
Journal of Mathematical Modelling and Algorithms [SJR: 0.358] [HI: 19] [2 followers] Follow Hybrid journal (It can contain Open Access articles) ISSN (Print) 15701166  ISSN (Online) 15729214 Published by SpringerVerlag [2335 journals] 
 Multiobjective Compromise Allocation in Multivariate Stratified Sampling
Using Extended Lexicographic Goal Programming with Gamma Cost Function Abstract: Abstract
In the present paper, a new Gamma cost function is proposed for an optimum allocation in multivariate stratified random sampling with linear regression estimator. Extended lexicographic goal programming is used for solution of multiobjective nonlinear integer allocation problem. A real data set is used to illustrate the application.
PubDate: 20150801
 Abstract: Abstract
In the present paper, a new Gamma cost function is proposed for an optimum allocation in multivariate stratified random sampling with linear regression estimator. Extended lexicographic goal programming is used for solution of multiobjective nonlinear integer allocation problem. A real data set is used to illustrate the application.
 A New Biobjective Locationrouting Problem for Distribution of Perishable
Products: Evolutionary Computation Approach Abstract: Abstract
Supply, manufacture, and distribution of perishable products are challenging jobs in supply chains. Location of warehouses and routing of vehicles are essential issues to distribute perishable products properly. In this paper, a new biobjective mixed integer mathematical programming is proposed to reduce the total cost of the supply chain and to balance the workload of distribution centers while the due dates of delivery of perishable product are met, concurrently. The considered properties and constraints of proposed model made it wellposed to illustrate the real life situation. As the proposed model is NPHard, an evolutionary algorithm called NonDominated Sorting Genetic AlgorithmII (NSGAII) is customized to solve the problem. The structure of chromosome and genetic operators are customized for the problem. The performance of proposed NSGAII and an efficient exact Multiobjective method, called εconstraint, is compared using accuracy and diversity metrics on several benchmark instances.
PubDate: 20150801
 Abstract: Abstract
Supply, manufacture, and distribution of perishable products are challenging jobs in supply chains. Location of warehouses and routing of vehicles are essential issues to distribute perishable products properly. In this paper, a new biobjective mixed integer mathematical programming is proposed to reduce the total cost of the supply chain and to balance the workload of distribution centers while the due dates of delivery of perishable product are met, concurrently. The considered properties and constraints of proposed model made it wellposed to illustrate the real life situation. As the proposed model is NPHard, an evolutionary algorithm called NonDominated Sorting Genetic AlgorithmII (NSGAII) is customized to solve the problem. The structure of chromosome and genetic operators are customized for the problem. The performance of proposed NSGAII and an efficient exact Multiobjective method, called εconstraint, is compared using accuracy and diversity metrics on several benchmark instances.
 Solving Large 0–1 Multidimensional Knapsack Problems by a New
Simplified Binary Artificial Fish Swarm Algorithm Abstract: Abstract
The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many realworld optimization problems are described by 0–1 multidimensional knapsack problems that are NPhard. In the last decades, several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simplified binary version of the artificial fish swarm algorithm is presented, where a point/fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fish behavior that are randomly selected by using two user defined probability values. In order to make the points feasible, the presented algorithm uses a random heuristic dropitem procedure followed by an additem procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50 % of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.
PubDate: 20150801
 Abstract: Abstract
The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many realworld optimization problems are described by 0–1 multidimensional knapsack problems that are NPhard. In the last decades, several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simplified binary version of the artificial fish swarm algorithm is presented, where a point/fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fish behavior that are randomly selected by using two user defined probability values. In order to make the points feasible, the presented algorithm uses a random heuristic dropitem procedure followed by an additem procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50 % of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.
 Balking and Reneging Multiple Working Vacations Queue with Heterogeneous
Servers Abstract: Abstract
This paper presents the analysis of a renewal input multiple working vacations queue with balking, reneging and heterogeneous servers. Whenever the system becomes empty the second server leaves for a working vacation whereas the first server remains idle in the system. During a working vacation the second server provides service at a slower rate rather than completely stopping service. The steadystate probabilities of the model are obtained using supplementary variable and recursive techniques. Various performance measures of the model such as expected system length, expected balking rate, etc., have been discussed. Finally, some numerical results have been presented to show the effect of model parameters on the system performance measures.
