Sequence Of Problems Research Articles

Deficiencies and Improvement Proposals for Retaliation Mechanism in WTO Dispute Settlement Understanding

As the last-resort enforcement mechanism within the WTO framework, the effectiveness and regime of Retaliation have been subjects of ongoing concern and scrutiny. This paper presents an in-depth analysis of the Retaliation Mechanism in WTO Dispute Settlement Understanding (DSU), highlighting its notable deficiencies and the various proposals aimed at enhancing its function. The defects can be split into procedural problems and substantial problems, including the “sequencing problem” of DSU Article21.5 and Article 22, flaws in the arbitration process, and the mechanism’s overall limited effectiveness in ensuring compliance and rebalancing interests among member states. Through detailed analysis of relevant judicial cases and consideration of suggested reforms, this paper explores the potential future improvements to make the Retaliation Mechanism more robust and equitable. This review aims not only to assess the current challenges but also to suggest feasible enhancements that could significantly optimize the mechanism's efficacy in resolving international trade disputes.

Comparative Analysis of Quasi-Linear Kalman-Type Algorithms in Estimating a Markov Sequence with Nonlinearities in the System and Measurement Equations

The so-called Kalman type algorithms (KTA) are considered, among them quasi-linear KTAs introduced as a separate class, the features of which are the Gaussian approximation of the a posteriori probability density function (p.d.f.) at each step and the procedure for processing the current measurement based on the ideology of a linear optimal algorithm. The unified structure of such algorithms and their features are discussed. Two groups are distinguished the quasi-linear KTA: the first is algorithms using Taylor series expansion of the nonlinear functions, and the second is the so-called linear regression KTA. The methods of their designing are considered, and the common features are described. Detailed attention is paid to the following KTAs: the extended Kalman filter (EKF), polynomial filters of the second and third order (PF2 and PF3), as representatives of the first group, and the Unscented and Cubature Kalman filters (UKF and CKF), as representatives the second one. Their comparative analysis is carried out using the estimation problem of a scalar Markov sequence in the presence of nonlinearities in the shaping filter and in the measurement equations. For all the studied algorithms, the formulas are given in a form convenient for comparison. Based on these formulas, possible causes of a decrease in accuracy and a violation of the consistency properties are identified. Using the previously proposed procedure based on the method of statistical tests, predictive simulation was carried out, which made it possible to confirm the conclusions obtained previously on the basis of an analysis of the formulas for the algorithms being compared. The simulation also allowed to compare the computational complexity of the compared algorithms. The results of the study may be useful to developers involved in the processing of measurement information when choosing a filtering algorithm for solving specific practical estimation problems.

A multi-objective constraint programming approach to address clustering problems in mine planning

PurposeThe mine sequencing problem is NP-hard. Therefore, simplifying it is necessary. One way to do this is to employ clusters as input instead of individual blocks. The mining cut clustering problem has been little addressed in the literature, and the solutions used are almost always heuristic. We solve the mining cut clustering problem, which is NP-hard, through single- and multi-objective optimization, finding results that are local optima in acceptable computational time.Design/methodology/approachWe first elaborate an ILP-based model to address the mining cut clustering problem. We employ a mono-objective approach and two multi-objective approaches, solving all these models by constraint programming. To choose the best solutions generated by multi-objective approaches, we employ two multi-criteria decision analysis approaches, considering different weight configurations. We developed a case study using real data.FindingsWe verified that the approaches based on multi-objective optimization performed better than the mono-objective approach for the economic return criterion. The weighted-sum multi-objective approach presented the best results considering all objective functions used. Once viable solutions were obtained through multi-objective optimization, multi-criteria decision analysis approaches almost always selected the same solution. We obtained solutions that are local optima in acceptable computational time.Research limitations/implicationsThis study solves an instance with 80 blocks. Consequently, it is aimed at short-term mine planning. The methodology has not yet been evaluated in large instances related to medium- and long-term mine planning.Originality/valueThis is the first time that multi-objective optimization has been employed to solve the mining cut custering problem. Even other problems related to mine planning were, at most, solved by goal programming, so that multi-objective optimization is a knowledge that is not widespread among mining researchers. The results are consistent, and the study achieves the objective of finding quality solutions to an NP-hard problem in an acceptable computational time.

