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The NP-hard single machine sequence-dependent group scheduling problem with minimization of total weighted earliness and tardiness is investigated. An arc-time-indexed formulation is presented and a Lagrangian-based branch-and-bound algorithm is proposed. The Lagrangian relaxation of the arc-time-indexed formulation is solved as a shortest path problem. The results of an extensive computational study demonstrate the efficacy of the proposed algorithm and establish characteristics of some hard to solve instances. [ABSTRACT FROM AUTHOR]

Motivated by some practical applications, we study a new integrated loading and transportation scheduling problem. Given a set of jobs, a single crane is available to load jobs, one by one, onto semitrailers with a given capacity. Loaded semitrailers are assigned to tractors for transportation tasks. Subject to limited resources (crane, semitrailers, and tractors), the problem is to determine (1) an assignment of jobs to semitrailers for loading tasks, (2) a sequence for the crane to load jobs onto semitrailers, (3) an assignment of loaded semitrailers to tractors for transportation tasks, and (4) a transportation schedule of assigned tractors such that the completion time of the last transportation task is minimized. We first formulate the problem as a mixed integer linear programming model (MILPM) and prove that the problem is strongly NP-hard. Then, optimality properties are provided which are useful in establishing an improved MILPM and designing solution algorithms. We develop a constructive heuristic, two LP-based heuristics, and a recovering beam search heuristic to solve this problem. An improved procedure for solutions by heuristics is also presented. Furthermore, two branch-and-bound (B&B) algorithms with two different lower bounds are developed to solve the problem to optimality. Finally, computational experiments using both real data and randomly generated data demonstrate that our heuristics are highly efficient and effective. In terms of computational time and the number of instances solved to optimality in a time limit, the B&B algorithms are better than solving the MILPM. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 416-433, 2015 [ABSTRACT FROM AUTHOR]

Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and-bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs. [ABSTRACT FROM AUTHOR]

This paper addresses a certain type of scheduling problem that arises when a parallel computation is to be executed on a set of identical parallel processors. It is assumed that if two precedence-related tasks are processed on two different processors, due to the information transferring, there will be a task-dependent communication delay between them. For each task, a processing time, a due date and a weight is given while the goal is to minimize the total weighted late work. An integer linear mathematical programming model and a branch-and-bound algorithm have been developed for the proposed problem. Comparing the results obtained by the proposed branch-and-bound algorithm with those obtained by CPLEX, indicates the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In this paper we present improvements to one of the most recent and fastest branch-and-bound algorithm for the maximum clique problem-MCS algorithm by Tomita et al. (Proceedings of the 4th international conference on Algorithms and Computation, WALCOM'10, pp. 191-203, ). The suggested improvements include: incorporating of an efficient heuristic returning a high-quality initial solution, fast detection of clique vertices in a set of candidates, better initial colouring, and avoiding dynamic memory allocation. Our computational study shows some impressive results, mainly we have solved p_hat1000-3 benchmark instance which is intractable for MCS algorithm and got speedups of 7, 3000, and 13000 times for gen400_p0.9_55, gen400_p0.9_65, and gen400_p0.9_75 instances correspondingly. [ABSTRACT FROM AUTHOR]

Many problems in economy may be formulated as global optimization problems. Most numerically promising methods for solution of multivariate unconstrained Lipschitz optimization problems of dimension greater than 2 use rectangular or simplicial branch‐and‐bound techniques with computationally cheap, but rather crude lower bounds. The proposed branch‐and‐bound algorithm with simplicial partitions for global optimization uses a combination of 2 types of Lipschitz bounds. One is an improved Lipschitz bound with the first norm. The other is a combination of simple bounds with different norms. The efficiency of the proposed global optimization algorithm is evaluated experimentally and compared with the results of other well‐known algorithms. The proposed algorithm often outperforms the comparable branch‐and‐bound algorithms. Santrauka Daug įvairių ekonomikos uždavinių yra formuluojami kaip globaliojo optimizavimo uždaviniai. Didžioji dalis Lipšico globaliojo optimizavimo metodų, tinkamų spręsti didesnės dimensijos, t. y. n > 2, uždavinius, naudoja stačiakampį arba simpleksinį šakų ir rėžių metodus bei paprastesnius rėžius. Šiame darbe pasiūlytas simpleksinis šakų ir rėžių algoritmas, naudojantis dviejų tipų viršutinių rėžių junginį. Pirmasis yra pagerintas rėžis su pirmąja norma, kitas – trijų paprastesnių rėžių su skirtingomis normomis junginys. Gautieji eksperimentiniai pasiūlyto algoritmo rezultatai yra palyginti su kitų gerai žinomų Lipšico optimizavimo algoritmų rezultatais. First published online: 21 Oct 2010 Reikšminiai žodžiai: šakų ir rėžių algoritmas, globalusis optimizavimas, Lipšico optimizavimas, Lipšico rėžis.

