Set Of Instances Research Articles

An exact algorithm for two-dimensional cutting problems based on multi-level pattern

A multi-level pattern is proposed for the unconstrained two-dimensional cutting problems of rectangular items, and an exact generation algorithm is constructed. The arrangement of rectangular items with the same type in multiple rows and columns is referred to as a 0-level pattern. An n-level pattern is the horizontal or vertical combination of an n-1 level pattern with a pattern whose level will not exceed n-1. The generation algorithm of multi-level pattern is constructed on the base of dynamic programming, and the multi-level patterns with various sizes are generated with increased level order. The normal size is chosen to reduce unnecessary computation in the algorithm. Three sets of benchmark instances and one set of random production instance from the literatures are used for comparison. Comparing to the exact algorithm in the literature, the results in this paper are equivalent, but the computation time is shorter. Comparing to heuristic algorithms in literatures, the results in this paper are better and the computation time is still good enough for practical applications.

Clinical and genetic features of dominant Essential Tremor in Tuscany, Italy: FUS, CAMTA1, ATXN1 and beyond

ObjectiveEssential Tremor (ET) is one of the most common neurological disorders. In most instances ET is inherited as an autosomal dominant trait with age-related penetrance (virtually complete in advanced age); however, ET genetics remains elusive. The current study aims to identify possibly pathogenic genetic variants in a group of well-characterized ET families. Methods34 individuals from 14 families with dominant ET were clinically evaluated and studied by whole exome sequencing studies (after excluding trinucleotide expansion disorders). ResultsMost patients had pure ET. In 4 families, exome studies could identify a genetic variant potentially able to significantly alter the protein structure (CADD >20, REVEL score > 0.25), shared by all the affected individuals (in CAMTA1, FUS, MYH14, SGCE genes). In another family there were two variants in dominant genes (PCDH9 and SQSTM1). Moreover, an interrupted “intermediate” trinucleotide expansion in ATXN1 (“SCA1”) was identified in a further family with pure ET. ConclusionCombining our observations together with earlier reports, we can conclude that ET genes confirmed in at least two families to date include CAMTA1 and FUS (reported here), as well as CACNA1G, NOTCH2NLC and TENM4. Most cases of familial ET, inherited with an autosomal dominant inheritance, may result from “mild” variants of many different genes that, when affected by more harmful genetic variants, lead to more severe neurological syndromes (still autosomal dominant). Thus, ET phenotype may be the “mild”, incomplete manifestation of many other dominant neurogenetic diseases. These findings further support evidence of genetic heterogeneity for such disease(s).Author's keywords: cerebellar ataxias, movement disorders, neurogenetics, rare neurological disorders, tremor.

Multi-Instance Learning with One Side Label Noise

Multi-instance Learning (MIL) is a popular learning paradigm arising from many real applications. It assigns a label to a set of instances, which is called a bag, and the bag’s label is determined by the instances within it. A bag is positive if and only if it has at least one positive instance. Since labeling bags is more complicated than labeling each instance, we will often face the mislabeling problem in MIL. Furthermore, it is more common that a negative bag has been mislabeled to a positive one, since one mislabeled instance will lead to the change of the whole bag label. This is an important problem that originated from real applications, e.g., web mining and image classification, but little research has concentrated on it as far as we know. In this article, we focus on this MIL problem with one side label noise that the negative bags are mislabeled as positive ones. To address this challenging problem, we propose, to the best our our knowledge, a novel multi-instance learning method with one side label noise. We design a new double weighting approach under traditional framework to characterize the “faithfulness” of each instance and each bag in learning the classifier. Briefly, on the instance level, we employ a sparse weighting method to select the key instances, and the MIL problem with one size label noise is converted to a mislabeled supervised learning scenario. On the bag level, the weights of bags, together with the selected key instances, will be utilized to identify the real positive bags. In addition, we have solved our proposed model by an alternative iteration method with proved convergence behavior. Empirical studies on various datasets have validated the effectiveness of our method.