Expanding Digital Literacies Beyond the Digital: Infusing Computational Thinking into Unplugged Pedagogical Tools - Two Case Studies from Mathematics Education

Computational Thinking (CT), the conceptual foundations required for solving complex problems effectively and efficiently, is an imperative skill for today’s learners, across disciplines and across ages. As for its importance in the wide network of digital literacies, CT has been implemented in various educational contexts, however mostly via digital artifacts, specifically in programming-related activities. This approach does not fulfill the potential of such integration for promoting learning. In this paper, we argue that digital literacies should not be bound to the digital realm, and present a novel approach to fully integrate CT into learning and teaching by the development of CT-infused “unplugged” pedagogical tools that are fully embedded in the subject matter. We demonstrate the advantages to student learning of these tools in two case studies from K-12 mathematics education: 1) An assistive tool for function investigation in high-school calculus – in this case, we point out to how CT skills that were implemented in the tool promoted students’ problem solving; 2) A teaching unit for pattern sequences in elementary school – in this case, we show how students in the CT research group outperformed students in the other groups, and how they implemented CT to solve pattern sequence problems effectively and efficiently. We highlight that besides enhancing learning, this approach can help address the important issue of equity in education, and suggest ways to promote it via dedicated professional development.

An optimal scheduling method for heavy haul trains with virtual coupling technology

ABSTRACT Heavy haul transportation enjoys the benefits of cost-efficiency by using long trains, but suffers from high cost in combination process and low efficiency in bottleneck areas. Virtual coupling technology (VCT) is helpful in resolving the dilemma. In this paper, we propose an optimal method for scheduling the route, timetable, and combination scheme of heavy haul trains with VCT in an integrated way. Specifically, six groups of constraints are first established, in which the sequence and train length problems incorporated by virtual coupling are properly handled. Then, a combined objective function comprised makespan and total flowtime indicators is proposed to optimize the throughput and turnover rate and further speeds up the optimization convergence. Finally, the optimization problem equipped with the proposed constraints and the combined objective function is transformed into an integer linear programming problem and solved by GUROBI. Simulation results demonstrate the effectiveness and advantages of the proposed method in leveraging VCT, and the benefits of virtual coupling over traditional physical coupling for heavy haul transportation.

Optimizing Road–Rail Multimodal Transport Schedule for Emergency Response with Congestion and Transshipment Sequence Selection

The current literature mainly uses hub capacity or transport route selection to manage the congestion of emergency multimodal transport and pays less attention to transshipment scheduling. This paper proposes an integrated optimization problem of transport routes and transshipment sequences (ITRTSP) and constructs a hybrid flow shop scheduling model to describe it. Based on this model, a recursive method is proposed to calculate the minimum waiting times that cargoes consume in queues at hubs, given the transport routes and transshipment sequences. Furthermore, a memetic algorithm is designed with route selection as the outer layer and transshipment sequence selection as the inner layer for solving ITRTSP. Compared with existing achievements, the model and algorithms can quantify the dependency between transshipment sequence selection and emergency transport time in multimodal transport network settings. The model and algorithms are applied to solve some real-scale examples and compared with the first-come-first-served (FCFS) rule commonly used in the current literature. The results indicate that the makespan is reduced by up to approximately 4.2%, saving 33.68 h. These findings demonstrate that even with given hub capacities and transport routes, congestion can still be managed and the schedule optimized through transshipment scheduling, further improving emergency transport efficiency.

An effective two-stage heuristic for scheduling the distributed assembly flowshops with sequence dependent setup times