PubDate: 20150801
 Abstract: Abstract
This paper presents the analysis of a renewal input multiple working vacations queue with balking, reneging and heterogeneous servers. Whenever the system becomes empty the second server leaves for a working vacation whereas the first server remains idle in the system. During a working vacation the second server provides service at a slower rate rather than completely stopping service. The steadystate probabilities of the model are obtained using supplementary variable and recursive techniques. Various performance measures of the model such as expected system length, expected balking rate, etc., have been discussed. Finally, some numerical results have been presented to show the effect of model parameters on the system performance measures.
 Reducing the Structure Space of Bayesian Classifiers Using Some General
Algorithms Abstract: Abstract
The use of Bayesian Networks (BNs) as classifiers in different application fields has recently witnessed a noticeable growth. Yet, using the Naïve Bayes application, and even the augmented Naïve Bayes, to classifierstructure learning, has been vulnerable to some extent, which accounts for the resort of experts to other more sophisticated types of algorithms. Consequently, the use of such algorithms has paved the way for raising the problem of superexponential increase in computational complexity of the Bayesian classifier learning structure, with the increasing number of descriptive variables. In this context, the main objective of our present work lies in trying to conceive further solutions to solve the problem of the intricate algorithmic complexity imposed during the learning of Bayesian classifiers structure through the use of sophisticated algorithms. Our results revealed that the newly suggested approach allows us to considerably reduce the execution time of the Bayesian classifier structure learning without any information loss.
PubDate: 20150601
 Abstract: Abstract
The use of Bayesian Networks (BNs) as classifiers in different application fields has recently witnessed a noticeable growth. Yet, using the Naïve Bayes application, and even the augmented Naïve Bayes, to classifierstructure learning, has been vulnerable to some extent, which accounts for the resort of experts to other more sophisticated types of algorithms. Consequently, the use of such algorithms has paved the way for raising the problem of superexponential increase in computational complexity of the Bayesian classifier learning structure, with the increasing number of descriptive variables. In this context, the main objective of our present work lies in trying to conceive further solutions to solve the problem of the intricate algorithmic complexity imposed during the learning of Bayesian classifiers structure through the use of sophisticated algorithms. Our results revealed that the newly suggested approach allows us to considerably reduce the execution time of the Bayesian classifier structure learning without any information loss.
 Sufficiency and Duality for Nonsmooth Multiobjective Programming Problems
Involving Generalized (Φ, ρ ) V Type I Functions Abstract: Abstract
In this paper, a class of nonsmooth multiobjective programming problems is considered. We introduce the new concepts of (Φ, ρ)Vtype I, (pseudo, quasi) (Φ, ρ)Vtype I and (quasi, pseudo) (Φ, ρ)Vtype I functions, in which the involved functions are locally Lipschitz. Based upon these generalized (Φ, ρ)Vtype I functions, the sufficient optimality conditions for weak efficiency, efficiency and proper efficiency are derived. MondWeir duality results are also established under the aforesaid functions.
PubDate: 20150601
 Abstract: Abstract
In this paper, a class of nonsmooth multiobjective programming problems is considered. We introduce the new concepts of (Φ, ρ)Vtype I, (pseudo, quasi) (Φ, ρ)Vtype I and (quasi, pseudo) (Φ, ρ)Vtype I functions, in which the involved functions are locally Lipschitz. Based upon these generalized (Φ, ρ)Vtype I functions, the sufficient optimality conditions for weak efficiency, efficiency and proper efficiency are derived. MondWeir duality results are also established under the aforesaid functions.