Solution algorithms for dock scheduling and truck sequencing in cross-docks: A neural branch-and-price and a metaheuristic

In this study, we present a novel mathematical model for the integrated challenge of dock scheduling and truck sequencing in cross-docking facilities. Many existing models for variations of this problem in the literature rely on big-M constraints, known for their poor performance when applied to general-purpose mixed-integer programming solvers. This introduces significant limitations, especially when tackling smaller instances. Consequently, most solution methods in the literature gravitate towards metaheuristic approaches. Our proposed model offers a compact formulation without any big-M constraints, utilizing 4-index variables. It is strategically designed to lend itself well to a dual decomposition approach (Dantzig–Wolfe) and can be effectively solved using a branch-and-price methodology. We address the pricing problem through a branch-and-cut scheme and illustrate its efficiency in handling large instances. Recognizing the pricing problem as the primary bottleneck, we employ a deep neural network trained on a comprehensive set of instances with the same distribution to predict promising duals. Extensive computational experiments demonstrate a notable reduction of over 50% in overall computational times. Additionally, we introduce a heuristic sharing similarities with certain Variable Neighborhood Search (VNS) approaches. Our proposed heuristic has proven highly efficient in extensive computational experiments.

Optimal decomposition approach for solving large nesting and scheduling problems of additive manufacturing systems

This paper addresses the challenges associated with nesting and production scheduling in additive manufacturing (AM). The problem studied consists of grouping a set of parts into batches, which are then assigned to and sequenced across the available machines, guaranteeing the production of all parts. This work stands out by proposing exact methods for the AM nesting and scheduling problem considering irregular-shaped parts with specific release dates, processing times, and due dates, with the aim of minimizing the cumulative tardiness. The proposed approaches include two logic-based Benders decompositions: one combining Mixed Integer Programming (MIP) and Constraint Programming (CP), and the other relying solely on CP. To deal with the sub-problems, a strategic procedure was developed to reduce the solution space while maintaining low resolution times per iteration. Problem-specific cuts are also generated to improve the efficiency of these approaches. Computational experiments show that both decompositions significantly outperform a prior monolithic CP model, with the decomposition based solely on CP yielding the best results. Moreover, the results show that this approach has the potential to achieve similar computational performance of non-exact approaches that are currently considered state-of-the-art. A set of instances is provided to serve as a benchmark for future studies.

A Practical Methodology for Reproducible Experimentation: An Application to the Double-Row Facility Layout Problem.

Reproducibility of experiments is a complex task in stochastic methods such as evolutionary algorithms or metaheuristics in general. Many works from the literature give general guidelines to favor reproducibility. However, none of them provide both a practical set of steps or software tools to help in this process. In this article, we propose a practical methodology to favor reproducibility in optimization problems tackled with stochastic methods. This methodology is divided into three main steps, where the researcher is assisted by software tools which implement state-of-the-art techniques related to this process. The methodology has been applied to study the double-row facility layout problem (DRFLP) where we propose a new algorithm able to obtain better results than the state-of-the-art methods. To this aim, we have also replicated the previous methods in order to complete the study with a new set of larger instances. All the produced artifacts related to the methodology and the study of the target problem are available in Zenodo.

QAL-BP: an augmented Lagrangian quantum approach for bin packing

The bin packing is a well-known NP-Hard problem in the domain of artificial intelligence, posing significant challenges in finding efficient solutions. Conversely, recent advancements in quantum technologies have shown promising potential for achieving substantial computational speedup, particularly in certain problem classes, such as combinatorial optimization. In this study, we introduce QAL-BP, a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation designed specifically for bin packing and suitable for quantum computation. QAL-BP utilizes the Augmented Lagrangian method to incorporate the bin packing constraints into the objective function while also facilitating an analytical estimation of heuristic, but empirically robust, penalty multipliers. This approach leads to a more versatile and generalizable model that eliminates the need for empirically calculating instance-dependent Lagrangian coefficients, a requirement commonly encountered in alternative QUBO formulations for similar problems. To assess the effectiveness of our proposed approach, we conduct experiments on a set of bin packing instances using a real Quantum Annealing device. Additionally, we compare the results with those obtained from two different classical solvers, namely simulated annealing and Gurobi. The experimental findings not only confirm the correctness of the proposed formulation, but also demonstrate the potential of quantum computation in effectively solving the bin packing problem, particularly as more reliable quantum technology becomes available.

Approximation of the Objective Function of Single-Machine Scheduling Problem

The problem of the approximation of the coefficients of the objective function of a scheduling problem for a single machine is considered. It is necessary to minimize the total weighted completion times of jobs with unknown weight coefficients when a set of problem instances with known optimal schedules is given. It is shown that the approximation problem can be reduced to finding a solution to a system of linear inequalities for weight coefficients. For the case of simultaneous job release times, a method for solving the corresponding system of inequalities has been developed. Based on it, a polynomial algorithm for finding values of weight coefficients that satisfy the given optimal schedules was constructed. The complexity of the algorithm is O(n2(N+n)) operations, where n is the number of jobs and N is the number of given instances with known optimal schedules. The accuracy of the algorithm is estimated by experimentally measuring the function ε(N,n)=1n∑j=1n∣wj−wj0∣wj0, which is an indicator of the average modulus of the relative deviation of the found values wj from the true values wj0. An analysis of the results shows a high correlation between the dependence ε(N,n) and a function of the form α(n)/N, where α(n) is a decreasing function of n.

