successfully applied to small and medium sized HIWN problems. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model for the design of a non-cooperative supply chain where transportation activities are provided by logistics companies. We consider a four-layer network comprised of manufacturers, retailers, customers, and logistics service providers (LSPs). In this problem, manufacturers, retailers, and LSPs do not collaborate or engage in any type of bargaining strategy among one other. Instead, they compete to supply products to customers at demand markets, while each agent seeks to maximize his own profit. LSPs compete among themselves to provide logistics (transportation/warehousing) services to manufacturers. It is considered that manufacturers, LSPs, and retailers collaborate to maximize services. Normally this problem cannot be modeled as an optimization problem, so we use a variational inequality approach to formulate it. To the best of our knowledge, this important problem, which helps evaluate options and make decisions when logistics activities are provided by LSPs, has not been modeled in the literature and this constitutes our main contribution. The model determines the optimal level of production for each manufacturer, the flow of products between manufacturers and retailers, the flow of products to be handled by each logistics service provider and the flow of products between retailers and demand markets in a non-cooperative environment. Numerous experiments are presented and adapted from test examples that are available in the literature, while results and important findings are discussed. [ABSTRACT FROM AUTHOR]

This article introduces an algorithm that determines the thermodynamic conditions behind incident and reflected shocks in aerosol-laden flows. Importantly, the algorithm accounts for the effects of droplet evaporation on post-shock properties. Additionally, this article describes an algorithm for resolving the effects of multiple-component-fuel droplets. This article presents the solution methodology and compares the results to those of another similar shock calculator. It also provides examples to show the impact of droplets on post-shock properties and the impact that multi-component fuel droplets have on shock experimental parameters. Finally, this paper presents a detailed uncertainty analysis of this algorithm's calculations given typical experimental uncertainties. [ABSTRACT FROM AUTHOR]

For using the surface electromyography (sEMG) in various applications, the process consists of three parts: an onset time detection for detecting the first point of movement signals, a feature extraction for extracting the signal attribution, and a feature classification for classifying the sEMG signals. The first and the most significant part that influences the accuracy of other parts is the onset time detection, particularly for automatic systems. In this paper, an automatic and simple algorithm for the real-time onset time detection is presented. There are two main processes in the proposed algorithm: a smoothing process for reducing the noise of the measured sEMG signals and an automatic threshold calculation process for determining the onset time. The results from the algorithm analysis demonstrate the performance of the proposed algorithm to detect the sEMG onset time in various smoothing-threshold equations. Our findings reveal that using a simple square integral as the smoothing-threshold equation with the given sEMG signals gives the best performance for the onset time detection. Additionally, our proposed algorithm is also implemented on a real hardware platform, namely NI myRIO. Using the real-time simulated sEMG data, the experimental results guarantee that the proposed algorithm can properly detect the onset time in the real-time manner. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal arrangement and design of a dual pressure heat recovery steam generator coupled to two steam turbines. A superstructure that embeds various alternative configurations is optimized considering the following two single objective functions: (a) the maximization of the total net power generation for a given total heat transfer area and (b) the minimization of the total heat transfer area for a given total net power. The optimal number of heat exchangers and pumps and how they should be connected are the discrete decisions. The dimensions and operating conditions are the continuous decisions. A discrete and continuous mathematical model is developed and logical propositions are used for discrete decisions. The results are compared with a reference case reported by other authors. The results indicated that the optimization of the proposed superstructure allowed to find a more efficient HRSG configuration. The obtained configurations differ from the configuration of the reference case in how the heat exchangers and pumps are connected. A considerable increase in about 8% of the total net power generation in (a) and a significant reduction in about 24% of the total heat transfer area in (b) are achieved when compared to the reference case. [ABSTRACT FROM AUTHOR]