This paper studies the Distributed Assembly Permutation Flowshop Scheduling Problem with Sequence Dependent Setup Times (DAPFSP-SDST). The optimization objective is minimization of maximal completion time (makespan), and it is shown that minimizing the sum of Total Setup Times for Assembling Products (TSTAP) and Total Idle Times on Assembly Machine (TITAM) is equivalent to minimizing the makespan. Additionally, minimization of TSTAP and TITAM can be transformed into sequencing problems of products and jobs within critical products, respectively. We also find out that a product sequence essentially explores an area in the solution space, with solutions in the area having the same TSTAP but different TITAMs determined by Critical-Jobs-Sequences (CJSs). Based on the new findings, an effective Two-Stage Heuristic Algorithm (TSHA) is proposed to first obtain promising product sequences and then exploit the corresponding areas for the DAPFSP-SDST. At the first stage of TSHA, high-quality initial product sequences are obtained through a constructive method and two Neighborhood Descent for Product Sequence (NDPS) algorithms are presented for more potential searching areas. At the second stage, a Neighborhood Descent for CJS (NDCJS) is designed to find CJSs with TITAM as small as possible. Evaluations on a benchmark instance set show that TSHA achieves better performance compared to the existing meta-heuristic and hyper-heuristic algorithms on the DAPFSP-SDST. Another impressive advantage of the TSHA is its low computational cost, as it is a heuristic algorithm with some neighborhood search operators.

Constructability-driven design of frame structures with state-space search methods

In the design of frames and trusses, the relationship between the structural form, the construction sequence, and the structural behavior during construction is rarely systematically considered. This paper proposes a method to systematically consider constructability in design, specifically focusing on minimizing the maximum displacement or stress during construction. The paper presents the formulation of the optimal assembly sequencing problem as a state-space search with a unique design of the search heuristic and constraint bounding schemes. It proposes three variants of heuristic search algorithms for finding a feasible sequence, a diverse set of feasible sequences, and an optimal sequence, respectively. These algorithms are tested on a case study structure to showcase their ability to navigate the combinatorial space of assembly sequences, and provide means for designers to incorporate sequence-related performance into conceptual structural design.

Combinatorial lower bounds for the Generalized Traveling Salesman Problem with Neighborhoods

In this paper, we study the Generalized Traveling Salesman Problem with Neighborhoods (GTSPN), a variant of the Traveling Salesman Problem (TSP), where the goal is to find the shortest path visiting each of the given neighborhood sets represented as a set of convex regions. The GTSPN is motivated by the sequencing problem of robotic manipulators, where an operation can be achieved from multiple locations, such as the visual inspection that can be performed from several possible views. The GTSPN formulation allows for exploiting continuous optimization to find the most suitable locations for the inspection, yielding possible solution cost reduction. Moreover, instances with overlapping high-dimensional convex regions further allow modeling neighborhood sets with complex shapes. We propose a novel approach to determine the first lower bounds to the studied GTSPN by employing the Branch-and-Bound (BB) method and the Mixed-Integer Second-Order Cone Programming (MISOCP) model for particular BB subproblems. In addition, the proposed method allows for solving the GTSPN to optima. The developed lower bound determination is further exploited in the empirical evaluation of existing heuristic approaches to the GTSPN and assesses the solution quality using the relative optimality gap. Regarding the presented results, the proposed BB-based approach provides tight lower bounds and solutions with up to 20% optimality gap for the GTSPN instances with less than 15 neighborhood sets for the given limited computational time. Furthermore, the presented results support that the proposed approach is suitable for solving high-dimensional instances of the GTSPN that can be found in inspection tasks with robotic manipulators.

An Approach for Solving Fuzzy Sequencing Problem with Triangular Fuzzy Neutrosophic Numbers Using Ranking Function

This study presents a modified approach to address the sequencing problem, specifically when the values are expressed using Triangular Fuzzy Neutrosophic Numbers. First, we have to convert Triangular Neutrosophic fuzzy numbers into crisp one by using Score function. A numerical example has been considered and solved for illustration purpose.

Augmented multi-agent algorithm utilizing intelligent search range detection heuristic to solve assembly line sequencing problem: A case study in the truck industry

Car sequencing is a widely adopted approach to implicitly enhance the short-term balancing of mixed-model assembly lines in practical applications. In this research, we investigate this problem in the context of a real-world case taken from a truck final assembly line. Our study considers cross-ratio constraints that address limitations related to dependent features of the products. Additionally, to manage the distribution of violation errors in cases where predefined congestion level thresholds (soft rules) exist, we propose a piecewise linear penalty function. To tackle these considerations, we formulate a mathematical programming model aimed at minimizing the total weighted penalty incurred from violating both independent and dependent sequencing rules. As the problem falls into the NP-hard category, we develop a multi-start parallel local search algorithm based on multi-agent approach to efficiently solve it. The algorithm is augmented by incorporating a self-adaptive diversification mechanism and a novel intelligent search range detection heuristic. The proposed heuristic enables the searching agents to intelligently explore the solution space rather than relying on a purely random search. The numerical experiments are conducted using 30 real-world instances from three truck assembly lines to validate the quality of the proposed algorithm. The comparison of the results indicates that our algorithm outperforms state-of-the-art algorithms. Furthermore, computational experiments demonstrate the effectiveness of each proposed mechanism in enhancing the algorithm's performance.