Attacking Click-through Rate Predictors via Generating Realistic Fake Samples

How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack methods may fail to construct realistic fake samples. Meanwhile, most recommendation models adopt click-through rate (CTR) predictors, which usually utilize black-box deep models with discrete features as input. Thus, how to efficiently construct realistic fake samples for black-box recommender systems is still full of challenges. In this article, we propose a hierarchical adversarial attack method against black-box CTR models via generating realistic fake samples, named CTRAttack. To better train the generation network, the weights of its embedding layer are shared with those of the substitute model, with both the similarity loss and classification loss used to update the generation network. To ensure that the discrete features of the generated fake samples are all real, we first adopt the similarity loss to ensure that the distribution of the generated perturbed samples is sufficiently close to the distribution of the real features, and then the nearest neighbor algorithm is used to retrieve the most appropriate features for non-existent discrete features from the candidate instance set. Extensive experiments demonstrate that CTRAttack can not only effectively attack the black-box recommender systems but also improve the robustness of these models while maintaining prediction accuracy.

Generalized planning as heuristic search: A new planning search-space that leverages pointers over objects

Planning as heuristic search is one of the most successful approaches to classical planning but unfortunately, it does not trivially extend to Generalized Planning (GP); GP aims to compute algorithmic solutions that are valid for a set of classical planning instances from a given domain, even if these instances differ in their number of objects, the initial and goal configuration of these objects and hence, in the number (and possible values) of the state variables. State-space search, as it is implemented by heuristic planners, becomes then impractical for GP. In this paper we adapt the planning as heuristic search paradigm to the generalization requirements of GP, and present the first native heuristic search approach to GP. First, the paper introduces a new pointer-based solution space for GP that is independent of the number of classical planning instances in a GP problem and the size of those instances (i.e. the number of objects, state variables and their domain sizes). Second, the paper defines an upgraded version of our GP algorithm, called Best-First Generalized Planning (BFGP), that implements a best-first search in our pointer-based solution space for GP. Lastly, the paper defines a set of evaluation and heuristic functions for BFGP that assess the structural complexity of the candidate GP solutions, as well as their fitness to a given input set of classical planning instances. The computation of these evaluation and heuristic functions does not require grounding states or actions in advance. Therefore our GP as heuristic search approach can handle large sets of state variables with large numerical domains, e.g. integers.

Enhanced methods for the weight constrained shortest path problem

AbstractThe classic problem of constrained pathfinding is a well‐studied, yet challenging, network optimization problem with a broad range of applications in various areas such as communication and transportation. The weight constrained shortest path problem (WCSPP), the base form of constrained pathfinding with only one side constraint, aims to plan a cost‐optimum path with limited weight/resource usage. Given the bi‐criteria nature of the problem (i.e., dealing with the cost and weight of paths), methods addressing the WCSPP have some common properties with bi‐objective search. This article leverages the recent state‐of‐the‐art techniques in both constrained pathfinding and bi‐objective search and presents two new solution approaches to the WCSPP on the basis of A* search, both capable of solving hard WCSPP instances on very large graphs. We empirically evaluate the performance of our algorithms on a set of large and realistic problem instances and show their advantages over the state‐of‐the‐art algorithms in both time and space metrics. This article also investigates the importance of priority queues in constrained search with A*. We show with extensive experiments on both realistic and randomized graphs how bucket‐based queues without tie‐breaking can effectively improve the algorithmic performance of exhaustive A*‐based bi‐criteria searches.

A comparative study of alternative formulations for the periodic vehicle routing problem

This study investigates the periodic vehicle routing problem (PVRP) and its variant with time windows with a particular focus on alternative formulation approaches that can be solved by a state-of-the-art commercial solver. We propose a new vehicle flow formulation for the PVRP and strengthen it with valid inequalities. We also investigate two prominent formulations for the PVRP available in the literature: a commodity flow formulation, referred to as the load-based formulation, and a cut-based formulation which is adapted from a formulation originally developed for a variant of the PVRP. We also extend these formulations to model the PVRP with time windows (PVRPTW) and employ valid inequalities to tighten the resulting formulations. A comprehensive computational study is then carried out to compare the performances of alternative PVRP and PVRPTW formulations on various sets of benchmark instances with different characteristics. The results attest to the robustness and the consistency of the proposed formulation and its strengthened versions in producing good quality solutions, especially for large instances although the load-based formulations tend to perform well in small instances.