Over the last few decades, research on resource constrained project scheduling has focused on the development of mathematical programming based approaches for the generation of a nominal schedule under a deterministic environment. During the implementation phase, however, the nominal schedule may need to be revised when one or more resources are disrupted for a length of time. In this paper, we formulate two discrete time based models to deal with two different disruption scenarios for multi-mode resource constrained problems. We propose a reactive re-scheduling procedure for a single, as well as a series of disruptions, without having any disruption information in advance. To test the proposed approaches, sets of ten, twenty and thirty-activity multi-mode test instances from Project Scheduling Library (PSLIB) were used after introducing randomly generated disruption events. The experimental studies were also carried out to determine the effect of different factors related to the disruption recovery process. [ABSTRACT FROM AUTHOR]

In this paper, an online optimal learning algorithm based on adaptive dynamic programming (ADP) approach is designed to solve the finite-horizon optimal control for multi-player nonzero-sum games with partially unknown dynamics and constrained control inputs. Firstly, it is proved that the online policy iteration (PI) algorithm is equivalent to Newton׳s iteration. Secondly, the single neural networks (NNs) with time-varying activation functions for each player are used to approximate the time-varying solution to the coupled Hamilton–Jacobi–Bellman (HJB) equations in an online and forward-in-time manner. Control constraints are handled through non-quadratic functions. The convergence of NN-based online optimal learning algorithm for the multi-player nonzero-sum games is also proved. Finally, a simulation example illustrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Water and energy are key resources in the process industry. The increasing pressure on freshwater and energy resources coupled with stringent environmental regulations on effluent discharge limits have called for innovative designs for sustainable use of water and energy. This can be achieved through process integration techniques that are environmentally benign and economically feasible. This work proposes a robust water network superstructure optimization approach for the synthesis of a multiregenerator network for simultaneous water and energy minimization. Two types of membrane regenerators are considered for this work, namely, electrodialysis and reverse osmosis. In each of the membrane regenerators, a detailed design model is developed and incorporated into the water network model. The detailed model presented in this work is compared to the more common "blackbox" model, which uses linear expressions to represent costs of regeneration units. The results show that the "blackbox" model gives inaccurate cost representation as compared to the detailed model and also the blackbox model framework does not give insights on optimal design of the regeneration units for minimum energy usage. The presence of continuous and integer variables, as well as nonlinear constraints renders the problem mixed integer nonlinear programming (MINLP). The developed model is applied to a pulp and paper case study to demonstrate its applicability, assuming a single contaminant scenario. The model was solved in GAMS using a solver BARON, and the results indicate a 43.7% freshwater reduction, 50.9% decrease in wastewater generation, and 46% savings in total annualized cost. [ABSTRACT FROM AUTHOR]

Robust optimization has attracted great attention in recent decade owing to its ability to cope with risk and uncertainty in real-life optimization problems. Recently, the scope of robust optimization is extended into fuzzy mathematical programming by proposing robust possibilistic programming approach. As a complementary for robust possibilistic programming approach, this paper proposes different novel robust flexible programming and robust mixed possibilistic-flexible programming models. The advantages and strengths of the proposed models are described and a real-life problem is employed to show the efficiency and practicability of the propounded models against the traditional fuzzy programming approach. [ABSTRACT FROM AUTHOR]

A multi-depot Chinese postman problem (MDCP) arises from a network (e.g. cityplan) in which several depots are located wherefrom edges (e.g. streets) have to be served. Since costs are involved with each visit to an edge, the objective is to find a minimum cost tour in the network that visits all edges of the network. Such a minimum cost tour consists of a collection of subtours such that the subtours originate from different depots, and each subtour starts and ends at the same depot. This typical OR problem turns into a multi decision maker problem if agents are assigned to the streets. In this new setting the cost of a minimum cost tour that visits all edges have to be paid by the agents. However, now each group of agents (coalition) has the opportunity to find its own minimum cost tour, i.e. a minimum cost tour that only visits the edges owned by the group of agents. Therefore, the main objective is to find allocations of the cost of a minimum tour that visits all agents in such a way that no coalition has higher costs than the costs incurred by its own minimum tour. We will use cooperative game theory to investigate whether these so-called core allocations exist. Therefore, we consider a cooperative Chinese postman (CP) game that is induced by an MDCP by associating every edge of the network with a different agent. In this paper, we characterize classes of networks that ensure the existence of core allocations, the so-called CP balanced graphs, and the existence of specific core allocations, the so-called CP submodular graphs. [ABSTRACT FROM AUTHOR]