 Multiperiod Possibilistic Mean Semivariance Portfolio Selection with
Cardinality Constraints and its Algorithm Abstract: Abstract
In this paper, we consider a multiperiod portfolio selection problem in a fuzzy investment environment, in which the return and risk of assets are characterized by possibilistic mean value and possibilistic semivariance, respectively. Based on the theories of possibility, a new multiperiod possibislistic portfolio selection model is proposed, which contains risk control, transaction costs, borrowing constraints, threshold constraints and cardinality constraints. the proposed model can be transformed into a crisp nonlinear dynamic optimization problem by using fuzzy programming approach. Because of the transaction costs and cardinality constraints, the multiperiod portfolio selection is a mix integer dynamic optimization problem with path dependence A forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, a comparison analysis of the different cardinality constraints is provided to illustrate the efficiency of the proposed approaches and the designed algorithm.
PubDate: 20150601
 Abstract: Abstract
In this paper, we consider a multiperiod portfolio selection problem in a fuzzy investment environment, in which the return and risk of assets are characterized by possibilistic mean value and possibilistic semivariance, respectively. Based on the theories of possibility, a new multiperiod possibislistic portfolio selection model is proposed, which contains risk control, transaction costs, borrowing constraints, threshold constraints and cardinality constraints. the proposed model can be transformed into a crisp nonlinear dynamic optimization problem by using fuzzy programming approach. Because of the transaction costs and cardinality constraints, the multiperiod portfolio selection is a mix integer dynamic optimization problem with path dependence A forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, a comparison analysis of the different cardinality constraints is provided to illustrate the efficiency of the proposed approaches and the designed algorithm.
 Partial Solutions and MultiFit Algorithm for Multiprocessor Scheduling
 Abstract: Abstract
A new polynomial algorithm is developed for the classical multiprocessor scheduling problem in which independent jobs are nonpreemptively scheduled on identical parallel machines with the objective of minimizing the makespan, i.e. the latest job finishing time. The algorithm at first generates and merges a set of partial solutions in order to obtain a feasible solution for the multiprocessor scheduling problem. Then a set of bin packing problems are solved in order to improve the solution, by iteratively using a M
u
l
t
i
F
i
t type procedure on different job sets. The effectiveness of this approach is evaluated by solving a large number of benchmark instances. The results indicate that the proposed algorithm, called PSMF, is very competitive with well known constructive algorithms for a wide range of instances. Furthermore, PSMF is competitive with respect to some of the best heuristics for parallel machine scheduling problems only when it uses a 2exchange procedure.
PubDate: 20150601
 Abstract: Abstract
A new polynomial algorithm is developed for the classical multiprocessor scheduling problem in which independent jobs are nonpreemptively scheduled on identical parallel machines with the objective of minimizing the makespan, i.e. the latest job finishing time. The algorithm at first generates and merges a set of partial solutions in order to obtain a feasible solution for the multiprocessor scheduling problem. Then a set of bin packing problems are solved in order to improve the solution, by iteratively using a M
u
l
t
i
F
i
t type procedure on different job sets. The effectiveness of this approach is evaluated by solving a large number of benchmark instances. The results indicate that the proposed algorithm, called PSMF, is very competitive with well known constructive algorithms for a wide range of instances. Furthermore, PSMF is competitive with respect to some of the best heuristics for parallel machine scheduling problems only when it uses a 2exchange procedure.
 On Soft Power Diagrams
 Abstract: Abstract
Many applications in operations research begin with a set of points in a Euclidean space that is partitioned into clusters. Common data analysis tasks then are to devise a classifier deciding to which of the clusters a new point is associated, finding outliers with respect to the clusters, or identifying the type of clustering used for the partition. One of the common kinds of clusterings are (balanced) leastsquares assignments with respect to a given set of sites. For these, there is a ‘separating power diagram’ for which each cluster lies in its own cell. In the present paper, we aim to develop new, efficient algorithms for outlier detection and the computation of thresholds that measure how similar a clustering is to a leastsquares assignment for fixed sites. For this purpose, we devise a new model for the computation of a ‘soft power diagram’, which allows a soft separation of the clusters with ‘point counting properties’; e.g. we are able to prescribe the maximum number of points we wish to classify as outliers. As our results hold for a more general nonconvex model of free sites, we describe it and our proofs in this more general way. We show that its locally optimal solutions satisfy the aforementioned point counting properties, by studying the corresponding optimality conditions. For our target applications that use fixed sites, our algorithms are efficiently solvable to global optimality by linear programming.
