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This paper proposes a new multi-objective interior search algorithm (MOISA) for solving multi-objective optimization problems. Multi-objective complex mathematical models need to be solved by meta-heuristic algorithms in such a way that Pareto-optimal solutions are obtained; therefore, a new algorithm is presented in this paper for solving such models. The process of the interior search algorithm (ISA) is based on principles of interior design and decoration. This algorithm divides all elements, except the most suitable one, into two groups. In the first group, which is called the artistic composition group, algorithm changes the composition of elements to achieve a more desirable view. In the second group, which is called the mirror group, the algorithm places a mirror between the group elements and the most suitable element to find a better view. This paper uses the principles of the ISA in conjunction with the concepts of the non-dominated sorting and crowding distance to present the proposed MOISA, which is capable of obtaining near-optimal non-dominated solutions from solution space and identifying accurate Pareto fronts. To evaluate the performance of the foregoing algorithm, the related results of solving six models and a maximal covering location-allocation model are compared with several standard multi-objective evolutionary algorithms in terms of different metrics. This comparison shows that the results of the proposed MOISA are better than those obtained from other tested algorithms. Based on the solved numerical examples, the algorithm presented in this paper has many advantages over existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new group of optimization algorithms named Human Strategy Algorithm (HS) is proposed which is inspired by human strategies to problem solving. The main idea of HS is based on human actions to find the problem’s optima by means of accessible instruments. As the environment of an unknown problem assumed to be a black box, it is supposed that the environment of our problem is a dark room occupied by several men named blind men. The main mission of these men is to look for the optimum solution. Each man has at least one instrument as his assistance. Like real life, the instrument might be any tool such as stick, billy, rope, stone, yoyo, sweep. Any instrument by its unique features is suitable in some situations. In fact, this algorithm maps problem space and searches agents to dark room and people, respectively. In this paper, one sample algorithm of the group of human strategy, YOYO Blind Man Algorithm (YOYO-BMA), is introduced which uses yoyos as men’s accessible instruments. The performance of the YOYO-BMA is evaluated on a set of benchmark problems provided for CEC’2010 Special Session and Competition on Large-Scale Global Optimization (Tang et al. 2010). The results show superior performance of proposed algorithm in comparison with others. Moreover, the problem of designing urban traffic network is solve to evaluate the algorithm using a real complex problem. [ABSTRACT FROM AUTHOR]

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively. [ABSTRACT FROM AUTHOR]

In the field of service management plays a decisive role the location of the facilities to improve the quality of services. The maximal covering location problem allows locating a known number of facilities in order to maximize the demand covered. An important aspect to take into account is the varying of demand of the nodes with respect to the time (multi-period model). In addition, each facility could be of different types. A model that takes into account the existence of different types of facilities in order to cover the demand in multi-period environments has not been found in the literature. In this paper we propose a new generalization of the dynamic maximal covering location problem where different types of facilities (with different radius of coverage) could be open in each location. In this work we used the model on case study with the objective to locate the police patrol. [ABSTRACT FROM AUTHOR]

In this paper, we address the non-permutation flowshop scheduling problem with minimal and maximal time lags between successive operations of each job. For this problem, the set of permutation schedules is not a dominant set but not all non-permutation schedules are feasible because they are not always able to satisfy all time lag constraints. We present a theoretical study, limited to the two-machine case, and related to the change on one machine of the order of two successive jobs of a permutation schedule. This study gives first the necessary conditions to make such move with regard to the feasibility of the schedule; secondly the necessary conditions to make such move interesting with regard to either the makespan or the number of tardy jobs. Through this analysis, we obtain new properties of dominance of permutation schedules. The results of the study are incorporated into a heuristic algorithm which starts the search with optimal permutation schedules and tries to improve them so as to obtain better non-permutation schedules. We also propose a mixed integer linear programming model. The objective function is to minimize lexicographically the number of tardy jobs as primary criterion and the makespan as secondary one. Computational experiments are performed to compare permutation with non-permutation schedules. [ABSTRACT FROM AUTHOR]

Analyzing the behavior and stability properties of a local optimum in an optimization problem, when small perturbations are added to the objective functions, are important considerations in optimization. The tilt stability of a local minimum in a scalar optimization problem is a well-studied concept in optimization which is a version of the Lipschitzian stability condition for a local minimum. In this paper, we define a new concept of stability pertinent to the study of multiobjective optimization problems. We prove that our new concept of stability is equivalent to tilt stability when scalar optimizations are available. We then use our new notions of stability to establish new necessary and sufficient conditions on when strict locally efficient solutions of a multiobjective optimization problem will have small changes when correspondingly small perturbations are added to the objective functions. [ABSTRACT FROM AUTHOR]

The analysis of the principal-agent problem usually requires the classical first-order approach (FOA). However, the validity of the FOA makes restrictive assumptions on the problem under consideration such as the convexity of the distribution function condition. The main aim of this paper is to compute the optimal wages and characterize a closed form solution to the risk-neutral principal-agent problem with limited liability constraints. The development in this paper mainly invokes certain techniques in the semi-infinite programming rather than the FOA. [ABSTRACT FROM AUTHOR]

In this paper, global optimization model is designed for grid computing system. It is provided as a promising model for grid resource scheduling algorithm. It aims at solving the problem of optimally allocating services on the grid to optimize the grid service reliability, deadline and cost. In this paper, the problem of optimizing the reliability of grid systems has been modeled as a multi-objective optimization problem where apart from the grid system reliability; the system cost, deadline and redundancy are also considered as its constraints. The algorithm considers failure rate of computational and network resources to do the reliability analysis of the grid system. Based on the service reliability of the grid system, the proposed RORS algorithm selects the set of optimal resources among the candidate resources based on reliability, application execution time and cost that achieves optimal performance using a genetic algorithm. The proposed algorithm has been demonstrated using Java-based GridSim tool. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new mathematical programming model for integrating production and procurement transport planning decisions in manufacturing systems in a unique optimization model. This problem was introduced conceptually and dubbed as MRP IV by Díaz-Madroñero et al. (2012) to extend the current MRP (material requirement planning) systems. This proposal simultaneously considers material, production resources capacities and procurement transport planning decisions with different shipping modes (such as full-truckload, less-than-truckload and milk-run) in the supply chain to avoid suboptimal results, which are usually generated due to sequential and independent plans. We considered an industrial automobile company to validate the proposed model using real world data. The results obtained by the MRP IV proposed model, in terms of total planning costs and transport efficiency indicators, are better than those obtained in the current heuristic procedures followed in the company under study. [ABSTRACT FROM AUTHOR]

Modern computer technology allows conjoining shakedown theory, optimization and ever stricter standardized design requirements in a single mathematical problem formulation. However it raises a question of reliability: easily achieved solution should not be taken for granted but should be adequately assessed. This paper focuses on the physical validation technique for optimal shakedown design problem solution in the aspect of Melan theorem (statics) and residual deformation compatibility (kinematics). For that purpose Rosen gradient projection method is used. Optimization problem of bending circular, symmetric plate at shakedown, which is subjected by a variable repeated load, is considered for illustration of the validation technique. [ABSTRACT FROM AUTHOR]

This paper proposes a novel decentralized adaptive excitation control scheme to globally stabilize large-scale power systems and enhance the transient stability. Two smooth functions are introduced to counteract the effect of unknown time-varying interactions, then a completely decentralized adaptive controller is constructed on the basis of the adaptive backstepping approach. The proposed controller utilizes only local measurements and has no requirement for the bounds of interconnections in the power system. All signals of the overall closed-loop large-scale power system are proved to be globally uniformly bounded. The proposed control scheme is tested on a two-area benchmark power system in the face of a symmetrical three-phase short circuit fault. Simulation results have showed better transient performances in comparison with existing controllers. [ABSTRACT FROM AUTHOR]

In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new tensor-based representation algorithm for image classification. The algorithm is realized by learning the parameter tensor for image tensors. One novelty is that the parameter tensor is learned according to the Tucker tensor decomposition as the multiplication of a core tensor with a group of matrices for each order, which endows that the algorithm preserved the spatial information of image. We further extend the proposed tensor algorithm to a semi-supervised framework, in order to utilize both labeled and unlabeled images. The objective function can be solved by using the alternative optimization method, where at each iteration, we solve the typical ridge regression problem to obtain the closed form solution of the parameter along the corresponding order. Experimental results of gray and color image datasets show that our method outperforms several classification approaches. In particular, we find that our method can implement a high-quality classification performance when only few labeled training samples are provided. [ABSTRACT FROM AUTHOR]

Abstract: Since the observed values of security returns in real-world problems are sometimes imprecise or vague, an increasing effort in research is devoted to study the properties of risk measures in fuzzy portfolio optimization problems. In this paper, a new risk measure is suggested to gauge the risk resulted from fuzzy uncertainty. For this purpose, the absolute deviation and absolute semi-deviation are first defined for fuzzy variable by nonlinear fuzzy integrals. To compute effectively the absolute semi-deviations of single fuzzy variable as well as its functions, this paper discusses the methods of computing the absolute semi-deviation by classical Lebesgue–Stieltjes (L–S) integral. After that, several useful absolute deviation and absolute semi-deviation formulas are established for common triangular, trapezoidal and normal fuzzy variables. Applying the absolute semi-deviation as a new risk measure in portfolio optimization, three classes of fuzzy portfolio optimization models are developed by combining the absolute semi-deviation with expected value operator and credibility measure. Based on the analytical representation of absolute semi-deviations, the established fuzzy portfolio selection models can be turned into their equivalent piecewise linear or fractional programming problems. Since the absolute semi-deviation is a piecewise fractional function and pseudo-convex on the feasible subregions of deterministic programming models, we take advantage of the structural characteristics to design a domain decomposition method to separate a deterministic programming problem into three convex subproblems, which can be solved by conventional solution methods or general-purpose software. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the effectiveness of the solution method. [Copyright &y& Elsevier]

Abstract: Many authors have investigated multiattribute decision making (MADM) problems under interval-valued intuitionistic fuzzy sets (IVIFSs) environment. This paper presents an optimization model to determine attribute weights for MADM problems with incomplete weight information of criteria under IVIFSs environment. In this method, a series of mathematical programming models based on cross-entropy are constructed and eventually transformed into a single mathematical programming model to determine the weights of attributes. In addition, an extended technique for order preference by similarity to ideal solution (TOPSIS) is suggested to ranking all the alternatives. Furthermore, an illustrative example is provided to compare the proposed approach with existing methods. Finally, the paper concludes with suggestions for future research. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to establish that there exist suitable objective functions for mathematical programming formulations of dynamic user equilibrium assignment in the sense of . Recently, showed that we cannot necessarily calculate a dynamic user equilibrium assignment by minimising the integral over time of Beckmann’s objective function that is widely used to solve for static user equilibrium assignment. In the present paper, two distinct objective functions are presented that result from formulations of assignment either as a variational inequality or as an infinitesimal optimisation: each of these formulations can be solved as a forward dynamic programme. With these formulations and solution approaches, we solve an example based upon Lin and Lo’s one correctly. [Copyright &y& Elsevier]

In this paper the fairness of some methods of allocating seats in a proportional representation (PR) voting system is investigated. Different PR systems are in use throughout the democratic world, but the primary focus here is the method used in South Africa, namely the largest remainder method with a Droop quota. It is shown that as the number of parties increases, the number of lost votes (votes not used to allocate seats) increases when using this method. Other existing allocation methods are discussed and compared with each other as well as with three optimisation methods (based on mathematical programming) introduced in this paper. Applying these mathematical programming methods results in allocations that are more fair than the existing methods of seat allocation, if South African voting data are used. These mathematical models attempt to minimise a number of different measures of the deviation between the actual percentage of votes received and the percentage of seats allocated to a certain party. Ideally this deviation should be zero, but due to the discrete nature of seats this is virtually impossible to achieve. [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]

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]

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]

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]