In this study, we consider a permutation flow shop scheduling problem on a three-machine with deteriorating jobs (a deteriorating job means that the job's processing time is an increasing function of its starting time) so as to minimize the makespan. We model job deterioration as a function that is proportional to a linear function of time. For some special cases, we prove that the problem can be solved in polynomial time. We develop branch-and-bound and heuristic procedures for the general case. Computational experiments for the branch-and-bound algorithm and heuristic algorithm are presented. [ABSTRACT FROM AUTHOR]

In oncology, single-arm two-stage designs with binary endpoint are widely applied in phase II for the development of cytotoxic cancer therapies. Simon's optimal design with prefixed sample sizes in both stages minimizes the expected sample size under the null hypothesis and is one of the most popular designs. The search algorithms that are currently used to identify phase II designs showing prespecified characteristics are computationally intensive. For this reason, most authors impose restrictions on their search procedure. However, it remains unclear to what extent this approach influences the optimality of the resulting designs. This article describes an extension to fixed sample size phase II designs by allowing the sample size of stage two to depend on the number of responses observed in the first stage. Furthermore, we present a more efficient numerical algorithm that allows for an exhaustive search of designs. Comparisons between designs presented in the literature and the proposed optimal adaptive designs show that while the improvements are generally moderate, notable reductions in the average sample size can be achieved for specific parameter constellations when applying the new method and search strategy. [ABSTRACT FROM AUTHOR]

We solve the problem of finding the lowest stable-equilibrium pose of a rigid body subjected to gravity and suspended in space by an arbitrary number of cables. Besides representing a contribution to fundamental rigid-body mechanics, this solution finds application in two areas of robotics research: underconstrained cable-driven parallel robots and cooperative towing. The proposed approach consists in globally minimizing the rigid-body potential energy. This is done by applying a branch-and-bound algorithm over the group of rotations, which is partitioned into boxes in the space of Euler-Rodrigues parameters. The lower bound on the objective is obtained through a semidefinite relaxation of the optimization problem, whereas the upper bound is obtained by solving the same problem for a fixed orientation. The resulting algorithm is applied to several examples drawn from the literature. The reported Matlab implementation converges to the lowest stable equilibrium pose generally in a few seconds for cable-robot applications. Interestingly, the proposed method is only mildly sensitive to the number of suspending cables, which is shown by solving an example with 1000 cables in two hours. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two-machine flow shop scheduling problem with decreasing linear deterioration. By decreasing linear deterioration, we mean that the processing time is a decreasing function of its execution start time. The objective is to find a sequence that minimizes the total weighted completion time. Several dominance properties and some lower bounds are derived to speed up the elimination process of a branch-and-bound algorithm. Two heuristic algorithms are also proposed to overcome the inefficiency of the branch-and-bound algorithm. Computational results for randomly generated problem instances are presented. [ABSTRACT FROM AUTHOR]

In this paper, we consider scheduling problem in a new product development project. Each research and development project consists of a set of activities in which each activity may be executed in several ways. Each way of execution of an activity is a different technology, called an alternative technology, which may fail due to technical risks. In this work, we focus on alternative technologies for scheduling activities to maximize the expected net present value. We assume that the activity payoff is obtained as soon as any single technology is completed successfully. Therefore, we analyze the problem and develop a two-phase solution procedure, consisting of a branch-and-bound algorithm and a recursive search procedure. The efficiency of the proposed algorithm has been evaluated on a set of randomly generated test instances. [ABSTRACT FROM AUTHOR]

