Insertion Algorithm Research Articles

A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

ABSTRACT Sustainable development denotes the enhancement of living standards in the present without compromising future generations’ resources. Sustainable Development Goals (SDGs) quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations. Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data, as intended by this work. We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves (HSFCs) in order to semantically cluster new uncategorised SDG data and novel indicators, and efficiently place them in the environment of a distributed knowledge graph store. First, a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment, for retrieval of indicators and load-balancing along with an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching, as semantic cohesion of data is preserved.

Optimizing Distribution Routes for Chain Supermarket Considering Carbon Emission Cost

The low-carbon economy and sustainable development have become a widespread consensus. Chain supermarkets should pay attention to path optimization in the process of distribution to reduce carbon emissions. This study takes chain supermarkets as the research object, focusing on the optimization of the vehicle routing problem (VRP) in supermarket store distribution. Firstly, based on the concept of cost-effectiveness, we constructed a green and low-carbon distribution route optimization model with the lowest cost. With cost minimization as the objective function, the total distribution cost in the vehicle delivery process includes fixed cost, transportation cost, and carbon emission cost. The carbon emission cost is calculated using the carbon tax mechanism. Secondly, through integrating the Floyd algorithm, the nearest neighbor algorithm, and the insertion algorithm, a fusion heuristic algorithm was proposed for model solving, and an empirical study was conducted using the W chain supermarket in Wuhan as an example. The experimental results show that optimizing distribution routes considering carbon emission cost can effectively reduce carbon emissions. At the same time, it can also reduce the total costs of enterprises and society, thereby achieving greater social benefits at lower costs. The research results provide effective suggestions for chain supermarkets to control carbon emissions during the distribution process.

A knowledge graph-based deep learning framework for efficient content similarity search of Sustainable Development Goals data

Abstract Sustainable development denotes the enhancement of living standards in the present without compromising future generations’ resources. Sustainable Development Goals (SDGs) quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations. Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data, as intended by this work. We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves (HSFCs) in order to semantically cluster new uncategorised SDG data and novel indicators, and efficiently place them in the environment of a distributed knowledge graph store. First, a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment, for retrieval of indicators and load-balancing along with an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching, as semantic cohesion of data is preserved.

Time- and resource-based robust scheduling algorithms for multi-skilled projects

The presence of uncertainties often disrupts the implementation of a project plan. This paper investigates robust scheduling methods for multi-skilled projects with stochastic duration. A new indicator for evaluating robustness is designed, which is used to propose a reinforcement learning-based time buffer insertion algorithm and a resource flow optimization process. An integrated time- and resource-based robust scheduling algorithm is also developed, which considers the interaction between time buffers and resource flow. Computational experiments show that the new indicator accurately evaluates the robustness of a plan, and the proposed integrated algorithm significantly outperforms traditional methods. The results demonstrate that systematically optimizing time and resources provides greater assurance that the project will proceed as planned compared to optimizing them separately.

US Army Aviation air movement operations assignment, utilization and routing

PurposeThe purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost.Design/methodology/approachIn this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period.FindingsThe MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time.Research limitations/implicationsDue to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight.Originality/valueThis research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits.

Distribution Path Segmentation Using Route Relocation and Savings Heuristics for Multi-Depot Vehicle Routing

This paper uses routing segmentation optimization for the planning of optimal distribution networks between urban depots and their respective customers. In this experiment, three steps are proposed in concession: search for the initial solution using local search properties, improve the solution using route relocation and perturb the solution using tabu search incorporating the savings heuristic. By applying multi-depot simultaneous deployment with ideal scheduling strategies and routing heuristics ensuring cost-optimal routing, the study presents an alternative to enhanced scheduling system optimization. Based on repopulation and sequential insertion algorithms, the initial solution is created, while route relocation and tabu swap mechanisms constitute the improvement strategy and perturbation. Test results comparing the proposed solution strategy to the previous genetic algorithm solution result in a better arrangement of route segregation aspects representing customer clusters. This strategy has proven to be more successful in optimizing route segregation than the original genetic algorithm solution. This demonstrates a significant improvement in route optimization.