This paper focuses on a multidemand multisource order quantity allocation problem with multiple transportation alternatives. To solve this problem, a bilevel multiobjective programming model under a mixed uncertain environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the purchaser aims to allocate order quantity to multiple suppliers for each demand node with the consideration of three objectives: total purchase cost minimization, total delay risk minimization, and total defect risk minimization. On the lower level, each supplier attempts to optimize the transportation alternatives with total transportation and penalty costs minimization as the objective. In contrast to prior studies, considering the information asymmetry in the bilevel decision, random and fuzzy random variables are used to model uncertain parameters of the construction company and the suppliers. To solve the bilevel model, a solution method based on Kuhn-Tucker conditions, sectional genetic algorithm, and fuzzy random simulation is proposed. Finally, the applicability of the proposed model and algorithm is evaluated through a practical case from a large scale construction project. The results show that the proposed model and algorithm are efficient in dealing with practical order quantity allocation problems. [ABSTRACT FROM AUTHOR]

In this paper, a scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system is proposed and formulated. Considering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Earliness results in AGVs waiting and tardiness causes temporary part storages in the shop floor. Therefore, we propose a mathematical program to minimize the penalized earliness and tardiness. Since the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example and compared with several methods in the current literature. [ABSTRACT FROM AUTHOR]

This paper presents an approach for weighting indices in the comprehensive evaluation. In accordance with the principle that the entire difference of various evaluation objects is to be maximally differentiated, an adjusted weighting coefficient is introduced. Based on the idea of maximizing the difference between the adjusted evaluation scores of each evaluation object and their mean, an objective programming model is established with more obvious differentiation between evaluation scores and the combined weight coefficient determined, thereby avoiding contradictory and less distinguishable evaluation results of single weighting methods. The proposed model is demonstrated using 2,044 observations. The empirical results show that the combined weighting method has the least misjudgment probability, as well as the least error probability, when compared with four single weighting methods, namely, G1, G2, variation coefficient, and deviation methods. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of infeasibility of systems defined by convex inequality constraints, where some or all of the variables are integer valued. In particular, we provide a polynomial time algorithm to identify a set of all constraints which may affect a feasibility status of the system after some perturbation of the right-hand sides. We establish several properties of the irreducible infeasible sets and infeasibility sets in the systems with integer variables, proving in particular that all irreducible infeasible sets and infeasibility sets are subsets of the set of constraints critical to feasibility. Furthermore, the well-known Bohnenblust-Karlin-Shapley Theorem, which requires that a system of convex inequality constraints must be defined over a compact convex set, is generalized to convex systems without the assumption on compactness of the convex region. Extension of the latter result to convex systems defined over the set of integers is also provided. [ABSTRACT FROM AUTHOR]

Statistical model checking of unbounded time properties is challenging, because it requires an algorithm to estimate the measure of paths satisfying an unbounded until property from samples of finite length paths. In this paper, we survey all proposed algorithms for this problem, and critically evaluate them. [ABSTRACT FROM AUTHOR]