Given an edge-weighted graph G of order n, the minimum cut linear arrangement problem (MCLAP) asks to find a one-to-one map from the vertices of G to integers from 1 to n such that the largest of the cut values c,..., c is minimized, where c, i∈{1,..., n−1}, is the total weight of the edges connecting vertices mapped to integers 1 through i with vertices mapped to integers i+1 through n. In this paper, we present a branch-and-bound algorithm for solving this problem. A salient feature of the algorithm is that it employs a dominance test which allows reducing the redundancy in the enumeration process drastically. The test is based on the use of a tabu search procedure developed to solve the MCLAP. We report computational results for both the unweighted and weighted graphs. In particular, we focus on calculating the cutwidth of some well-known graphs from the literature. [ABSTRACT FROM AUTHOR]

We consider a two-machine flow shop scheduling problem with effects of deterioration and learning. By the effects of deterioration and learning, we mean that the processing time of a job is a function of its execution starting time and its position in a sequence. The objective is to find a sequence that minimizes the makespan. Several dominance properties and two lower bounds are derived, which are used to speed up the elimination process of a branch-and-bound algorithm proposed to solve the problem. Two heuristic algorithms are also proposed to obtain near-optimal solutions. Computational results are presented to evaluate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

We present a branch-and-bound algorithm for minimizing a convex quadratic objective function over integer variables subject to convex constraints. In a given node of the enumeration tree, corresponding to the fixing of a subset of the variables, a lower bound is given by the continuous minimum of the restricted objective function. We improve this bound by exploiting the integrality of the variables using suitably-defined lattice-free ellipsoids. Experiments show that our approach is very fast on both unconstrained problems and problems with box constraints. The main reason is that all expensive calculations can be done in a preprocessing phase, while a single node in the enumeration tree can be processed in linear time in the problem dimension. [ABSTRACT FROM AUTHOR]

This paper extends the worst-case norm (WCN) of linear systems subject to inputs with magnitude and rate bounds to the WCN of uncertain linear systems under the same inputs. While the WCN for linear systems can be accurately approximated by simply solving a sparse linear programming, the computation of the WCN for uncertain linear systems leads to an NP-hard problem. In this paper, a branch-and-bound algorithm is applied to calculate the WCN in the presence of uncertainty. Subsequently, we derive the bounds for two approximation errors, namely, the truncation error and the discretization error, which are resulted from the proposed WCN computation method. Based on these error bounds, we give a brief guideline for choosing appropriate values of the terminal time and the sampling time. Numerical examples demonstate that computation time of the proposed algorithm is reasonable within certain problem dimensions. An exhaustive search is employed to validate the branch-and-bound algorithm. Finally, we suggest a means to improve the WCN computation for problems with higher dimension. [ABSTRACT FROM AUTHOR]

Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancelation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach for optimizing the system performance. If the interference is treated as noise, the power allocation optimization problem aiming at maximizing the sum rate with a total power constraint is nonconvex and up to now an open problem. In the present paper, the solution is found by reformulating the nonconvex objective function of the sum rate as a difference of two concave functions. A globally optimum power allocation is found by applying a branch-and-bound algorithm to the new formulation. In principle, the algorithm partitions the feasible region recursively into subregions where for every subregion the objective function is upper and lower bounded. For each subregion, a linear program is applied for estimating the upper bound of the sum rate which is derived from a convex maximization formulation of the problem with piecewise linearly approximated constraints. The performance is investigated by system-level simulations. The results show that the proposed algorithm outperforms the known conventional suboptimum schemes. Furthermore, it is shown that the algorithm asymptotically converges to a globally optimum power allocation. [ABSTRACT FROM PUBLISHER]

In this paper, we deal with the single-row equidistant facility layout problem (SREFLP), which asks to find a one-to-one assignment of n facilities to n locations equally spaced along a straight line so as to minimize the sum of the products of the flows and distances between facilities. We develop a branch-and-bound algorithm for solving this problem. The lower bound is computed first by performing transformation of the flow matrix and then applying the well-known Gilmore-Lawler bounding technique. The algorithm also incorporates a dominance test which allows to drastically reduce redundancy in the search process. The test is based on the use of a tabu search procedure designed to solve the SREFLP. We provide computational results for problem instances of size up to 35 facilities. For a number of instances, the optimal value of the objective function appeared to be smaller than the best value reported in the literature. [ABSTRACT FROM AUTHOR]