An Iterative Re-Optimization Framework for the Dynamic Scheduling of Crossover Yard Cranes with Uncertain Delivery Sequences

In yard-crane scheduling problems, as loading operations take priority over unloading, the delivery sequence of unloading from the quaysides to the yard is uncertain. The delivery sequence changes may make crane scheduling more difficult. As a result, the crane operations schedules developed statically become suboptimal or even infeasible. In this paper, we propose a dynamic scheduling problem considering uncertain delivery sequences. A mixed-integer linear program is developed to assign tasks to cranes and minimize the makespan of crane operations. We propose an iterative solution framework in which the schedules are re-optimized whenever the delivery sequence change is revealed. A genetic algorithm is proposed to solve the problem, and a greedy algorithm is designed to re-optimize and update the solution. To make the updated solution take effect as soon as possible, regarding batch-based task assignment, the tasks in the scheduling period are divided into several batches. In this case, the instant requests arising from the delivery sequence change are added to corresponding batch tasks and re-optimized together with the tasks of this batch. In addition, a relaxation model is formulated to derive a lower bound for demonstrating the performance of the proposed algorithm. Experimental results show that the average gap between the algorithm and the lower bound does not exceed 5%. The greedy insertion algorithm can re-optimize the instant requests in time. Therefore, the proposed iterative re-optimization framework is feasible and has the advantages (necessity) of batch-based task assignment.

Dynamic Ridesharing With Minimal Regret: Towards an Enhanced Engagement Among Three Stakeholders

In dynamic ridesharing, the platform serves as the mediator by tailoring the assignment result between workers and riders with a focus on a certain objective. Existing studies generally focus on either one or two stakeholders when modelling the problem while the wellbeing of the other parties may be ignored or even undermined. For example, purely maximizing the total revenue of the ridesharing platform may cause the loss of riders and in turn lead to a low served rate, because those expensive orders will be processed in priority. In this paper, we for the first time study how to incorporate the willingness of all stakeholders (i.e., the platform, workers and riders). Given a set of workers and a set of rider requests, we aim to return the matchable worker-rider pairs in order to minimize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">regret</i> . Specifically, two types of regret are defined: (i) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">served rate regret</i> , which refers to the rate of unserved requests, catering for the reputation and profit of the platform and workers; (ii) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">revenue regret</i> , which considers the portion of revenue loss from unserved riders, catering for the focus of workers and riders in the trip schedule. We prove the NP-hardness of this problem. To tackle this problem, we first propose a dynamic programming insertion algorithm to improve the efficiency of inserting a rider request into a trip schedule of a worker. Furthermore, two kinds of heuristic algorithms are devised to match rider requests with workers effectively. Comprehensive experiments on two real-world datasets verify the effectiveness, efficiency and scalability of our solutions in dealing with different supply-demand relationships in practice.

Lazy tournaments and multidegrees of a projective embedding of \(\overline{M}_{0,n}\)

We consider the (iterated) Kapranov embedding \(\Omega_n:\overline{M}_{0,n+3} \hookrightarrow \mathbb{P}^1 \times \cdots \times \mathbb{P}^n\), where \(\overline{M}_{0,n+3}\) is the moduli space of stable genus \(0\) curves with \(n+3\) marked points. In 2020, Gillespie, Cavalieri, and Monin gave a recursion satisfied by the multidegrees of \(\Omega_n\) and showed, using two combinatorial insertion algorithms on certain parking functions, that the total degree of \(\Omega_n\) is \((2n-1)!!=(2n-1)\cdot (2n-3) \cdots 5 \cdot 3 \cdot 1\). In this paper, we give a new proof of this fact by enumerating each multidegree by a set of boundary points of \(\overline{M}_{0,n+3}\), via an algorithm on trivalent trees that we call a lazy tournament. The advantages of this new interpretation are twofold: first, these sets project to one another under the forgetting maps used to derive the multidegree recursion. Second, these sets naturally partition the complete set of boundary points on \(\overline{M}_{0,n+2}\), of which there are \((2n-1)!!\), giving an immediate proof of the total degree formula.Mathematics Subject Classifications: 05E14, 14N10, 05C05, 14H10, 05A19, 05C85Keywords: Moduli spaces of curves, projective embeddings, multidegrees, trivalent trees