Modeling of transportation systems must invariably balance behavioral richness and computational tractability. In this paper, we develop a transportation equilibrium model and an algorithm for the simultaneous prediction of trip generation, trip distribution, modal split, and trip assignment on large-scale networks. The model achieves a practical compromise between behavioral and computational aspects of modeling the problem. It is formulated as an equivalent convex optimization problem, yet it is behaviorally richer than other models that can be cast as equivalent convex programs. Although the model is not as behaviorally rich as the most general equilibrium models, it has computational advantages. The applications reported in this paper of the model to real systems, i.e., the intercity transport network of Egypt and the urban transportation network of Austin, Texas, illustrate the computational attractiveness of the approach. These results indicate that equivalent optimization formulations are not as restrictive as previously thought, and hence, the equivalent convex programming approach for modeling and predicting equilibrium on transportation systems remains a viable and fruitful avenue for future consideration. [ABSTRACT FROM AUTHOR]

This paper points out the abstract similarities between problems arising in programming, discrete systems analysis in engineering, and network flow problems in operations research. The highly developed techniques of analyzing discrete systems of two terminal elements in electrical engineering become applicable to analyzing the complexity and execution time of computer programs. Similarly, the concept of the cost-oriented flows in network flow problems has significance in determining execution times in program segments. The abstract similarities explored in this paper are predicated on the concept of a flowgraph, which is a dual graph of a more traditional view of a computer program as a control graph. This slightly different view- point of computer programs results in the abstract similarity to discrete systems which suggests many new ideas and permits the use of results from the well-developed fields of electrical engineering and operations research. [ABSTRACT FROM AUTHOR]

Building on the work of Noam Chomsky (1963), this paper presents a hierarchy of grammars and associated computational automata in order to inform social theory construction and method. A detailed exposition of linguistic forms within the grammar hierarchy reveals clear analogues with common social scientific paradigms. Two of these paradigms (which are termed structural and process approaches) are already being widely exploited by formal methodological techniques. A third paradigm, which is rooted in a tradition of interpretive sociology, has been more resistant to formalization. Using arguments from theoretical computer science, the paper suggests that existing quantitative methodologies can be extended to accommodate qualitative arguments which subsume empirical domains as diverse as natural language and structurational phenomena. [ABSTRACT FROM AUTHOR]

This paper is written with three objectives in mind. First, to point out that the problem of grouping, where a larger number of elements n are combined into m mutually exclusive groups (m ~n) should be recognized as a problem in Integer Programming, and that such recognition can help us in avoiding complete enumeration of stages in grouping from n to n - 1 ... to m, and of alternative possibilities in each stage. Second, to formulate mathematically some simple versions of the relevant Integer Programming Problem, so that the available computer codes can solve it. When the grouping attempts to minimize the within groups sums of squares, the so-called string property is proved to be necessary for the minimum (Lemma 1). It is shown that the string property can be exploited to write the non-linear within group sums of squares as a linear function (Lemma 2). An attempt is made to generalize the string property for the higher dimensional case. Third, to give some numerical examples to clarify the mathematical models and to illustrate the advantages of Integer Programming formulation by comparing with the numerical examples appearing in the literature [8], [13 ]. Zero-one programming where the integer variables can take values of zero or one only appears to be most efficient from the limited experience of the author. The paper is divided into three sections which closely correspond to the three objectives noted above. [ABSTRACT FROM AUTHOR]

In this paper, let H be a real Hilbert space and let C be a nonempty, closed, and convex subset of H. We assume that $(A+B)^{-1}0\cap U\neq\emptyset$, where $A:C\rightarrow H$ is an α-inverse-strongly monotone mapping, $B:H\rightarrow H$ is a maximal monotone operator, the domain of B is included in C. Let U denote the solution set of the constrained convex minimization problem. Based on the viscosity approximation method, we use a gradient-projection algorithm to propose composite iterative algorithms and find a common solution of the problems which we studied. Then we regularize it to find a unique solution by gradient-projection algorithm. The point $q\in (A+B)^{-1}0\cap U$ which we find solves the variational inequality $\langle(I-f)q, p-q\rangle\geq0$, $\forall p\in(A+B)^{-1}0\cap U$. Under suitable conditions, the constrained convex minimization problem can be transformed into the split feasibility problem. Zeros of the sum of two operators can be transformed into the variational inequality problem and the fixed point problem. Furthermore, new strong convergence theorems and applications are obtained in Hilbert spaces, which are useful in nonlinear analysis and optimization. [ABSTRACT FROM AUTHOR]