A dynamic programming algorithm for order picking in robotic mobile fulfillment systems

AbstractThe order scheduling and rack sequencing problem deals with the order picking process in robotic mobile fulfillment systems: automated guided vehicles lift and transport movable storage racks to picking stations to supply items requested by customer orders which are put together in cardboard boxes on a workbench of limited capacity. To efficiently operate the station, the sequence in which racks visit the station one after another and the intervals at which customer orders are scheduled must be coordinated. We present a dynamic programming algorithm for the order scheduling and rack sequencing problem at a single picking station minimizing the number of rack visits. Despite monolithic mixed integer linear programming formulations, our approach appears to be the first combinatorial solution method for the problem in literature. A computational study demonstrates the effectiveness of the approach.

Arrival and Departure Sequencing, Considering Runway Assignment Preferences and Crossings

Aircraft sequencing has the potential to decrease flight delays and improve operational efficiency at airports. This paper presents the aircraft sequencing problem (ASP) on multiple runways with complex interactions by allocating flights on runways and optimizing landing times, take-off times, and crossing times simultaneously in a uniform framework. The problem was formulated as a mixed-integer program considering realistic operational constraints, including runway assignment preferences based on the entry/exit fixes of the terminal maneuvering area (TMA), minimum runway separation, time window, and arrival crossing rules. Variable-fixing strategies were applied, to strengthen the formulation. A first-come-first-served (FCFS) heuristic was proposed for comparison. Various instances from the literature and from realistic data sets were tested. Our computational study showed that the solution approach optimizes runway schedules, to achieve significantly fewer flight delays, taking runway assignment preferences and arrival crossings into account.

Generalized welfare lower bounds and strategyproofness in sequencing problems

Generalized welfare lower bounds and strategyproofness in sequencing problems

Combining balancing, sequencing and buffer allocation decisions to improve the efficiency of mixed-model asynchronous assembly lines

When the production of a variety of models is considered in assembly lines, the more suitable control system is asynchronous: the pace of the line is not imposed but depends on the actual time needed in each work centre to complete a product. In asynchronous lines blocking and starvation phenomena are responsible of performances deterioration. Three popular techniques able to limit these phenomena are: balancing the workload among stations by tasks assignment, allocating buffers between stations, optimizing of the sequence of models entering the line. These three techniques correspond to the Mixed-model Assembly Line Balancing Problem (MALBP), the buffer allocation problem (BAP) and the sequencing problem (SP). The presented approach is able to simultaneously solve these three problems, which are strictly connected, and is based on a Genetic Algorithm procedure that uses discrete event simulation to evaluate the fitness function of individuals. Tests conducted on a set of benchmark instances allow quantifying the effect on line efficiency of sequencing and buffer allocation decisions separately, with the aim to assess which of the two techniques is more effective and to investigate whether if combining sequencing optimization and buffer allocation gives additional advantages in terms of increasing line efficiency.

Continual learning approaches to hand–eye calibration in robots

This study addresses the problem of hand–eye calibration in robotic systems by developing Continual Learning (CL)-based approaches. Traditionally, robots require explicit models to transfer knowledge from camera observations to their hands or base. However, this poses limitations, as the hand–eye calibration parameters are typically valid only for the current camera configuration. We, therefore, propose a flexible and autonomous hand–eye calibration system that can adapt to changes in camera pose over time. Three CL-based approaches are introduced: the naive CL approach, the reservoir rehearsal approach, and the hybrid approach combining reservoir sampling with new data evaluation. The naive CL approach suffers from catastrophic forgetting, while the reservoir rehearsal approach mitigates this issue by sampling uniformly from past data. The hybrid approach further enhances performance by incorporating reservoir sampling and assessing new data for novelty. Experiments conducted in simulated and real-world environments demonstrate that the CL-based approaches, except for the naive approach, achieve competitive performance compared to traditional batch learning-based methods. This suggests that treating hand–eye calibration as a time sequence problem enables the extension of the learned space without complete retraining. The adaptability of the CL-based approaches facilitates accommodating changes in camera pose, leading to an improved hand–eye calibration system.