An Exact Approach to the Multi-Compartment Vehicle Routing Problem: The Case of a Fuel Distribution Company

Over the years, the vehicle routing problem has been studied by several authors, creating several extensions, such as the multi-compartment vehicle routing problem. Several studies in the literature have addressed this problem, but few have solved it through exact approaches owing to model convolution. In this way, a mathematical model is proposed for the multi-compartment vehicle routing problem with time windows, in which three types of fuel products are distributed to a set of customers using a limited homogeneous fleet. The model explicitly considers time windows, as well as regulatory rest times for the drivers and time limits for each trip and for working schedules, addressing a real company’s decision support requirements, which is scarce in the literature. The optimal solution determines, for each vehicle, the distribution route and time to carry out the deliveries with the corresponding loading of products to compartments, complemented by the calculation of carbon emissions. The main objective is to minimize the total distance traveled, which corresponds to the sum of the distances traveled by each one of the allocated vehicles. The results allow the assessment of the solution optimization applied to a set of instances for a Portuguese company to evaluate the performance and compare decision support improvements with current baseline company procedures.

Multiobjective Multiverse Optimizer for Multirobotic U-Shaped Disassembly Line Balancing Problems

The development of technology accelerates the upgrade of products, which results in a significant number of obsolete products. This research aims to solve the Multi-robotic Multi-product U-shaped Disassembly-line-balancing Problem (M2UDP), in which different products are disassembled on a U-shaped model in a pre-set cycle time and assigned tasks to robots in each workstation reasonably. A linear mixed-integer model is established to maximize disassembly profit and minimize carbon emissions. An improved multi-objective multi-verse optimizer (IMMO) that utilizes the sigmoid activation function in neural networks is proposed to find the optimal plan for the model. The improved algorithm is verified via a set of real-life instances and compared with three classical multi-objective optimization algorithms. The experimental results show that the proposed IMMO performs better than those peers in solving the M2UDP problems.

Multi-period time window assignment for attended home delivery

We study a multi-period stochastic variant of the Time Window Assignment Vehicle Routing Problem, where customers’ demands, locations, and service times are uncertain. Customers are partitioned into geographical zones, each of which has to be visited a predetermined number of times over a planning period of several days. Whenever a zone is visited, a time window is assigned. Time windows are decided before knowing customers and their demands. A fleet of homogeneous vehicles is available to serve customers each day. At a tactical level, the problem looks for a static time window assignment that minimizes the expected traveling costs plus the expected penalty costs for unserved customers. We propose a two-stage formulation and a solution approach, which relies on the Sample Average Approximation Method, while encompassing a perturbation method to assign time windows in the first stage and an Adaptive Large Neighborhood Search to optimize routes in the second stage. We experimentally evaluate three instance sets, including real ones from a Canadian company, comparing our results to lower bounds from the exact solution of a deterministic equivalent formulation over a finite number of scenarios. Our method outperforms the manual approach used by the company.

Transfer learning based covariance matrix adaptation for evolutionary many-objective optimization

Evolutionary Algorithms (EA) have proven successful in solving Multi- and Many-objective Optimization Problems (MOPs/MaOPs) in numerous application areas. However, the various linkages among the decision variables may have posed a challenge for traditional evolutionary algorithms for solving such problems. In this paper, a transfer learning based covariance matrix adaptation algorithm, shortened as TL-M2M-CMA, is proposed to handle MOPs/MaOPs with complex decision variable linkage. In TL-M2M-CMA, the search population is first decomposed into a set of subpopulations and each subpopulation corresponds to a specific part of the Pareto-optimal Front (PF) of the MOP/MaOP. During the search, a covariance matrix adaptation process is utilized to learn the linkage among the decision variables, and then the covariance matrix is incorporated into the crossover and mutation operators for better efficiency. To facilitate performance comparison, a set of scalable MOP test instances with various linkage complexities is constructed for experimental studies. The performance of the proposed TL-M2M-CMA on these constructed instances is verified by comparing it to the performance of five state-of-the-art EA methods.