Electric power is an important energy in steel industry. Electricity accounts for roughly 20%to 30% of the gross energy consumption and costs about 10% of the gross cost of energy. In this paper, under the premise of ensuring the stability of energy supply and the normal production safety, the mathematical programming method and the dynamic mathematical optimization model were used to set up the surplus gas in the optimal allocation among the buffer users and steam production dispatching for the production equipment. The application of this optimization model can effectively improve the energy efficiency and the accuracy of power generation, making full use of secondary energy and residual heat. It also can realize the rationalization of the electricity production structure optimization which can effectively reduce the flare of the gas and steam on one hand, and save energy and decrease production cost on the other. [ABSTRACT FROM AUTHOR]

The first of this two-paper series proposes a methodology to characterize generation investment equilibria in a pool-based network-constrained electricity market, where the producers behave strategically. To this end, the investment problem of each strategic producer is represented using a bilevel model, whose upper-level problem determines the optimal investment and the supply offering curves to maximize its profit, and whose several lower-level problems represent different market clearing scenarios. This model is transformed into a mathematical program with equilibrium constraint (MPEC) through replacing the lower-level problems by their optimality conditions. The joint consideration of all producer MPECs, one per producer, constitutes an equilibrium problem with equilibrium constraints (EPEC). To identify the solutions of this EPEC, each MPEC problem is replaced by its Karush-Kuhn-Tucker (KKT) conditions, which are in turn linearized. The resulting mixed-integer linear system of equalities and inequalities allows determining the EPEC equilibria through an auxiliary MILP problem. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a class of second order (C, α, ρ, d)-convexity. Under the (C, α, ρ d)-convexity assumptions on the functions involved, weak, strong and strict converse duality theorems are established for a second order Mond-Weir type multiobjective dual. Our results generalize these existing dual results which were discussed by Ahmad et al [Second-order (F, α, ρ d)-convexity and duality in multiobjective programming, Information Science, 176(2006)3094-3103]. [ABSTRACT FROM AUTHOR]

Abstract: The 2003 CAP reform considerably affects cropping patterns in European agriculture. At the same time the imperatives of the Water Framework Directive (WFD) are expected to modify irrigation decisions especially in Southern Europe where irrigated agriculture utilizes about 70–80% of total water. The paper uses a nonlinear optimization model that incorporates rain-fed, irrigated and fodder crops to examine the impacts of water pricing and CAP reform on cropping patterns, water use, irrigation technology use and farm returns in the region of Thessaly, Greece. The results indicate that, although there are important trade-offs between water pricing (WFD) and decoupling (CAP reform), water use is more sensitive to water pricing in the post-reform CAP. [Copyright &y& Elsevier]

Abstract: Optimal allocation of emergency resources is a crucial content of emergency management. It is a key step in emergency rescue and assistance. Multiple resources and potential secondary disasters are often neglected in the existing methods, which desperately need to be improved. In this paper, we formulate the emergency resource allocation problem with constraints of multiple resources and possible secondary disasters, and model the multiple resources and multiple emergency response depots problem considering multiple secondary disasters by an integer mathematical programming. For the complexity, a heuristic algorithm is designed to efficiently solve it based on linear programming and network optimization. The algorithm modifies the solutions of the linear programming by setting a priority of preference for each location where the secondary disasters will take place with certain possibilities. The numerical simulation provides evidence for its effectiveness and efficiency. Our method and algorithm can also be implemented in the practical applications with large-scale scenario. [Copyright &y& Elsevier]

Abstract: This paper deals with the simultaneously optimization of operating conditions (pressures, temperatures and flow-rates) and dimensions (diameter and height) of the amine regeneration unit in the post-combustion CO2 capture process. The proposed model takes into account the effect of kinetic reactions on the mass transfer, the hydraulics of the random packing and the pressure drop along the column. In addition, profiles of temperature, composition and flow-rate along the height of the regenerator are also predicted. The resulting mathematical model is implemented into the optimization environment General Algebraic Modeling System (GAMS) which is a high-level modeling system for mathematical programming and optimization. The benefits of the mathematical programming techniques (equation-oriented modeling tools) are exploited for the simultaneous optimization not only of the operating conditions but also the dimensions of the all piece of equipments (heat exchangers, regeneration unit, condenser and reboiler). The mathematical model was successfully verified by comparison of the obtained results with published experimental data and simulated solutions obtained by a process simulator (HYSYS). Once validated, the model was used for optimization purpose. Finally, in order to study the effect of the main process parameters on the optimized results a sensitivity analysis is also investigated and discussed in detail. [Copyright &y& Elsevier]

Community structure detection is widely accepted as a means of elucidating the functional properties of complex networks. The problem statement is ever evolving, with the aim of developing more flexible and realistic modeling procedures. For example, a first step in developing a more informative model is the inclusion of weighted interactions. In addition to the standard community structure problem, interest has increased in the detection of overlapping communities. Adopting such constraints may, in some cases, represent a more true to life abstraction of the system under study. In this paper, two novel mathematical programming algorithms for module detection are presented. First, disjoint modules in weighted and unweighted networks are detected by formulating modularity maximization as a mixed integer nonlinear programming (MINLP) model. The solution obtained is then used to detect overlapping modules through a further MINLP model. The inclusion of two parameters controlling the extent of overlapping offers flexibility in user requirements. Comparative results show that these methodologies perform competitively to previously proposed methods. The methodologies proposed here promote the detection of topological relationships in complex systems. Together with the amenable nature of mathematical programming models, we show that both algorithms offer a versatile solution to the community detection problem. [ABSTRACT FROM AUTHOR]

Abstract: Optimization of industrial water networks made up of several water-using processes can reduce freshwater usage and wastewater production. A common feature of previous water network studies is that each water-using operation is assumed to have a single outlet stream. In this paper, a superstructure and a mathematical programing model are proposed for optimization of a water network with both single and two outflow water-using processes. A two outflow water-using process has two outlet streams with different impurity concentrations, in which one has a lower concentration and the other a higher concentration. Outlet stream with a high concentration can be reused in other water-using processes, regenerated in a regeneration system or discharged, while the outlet stream with a low concentration can only be reused in other water-using processes. These intricacies are accounted for in the mathematical model. For a two outflow water-using process, the main parameters are the allocation rates of flowrate and mass load which may be determined through consideration of the process’s performance characteristics. The optimization methodology is demonstrated through a case study of a water network consisting of four single outflow water-using units, one two outflow water-using unit and a regeneration unit. [Copyright &y& Elsevier]

Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modeled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system and an experiment on an industrial robot are presented. They show that there exists a real possibility for a significant reduction of the energy consumption in comparison to state-of-the-art scheduling approaches. [ABSTRACT FROM PUBLISHER]

With the increasing reliance on modeling optimization problems in practical applications, a number of theoretical and algorithmic contributions of optimization have been proposed. The approaches developed for treating optimization problems can be classified into deterministic and heuristic. This paper aims to introduce recent advances in deterministic methods for solving signomial programming problems and mixed-integer nonlinear programming problems. A number of important applications in engineering and management are also reviewed to reveal the usefulness of the optimization methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new mathematical programming model for the optimal synthesis of recycle and reuse networks considering simultaneously the integration of the water network system and the surrounding watershed to satisfy process and environmental constraints. The model considers the optimal location of the new industrial facility to integrate its wastewater discharge to the environment with the surrounding watershed through a disjunctive formulation. The pollutants discharged for the new plant are tracked simultaneously with the other discharges to the watershed (i.e., residential, sanitary, industrial and extractions), and the natural phenomena that affect the composition of the watershed (i.e., evaporation, filtration, etc.), in addition to the chemical reactions that are carried out in the rivers. The objective function consists in minimizing the total annual cost that is constituted by the installation of the new plant cost (including the transportation for raw materials, products and services, as well as the land cost), the wastewater treatment costs (including the piping cost) and the fresh sources cost. Two example problems were used to show the applicability of the proposed methodology. [Copyright &y& Elsevier]

Abstract: In this paper, assuming cooperative behavior of the decision makers, solution methods for decision making problems in hierarchical organizations under fuzzy random environments are considered. To deal with the formulated two-level linear programming problems involving fuzzy random variables, -level sets of fuzzy random variables are introduced and an -stochastic two-level linear programming problem is defined for guaranteeing the degree of realization of the problem. Taking into account the vagueness of judgments of decision makers, fuzzy goals are introduced and the -stochastic two-level linear programming problem is transformed into the problem to maximize the satisfaction degree for each fuzzy goal. Through the use of the fractile criterion optimization model, the transformed stochastic two-level programming problem can be reduced to a deterministic one. Interactive fuzzy programming to obtain a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. It is shown that all of the problems to be solved in the proposed interactive fuzzy programming can be easily solved by the simplex method, the sequential quadratic programming or the combined use of the bisection method and the sequential quadratic programming. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed method. [Copyright &y& Elsevier]

Abstract: The goal of this paper is to study Nash Equilibrium (NE) existence of some game-theoretic pricing models. In Soon et al. , deterministic pricing models incorporating a complete demand system were proposed. As in those models, the demand function is defined via a Nonlinear Complementarity Problem (NCP), the models’ pricing constraints include complementarity conditions. When incorporated within a game, the best response problem facing each seller is a Mathematical Program with Equilibrium Constraints. A randomized version of this pricing problem will be introduced in this work and the issue of NE existence will be discussed for both the deterministic and random pricing games. [Copyright &y& Elsevier]

The presented paper addresses the problem of optimization of the fatigue life of threaded connections by the positioning method. This method presents a technique operating upon the periodic mutual change of nut and stud positions in order to increase the fatigue life. The modelling of the fatigue life of threaded connections using the positioning method is based on concept of damageability curve. A mathematical model is formulated in terms of mathematical programming taking into account technological and operation constraints. The performance and possibilities of the model are demonstrated by numerical experiments. The influence of weighting and constraints on the fatigue life of threaded connection, the influence of the heights of inter-turnings on threaded connection fatigue life and the influence of a number of positioning periods on threaded connection fatigue life when the height of inter-turnings is constant are presented. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a systematic methodology is presented for the simultaneous optimal selection of raw materials, fossil fuels and alternative fuels in cement production. The aim is to offer a generic mathematical formulation that can be used as the basis for developing case specific mathematical formulations that can assist the strategic decision-making process. The mathematical model presented takes into consideration the essential elements of a cement plant operation. The final formulation is a mixed integer linear programming problem that aims at minimizing the overall operating cost. A realistic case study is presented which demonstrates the usefulness of the proposed mathematical programming methodology. [Copyright &y& Elsevier]

Maximising value is the main objective when developing long term mine production schedules. These results provide input for the development of a short term schedule that aims to meet process plant feed requirements so as to produce a quality saleable product. This paper reviews previous work on optimised short- and long term production scheduling and real time fleet management systems. A new dynamic mathematical model using mixed integer programming is proposed to optimise short term production scheduling and machine allocation for application in sublevel stoping operations. The objective of the model is to minimise deviation from targeted metal production. The dynamic nature of the model not only optimises the shift based schedule but also allows rapid equipment reassignment to take place as underground operating conditions change. Optimal results are generated in less than 1 min when trialled on a conceptual sublevel stoping dataset. [ABSTRACT FROM AUTHOR]

Abstract: The multi-objective optimization strategy called physical programming (PP) provides engineers with a flexible tool to express design preferences with a ‘physical’ meaning. For each objective or specification design, preferences are established through linguistic categories to which numerical values are assigned. In PP, this mapping is made using preference functions as piecewise splines whose curvatures are calculated with an expensive and iterative algorithm. However, mapping between design parameter space and objective space may be largely non-convex and is uninfluenced by the use of gradient-based methods for solving the optimization problem. In this paper, the philosophy of the PP method has been used, but two components have been totally redesigned: a simpler algorithm is used for the construction of preference functions; and the optimizer is replaced by a genetic algorithm that avoids possible local minima problems. Three engineering applications are shown to illustrate the value of this new method. [ABSTRACT FROM AUTHOR]