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Cloud computing is widely applied in modern industrial areas due to its technological advancement, cost reduction, and applicability. Packets (tasks) belonging to different applications (agents) compete to share the common cloud resource through a series of edge nodes (processors) in pursuit of fast transmission. This paper abstracts the cloud computing system as a multi-agent flow-shop scheduling (MAFS) problem. The objective is to minimise the total completion time of several agents with the restriction that the maximum lateness cannot exceed a given bound. Given the complexity of the considered problem, a branch and bound algorithm combined with several pruning rules and lower bounds is proposed to obtain optimal solutions. Furthermore, the considered problem is generalised to a bi-scenario version, and a bi-population cooperative co-evolutionary (BCCE) algorithm is proposed to solve it. A reinforcement learning-based method is presented to generate the initial population. Several problem-specific intensification strategies are constructed to explore promising solutions. Comprehensive experiments verified the effectiveness of the proposed algorithms. The industrial data from the China Earthquake Network Centre further confirmed the superiority of the BCCE algorithm. Overall, the MAFS model and the proposed algorithms effectively enhance the user experience and reasonably guarantee revenue. [ABSTRACT FROM AUTHOR]

This study explores the problem of train scheduling (or) train timetabling and its impact on the administration of railway management. This is a highly dependable and effective public transportation system. The problem considers both single and multiple tracks along with multiple platforms with varying train capacities (like speed, passengers, and so on). Let the tracks link two major stations source and destination with intermediate stations. A subset of intermediate stations will function as junctions. A finite number of trains be available at the source for the passengers/cargo transit movements. The profit which is generated from the shipment of passengers between a pair of stations is known in advance. The train's travel time between the stations and halting time at the stations are predefined. The arrival and departure times at the stations are calculated through the travel time and halting time. All the trains should starts from the source station and continue the journey through the intermediate stations to reach the destination station. Overtaking of the trains is permitted only at the intermediate stations. The trains must make a halt at one or more intermediate stations before reaching the destination. Now the objective is to find the best train operating schedule that maximizes the profit within the admissible travel time threshold. A zero–one integer linear programming is used to model this problem mathematically. For a better understanding of this problem, a case study is considered from the Indian railway network with two major stations Chennai and Hyderabad. A branch and bound algorithm is proposed to determine an optimal operating schedule. In addition, the experiments are carried out on a wide range of randomly generated instances of small and medium sizes, to test the efficiency of the algorithm. The computational results indicate that the algorithm is capable of finding the optimal schedules within a reasonable amount of time. [ABSTRACT FROM AUTHOR]

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Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Preventive Maintenance (PM) plays an important role in maximizing machine reliability, improving production efficiency and reducing repair costs. Due to the importance of PM in manufacturing environments, it is necessary to develop an integrated model for scheduling production jobs and maintenance interventions on the machines. On the other side, with the advent of Industry 4.0 and the transformation of factories into smart factories, the production and maintenance processes generate huge volume of data on real-time basis. Despite the importance of the issue and the competitiveness of manufacturing companies in the world, past studies show that the integration of production scheduling and maintenance in Industry 4.0 platform has not been paid much attention. Therefore, in this paper, we propose an optimal parallel machine-scheduling problem with PM activities. A mathematical model is formulated to optimize the scheduling of production and maintenance operations. Industry 4.0 conceptual model is also presented in this paper, in which the smart sensors are considered as the real enablers for industrial digitalization. The optimization problem is solved using GAMS software and branch and bound algorithm. The results of the model provide a suitable schedule for scheduling production and maintenance, and the comparison of solution methods shows that the branch-and-bound algorithm achieves a suitable output in a shorter time. [ABSTRACT FROM AUTHOR]

In this research, two new methods for solving the Internet shopping optimization problem with sensitive prices are proposed, incorporating adaptive adjustment of control parameters. This problem is classified as NP-hard and is relevant to current electronic commerce. The first proposed solution method corresponds to a Memetic Algorithm incorporating improved local search and adaptive adjustment of control parameters. The second proposed solution method is a particle swarm optimization algorithm that adds a technique for diversification and adaptive adjustment of control parameters. We assess the effectiveness of the proposed algorithms by comparing them with the Branch and Bound algorithm, which presents the most favorable outcomes of the state-of-the-art method. Nine instances of three different sizes are used: small, medium, and large. For performance validation, the Wilcoxon and Friedman non-parametric tests are applied. The results show that the proposed algorithms exhibit comparable performance and outperform the Branch and Bound algorithm. [ABSTRACT FROM AUTHOR]