PubDate: 20150601
 Abstract: Abstract
Many applications in operations research begin with a set of points in a Euclidean space that is partitioned into clusters. Common data analysis tasks then are to devise a classifier deciding to which of the clusters a new point is associated, finding outliers with respect to the clusters, or identifying the type of clustering used for the partition. One of the common kinds of clusterings are (balanced) leastsquares assignments with respect to a given set of sites. For these, there is a ‘separating power diagram’ for which each cluster lies in its own cell. In the present paper, we aim to develop new, efficient algorithms for outlier detection and the computation of thresholds that measure how similar a clustering is to a leastsquares assignment for fixed sites. For this purpose, we devise a new model for the computation of a ‘soft power diagram’, which allows a soft separation of the clusters with ‘point counting properties’; e.g. we are able to prescribe the maximum number of points we wish to classify as outliers. As our results hold for a more general nonconvex model of free sites, we describe it and our proofs in this more general way. We show that its locally optimal solutions satisfy the aforementioned point counting properties, by studying the corresponding optimality conditions. For our target applications that use fixed sites, our algorithms are efficiently solvable to global optimality by linear programming.
 Upgrading p Median Problem on a Path
 Abstract: Abstract
Upgrading pmedian problem is a problem of finding the best median of the given graph through modification of its parameters. The current paper develops a polynomialtime model to address this problem when the weights of vertices can be varying under a given budget. Moreover, in the case where the considering graph has a special structure, namely a path, a linear time algorithm will be proposed for solving the problem with uniform cost.
PubDate: 20150601
 Abstract: Abstract
Upgrading pmedian problem is a problem of finding the best median of the given graph through modification of its parameters. The current paper develops a polynomialtime model to address this problem when the weights of vertices can be varying under a given budget. Moreover, in the case where the considering graph has a special structure, namely a path, a linear time algorithm will be proposed for solving the problem with uniform cost.
 Planning a Capacitated Road Network with Flexible Travel Times: A Genetic
Algorithm Abstract: Abstract
We are concerned with a capacitated locationmulti allocationrouting problem in a road network with flexible travel times. It is assumed that all links are twoway and capacities of the server nodes and arcs for accepting of population are limited. The aim of our work is to find numbers and locations of server nodes, allocation of the existing population in existing demand nodes on the network to the servers and the allocation of existing population in each node to different routes to determine the decided server for each member so that total transportation time is minimized. Here, two basic concepts are considered: multi allocation and flexible travel times. The concept of multi allocation arises from the possibility of allocating the existing population in a demand node to more than one server node. Also, flexible travel times concentrate on impact of traveling population on the times of links simultaneously, that is, depending on how the population is distributed on the network, the travel times on links may be increased. So, to have the least increase in the time of each link, it is necessary to decide upon the distribution of population in the network. We formulate the proposed problem as a mixedinteger nonlinear programming model and then present a genetic algorithm (GA) for solving large problems Finally, we make two sets of numerical experiments and analyze the obtained results by LINGO solver and GA. Numerical results show the proposed GA to be highly efficient.
PubDate: 20150519
 Abstract: Abstract
We are concerned with a capacitated locationmulti allocationrouting problem in a road network with flexible travel times. It is assumed that all links are twoway and capacities of the server nodes and arcs for accepting of population are limited. The aim of our work is to find numbers and locations of server nodes, allocation of the existing population in existing demand nodes on the network to the servers and the allocation of existing population in each node to different routes to determine the decided server for each member so that total transportation time is minimized. Here, two basic concepts are considered: multi allocation and flexible travel times. The concept of multi allocation arises from the possibility of allocating the existing population in a demand node to more than one server node. Also, flexible travel times concentrate on impact of traveling population on the times of links simultaneously, that is, depending on how the population is distributed on the network, the travel times on links may be increased. So, to have the least increase in the time of each link, it is necessary to decide upon the distribution of population in the network. We formulate the proposed problem as a mixedinteger nonlinear programming model and then present a genetic algorithm (GA) for solving large problems Finally, we make two sets of numerical experiments and analyze the obtained results by LINGO solver and GA. Numerical results show the proposed GA to be highly efficient.