This study describes a single phase algorithm for the fixed destination multi-depot multiple traveling salesman problem with multiple tours (mdmTSP). This problem widely appears in the field of logistics mostly in connection with maintenance networks. The general model of the technical inspection and maintenance systems is shown in the first part, where the solution of this problem is an important question. A mathematical model of the system’s object expert assignment is proposed with the constraints typical of the system, like experts’ capacity minimum and maximum and constraints on maximum and daily tours of the experts. In the second part, the developed evolutionary programming algorithm is described which solves the assignment, regarding the constraints introducing penalty functions in the algorithm. In the last part of the paper, the convergence of the algorithm and the run times and some examination of the parallelization are presented. [ABSTRACT FROM AUTHOR]

The definition of weighted entropy allows to calculate the entropy of the mixture of measures. In this paper, we investigate the problem of equivalent definition of the general entropy function in the weighted form. We show that under a reasonable condition, which is satisfied by the well-known Shannon, Rényi and Tsallis entropies, every entropy function can be defined equivalently in the weighted way. As a corollary, we show how to use the weighted form to compute Tsallis entropy of the mixture of measures. [ABSTRACT FROM AUTHOR]

This paper presents a new mathematical programming model for the retrofit of heat exchanger networks (HENs), wherein the pressure recovery of process streams is conducted to enhance heat integration. Particularly applied to cryogenic processes, HENs retrofit with combined heat and work integration is mainly aimed at reducing the use of expensive cold services. The proposed multi-stage superstructure allows the increment of the existing heat transfer area, as well as the use of new equipment for both heat exchange and pressure manipulation. The pressure recovery of streams is carried out simultaneously with the HEN design, such that the process conditions (streams pressure and temperature) are variables of optimization. The mathematical model is formulated using generalized disjunctive programming (GDP) and is optimized via mixed-integer nonlinear programming (MINLP), through the minimization of the retrofit total annualized cost, considering the turbine and compressor coupling with a helper motor. Three case studies are performed to assess the accuracy of the developed approach, including a real industrial example related to liquefied natural gas (LNG) production. The results show that the pressure recovery of streams is efficient for energy savings and, consequently, for decreasing the HEN retrofit total cost especially in sub-ambient processes. [ABSTRACT FROM AUTHOR]

This paper puts forward an integrated model to support the process of classifying projects and project managers (PM) using a multiple criteria decision aid (MCDA) approach and allocating projects according to organizational restrictions through mathematical programming. The model was formulated after reviewing the literature and being guided by the findings of searches; we used a MCDA method for project and PM classification (first stage) and mathematical programming for project allocation (second stage). A practical application of the proposed model was implemented at a Brazilian electric energy company. The results demonstrated that it was possible to classify projects and project managers into definable categories, thereby enabling the process of project allocation to be undertaken more effectively. The project allocation process can be conducted in a systematic and more efficient way. The proposed model can support an organization by allocating its most critical projects to its best qualified and experienced professionals. [ABSTRACT FROM AUTHOR]