Students' Specializing Thinking in Solving Arithmetic Sequence and Series Problems

Specializing thinking is a mathematical thinking process that is very important in the mathematics learning process. Specializing thinking is thinking by starting with special things. The application of specializing thinking can be an effective strategy for teachers to improve students' mathematical thinking abilities in dealing with various problems. This research aims to describe students' specializing forms of thinking in solving problems of arithmetic sequences and series. This research method is descriptive qualitative research with a case study approach. The subjects in this research were 2 class X high school students who successfully solved arithmetic sequence and series problems. The instruments used are tests and interviews. The data in this research are the results of the subject's work and the results of interview transcripts. The research results show that two forms of specializing thinking were found in solving arithmetic sequences and series problems, that is explicit schematic representation and implicit schematic representation. It is hoped that the results of this research can provide a valuable contribution to mathematics teachers in designing more meaningful learning.

Fluency and Flexibility of Students in Solving Arithmetic Sequence Problems Based on Self Efficacy

This research is a descriptive qualitative research that aims to describe students' creative thinking in solving arithmetic sequence problems in terms of self-efficacy. This research was conducted at SMK Kesehatan Dhermajaya. The subjects of this study were 3 grade XI students who were taken according to the aspects of self-efficacy, namely magnitude, strength, generality. Creative thinking indicators in this study are fluency, flexibility, and novelty. The results of this study indicate that the magnitude subject in the fluency aspect is that students can provide 2 possible jars needed. The flexibility aspect is that students can solve with 2 alternative solutions with the arithmetic sequence formula, and write an arithmetic sequence that is at odds with 2 and if added together the result is 90. The strength of the fluency aspect is that students provide 2 possible jars needed. At the same time, the aspects of flexibility and novelty have not been fulfilled. The subject of generality in the fluency aspect is that students can provide 3 possible jars. The flexibility aspect is that students can provide 2 different alternative solutions, namely using the arithmetic sequence formula and writing an arithmetic sequence that is 2 apart and then adding them up. The three subjects, still have not shown the uniqueness of the answer or used different strategies so that the novelty aspect has not been fulfilled.

Solving an Order Batching and Sequencing Problem with Reinforcement Learning

The purpose of this research is to determine whether a DRL solution would be a suitable solution for the OBSP problem and to compare it with traditional methods. For this purpose, models trained with the PPO algorithm were tested in a complex and realistic warehouse environment, and an attempt was made to measure whether a strategy was developed to decrease the number of orders being late. A heuristic method was also applied and the results were compared on the same environment and data. The results showed that DRL approach that combines heuristics with the PPO algorithm has a better performance than the heuristics in minimizing the tardy order percentage in all tested scenarios.

From Protein to a Bioactive Peptide: Potent IL‐6 Peptide Antagonist Generated by a Novel Method

ABSTRACTHere, we describe a new technology, designed to accelerate peptide discovery by quick identification and optimization of the residues critical for protein–protein interactions or ligand binding. We called it PepFusion. It is based on ligation of short DNA sequences generated from known ligand‐binding regions. We tested it by selecting peptide antagonists of interleukin‐6 (IL‐6), a key mediator of inflammatory diseases such as rheumatoid arthritis (RA), Crohn's disease, and Castleman disease (CD). The PepFusion library demonstrated superiority over a random library by yielding a peptide with low micromolar affinity for IL‐6, whereas the random library failed. The affinity of the peptide from the PepFusion library was further enhanced by additional rounds of mutagenesis leading to peptide variants with low nanomolar IL‐6 affinity. In addition to generating high‐affinity peptides, our method opens the way to solve the problem of the false positive sequences, which are common with all display technologies.