The rapid development of manufacturing sectors in the contemporary Industry 4.0 era has prompted enterprises to adopt increasingly systematic, streamlined, and efficient organizational structures. In this scenario, production scheduling has arisen as an essential function that contributes to the effective allocation of organizational resources in the production process, while shortening production time and satisfying customer deadlines. Consequently, the objective of this study is to develop an advanced method to optimize the production scheduling problem. Accordingly, this study proposes an innovative genetic algorithmbased master schedule (GA-MS) focused on minimizing makespan. The proposed method is implemented in various benchmark datasets on job shop scheduling problems. Compared with some benchmark methods such as priority rules-based approaches, branch and bound algorithm (B&B), and shifting bottleneck algorithm (SB), the proposed GA-MS shows an outstanding performance concerning the tested datasets. Its application in practical manufacturing factories is highly recommended. [ABSTRACT FROM AUTHOR]

This paper proposes a new convex underestimator for general C2 nonconvex functions. The new underestimator can be used in the branch and bound algorithm αBB for solving global optimization problems. We show that the new underestimator is tighter than the classical underestimator in the αBB method. [ABSTRACT FROM AUTHOR]

This article proposes a rectangular branch‐and‐bound algorithm for solving linear multiplication problems (LMP) globally. In order to obtain a reliable lower bound of the original problem, this article designs a novel linear relaxation programming problem (LRP) that has not been seen in the existing literature. Based on the basic framework of the rectangular branch and bound algorithm, this article proposes an algorithm that can obtain a global solution. According to the structure of linear relaxation programming, the article designs a region reduction technology to improve the efficiency of the algorithm. This article also provides convergence analysis to ensure the reliability of the algorithm. Finally, several numerical experiments are used to demonstrate the effectiveness and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider some problems that arise in connected and autonomous vehicle (CAV) systems. Their simplified variants can be formulated as scheduling problems. Therefore, scheduling solution algorithms can be used as a part of solution algorithms for real-world problems. For four variants of such problems, mathematical models and solution algorithms are presented. In particular, three polynomial algorithms and a branch and bound algorithm are developed. These CAV scheduling problems are considered in the literature for the first time. More complicated NP-hard scheduling problems related to CAVs can be considered in the future. [ABSTRACT FROM AUTHOR]

The intricacies of air traffic departure scheduling, especially when numerous flights are delayed, frequently impede the implementation of automated decision-making for scheduling. To surmount this obstacle, a mathematical model is proposed, and a dynamic simulation framework is designed to tackle the scheduling dilemma. An optimization control strategy is based on adaptive dynamic programming (ADP), focusing on minimizing the cumulative delay time for a cohort of delayed aircraft amidst congestion. This technique harnesses an approximation of the dynamic programming value function, augmented by reinforcement learning to enhance the approximation and alleviate the computational complexity as the number of flights increases. Comparative analyses with alternative approaches, including the branch and bound algorithm for static conditions and the first-come, first-served (FCFS) algorithm for routine scenarios, are conducted. Moreover, perturbation simulations of ADP parameters validate the method's robustness and efficacy. ADP, when integrated with reinforcement learning, demonstrates time efficiency and reliability, positioning it as a viable solution for decision-making in departure management systems. [ABSTRACT FROM AUTHOR]

Linear least squares is one of the most widely used regression methods in many fields. The simplicity of the model allows this method to be used when data is scarce and allows practitioners to gather some insight into the problem by inspecting the values of the learnt parameters. In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables that they require to contribute similarly to the final result. We show that the new formulation is not convex and provide two alternative methods to deal with the problem: one non-exact method based on an alternating least squares approach; and one exact method based on a reformulation of the problem. We show the correctness of the exact method and compare the two solutions showing that the exact solution provides better results in a fraction of the time required by the alternating least squares solution (when the number of partitions is small). We also provide a branch and bound algorithm that can be used in place of the exact method when the number of partitions is too large as well as a proof of NP-completeness of the optimization problem. [ABSTRACT FROM AUTHOR]