We consider a problem of scheduling a set of independent jobs by two agents on a single machine. Every agent has its own subset of jobs to be scheduled and uses its own optimality criterion. The processing time of each job proportionally deteriorates with respect to the starting time of the job. The problem is to find a schedule that minimizes the total tardiness of the first agent, provided that no tardy job is allowed for the second agent. We prove basic properties of the problem and give a lower bound on the optimal value of the total tardiness criterion. On the basis of these results, we propose a branch-and-bound algorithm and an evolutionary algorithm for the problem. Computational experiments show that the exact algorithm solves instances up to 50 jobs in a reasonably short time and that solutions obtained by the metaheuristic are close to optimal ones. [ABSTRACT FROM AUTHOR]

We solve for the first time to proven optimality the small instances in the classical literature benchmark of Minimum Linear Arrangement. This is achieved by formulating the problem as an ILP in a somehow unintuitive way, using variables expressing the fact that a vertex is between two other adjacent vertices in the arrangement. Using (only) these variables appears to be the key idea of the approach. Indeed, with these variables already the use of very simple constraints leads to good results, which can however be improved with a more detailed study of the underlying polytope. [ABSTRACT FROM AUTHOR]

The most widely used progress measure for branch-and-bound (B&B) algorithms when solving mixed-integer programs (MIPs) is the MIP gap. We introduce a new progress measure that is often much smoother than the MIP gap. We propose a double exponential smoothing technique to predict the solution time of B&B algorithms and evaluate the prediction method using three MIP solvers. Our computational experiments show that accurate predictions of the solution time are possible, even in the early stages of B&B algorithms. [ABSTRACT FROM AUTHOR]

In branch-and-bound (B&B) schemes for solving a minimization problem, a better lower bound could prune many meaningless branches which do not lead to an optimum solution. In this paper, we propose several techniques to refine the lower bound on the makespan in the multiprocessor scheduling problem (MSP). The key idea of our proposed method is to combine an efficient quadratic-time algorithm for calculating the Fernández's bound, which is known as the best lower bounding technique proposed in the literature with two improvements based on the notions of binary search and recursion. The proposed method was implemented as a part of a B&B algorithm for solving MSP, and was evaluated experimentally. The result of experiments indicates that the proposed method certainly improves the performance of the underlying B&B scheme. In particular, we found that it improves solutions generated by conventional heuristic schemes for more than 20 percent of randomly generated instances, and for more than 80 percent of instances, it could provide a certification of optimality of the resulting solutions, even when the execution time of the B&B scheme is limited by one minute. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address a two-machine flow shop scheduling problem under simple linear deterioration. By a simple linear deterioration function, we mean that the processing time of a job is a simple linear function of its execution start time. The objective is to find a sequence that minimizes total weighted completion time. Optimal schedules are obtained for some special cases. For the general case, several dominance properties and two lower bounds are derived to speed up the elimination process of a branch-and-bound algorithm. A heuristic algorithm is also proposed to overcome the inefficiency of the branch-and-bound algorithm. Computational analysis on randomly generated problems is conducted to evaluate the branch-and-bound algorithm and heuristic algorithm. [ABSTRACT FROM AUTHOR]

This article investigates a scheduling problem for passenger trains on a single or partially double-track railway in which the total passengers' trip time with a penalty function is minimized. Owing to the uncertainty of the real traffic system, the number of passengers boarding (leaving) the train at each station is treated as a random variable. Three kinds of criteria are introduced to compute the total passengers' trip time, including the expected value criterion, the pessimistic value criterion and the optimistic value criterion. A 0-1 mixed integer programming model is constructed for the problem, and a branch-and-bound algorithm is also designed to solve the model, in which two strategies are introduced to resolve the conflicts on tracks. Finally, some numerical experiments are performed to show the performance of the model and the algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers the problem of finding the global optimum of the camera rotation, translation and the focal length given a set of 2D–3D point pairs. The global solution is obtained under the L-infinity optimality by a branch-and-bound algorithm. To obtain the goal, we firstly extend the previous branch-and-bound formulation and show that the image space error (pixel distance) may be used instead of the angular error. Then, we present that the problem of camera pose plus focal length given the rotation is a quasi-convex problem. This provides a derivation of a novel inequality for the branch-and-bound algorithm for our problem. Finally, experimental results with synthetic and real data are provided. [Copyright &y& Elsevier]