 L (0,1)labelling of Permutation Graphs
 Abstract: Abstract
L(0,1)labelling of a graph G=(V,E) is a function f from the vertex set V(G) to the set of nonnegative integers such that adjacent vertices get number zero apart, and vertices at distance two get distinct numbers. The goal of L(0,1)labelling problem is to produce a legal labelling that minimize the largest label used. In this article, it is shown that, for a permutation graph G with maximum vertex degree Δ, the upper bound of λ
0,1(G) is Δ−1. Finally, we prove that the result is exact for bipartite permutation graph.
PubDate: 20150501
 Abstract: Abstract
L(0,1)labelling of a graph G=(V,E) is a function f from the vertex set V(G) to the set of nonnegative integers such that adjacent vertices get number zero apart, and vertices at distance two get distinct numbers. The goal of L(0,1)labelling problem is to produce a legal labelling that minimize the largest label used. In this article, it is shown that, for a permutation graph G with maximum vertex degree Δ, the upper bound of λ
0,1(G) is Δ−1. Finally, we prove that the result is exact for bipartite permutation graph.
 Fuzzy Geometric Programming Approach in Multivariate Stratified Sample
Surveys Under Two Stage Randomized Response Model Abstract: Abstract
This paper provide a profound study on optimum allocation of two stage multivariate stratified Warner’s randomized response (RR) model with linear and non linear cost function. The multiobjective problem is formulated as a Geometric Programming Problem (GPP). The fuzzy programming approach is adopted to solve the formulated problem. A numerical example is given to illustrate the computational details of the suggested method.
PubDate: 20150422
 Abstract: Abstract
This paper provide a profound study on optimum allocation of two stage multivariate stratified Warner’s randomized response (RR) model with linear and non linear cost function. The multiobjective problem is formulated as a Geometric Programming Problem (GPP). The fuzzy programming approach is adopted to solve the formulated problem. A numerical example is given to illustrate the computational details of the suggested method.
 GPU implementation of Jacobi Method and GaussSeidel Method for Data
Arrays that Exceed GPUdedicated Memory Size Abstract: Abstract
The paper proposes a method to extend the dimension of grids that GPUaided implicit finite difference method is capable to work with. The approach is based on the pyramid method. A predictive mathematical model for computation duration is proposed. This model allows to find optimal algorithm parameters. The paper provides computation experiment results that has shown the model to be accurate enough to predict optimal algorithm parameters.
PubDate: 20150421
 Abstract: Abstract
The paper proposes a method to extend the dimension of grids that GPUaided implicit finite difference method is capable to work with. The approach is based on the pyramid method. A predictive mathematical model for computation duration is proposed. This model allows to find optimal algorithm parameters. The paper provides computation experiment results that has shown the model to be accurate enough to predict optimal algorithm parameters.
 Partial Trade Credit Policy of Retailer in Economic Order Quantity Models
for Deteriorating Items with Expiration Dates and Price Sensitive Demand Abstract: Abstract
In a supplierretailercustomer supply chain, a creditworthy retailer frequently receives a permissible delay on the entire purchase amount without collateral deposits from his/her supplier (i.e., an upstream full trade credit). By contrast, a retailer usually requests his/her creditrisk customers to pay a fraction of the purchase amount at the time of placing an order, and then grants a permissible delay on the remaining balance (i.e., a downstream partial trade credit). Also, in selecting an item for use, the selling price of that item is one of the decisive factors to the customers. It is well known that the higher selling price of item decreases the demand rate of that item where the lesser price has the reverse effect. Hence, the demand rate of an item is dependent on the selling price of that item. In addition, many products such as fruits, vegetables, hightech products, pharmaceuticals, and volatile liquids not only deteriorate continuously due to evaporation, obsolescence and spoilage but also have their expiration dates. However, only a few researchers take the expiration date of a deteriorating item into consideration. This paper proposes an economic order quantity model to allow for: (a) the strategy that supplier offers retailer a full trade credit policy whereas the retailer offers their customers a partial trade credit policy, (b) selling price dependent demand rate, (c) a profit maximization objective and (d) deteriorating items not only deteriorate continuously but also have their expiration dates. For the objective function sufficient conditions for the existence and uniqueness of the optimal solution are provided. An efficient algorithm is designed to determine the optimal pricing and inventory policies for the retailer. Finally, numerical examples are presented to illustrate the proposed model and the effect of key parameters on optimal solution is examined.