In this paper, we develop a branch and bound algorithm for globally solving linear multiplicative programs (LMP). The problem LMP is firstly converted to an equivalent problem (EP2), and then a novel linear relaxation technique is constructed for (EP2) to get the lower bound of the optimum of LMP. Subsequently, the presented region pruning technique aims to eliminate as many areas as possible where the optimal solution does not exist. The convergence analysis of the algorithm is provided, and the number of worst-case iterations required is estimated to obtain an ε -optimal solution. Finally, the numerical experiments show the effectiveness of our presented algorithm. [ABSTRACT FROM AUTHOR]

While branch and bound based algorithms are a standard approach to solve single-objective (mixed-)integer optimization problems, multi-objective branch and bound methods are only rarely applied compared to the predominant objective space methods. In this paper we propose modifications to increase the performance of multi-objective branch and bound algorithms by utilizing scalarization-based information. We use the hypervolume indicator as a measure for the gap between lower and upper bound set to implement a multi-objective best-first strategy. By adaptively solving scalarizations in the root node to integer optimality we improve both, upper and lower bound set. The obtained lower bound can then be integrated into the lower bounds of all active nodes, while the determined solution is added to the upper bound set. Numerical experiments show that the number of investigated nodes can be significantly reduced by up to 83% and the total computation time can be reduced by up to 80%. [ABSTRACT FROM AUTHOR]

Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an acceptable average normalized root-mean-square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology. [ABSTRACT FROM AUTHOR]

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system's operating cost, and the introduction of P2G-CCS can effectively help the park's integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system's operating costs and improve the economic benefits of the park. [ABSTRACT FROM AUTHOR]

Car mass production commonly involves a moving assembly line that mixes several car models. This requires plenty of material supplies at the line side, but available space is scarce. Thus, material is placed apart from ideal positions. Then, picking it up involves walking along the line. This time is non-productive and can encompass 10–15% of total production time. Thus, it is important to estimate and minimize it during production planning. However, the calculations are difficult because the conveyor continuously moves. Therefore, most literature bounds walking time by a constant, but this discards valuable potential. To better approximate it, we use a time-dependent V-shaped function. A comparison indicates that for a majority of instances, constant walking time estimates of 95% confidence are at least 51% higher. Then, we introduce a model to optimize material positions such that the model-mix walking time is minimized. This poses an NP-hard sequencing problem with a recursive and nonlinear objective function. Our key discovery is a lower bound on the objective of partial solutions, established by a Lagrangian relaxation that can be solved in quadratic time. Resulting branch and bound based algorithms allow to quickly and reliably optimize up to the largest real-world sized instances. [ABSTRACT FROM AUTHOR]

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost, consisting of the price of raw materials, purchasing set-up costs, and inventory costs, and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch and bound algorithm while solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution. [ABSTRACT FROM AUTHOR]

Customer satisfaction is a prevalent issue amongst manufacturing enterprises. Multi-agent scheduling models aim to optimise the given criteria for improving customer satisfaction by fulfilling the customisation requirements. An investigation is executed on a bi-agent flowshop scheduling model, where a mass of tasks maintained by two competitive agents share a group of successive processors over time. The objective is to determine a feasible schedule that minimises the weighted combination of makespans belonging to two different agents. Asymptotic and worst-case analyses are conducted on a class of dominant-agent-based heuristics proposed to find approximate solutions for large-scale instances. An effective branch and bound algorithm is presented to achieve optimal solutions for small-scale instances, where the release-date-based branching rules and the preemption-based lower bounds significantly speed up the convergence of the proposed algorithm. A discrete artificial bee colony algorithm is introduced to find high-quality solutions for medium-scale instances. Extensive computational experiments are conducted to reveal the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

David A. Kendrick, a respected scholar and co-founder of the Journal of Computational Economics, has passed away at the age of 86. Kendrick began his career in politics but later pursued science, earning a Ph.D. from MIT and becoming a professor at Harvard and the University of Texas. He made significant contributions to computational economics, particularly in stochastic control theory and the development of GAMS software. Kendrick's legacy includes numerous books and articles, as well as the establishment of the David A. Kendrick Distinguished Service Award. He will be remembered for his kindness, generosity, and dedication to his students and colleagues. The document provided is a list of publications by Hans Amman, Ruben Mercado, and Berç Rustem. These publications cover a range of topics in economics, such as stochastic control, macroeconomic models, computational economics, and optimal control models. The authors discuss various methods and approaches used in economic analysis and offer insights into the challenges and advancements in the field. The publications span from 1981 to 2020, providing a comprehensive overview of the authors' contributions to economics. [Extracted from the article]