We introduce a branch-and-cut algorithm to aggregate published journal rankings based on subsets of the accounting literature in order to create a consensus ranking. The aggregate ranking allows specialist and regional journals, which may only be ranked in a limited number of studies, to be placed with respect to each other and with respect to the generalist journals that are usually included in ranking studies. The approach we develop is a significant advance over ad hoc approaches to aggregating journal rankings that have appeared in the literature and may provide a theoretically sound and replicable basis for further exploration of the concept of journal quality and the stability of journal rankings over time and ranking methods. [ABSTRACT FROM AUTHOR]

The article focuses on the formulation and solving a discrete-time path optimization problem where a single searcher that operates in a discretized three-dimensional airspace search for a moving target in finite set of cells. It says that a specialized branch-and-bound algorithm was developed for the problem using several network reduction procedures and a new bounding strategy based on Lagrangian network expansion and relaxation. It adds that the algorithm solve multi-constrained problems.

Many warehouses store at least some goods in two areas, a reserve area that is efficient for storage and a forward area that is efficient for order picking. The forward-reserve allocation problem determines the set of Stock-Keeping Units and their space allocations in the forward area to maximize the forward area's benefit by trading off the relevant costs of order picking and internal replenishment. The mathematical model of this decision resembles the classical knapsack problem with the additional complexity that it has a discontinuous nonlinear cost function. A simple greedy heuristic has been proposed in the literature to solve this problem. This paper proposes an alternative branch-and-bound algorithm that can quickly solve the problem to optimality. Heuristic and optimal solutions are numerically compared using problem instances based on real warehouse data. Results suggest that the heuristic solutions are very close to the optimal ones in terms of both the objective value and the forward assignment. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. [Copyright &y& Elsevier]

We consider the famous Hamiltonian cycle problem (HCP) embedded in a Markov decision process (MDP). More specifically, we consider the HCP as an optimisation problem over the space of occupation measures induced by the MDP's stationary policies. In recent years, this approach to the HCP has led to a number of alternative formulations and algorithmic approaches. In this paper, we focus on a specific embedding, because of the work of Feinberg. We present a "branch-and-fix" type algorithm that solves the HCP. At each branch of the algorithm, only a linear program needs to be solved and the dimensions of the successive linear programs are shrinking rather than expanding. Because the nodes of the branch-and-fix tree correspond to specially structured 1-randomised policies, we characterise the latter. This characterisation indicates that the total number of such policies is significantly smaller than the subset of all 1-randomised policies. Finally, we present some numerical results. [ABSTRACT FROM AUTHOR]

Many problems in economy may be formulated as global optimization problems. Most numerically promising methods for solution of multivariate unconstrained Lipschitz optimization problems of dimension greater than 2 use rectangular or simplicial branch-and-bound techniques with computationally cheap, but rather crude lower bounds. The proposed branch-and-bound algorithm with simplicial partitions for global optimization uses a combination of 2 types of Lipschitz bounds. One is an improved Lipschitz bound with the first norm. The other is a combination of simple bounds with different norms. The efficiency of the proposed global optimization algorithm is evaluated experimentally and compared with the results of other well-known algorithms. The proposed algorithm often outperforms the comparable branch-and-bound algorithms. [ABSTRACT FROM AUTHOR]

The aim of the train timetable problem is to determine arrival and departure times at each station so that no collisions will happen between different trains and the resources can be utilized effectively. Due to uncertainty of real systems, train timetables have to be made under an uncertain environment under most circumstances. This paper mainly investigates a passenger train timetable problem with fuzzy passenger demand on a single-line railway in which two objectives, i.e., fuzzy total passengers' time and total delay time, are considered. As a result, an expected value goal-programming model is constructed for the problem. A branch-and-bound algorithm based on the fuzzy simulation is designed in order to obtain an optimal solution. Finally, some numerical experiments are given to show applications of the model and the algorithm. [ABSTRACT FROM AUTHOR]

• An effective model has been established for conflict-free route and speed optimization of AGVs with the lowest energy consumption, namely RSOM. • RSOM is for the AGVs in the warehouse that adopt Quick-Response (QR) code navigation system. • The analysis of all node conflicts and arc conflicts provides a basis for solving path conflicts of large-scale AGVs. • According to various schemes of adjusting speed and time, the branch and bound algorithm is constructed to effectively obtain optimal solution to RSOM of large-scale AGVs. • The examples of AGVs using QR code navigation system have saved 30%-60% of the solving time as the scale increased, in comparison with path following techniques. Similarly, RSOM can save more time and energy, in comparison with path and time priority strategies. This paper focuses on exploring conflict-free routes for automated guided vehicles (AGVs) based on quick response (QR) code technology. Intelligent warehousing has become an industrial development trend, with an increasing number of enterprises deploying AGVs based on QR code technology for transportation tasks, especially in large warehouses. Unlike the early single lane path following navigation systems, AGVs using QR code technology can freely switch tracks at any position. And two AGVs can be accommodated side by side. The conditions of conflict-free routes are analyzed firstly when AGVs use QR code navigation system. After that, a route and speed optimization model (RSOM) that aims at minimizing energy consumption and taking conflict-free routes with time windows as the constraint condition is established. Then a branch-and-bound (B&B) algorithm scheme is proposed by altering AGV routes or speed to arrive at schemes with conflict-free routes to complete transportation tasks. Lastly, the effectiveness and validity of our proposed model and algorithm are verified by numerical examples and a case study. The results show that resolving conflicts during route planning for AGVs guided through QR code have advantages over avoiding collision in the running phase by technology and path following techniques in both aspects of solution time and energy. In addition, the established B&B algorithm has better performance than the rapid-exploring random tree (RRT) algorithm in numerical examples. The gaps between them widen as the size of AGVs increases. The research work has potential for application to meet the requirements of AGV development. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithmic technique for solving certain problems in geometric computer vision. The main novelty of the method is a branch-and-bound search over rotation space, which is used in this paper to determine camera orientation. By searching over all possible rotations, problems can be reduced to known fixed-rotation problems for which optimal solutions have been previously given. In particular, a method is developed for the estimation of the essential matrix, giving the first guaranteed optimal algorithm for estimating the relative pose using a cost function based on reprojection errors. Recently convex optimization techniques have been shown to provide optimal solutions to many of the common problems in structure from motion. However, they do not apply to problems involving rotations. The search method described in this paper allows such problems to be solved optimally. Apart from the essential matrix, the algorithm is applied to the camera pose problem, providing an optimal algorithm. The approach has been implemented and tested on a number of both synthetically generated and real data sets with good performance. [ABSTRACT FROM AUTHOR]

The ability of using dispersed generation (DG) in the distribution system restoration service in the context of smart networks is presented in this paper. The objectives are to reduce the consequences due to a major blackout in terms of the out-of-service load volume, and the duration of restoration process. Based on knapsack problem formulation and network represented graph modeling, a new restoration procedure for distribution network is proposed. An adapted branch-and-bound algorithm is then used to solve the problem. It maximizes the restored loads in distribution by using the DG availability. Simulation results on a study case will be shown to illustrate the proposed procedure and quantify the benefit of using DG in critical situations. [ABSTRACT FROM AUTHOR]

Two-sided (left- and right-side) assembly lines are often used in assembly of large-sized products, such as buses and trucks. A large number of exact algorithms and heuristics have been proposed to balance the well-known classical one-sided assembly lines. However, little attention has been paid to balancing the two-sided lines. Moreover, according to our best knowledge, there is no published work in balancing the two-sided assembly line exactly. In this study, a branch-and-bound algorithm is proposed to solve the balancing problem optimally. Experiments are carried out to demonstrate the performance of the proposed method and the results are promising. [ABSTRACT FROM AUTHOR]

This article investigates identical parallel machines scheduling with family setup times. The objective function being the weighted sum of completion times, the problem is known to be strongly NP-hard. We propose a constructive heuristic algorithm and three complementary lower bounds. Two of these bounds proceed by elimination of setup times or by distributing each of them to jobs of the corresponding family, while the third one is based on a lagrangian relaxation. The bounds and the heuristic are incorporated into a branch-and-bound algorithm. Experimental results obtained outperform those of the methods presented in previous works, in term of size of solved problems. [ABSTRACT FROM AUTHOR]

In this paper we discuss a single-machine scheduling problem with machine maintenance. In many production systems, the sequence-dependent setup time of a job cannot be ignored when a switch between two different jobs occurs. In our research, we develop a heuristic to minimize the completion time, or equivalently the total setup time subject to maintenance and due dates. The heuristic consists of three procedures: the initialization procedure, the Stinson heuristic procedure and the iterative procedure. Computational performance of the heuristic is evaluated by comparing its solution with the solution of the branch-and-bound algorithm. The performance of the heuristic on various sizes problems is provided. [ABSTRACT FROM AUTHOR]