PubDate: 20150419
 Abstract: Abstract
In a supplierretailercustomer supply chain, a creditworthy retailer frequently receives a permissible delay on the entire purchase amount without collateral deposits from his/her supplier (i.e., an upstream full trade credit). By contrast, a retailer usually requests his/her creditrisk customers to pay a fraction of the purchase amount at the time of placing an order, and then grants a permissible delay on the remaining balance (i.e., a downstream partial trade credit). Also, in selecting an item for use, the selling price of that item is one of the decisive factors to the customers. It is well known that the higher selling price of item decreases the demand rate of that item where the lesser price has the reverse effect. Hence, the demand rate of an item is dependent on the selling price of that item. In addition, many products such as fruits, vegetables, hightech products, pharmaceuticals, and volatile liquids not only deteriorate continuously due to evaporation, obsolescence and spoilage but also have their expiration dates. However, only a few researchers take the expiration date of a deteriorating item into consideration. This paper proposes an economic order quantity model to allow for: (a) the strategy that supplier offers retailer a full trade credit policy whereas the retailer offers their customers a partial trade credit policy, (b) selling price dependent demand rate, (c) a profit maximization objective and (d) deteriorating items not only deteriorate continuously but also have their expiration dates. For the objective function sufficient conditions for the existence and uniqueness of the optimal solution are provided. An efficient algorithm is designed to determine the optimal pricing and inventory policies for the retailer. Finally, numerical examples are presented to illustrate the proposed model and the effect of key parameters on optimal solution is examined.
 An Adaptive Trust Region Method Based on Simple Conic Models
 Abstract: Abstract
A new adaptive trust region algorithm with simple conic models is proposed. By use of the simple conic model, the new method needs less memory capacitance and computational complexity. The nonmonotone and adaptive techniques are introduced to improve the efficiency of the proposed algorithm. The convergence results of the method are proved under certain conditions. Numerical tests show that the new algorithm is efficient and robust.
PubDate: 20150326
 Abstract: Abstract
A new adaptive trust region algorithm with simple conic models is proposed. By use of the simple conic model, the new method needs less memory capacitance and computational complexity. The nonmonotone and adaptive techniques are introduced to improve the efficiency of the proposed algorithm. The convergence results of the method are proved under certain conditions. Numerical tests show that the new algorithm is efficient and robust.
 A New Search Procedure for the Twodimensional Orthogonal Packing Problem
 Abstract: Abstract
In this paper we propose a new exact procedure for the twodimensional orthogonal packing problem, based on F. Clautiaux et al. approach (Clautiaux et al. Eur. J. Oper. Res. 183(3), 1196–1211, 2007). The principle consists in searching first the positions of the items on the horizontal axis, so as that, at each position, the sum of the heights of the items does not exceed the height of the bin. Each time a valid placement of all the items is encountered, another procedure determines if it can be extended to a solution of the packing problem, searching the positions of the items on the vertical axis. Novel aspects of our approach include a simple and efficient search procedure, which only generates restricted placements, at least in a first stage, in order to reduce the search space, and the memorization of unsuccessful configurations, which are then used to detect deadends. We tested our implementation on a selection of orthogonal packing problems and strip packing problems, and we compared our results with those of recent successful approaches.
PubDate: 20150314
 Abstract: Abstract
In this paper we propose a new exact procedure for the twodimensional orthogonal packing problem, based on F. Clautiaux et al. approach (Clautiaux et al. Eur. J. Oper. Res. 183(3), 1196–1211, 2007). The principle consists in searching first the positions of the items on the horizontal axis, so as that, at each position, the sum of the heights of the items does not exceed the height of the bin. Each time a valid placement of all the items is encountered, another procedure determines if it can be extended to a solution of the packing problem, searching the positions of the items on the vertical axis. Novel aspects of our approach include a simple and efficient search procedure, which only generates restricted placements, at least in a first stage, in order to reduce the search space, and the memorization of unsuccessful configurations, which are then used to detect deadends. We tested our implementation on a selection of orthogonal packing problems and strip packing problems, and we compared our results with those of recent successful approaches.
 Solving the Nonlinear Power Flow Problem Through General Solutions of
Underdetermined Linearised Systems Abstract: Abstract
This work focuses on the solution of a set of algebraic nonlinear equations representing the steady state operation of electrical power systems. The classical modelling of the socalled power flow problem requires the statement of the power balance equations and the specification of some network variables. Usually Newton method is applied to solve these equations, which requires the solution of a linear system at each iteration. Here, the formulation of the power flow problem is modified by increasing the number of variables to be computed, such that an underdetermined linear system is solved at each iteration. This strategy imparts flexibility to obtain solutions with respect to selected performance indexes. Numerical results obtained with testsystems ranging from 26 to 1916 equations and 30 to 2013 variables illustrate the main features of the proposed application.
PubDate: 20150308
 Abstract: Abstract
This work focuses on the solution of a set of algebraic nonlinear equations representing the steady state operation of electrical power systems. The classical modelling of the socalled power flow problem requires the statement of the power balance equations and the specification of some network variables. Usually Newton method is applied to solve these equations, which requires the solution of a linear system at each iteration. Here, the formulation of the power flow problem is modified by increasing the number of variables to be computed, such that an underdetermined linear system is solved at each iteration. This strategy imparts flexibility to obtain solutions with respect to selected performance indexes. Numerical results obtained with testsystems ranging from 26 to 1916 equations and 30 to 2013 variables illustrate the main features of the proposed application.
 An Adaptive Infeasible InteriorPoint Algorithm with Full NesterovTodd
Step for Semidefinite Optimization Abstract: Abstract
We present an adaptive full NesterovTodd step infeasible interiorpoint method for semidefinite optimization. The proposed algorithm requires two types of full NesterovTodd steps are called, feasibility steps and centering steps, respectively. At each iteration both feasibility and optimality are reduced exactly at the same rate. In each iteration of the algorithm we use the largest possible barrier parameter value θ. The value θ varies from iteration to iteration and it lies between the two values
\(\frac {1}{4n}\)
and
\(\frac {1}{5n}\)
, which results a faster algorithm.
PubDate: 20150301
 Abstract: Abstract
We present an adaptive full NesterovTodd step infeasible interiorpoint method for semidefinite optimization. The proposed algorithm requires two types of full NesterovTodd steps are called, feasibility steps and centering steps, respectively. At each iteration both feasibility and optimality are reduced exactly at the same rate. In each iteration of the algorithm we use the largest possible barrier parameter value θ. The value θ varies from iteration to iteration and it lies between the two values
\(\frac {1}{4n}\)
and
\(\frac {1}{5n}\)
, which results a faster algorithm.
 SecondOrder Nondifferentiable Multiobjective Mixed Symmetric Dual
Programs Over Cones Abstract: Abstract
In this paper, a pair of secondorder mixed symmetric nondifferentiable multiobjective dual programs over arbitrary cones where each of the objective functions contains a pair of support functions is considered. Further, weak, strong and converse duality theorems are established under the assumptions of secondorder K (F,ρ)convexity. Moreover, we have illustrated a numerical example which is secondorder K (F,ρ)convex but not secondorder Fconvex. Several special cases are also obtained.
PubDate: 20150301
 Abstract: Abstract
In this paper, a pair of secondorder mixed symmetric nondifferentiable multiobjective dual programs over arbitrary cones where each of the objective functions contains a pair of support functions is considered. Further, weak, strong and converse duality theorems are established under the assumptions of secondorder K (F,ρ)convexity. Moreover, we have illustrated a numerical example which is secondorder K (F,ρ)convex but not secondorder Fconvex. Several special cases are also obtained.