This paper addresses to the problem of flowshop scheduling under no-idle constraints with respect to the total completion time criterion. A branch and bound algorithm is proposed and a machine based lower bound is developed for the first time in the literature of this problem. Moreover, an Iterated Local Search (ILS) algorithm is used to provide the upper bounds of the problem. The computational results are promising, and the proposed algorithm appears able to find optimal solutions to problems with up to 20 jobs and 4 machines. [ABSTRACT FROM AUTHOR]

This article presents an inventory management model for profit maximization in a Peruvian retail company (FIDEL) using linear programming techniques and the Branch and Bound algorithm. To introduce this paper we refute, through a review of previous studies in the same field, the use of this approach for its effectiveness in business decision-making. Gathering data on costs, demand and the available capital for the company's different product lines was done through the use of interviews. The proposed model aims to maximize revenue based on the quantities of goods purchased, while considering minimum demand and capital constraints. Validation was performed with Lingo software to confirm the consistency and feasibility of the model. The resulting values revealed a significant increase in projected profits compared to actual data. [ABSTRACT FROM AUTHOR]

Friction stir welding (FSW) of AA2219-T87 and AA6061-T6 is carried out under six process parameters: rotational speed, tool profile, welding speed, tilt angle, AA2219 location, and backing plate. Response Surface Methodology (RSM) was used to develop the mathematical models for the responses (tensile strength, percentage elongation, and yield strength), and branch and bound algorithm and augmented ε-constraint (AUGMECON) method were used to optimize single and multi-responses. Utilizing a Cu backing plate in FSW led to a superior dissimilar joint, achieving 72% tensile strength, 65% yield strength (YS), and 80% elongation of the base material under 800rpm rotational speed, 100 mm/min welding speed and 3° tilt angle. The position of the parent materials determined the trade-off between the extreme Pareto solutions. Visual inspection and radiography confirmed minimal flashing and superior surface finish with no internal defect in Cu-backed optimized weldments. Tool profile and backing plate type influenced the tunnel and incomplete fusion formation respectively. Microstructural studies showed that Copper backing promotes uniform intermetallic compound dispersion at grain boundaries, with intermetallic layers at dynamic recrystallization (DR) and onion ring pattern (ORP) zones. Temperature profiling of FSW confirmed the uniform heat dissipation characteristics of Copper backing by producing a weld center temperature profile that is closer to its peak temperature but achieved more gradually. The micro-hardness study highlighted the nugget zone's lower hardness compared to parent materials. SEM fractography revealed a mixed ductile and brittle failure mode due to the presence of deep dimples filled with cracked second-phase particles. EDS and XRD analyses confirmed strengthening intermetallics presence favoring strong phases over brittle under Cu backing. Corrosion studies showcased reduced galvanic corrosion with Cu backing. [ABSTRACT FROM AUTHOR]

As a testbed for the development of artificial intelligence (AI) techniques, Mahjong has become a hot spot in computer game research due its characteristics of imperfect information. An important aspect of the game is the evaluation of the quality of a hand, which significantly influences the decision-making process for players, including discarding tiles, pong, kong, and so on. In this paper, an effective and efficient algorithm is proposed to measure the quality (i.e., deficiency) of a Mahjong hand by introducing a hierarchical branch and bound method. In the proposed approach, an updating knowledge-based octree search method is first developed to explore all possible quasi-decompositions for one hand to ensure the correctness of the result. Meanwhile, an updated knowledge-based evaluation method is designed to calculate the cost of each quasi-decomposition, and a hierarchical branch and bound method is presented to accelerate the efficiency of getting its deficiency by layering the quasi-decompositions according to the number of their tiles. Moreover, the block decomposition is further adopted in the process of finding all quasi-decompositions to enhance its efficiency. Compared to existing methods, the proposed algorithm not only demonstrates computational efficiency but also provides the exact deficiency in all cases. Experimental results on different types of hands show its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

In the biobjective branch-and-bound literature, a key ingredient is objective branching, that is, to create smaller and disjoint subproblems in the objective space, obtained from the partial dominance of the lower bound set by the upper bound set. When considering three or more objective functions, however, applying objective branching becomes more complex, and its benefit has so far been unclear. In this paper, we investigate several ingredients that allow us to better exploit objective branching in a multiobjective setting. We extend the idea of probing to multiple objectives for the 0-1 case, enhance it in several ways, and show that, when coupled with objective branching, it results in significant speedups in terms of CPU times. We also show how to adapt it to the general integer case. Furthermore, we investigate cut generation based on the objective branching constraints. Besides, we generalize the best bound idea for node selection to multiple objectives, and we show that the proposed rules outperform, in the multiobjective literature, the commonly employed depth-first and breadth-first strategies. We also analyze problem-specific branching rules. We test the proposed ideas on available benchmark instances for three problem classes with three and four objectives, namely, the capacitated facility location problem, the uncapacitated facility location problem, and the knapsack problem. Our enhanced multiobjective branch-and-bound algorithm outperforms the best existing branch-and-bound–based approach and is the first to obtain competitive and even slightly better results than a state-of-the-art objective space search method on a subset of the problem classes. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms – Discrete. Funding: This work was supported in whole, or in part, by the Austrian Science Fund [Grant P 31366]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2022.0299. [ABSTRACT FROM AUTHOR]

Topology synthesis in integrated design of wireless communication networks is considered. An iterative method has been developed for placing base stations of a broadband network along extended transportation routes. The problem of base station placement is formulated as an extremal problem on a finite set in the form of a special combinatorial model. An algorithm of the branch and bound method (BBM) has been developed to solve this problem. This algorithm serves as the basis for finding a sequence of best variants for the placement of base stations. Numerical calculations confirming the efficiency of the proposed algorithm are described. [ABSTRACT FROM AUTHOR]

Globally, Diabetic mellitus (DM) is the most common chronic health problem worldwide. It elevates the blood glucose levels in patients, resulting in severe health problems like fatigue, numbness on feet or hands, blurred vision, etc., particularly, type-2 DM prevalence rate is increasing day by day. However, it is a curable disease, so it is vital to identify the disease early to reduce the complications. Correspondingly, traditional screening methods for DM include a blood test, blood sugar meter, etc. It is a costly and time-consuming method. Moreover, it requires physicians to process the test. To resolve this, numerous existing researches focused on the detection of DM with Artificial Intelligence but lacked accuracy and speed. Therefore, the proposed system used a particular set of procedures to enhance the classification performance. Initially, the data is preprocessed to remove the noise and enhance the quality of the data. Then, the feature extraction is carried out using Kernel Principle Component Analysis to take the significant features in the data. Further, feature selection is processed using Novel Bisection Of Branch And Bound Algorithm to select the important features. Formerly, classification was attained through Novel-based Radial basis function based Kohonen Self-Organizing Map with Grey Wolf Optimizer. The performance of the system is verified through performance metrics. The outcome of the analysis signifies the proposed model accomplished an accuracy of 97.07%. The respective system is planned to contribute to the research related to diabetes and to support qualified doctors in diabetes treatment. [ABSTRACT FROM AUTHOR]

In this study, we develop a branch and bound solution algorithm to solve the workload smoothing problem. Our algorithm incorporates new formulas for dynamically computing a lower bound on the optimal value of the objective function and for determining the earliest workstations for tasks. It also uses a fast heuristic for computing a good initial upper bound. A comprehensive experimental analysis is conducted in this study. The analysis demonstrates the outstanding performance of the algorithm and its efficiency in solving medium-sized workload smoothing problems. [ABSTRACT FROM AUTHOR]

This document is a special issue of the journal Mathematics dedicated to recent advances in discrete optimization and scheduling. The issue includes 10 papers covering a range of topics such as batch loading and scheduling, optimization algorithms, scheduling surgeries, routing problems, and mathematical models for simplification. The papers present various approaches and algorithms for solving these problems, and they have been reviewed by experts in the field. The authors express their gratitude to the contributors, reviewers, and editorial staff, and hope that the readers will find stimulating ideas for further research in this area. [Extracted from the article]

• The mathematical model of the reachable domain of the contingency return trajectories. • Reachable domain calculation method of the contingency return trajectories based on interval analysis. • Parameter characteristic analysis of reachable domain. The investigation on reachable domain based on interval analysis is performed, which is aimed for the contingency return trajectories during the circumlunar flight stage in manned lunar landing engineering. First, the issue of contingency return trajectory, which adopts a non-coplanar maneuver during the circumlunar flight phase, is expounded upon. The mathematical model of the reachable domain of contingency return trajectories is established. Second, the contingency return trajectory is designed. An improved interval branch and bound algorithm is introduced and a reachable domain calculation method of the contingency return trajectories is proposed. Third, in numerical simulations, by comparing the reachable domain obtained by the traditional brute force method, the accuracy of the method presented in this paper is verified. It also indicates that this method has a higher calculation efficiency. Finally, numerous simulations are conducted to discuss the parameter characteristic of reachable domain by adopting this method. The study conclusions can serve as an important reference for the selection and decision of contingency return trajectory plans in the future manned lunar landing engineering. [ABSTRACT FROM AUTHOR]

As the first generic method for finding the optimal differentialand linear characteristics, Matsui's branch and bound search algorithm has played an important role in evaluating the security of symmetric ciphers. By combining Matsui's bounding conditions with automatic search models, search efficiency can be improved. In this paper, by studying the properties of Matsui's bounding conditions, we give the general form of bounding conditions that can eliminate all the impossible solutions determined by Matsui's bounding conditions. Then, a new method of combining bounding conditions with sequential encoding method is proposed. With the help of some small size Mixed Integer Linear Programming (MILP) models, we can use fewer variables and clauses to build Satisfiability Problem (SAT) models. As applications, we use our new method to search for the optimal differential and linear characteristics of some SPN, Feistel, and ARX block ciphers. The number of variables and clauses and the solving time of the SAT models are decreased significantly. In addition, we find some new differential and linear characteristics covering more rounds. [ABSTRACT FROM AUTHOR]

Traditional network architectures cannot meet user needs for content caching, and there is a conflict between low latency requirements and high communication costs in content delivery. To address these problems, an online content caching and delivery algorithm based on Lyapunov optimization and branch and bound method is proposed under the scenario of horizontal collaboration between edge nodes to balance delivery delay and cost and to make efficient decisions on content caching and content delivery. The proposed algorithm decomposes the continuous problem into a single slot online optimization problem based on Lyapunov optimization theory, and solves them using branch and bound algorithms. Simulation experiments showed that the proposed algorithm can achieve lower average content delivery delay and higher content hit rate under a limited content delivery cost budget. It can also adaptively balance content delivery delay and delivery cost. [ABSTRACT FROM AUTHOR]

This study addresses the integration of supplier selection and inventory management issues that involve discounts, with a unified decision-making support formulated through a mathematical programming approach. The challenges addressed encompass uncertain parameters such as defective goods rates, late delivery rates, and demand, some of which are treated as probabilistic/random variables under data availability assumptions, while others are managed as fuzzy variables where data is not explicitly required. The joint problems were synthesized into a piecewise fuzzy-probabilistic optimization model with the aim of minimizing total operational costs, and the optimal decision was deduced by solving this model. Further, the model was constructed incorporating multi-period observations, indicating its ability to generate optimal solutions for multiple procurement activity periods. Computational simulations were executed to demonstrate the calculation of the optimal decision and to appraise the proposed model. All calculations were performed in LINGO 19.0 optimization software, leveraging its uncertain programming package. The computational process employed the generalized reduced gradient - a popular method for solving optimization problems due to its requirement of only a differentiable objective function - in conjunction with the branch and bound algorithm - recognized for its simplicity in branching and bounding feasible solutions. The results affirmed that the proposed model successfully delivered the optimal solution for the problem at hand. Therefore, the proposed model is deemed appropriate for implementation by practitioners in manufacturing/retail industries as a decision-making tool to curtail operational costs associated with their procurement and warehousing operations. [ABSTRACT FROM AUTHOR]

Modeling high-level synthesis (HLS) as mixed integer linear programming (MILP) affords the opportunity to integrate constraints and optimization objectives of hardware design in the form of a mathematical intermediate representation. Consequently, it is possible to improve previously developed methods to solve MILP models and customize them for application in domain-specific functions. However, the problem remains NP-hard, and solving large models requires methods that investigate the state space intelligently. Despite the high potential of branch and bound (B&B) algorithms to solve MILP models quickly, computing the best answer is still an open challenge. In this paper, we first develop three model improvement techniques that reduce the size of the search space for MILP models derived from HLS. Then, we present a new B&B algorithm to tackle the computational challenge by considering the properties of the original HLS problem. In this regard, we propose two heuristic techniques that enable the B&B algorithm to prove that some branches are not promising and should be pruned. Moreover, we have developed a best-first strategy by suggesting a new priority key calculation scheme to improve tree traversal in the B&B algorithm. Besides, the sifting method has been employed as the LP relaxation method to achieve fast processing of large models. Our proposed approach was evaluated using a set of MILP models derived from the synthesis of Mediabench data flow graphs. The experimental results indicate that our approach outperforms modern MILP solvers in terms of speed and the scale of the MILP models which can solve. According to the test results on the large MILP models, we solved models with 7254 integer and binary variables in less than 13 minutes. [ABSTRACT FROM AUTHOR]

This study proposes a method for solving mixed integer multi-objective fractional signomial geometric programming (MIMOFSGP) problems. In this paper we firstly convert a multi-objective fractional signomial programming into a non-fractional problem by a new convenient reformulation strategy. Then the fuzzy programming technique is applied to find the optimal compromise solution and a convex relaxation is used to reach a global solution. A mixed integer compromise optimal solution of the convex programming problem can finally be found by use of nonlinear branch and bound algorithm. Then on using the Spacial branch and bound algorithm, we obtain a solution that has the shortest distance from the solution of the original problem. Finally two examples are included to demonstrate the correctness and effciency of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider the sum of linear ratios problem (SLR) that is known to be NP-hard and often arises in various practical applications such as data envelopment analysis and financial investment. We first introduce an equivalent problem (EP) of SLR that involves differences of square terms in inequality constraints. Subsequently, the concave parts of the non-convex constraints in problem (EP) are replaced with the piecewise linear functions. Using the resulting second-order cone program (SOCP), we design a spatial branch and bound algorithm, which iteratively refines the piecewise linear approximations by dividing rectangles and solving a series of problems (SOCP) to obtain the solution of the original problem. Also, a region compression technique is proposed to accelerate the convergence of the algorithm. Furthermore, we demonstrate that the bound on the optimality gap is a function of approximation errors at the iteration and estimate that the worst-case number of iterations is in the order of O (ε) to attain an ε-optimal solution. Numerical results illustrate that the proposed algorithm scales better than both the existing LP-based algorithms and the off-the-shelf solvers SCIP to solve the problem (SLR). It is worth mentioning that the proposed algorithm takes significantly less time to reach four-digit accuracy than the time required by the known algorithms on small to medium problem instances. [ABSTRACT FROM AUTHOR]

Recently, many firms have adopted joint production and distribution decisions for perishable products to achieve timely delivery at minimum costs and meet the requirements for high customisation more effectively. This study addresses a two-stage integrated production and distribution problem, explicitly considering that customised orders of perishable products are processed in parallel servers by scheduling in the first stage and delivered through vehicle routing in the second stage. The firm needs to assign orders to servers and vehicles, schedule the production and decide the routes for distribution to minimise the firm's total costs and total weighted waiting costs for customers. We exploit a novel mixed-integer program to make a trade-off between cost and service for the studied problem. We first transform the program into a set-partitioning formulation. Then, we illustrate some structural properties and propose an exact algorithm using the column generation and branch and bound algorithm. We further develop some acceleration techniques and an arc-based fix and price heuristic for practical application. The experimental results show that the algorithms are efficient. [ABSTRACT FROM AUTHOR]

Sum-based global tests are highly popular in multiple hypothesis testing. In this paper, we propose a general closed testing procedure for sum tests, which provides lower confidence bounds for the proportion of true discoveries (TDPs), simultaneously over all subsets of hypotheses. These simultaneous inferences come for free, i.e. without any adjustment of the α -level, whenever a global test is used. Our method allows for an exploratory approach, as simultaneity ensures control of the TDP even when the subset of interest is selected post hoc. It adapts to the unknown joint distribution of the data through permutation testing. Any sum test may be employed, depending on the desired power properties. We present an iterative shortcut for the closed testing procedure, based on the branch and bound algorithm, which converges to the full closed testing results, often after few iterations; even if it is stopped early, it controls the TDP. We compare the properties of different choices for the sum test through simulations, then we illustrate the feasibility of the method for high-dimensional data on brain imaging and genomics data. [ABSTRACT FROM AUTHOR]

This paper discusses a single-machine scheduling problem with periodic maintenance activities and position-based learning effect to minimize the makespan. To obtain exact solutions of small-scale problems, one new two-stage binary integer programming model is formulated. In addition, a branch and bound algorithm combining boundary method and pruning rules is also proposed. According to the property of the optimal solution, a special search neighborhood is constructed. A hybrid genetic-tabu search algorithm based on genetic mechanism with tabu technique as an operator is proposed to solve medium-scale and large-scale problems. Moreover, to improve the efficiency of genetic algorithm and hybrid genetic-tabu search algorithm, Taguchi method is used for parameter tuning. Furthermore, computational experiments are carried out to compare the efficiency and performance of these algorithms. [ABSTRACT FROM AUTHOR]