Reposition optimization in the free-floating bike-sharing system considering transferring travels from urban rail transit

The popular free-floating bike-sharing (FFBS) system serves as a first- and last-mile solution to connect urban rail transit (URT). Recently, a number of studies have investigated how to enhance the integration between FFBS and URT. Nevertheless, how to reposition the FFBS system to promote URT ridership is still a challenge. This study proposes a bike-sharing repositioning framework to balance the demand and supply of FFBS and promote URT ridership. First, a repositioning quantity determination method based on the geographically weighted regression model is applied to determine the number of bikes to be delivered to or collected from each transfer area. Second, we establish an optimization model to minimize the total repositioning cost considering carbon price. Both the repositioning of normal bikes and recycling of broken bikes are included in this model. Subsequently, a hybrid Cuckoo Search algorithm is designed to solve the problem. The extended 2-opt, random removal and insertion algorithms are introduced to generate new solutions, which enhance the search ability. Finally, several numerical studies are conducted using data collected in Shenzhen, China, to verify the effectiveness of the proposed methods. The results demonstrate that the methodology proposed in this study is reasonable and effective. The findings are expected to guide traffic authorities to get environmental and highly utilized scheduling schemes for the integration between FFBS and URT systems.

Comprehensive probability method of buffer insertion based on Gaussian fitting under process variation condition

A fast buffer insertion methodology based on comprehensive probability is proposed to solve the buffer insertion problem under the process variation condition. In the condition of considering the correlation between the required arrival time and downstream loading capacitance, the computation is large and complexity. We develop a fast and efficient Gauss-Hermite Quadrature and Gaussian fitting algorithm to solve it. Specially, we can use the mean of metric (required arrival time or performance cost ratio) based Gaussian fitting to get the optimal buffer insertion solution based on comprehensive probability. We verify our buffer insertion algorithm using timing analysis with both device and interconnect variations based on different circuits and metrics, and show that compared to reference's buffer insertion algorithm, our new buffer insertion methodology can obtain concordant results, but can greatly reduce the computation complexity.

Sign insertion and Kazhdan–Lusztig cells of affine symmetric groups

Combinatorics of Kazhdan–Lusztig cells in affine type A was originally developed by Lusztig, Shi, and Xi. Building on their work, Chmutov, Pylyavskyy, and Yudovina introduced the affine matrix-ball construction (abbreviated AMBC) which gives an analog of Robinson–Schensted correspondence for affine symmetric groups. An alternative approach to Kazhdan–Lusztig theory in affine type A was developed by Blasiak in his work on catabolism. He introduced a sign insertion algorithm and conjectured that if one fixes the two-sided cell, the recording tableau of the sign insertion process determines uniquely and is determined uniquely by the left cell. In this paper we unite these two approaches by proving Blasiak’s conjecture. In the process, we show that certain new operations we introduce called partial rotations connect the elements in the intersection of a left cell and a right cell. Lastly, we investigate the connection between Blasiak’s sign insertion and the standardization map acting on the set of semi-standard Young tableaux defined by Lascoux and Schützenberger.