The paper introduces a cutting-plane matheuristic for the design-balanced capacitated multicommodity network design problem, one of the premier formulations for the service network design problem with asset management concerns increasingly faced by carriers within their tactical planning processes. The matheuristic combines a cutting-plane procedure efficiently computing tight lower bounds and a variable-fixing procedure feeding a MIP solver. Learning mechanisms embedded into the cutting-plane procedure provide the means to identify promising variables and thus both reduce the dimension of the problem instance, making it addressable by a MIP solver, and guide the latter toward promising solution spaces. Extensive computational experiments show the efficiency of the proposed procedures in obtaining high-quality solutions, outperforming the current best methods from the literature. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-step bloodmobile collection planning framework. The first step is the annual planning to determine weeks of collection at each mobile site in order to ensure regional self-sufficiency of blood supply. The second step is the detailed weekly planning to determine days of collections at each mobile site and to form corresponding transfusion teams. Only key resource requirements are considered for annual planning while detailed resource requirements and transportation times are considered for weekly planning. Two Mixed Integer Programming models are proposed for annual planning by assuming fixed or variable mobile collection frequencies. A new donation forecast model is proposed based on population demographics, donor generosity, and donor availability. A new concept of bloodmobile collection configurations is proposed for compact and efficient mathematical modeling of weekly planning in order to minimize the total working time. Field data from the French Blood Service (EFS) in the Auvergne-Loire Region are used to design numerical experiments and to assess the efficiency of the proposed models. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to give an uncertainty distribution of the least cost of shipment of a commodity through a network with uncertain capacities. Uncertainty theory is used to deal with uncertain capacities, and an $$\alpha $$ -minimum cost flow problem model is proposed. After defining the $$\alpha $$ -minimum cost flow, the properties of the model are analyzed, and then an algorithm for uncertain minimum cost flow problem is developed. The algorithm can be considered as a general solution to the uncertain minimum cost flow problem. To demonstrate the efficiency of the proposed algorithm, a numerical example is illustrated. [ABSTRACT FROM AUTHOR]

Latent trait models for responses and response times in tests often lack a substantial interpretation in terms of a cognitive process model. This is a drawback because process models are helpful in clarifying the meaning of the latent traits. In the present paper, a new model for responses and response times in tests is presented. The model is based on the proportional hazards model for competing risks. Two processes are assumed, one reflecting the increase in knowledge and the second the tendency to discontinue. The processes can be characterized by two proportional hazards models whose baseline hazard functions correspond to the temporary increase in knowledge and discouragement. The model can be calibrated with marginal maximum likelihood estimation and an application of the ECM algorithm. Two tests of model fit are proposed. The amenability of the proposed approaches to model calibration and model evaluation is demonstrated in a simulation study. Finally, the model is used for the analysis of two empirical data sets. [ABSTRACT FROM AUTHOR]

In this paper, we present a fuzzy semi-infinite optimization problem. Moreover, we will deduce the Fritz-John and Kuhn-Tucker necessary conditions of this problem. Finally, a numerical example is given to illustrate the results. [ABSTRACT FROM AUTHOR]

Using the Multivariate Decomposition Method (MDM), we develop an efficient algorithm for approximating the ∞-variate integral for a class of functions f that are once differentiable with respect to each variable. MDM requires efficient algorithms for d-variate versions of the problem. Such algorithms are provided by Smolyak's construction which is based on efficient algorithms for the univariate integration Detailed analysis and development of (nearly) optimal quadratures for I( f) is the main contribution of the current paper. [ABSTRACT FROM AUTHOR]

Safety and performance are important factors in the design and operation of chemical plants. This paper describes the formulation of a mixed integer nonlinear programming model for the optimization of plant layout with safety considerations. The model considers a quantitative risk analysis to take safety into account, and a bowtie analysis is used to identify possible catastrophic outcomes. These effects are quantified through consequence analyses and probit models. The model allows the location of facilities at any available point, an advantage over grid-based models. Two case studies are solved to show the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

In the decades following World War II, the Cowles Commission for Research in Economics came to represent new technical standards that informed most advances in economic theory. The public emergence of this community was manifest at a conference held in June 1949 titled Activity Analysis of Production and Allocation. New ideas in optimization theory, linked to linear programming, developed from the conference's papers. The authors’ history of this event situates the Cowles Commission among the institutions of postwar science in-between National Laboratories and the supreme discipline of Cold War academia, mathematics. Although the conference created the conditions under which economics, as a discipline, would transform itself, the participants themselves had little concern for the intellectual battles that had defined prewar university economics departments. The authors argue that the conference signaled the birth of a new intellectual culture in economic science based on shared scientific norms and techniques un-interrogated by conflicting notions of the meaning of either science or economics. [ABSTRACT FROM PUBLISHER]