The facility location of a competing firm in a market has great importance in supply chain management. The two-stage competitive location model formulates the decision process of an entrant firm facing both location and price competition. In this paper, we incorporated the facility quantity as a decision variable into a two-stage competitive location model with the objective of maximized profit. Sequential location mode and simultaneous location mode were applied to simulate different location behavior. We developed an approximate branch and bound method to accelerate optimal location searching speed under the premise of accuracy. Greedy algorithm and approximate branch and bound method were used in two location modes. From algorithm evaluation, we found that the approximate branch and bound method is an ideal supplement of the traditional branch and bound method, especially for location problems with large-scale potential locations. Compare the results of the two modes, we found when a new firm is going to enter a market with both price and location competition, sequential location mode is an advantage strategy, since it can gain more profit than simultaneous location mode. [ABSTRACT FROM AUTHOR]

Cloud computing has experienced rapid growth in recent years and has become a critical computing paradigm. Combining multiple cloud services to satisfy complex user requirements has become a research hotspot in cloud computing. Service composition in multi-cloud environments is characterized by high energy consumption, which brings attention to the importance of energy consumption in cross-cloud service composition. Nonetheless, prior research has mainly focused on finding a service composition that maximizes the quality of service (QoS) and overlooks the energy consumption generated during service invocation. Additionally, the dynamic nature of multi-cloud environments challenges the adaptability and scalability of cloud service composition methods. Therefore, we propose the skyline-enhanced deep reinforcement learning approach (SkyDRL) to address these challenges. Our approach defines an energy consumption model for cloud service composition in multi-cloud environments. The branch and bound skyline algorithm is leveraged to reduce the search space and training time. Additionally, we enhance the basic deep Q-network (DQN) algorithm by incorporating double DQN to address the overestimation problem, incorporating Dueling Network and Prioritized Experience Replay to speed up training and improve stability. We evaluate our proposed method using comparative experiments with existing methods. Our results demonstrate that our approach effectively reduces energy consumption in cloud service composition while maintaining good adaptability and scalability in service composition problems. According to the experimental results, our approach outperforms the existing approaches by demonstrating energy savings ranging from 8% to 35%. [ABSTRACT FROM AUTHOR]

The frequent conceptual links is a descriptive data mining task which aims at describing a social network in term of the most connected type of nodes. This is done by grouping nodes into clusters or groups according to their attributes and checking the number of links between the nodes of each couple of groups, if this number is greater than a predefined threshold, the set of links is referred to as a frequent conceptual link (FCL). Although relatively recent, this task has received a number of research, chiefly in order to optimize the exploration of the search process. Indeed, the problem is defined as NP-hard, where the search process depends on the size of the network, the number of attributes and the set of their possible values whose combination can explode quickly. In this paper, we propose a new algorithm for mining the frequent conceptual links in a social network based on the technique of the branch and bound. In addition to defining an upper bound for the potential patterns in the search space, the algorithm implements other techniques which improve significantly the performance of the search process and allows to fix the shortcomings constated in the previous implementations. [ABSTRACT FROM AUTHOR]

This paper addresses the flight path planning problem for multiple missiles engaging stationary targets in high-risk areas. Targets protected by air defence are preferably engaged by a fleet or swarm of missiles, not individual missiles. The concept of a swarm attack is that a large number of approaching missiles overwhelm air defence. The deployment of missiles is often part of a broader mission including further participants. Flight path planning is then an integral element of mission planning, requiring strict timing coordination of all members involved. The flight times of the missiles are dictated by the master planning. We present algorithms for offline planning and online re-planning of flight paths for a fleet of missiles with flight time constraints. The algorithms are based on an advanced bidirectional RRT* algorithm that generates risk-minimizing flight paths with predefined flight times. Online planning generates the flight paths of the fleet sequentially, maintaining a safety distance between the missiles to prevent mutual collision. Offline planning uses a global optimization approach to determine an optimal selection of flight paths from a large set of potential paths. The selection is performed by a branch and bound algorithm that determines optimal cliques in the path compatibility graph. The optimization is embedded in an iterative algorithm that allows to successively improve the mission success. [ABSTRACT FROM AUTHOR]