Survey on Relational Database Watermarking Employing Evolutionary Methods

Ownership control, integrity, and tamper-proofing of relational data are essential concerns that must be addressed as the communication (network) system grows. Over the past several years, a plethora of potential solutions has been proposed to address these issues (including cryptography, steganography, digital signatures, watermarks, and many others). Digital watermarking (comparatively new) is a technology that aids in the resolution of issues such as data theft, authenticity, and copyright claims. This paper’s primary contributions are as follows: 1) Examine evolutionary algorithm-based watermarking in relational databases that provide optimize data encoding space in search space which provides high robustness and imperceptibility. 2) Examine contemporary database watermarking strategies based on evolutionary algorithms for high embedding capacity and watermark bit insertion. 3) Malicious agent tracing through cluster-based (mainly fuzzy c-mean clustering algorithm or based on a hash function) or multiple watermarking approaches over shared or collaborative networks. This article focuses on watermarking numeric relational databases for authentication and integrity. Also provide a brief overview of the development of relational database watermarking and emergence, its characteristics and application, and the popular research methods currently in use. In addition, gist on various possible attacks. At last, this paper suggests directions for further research in these areas. Researchers can use the findings of this study to build secure watermarking methods for databases.

Wavelet -Based Modified Intermediate Significant Bit Insertion for Text Steganography

In this paper, we propose and investigate the use of a modifiedversion of Intermediate Significant Bit (ISB) insertion algorithm, so-called here as MISB algorithm for secret text steganography. The secret text is passed through a sequence of ciphering techniques. A Modified Intermediate Significant Bit MISB insertion algorithm is proposed for hiding the bit sequences of the cipher text into the time-frequency domain using wavelet transform of the image pixels, in order to improve the robustness of the Steganography system. Results demonstrate the effectiveness of wavelet Modified Intermediate Significant Bit WMISB while evaluating its performance using Peak Signal to Noise Ratio PSNR measure. Moreover, additional experiments are evaluated for noising, and image compression for cover images to compare their Peak Signal to Noise Ratio PSNR. The drawn results confirm the effectiveness of the proposed Wavelet-based Modified Intermediate Significant Bit Insertion WMISB, Also, Bit Error Rate BER, is a key parameter that is used in assessing systems that transmit digital data from one location to another.

Data-Driven Flexible Vehicle Scheduling and Route Optimization

The flexible transit service reflects a trend of demand on the flexibility and convenience in urban public transport systems, within which the vehicle scheduling and passenger insertion are two challenging issues. Especially, finding the optimal solution for a flexible transit system can be viewed as an extension of the traveling salesman problem which is NP-complete. Yet most of the existing research mainly focuses on one aspect, i.e. route planning, stop selection or vehicle scheduling, where a combined integration and optimization of the whole system is largely neglected. In this paper, we propose a data-driven flexible transit system that integrates the origin-destination insertion algorithm and the milp-based (mixed-integer linear programming) scheduling scheme. Specifically, stops are mined from the historical datasets and some stops act as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$backbone$ </tex-math></inline-formula> stops that should be visited by the vehicles; and a heuristic backbone-based origin-destination insertion algorithm is proposed to schedule the routing path of vehicles, where the time loss caused by the optimal insertion positions is calculated for the vehicles to decide whether to accept the requests or not when constructing a path for the flexible routes. Moreover, a vehicle scheduling model based on milp is proposed to minimise the gap between the passenger flow and available seats. The proposed flexible transit systems are simulated in real-world taxi datasets, and experimental results show that the proposed flexible transit system can effectively increase the delivery ratio and decrease the passengers’ waiting time compared with existing methods.

Scheduling quay cranes and shuttle vehicles simultaneously with limited apron buffer capacity

How to schedule quay cranes (QCs) and shuttle vehicles (SVs) simultaneously or model them in an integrated scheduling problem is one of the most important problems in operations and management of a container terminal. This paper investigates an integrated QC and SV scheduling problem constrained by apron buffer capacity. In this problem, the decisions to make include the bay-to-QC assignment, the QC-bay sequence, the QC-job sequence, the job-to-SV assignment and the SV-job sequence. First, a mixed-integer linear programming model is formulated for this problem to minimize the makespan. Second, the relationships and interactive variables among the four segments of constraints of the proposed model (i.e., scheduling QCs for vessel bays, sequencing jobs handled by a QC in each bay, scheduling SVs for jobs, apron buffer capacity constraints for each vessel bay) are analyzed to present three rules: vessel block rule, near-to-far rule and full-buffer rule. Finally, a sequential insertion algorithm, a greedy insertion algorithm and an improved genetic algorithm are proposed to solve mid- and large-sized cases for the problem. Numerical experiments show that the algorithms perform well, compared to the off-the-shelf solver. Based on these experiments, managerial implications are discussed for container terminal operations and management.