The development of mathematical and optimization models for reverse supply network design has concerned considerable interest over the past decades. However, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of these developed tools. The aim of this paper is to propose a new mathematical programming model for recycling network design in the iron and steel industry. The considered recycling network is multi-echelon, multi-facility, multi-product, and multi-supplier. Moreover, another objective of this research is to introduce an interval-stochastic robust optimization methodology to deal with various uncertainties in the proposed model. Computational experiments are provided to demonstrate the applicability of the proposed model in recycling network design. [ABSTRACT FROM AUTHOR]

Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a generalized fusion model and parallel computing techniques. The developed modules, including eight typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. The experiments demonstrate that if parallel strategies are adopted, in the best cases the fastest times required to finish the entire fusion operation (including disk input/output (I/O) and computation) are close to the time required to directly read and write the images without any computation. The parallel processing implemented on a workstation with two CPUs is able to perform these operations up to 13.9 times faster than serial execution. An algorithm in the framework is 32.6 times faster than the corresponding version in the ERDAS IMAGINE software. Additionally, no obvious differences in the fusion effects are observed between the fusion results of different implemented versions. [ABSTRACT FROM AUTHOR]

In this paper we present a stochastic route choice model for transit networks that explicitly addresses route correlation due to overlapping alternatives. The model is based on a multi-objective mathematical programming problem, the optimality conditions of which generate an extension to the Multinomial Logit models. The proposed model considers a fixed point problem for treating correlations between routes, which can be solved iteratively. We estimated the new model on the Santiago (Chile) Metro network and compared the results with other route choice models that can be found in the literature. The new model has better explanatory and predictive power that many other alternative models, correctly capturing the correlation factor. Our methodology can be extended to private transport networks. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports the results of an analytical study to predict the edge breakout shear capacity of single adhesive anchors post-installed into uncracked hardened concrete. For this purpose, an experimental database for the adhesive anchors compiled by the ACI Committee 355 was obtained and utilized to construct training and test sets so as to derive the closed-form solution by means of gene expression programming (GEP). The independent variables used for development of the prediction model were anchor diameter, type of anchor, edge distance, embedment depth, clear clearance of the anchor, type of chemical adhesive, method of injection of the chemical, and compressive strength of the concrete. The generated prediction model yielded correlation coefficients of 0.98 and 0.92 for training and testing data sets, respectively. Moreover, the performance of the proposed model was compared with the existing models proposed by American Concrete Institute (ACI) and Prestressed/Precast Concrete Institute (PCI). The analyses showed that the proposed GEP model provided much more accurate estimation of the observed values as compared to the other models. [Copyright &y& Elsevier]

Abstract: The optimal placement of turbines in a wind farm is critical to the maximization of power production. In this paper, we develop a new mathematical programming approach for wind farm layout optimization. We use Jensen's wake decay model to represent multi-turbine wake effects. We develop mixed integer linear and quadratic optimization formulations and apply them to several example layout cases in the literature. Compared to previous approaches, our models produce layouts that tend to be more symmetric and that generate slightly more power. Our formulations solve quickly, allowing a decision maker to efficiently explore the impact of different turbine densities in a wind farm. [Copyright &y& Elsevier]

A comprehensive multi-objective mathematical programming model is proposed in this paper to design a cellular manufacturing system. The model considers machine redundancy, production volume, processing time, cost of machines, sequence of operations, and alternative processing plans. A fuzzy goal programming approach is used to convert the proposed multi-objective model into a single-objective one. Due to NP-hard nature of the model, a genetic algorithm is developed for solving the proposed model. Performance of the proposed genetic algorithm is evaluated by adopting four problems from literature. The results indicate effectiveness and efficiency of the proposed algorithm in comparison with those obtained by Lingo and NSGA-II algorithm. [ABSTRACT FROM AUTHOR]