Online hierarchical streaming feature selection based on adaptive neighborhood rough set

In the era of open machine learning, a kind of data is accompanied by a hierarchical structure between classes in the label space and the increasing number of features. Therefore, hierarchical classification learning in dynamic changing feature spaces remains an essential research challenge. To address this challenge, we propose an online hierarchical streaming feature selection algorithm based on adaptive neighborhood rough set in this paper, which effectively selects closely interactive features in high-dimensional data with a hierarchical structure. First, a subtree instance set is obtained for each internal node based on the parent–child relationship of the hierarchical structure. Then, an adaptive neighborhood rough set is constructed, and the neighborhood granularity of each instance is auto selected according to the subtree instance set of its parent node. Moreover, an online streaming feature selection framework for hierarchical data is proposed, in which, the streaming feature is evaluated by three steps: online significance analysis, online relevance analysis, and online redundancy analysis. Finally, experiments are conducted on seven hierarchical datasets to evaluate the performance of the proposed algorithm, and extensive results demonstrate that the proposed algorithm outperforms other state-of-the-art online streaming feature selection algorithms.

Knowledge-transfer based genetic programming algorithm for multi-objective dynamic agile earth observation satellite scheduling problem

The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually designed heuristics and iterative-based methods used in previous research, genetic programming based hyper heuristics (GPHH) can automatically evolve high-quality heuristic rules (HRs) for real-time scheduling without being highly dependent on expert knowledge. In this paper, a knowledge-transfer based multi-objective GPHH framework (KT-MOGP) is proposed, equipped with a heuristic-based simulation considering the idle monitoring, to evolve non-dominated HRs for solving MO-DAEOSSP. The heuristic-based simulation generates feasible schedules and returns fitness values for given HRs, which are the individuals evolved by KT-MOGP. KT-MOGP applies a knowledge transfer mechanism to accelerate convergence. Once a source problem is trained, its non-dominated solutions are extracted and their feature importance is transferred to guide the initialization of another target problem, by which the knowledge generated during the training process can be fully utilized. Experimental results on three sets of instances show that KT-MOGP outperforms the existing GPHH-based method and that the evolved HRs are competitive compared to several classical constructive heuristics and multi-objective evolutionary algorithms. The results also show the effectiveness of the proposed knowledge transfer-based initialization. To the best of our knowledge, this study is the first attempt to consider both multi-objective scenarios and real-time arrival requests.

Multi-product disassembly line balancing optimization method for high disassembly profit and low energy consumption with noise pollution constraints

Remanufacturing attains much attention from both industrial and academic sectors due to its beneficial roles in energy conservation and environment protection, where disassembly is a crucial part. To reach the comprehensive sustainability and global optimization of disassembling multiple end-of-life products, this paper suggests a multi-objective multi-product disassembly line balancing problem with considering disassembly profit, energy consumption and noise pollution simultaneously. According to the natures of the problem under consideration, a multi-objective integer programming model is constructed. Its goals are to reach maximum disassembly profit and realize minimum energy consumption while observing noise pollution requirements and resource constraints. Accordingly, a multi-objective group teaching optimization algorithm is specially devised. In it, rank and crowding distance methods are employed to partition the population into two groups. Moreover, precedence preserving crossover and mutation methods are severally used on the two groups to realize the teacher phase. Furthermore, to achieve the student phase, an adaptive local search method is applied to refine solutions in an external archive, and thus its exploitation ability is enhanced. By executing contrast experiments between the devised approach and its powerful competitors on a set of real-world test instances, the experimental results validate that it has highly-adaptive and well-superior performance in tackling the problem of concern.

A cooperative evolutionary algorithm with simulated annealing for integrated scheduling of distributed flexible job shops and distribution

Production and distribution are two essential parts in supply chains. An integration of production and distribution has received amount of attention from both academia and industry. This article investigates an integrated scheduling problem of distributed flexible job shops and distribution. First, a mixed integer programming model is developed to minimize maximum completion time. Second, a cooperative evolutionary algorithm with simulated annealing methods is proposed in consideration of the problem's characteristics. A three-link integer string is designed to represent three parts, i.e., factory assignment, machine assignment and operation sequence, in a solution. Three populations are constructed to search the above three parts via genetic operations and simulated annealing methods, respectively. A cooperation strategy is designed to generate a schedule. Then, a heuristic rule is devised to determine the delivery scheme for distribution. Finally, experiments are carried out via extending a set of benchmark instances. The proposed algorithm is compared to four state-of-the-art algorithms and an exact solver CPLEX. The results and discussions verify that the developed model and method have strong competitiveness for solving the considered problems.