Grammic monoids with three generators

Young tableaux are combinatorial objects whose construction can be achieved from words over a finite alphabet by row or column insertion as shown by Schensted sixty years ago. Recently Abram and Reutenauer studied the action of the free monoid on the set of columns defined by the famous insertion algorithm. Since the number of columns is finite, this action yields a finite transformation monoid. Here we consider the action on the set of rows. We investigate the corresponding infinite monoid in the case of a 3 letter alphabet. In particular we show that it is the quotient of the free monoid relative to a congruence generated by the classical Knuth rules plus a unique extra rule.

Optimization of a Rural Portfolio Credit Granting System Using Improved Two-Dimensional Strip Packing Grouping Delay Problem

Rural preferential loans usually take the form of portfolio credits. From the perspective of public interest, the total delay time for obtaining loans is expected to be minimized. To use rural portfolio credits effectively, the two-dimensional strip packing grouping delay problem (2SPGDP) is improved to optimize the rural portfolio credit granting system. First, 2SPGDP is established by adding grouping constraints and the latest start time constraints to the two-dimensional strip packing problem, and the total delay is taken as the optimization objective. Second, based on the depth search reverse spanning tree (DSRST) and the insert spare space (ISS) method, the branch-and-bound reverse order insert algorithm (BB-RIA) is designed. Finally, the lag pruning operator (LPO) is designed to reduce lag. The improved model (2SPGDP) and BB-RIA-LPO algorithm are used to solve several classical two-dimensional strip packing problems and a specific rural portfolio credit case. Compared with the Bottom-Left and Branch and Bound Algorithm, our model and algorithm improve the success rate by 25% and reduce the total delay by 6%. The case of rural portfolio credit illustrates the operability and effectiveness of this method.

Timing-Aware Fill Insertions With Design-Rule and Density Constraints

Metal fill insertion has become an essential step in reducing dielectric thickness variation and improving pattern uniformity, which is important in mitigating process variations, thereby achieving better manufacturing yield. However, metal fills could induce coupling capacitance, which is not often considered in existing works that typically focus more on pattern density uniformity, incurring significant problems in timing closure. However, it is a great challenge to consider three types of capacitances (i.e., area, fringe, and lateral capacitances) with design rules and density constraints at the fill insertion stage simultaneously. This article presents an efficient timing-aware fill insertion algorithm for minimizing the total capacitance and fill amount, considering the density constraints. First, we present an initial metal fill insertion and design-rule-aware legalization to obtain an initial fill insertion solution quickly. Second, from critical conductors to powers/grounds in a circuit, we divide conductors into different equivalent paths and then construct a capacitance graph to reduce the capacitance of each equivalent path globally. Third, we propose a density-aware coupling capacitance optimization method and a fast Monte Carlo-based fill selection to further reduce the coupling capacitance between any pair of conductors. Finally, we present a density-aware fill deletion method to reduce the fill amount. We evaluate the performance of our algorithm on the benchmarks of the 2018 CAD Contest at ICCAD and its official contest evaluator. Compared with the first-place team of the contest and the state-of-the-artwork, experimental results show that our algorithm achieves the lowest total capacitance and the least fill amount in a comparable runtime.

Three‐stage improved algorithm based on clustering decomposition and its application in drone demand and task allocation

This paper proposes a three-stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK-iD-N), aiming at the optimization task allocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual-objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK-D) with neighbourhood insertion algorithm into task allocation, to get high-quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK-iD-N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK-D to get better solutions of the iK-iD-N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K-iD-N) that iK- iD-N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions.