Feasible Sequences Research Articles

Automatic Column Sequencing and Optimization for Homogeneous Azeotropic Mixtures

A method is developed for determining feasible sequences for separating mixtures with azeotropes and optimizing each column in every sequence (including two-column subsequences for azeotropic disti...

A decomposition heuristic for rotational workforce scheduling

In rotational workforce planning, a schedule is constructed from a sequence of work and rest periods. Each employee starts at a different part of the schedule, and after a certain amount of time, the schedule repeats. The length of the schedule increases with a higher number of employees. At the same time, various constraints on work sequences and days off have to be considered. For a large number of employees, it is difficult to construct a schedule that meets the requirements. It is important to ensure low solution times independently of the problem instance characteristics. In this work, a novel decomposition approach for rotational shift scheduling is proposed. The decomposition exploits the fact that most constraints in rotational workforce scheduling are imposed on the work shift sequence. By considering a fixed set of blocks to cover the demand, the problem complexity can be greatly reduced. Given a fixed set of blocks, we propose a network model that determines whether a feasible sequence of shift blocks exists. The decomposition approach is applied to the problem structure of the Rotating Workforce Scheduling Problem but may be extended to different problem structures. In a computational study, the decomposition approach is compared to a mathematical formulation and previous exact and heuristic approaches. Computational results show that the decomposition approach greatly outperforms previous heuristics on the standard benchmarks.

Asynchronous parallel disassembly sequence planning for multi-manipulator based on improved shuffled frog leaping algorithm

To solve the problem of low disassembly efficiency of complex products, an asynchronous parallel disassembly sequence planning method of multi-manipulator based on improved shuffled frog leaping algorithm was proposed. Depending on the characteristics of this method, the disassembly manipulators are taken as the research object, and the minimum completion time of disassembly is taken as the optimization objective. Based on the basic disassembly time of parts, the disassembly preparation time caused by the change of disassembly tools and directions is considered, so that the objective function evaluation model is more realistic. Then, the mathematical model of asynchronous parallel disassembly sequence planning is constructed and the disassembly information modeling method is optimized. We proposed a new method based on the fastener constraint matrix and the part constraint matrix to build the information model of product disassembly. Based on this model, the detachable conditions are derived and the feasible disassembly sequence is obtained. In order to improve the performance of the proposed algorithm, a double-link encoding method and an efficient decoding operator is proposed. The adjustment orders which based on adjustment position and the substitution formulas are constructed to carry out the new segmental local evolution of frog individuals, and the update formula of frog position is reconstructed. Furthermore, the two-point mutation operator is added to the global information exchange strategy to enhance the search ability of the algorithm. The proposed algorithm is applied to a screw lifting mechanism and four products of varying complexity, and the results are compared with two algorithms. The results show that the proposed method can solve disassembly problems of different scales and is especially effective in solving large-scale disassembly problems.

Operation Sequencing Optimization in CAPP Using Hybrid Teaching-Learning Based Optimization (HTLBO)

Computer-aided process planning (CAPP) is an essential component in linking computer-aided design (CAD) and computer-aided manufacturing (CAM). Operation sequencing in CAPP is an essential activity. Each sequence of production operations which is produced in a process plan cannot be the best possible sequence every time in a changing production environment. As the complexity of the product increases, the number of feasible sequences increase exponentially, consequently the best sequence is to be chosen. This paper aims at presenting the application of a newly developed meta-heuristic called the hybrid teaching–learning-based optimization (HTLBO) as a global search technique for the quick identification of the optimal sequence of operations with consideration of various feasibility constraints. To do so, three case studies have been conducted to evaluate the performance of the proposed algorithm and a comparison between the proposed algorithm and the previous searches from the literature has been made. The results show that HTLBO performs well in operation sequencing problem.

Finite horizon constrained control and bounded-error estimation in the presence of missing data

In this paper, we propose an optimization-based design technique for constrained control and bounded-error state estimation for affine systems in the presence of intermittent measurements. We treat the affine system as a switched system where the measurement equation switches between two modes based on whether a measurement exists or is missing, and model potential missing data patterns with a finite-length language that constrains the feasible mode sequences. Then, we introduce a novel property, equalized recovery, that generalizes the equalized performance property and that allows us to tolerate missing observations. By utilizing Q-parametrization, we show that a finite horizon optimal estimator/controller can be constructed using time-based and prefix-based approaches, where the latter implicitly estimates the specific missing data pattern (i.e., mode sequence), within the given language, according to the prefix observed so far. We illustrate with numerical examples that the proposed approaches can provide desirable performance guarantees.

Modeling and Planning Multimodal Transport Paths for Risk and Energy Efficiency Using AND/OR Graphs and Discrete Ant Colony Optimization

Path sequence selection is important for multimodal transport processes. AND/OR graphs (AOG) have been applied to describe practical multimodal transport route planning problems by using `AND' and `OR' matrices. An AOG-based multimodal transport route planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While adjacency (AND) relationship issues can be addressed, contradiction (OR) relations are not well addressed by existing multimodal transport route planning methods. Thus, an ineffective result may be obtained in practice. The OR matrix is a conflict matrix that describes the choice of mode of transport in the process of multimodal transport. By using a contradiction matrix together with an adjacency matrix and tabu list, an approach used in existing work, this paper proposes an effective triple-phase generate route method (TPGR) to produce a feasible multimodal transport path sequence based on an AOG. This paper uses energy consumption to evaluate the multimodal transport energy efficiency. The information entropy is applied to describe the risks of the transport process. The energy consumption and the information entropy lead to a novel dual-objective optimization model where route energy consumption and route risk are minimized. An improved ant colony algorithm is developed to effectively generate a set of Pareto solutions for route selection, which are used for the dual-objective multimodal transport route optimization problem. This methodology is applied to practical multimodal transport route selection processes on two maps to verify its effectiveness and feasibility.

Automating production planning and control in pallet manufacturing – A case study

In this work, we investigate and formalize the production planning for a highly automated pallet production and provide a solution approach. According to the final pallet to be produced, in pallet production boards have to be cut and stacked in order to fulfil the demand at the assembly line. The overall production problem primarily consists of a cutting stock problem with a constraining open stack problem. They are mathematically modelled and solved using a heuristic approach that aims at minimizing the waste of material. Various different approaches to generate the possible cutting patterns are introduced based on the chronology of orders. In a next step, a feasible production sequence of the cutting patterns is computed. Furthermore, the number of generated patterns for the cutting stock problem and its impact on the feasibility of the production sequence as well as on the solution quality is investigated.

A Hybrid Estimation-of-Distribution Algorithm for Scheduling Flexible Job Shop With Limited Buffers Based on Petri Nets

This article focuses on the production scheduling problem in the flexible job shop (FJS) environment with limited buffers. Limited manufacturing resources and buffers may lead to blockage and deadlock phenomenon. In order to establish production scheduling with minimum makespan, the timed Petri net (PN) model of a production process is established. Based on this PN model, a novel Hybrid Estimation-of-Distribution Algorithm (HEDA) is proposed for solving the considered scheduling problem. A candidate solution for the problem is coded as an individual that consists of a route sequence for processing jobs and a permutation with repetition of jobs. A deadlock prevention policy is used to check the feasibility of individuals, such that it can be decoded into a feasible sequence of transitions, i.e., a feasible schedule. By using an effective voting procedure of elite individuals, two probability models in HEDA corresponding to different subsections of individuals are constructed. Based on the probability models, offspring individuals are then produced. As an improvement strategy, simulated-annealing-based local search is designed and incorporated into HEDA to enhance the entire algorithm’s search ability. The proposed hybrid HEDA is tested on FJS examples. The results show its feasibility and effectiveness.

A Derived Mechanism of Nervous System Functions Explains Aging-Related Neurodegeneration as a Gradual Loss of an Evolutionary Adaptation.

Solving the nervous system requires understanding how it generates inner sensations of "mind" within it. It was possible to derive a hypothesis of brain functions where the formation of a spectrum inter-postsynaptic (inter-spine) functional LINKs (IPLs) are the key structural changes responsible for encoding at the time of learning and are used for inducing the inner sensation of memory, both taking place at millisecond timescales. Since stages of ontogeny reflect possible stages of evolution, it is possible to examine whether IPLs have features of an evolved mechanism. To examine whether 1) IPLs have features of an evolved mechanism, 2) significant neuronal death during ontogeny leads to evolutionary adaptations for preventing cell death among the surviving neurons, and 3) loss of these adaptations lead to cellular changes that can cause agingrelated neurodegeneration. Key milestone changes of the ontogeny of the nervous system were examined to test whether they match with a feasible sequence of steps that lead to the formation of IPLs. Several developmental stages can explain a probable sequence of events that lead to IPL formation among synaptically-connected neurons. When internal sensations generated by the IPLs started providing survival advantage, evolution has started preserving the IPL circuitry. A stage of inter-spine fusion possibly leads to a) significant neuronal death during the early stages of development, and b) trigger an adaptation in the surviving cells to stabilize and prevent the IPLs from undergoing fusion. Since there are no irreversible steps for maintaining the stability of IPLs, agingrelated factors may destroy the adaptation mechanism and destabilize the IPLs predisposing them to cause neurodegeneration. The derived testable IPL mechanism that can explain nervous system functions is capable to have evolved. An adaptation to prevent IPL hemifusion from progressing to fusion is likely the last stage of nervous system evolution. Since the IPL mechanism is utilized during every event of learning, any aging-related factors that can weaken this adaptation can cause IPL fusion and lead to neurodegeneration.

A Greedy Randomized Adaptive Search Procedure for the Orienteering Problem with Hotel Selection

The Orienteering Problem with Hotel Selection (OPHS) is one of the most recent variants of the Orienteering Problem (OP). The sequence of hotels has a significant effect on the quality of the OPHS solutions. According to prior studies, it is not efficient to consider all feasible sequences of hotels for constructing a tour. For this reason, solution methods for this problem should be independent of the Total Number of Feasible Sequences of hotels (TNFS).This paper proposes a novel approach based on a well-known metaheuristic called Greedy Randomized Adaptive Search Procedure (GRASP) to tackle the OPHS. In the previously introduced algorithms for this problem, the OP solutions among all feasible pairs of hotels are considered to construct a proper sequence of hotels. Our suggested GRASP algorithm does not follow this policy; instead, it uses a novel, potent, and fast dynamic programming method for hotel selection. This dynamic idea makes the introduced GRASP approach independent of the TNFS and solving the OPs.Considering 400 benchmark instances with and five instances without known optimal values, the proposed method in this article obtains the optimal solutions for 231 instances (57.75%), as opposed to 174 instances (43.5%) for the best formerly suggested algorithm. GRASP constructs better tours than the state-of-the-art algorithm for 142 instances (35.5%) and finds new best results for three out of five instances with unknown optimal values. Moreover, GRASP can produce high quality solutions for 76 new large instances constructed using the instances of a similar problem to the OPHS.

Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing

Abstract Remanufacturing helps to improve the resource utilization rate and reduce the manufacturing cost. Disassembly is a key step of remanufacturing and is always finished by either manual labor or robots. Manual disassembly has low efficiency and high labor cost while robotic disassembly is not flexible enough to handle complex disassembly tasks. Therefore, human-robot collaboration for disassembly (HRCD) is proposed to flexibly and efficiently finish the disassembly process in remanufacturing. Before the execution of the disassembly process, disassembly sequence planning (DSP), which is to find the optimal disassembly sequence, helps to improve the disassembly efficiency. In this paper, DSP for human-robot collaboration (HRC) is solved by the modified discrete Bees algorithm based on Pareto (MDBA-Pareto). Firstly, the disassembly model is built to generate feasible disassembly sequences. Then, the disassembly tasks are classified according to the disassembly difficulty. Afterward, the solutions of DSP for HRC are generated and evaluated. To minimize the disassembly time, disassembly cost and disassembly difficulty, MDBA-Pareto is proposed to search the optimal solutions. Based on a simplified computer case, case studies are conducted to verify the proposed method. The results show the proposed method can solve DSP for HRC in remanufacturing and outperforms the other three optimization algorithms in solution quality.

Hybrid constrained permutation algorithm and genetic algorithm for process planning problem

In this research, a hybrid constrained permutation algorithm and genetic algorithm approach is proposed to solve the process planning problem and to facilitate the optimisation process. In this approach, the process planning problem is represented as a graph in which operations are clustered corresponding to their machine, tool, and tool access direction similarities. A constrained permutation algorithm (CPA) developed to generate a set of optimised feasible operations sequences based on the principles of minimising the number of setup changes and the number of tool changes. Due to its strong capability in global search through multiple optima, genetic algorithm (GA) is used to search for an optimal or near optimal process plan, in which the population is initialised according to the operations sequences generated by CPA. Furthermore, to prevent premature convergence to local optima, a mixed crossover operator is designed and equipped into GA. Four comparative case studies are carried out to evidence the feasibility and robustness of the proposed CPAGA approach against GA, simulated annealing, tabu search, ant colony optimisation, and particle swarm optimisation based approaches reported in the literature, and the results are promising.

An efficient metaheuristics for a sequence-dependent disassembly planning

Abstract Disassembly planning (DP) is critical in remanufacturing and value recovery from end-of-life products and has attracted increasing attention due to the recent resurgence of research on circular economy. DP problem is NP-hard and its complexity increases exponentially with the size of problem. Sequence-dependent cost due to varying quality of the parts to be retrieved further increases the complexity of DP problems. This paper investigates the DP considering sequence-dependent costs among disassembly operations. A mathematical model is proposed with the objective to maximize the recovery profit using an AND/OR graph (AOG) subject to sequence-dependent costs. A novel two-phase heuristic method is developed to effectively generate feasible disassembly sequence according to the AOG in reasonable computation time. In addition, an improved genetic algorithm (IGA) is proposed to solve the problem, in combination with the presented two-phase heuristic. The performance of IGA is measured on a series of test problem instances against exact methods including CPLEX and an iterative method. Results indicate that IGA successfully find the near-optimal/optimal solutions and outperforms the other methods in terms of computation time. Finally, the proposed method is applied to compute the disassembly solution of a HG5-20 triaxial five speed mechanical transmission. Compared to the existing disassembly solutions of the transmission, the obtained solutions by IGA can shorten about 11% disassembly time and increase by approximately 7% recovery profit.

Solving Assembly Sequence Planning Using Distance Evaluated Simulated Kalman Filter

This paper presents an implementation of simulated Kalman filter (SKF) algorithm for optimizing an assembly sequence planning (ASP) problem. The SKF search strategy contains three simple steps; predict-measure-estimate. The main objective of the ASP is to determine the sequence of component installation to shorten assembly time or save assembly costs. Initially, permutation sequence is generated to represent each agent. Each agent is then subjected to a precedence matrix constraint to produce feasible assembly sequence. In this paper, the distance evaluated SKF (DESKF) is proposed for solving ASP problem. The performance of the proposed DESKF is compared against previous works in solving ASP by applying BGSA, BPSO, and MSPSO. Using a case study of ASP, the results show that DESKF outperformed all the algorithms in obtaining the best solution.

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI

PurposeDiffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens through which DW-MRI data are interpreted. Numerous modern approaches offer opportunities to assess more complex intra-voxel structures. Nevertheless, there remains a substantial gap between intra-voxel estimated structures and ground truth captured by 3-D histology. MethodsHerein, we propose a novel data-driven approach to model the non-linear mapping between observed DW-MRI signals and ground truth structures using a sequential deep neural network regression using residual block deep neural network (ResDNN). Training was performed on two 3-D histology datasets of squirrel monkey brains and validated on a third. A second validation was performed using scan-rescan datasets of 12 subjects from Human Connectome Project. The ResDNN was compared with multiple micro-structure reconstruction methods and super resolved-constrained spherical deconvolution (sCSD) in particular as baseline for both the validations. ResultsAngular correlation coefficient (ACC) is a correlation/similarity measure and can be interpreted as accuracy when compared with a ground truth. The median ACC of ResDNN is 0.82 and median ACC's of different variants of CSD are 0.75, 0.77, 0.79. The mean, median and std. of ResDNN & sCSD ACC across 12 subjects from HCP are 0.74, 0.88, 0.31 and 0.61, 0.71, 0.31 respectively. ConclusionThis work highlights the ability of deep learning to capture linkages between ex-vivo ground truth data with feasible MRI sequences. The data-driven approach is applicable to human in-vivo data and results in intriguingly high reproducibility of orientation structure.

Scheduling sequence selection for generating test data to cover paths of MPI programs

Abstract Context: As one of key tasks in software testing, test data generation has been receiving widespread attention in recent years. Message-passing Interface (MPI) programs, which are one representative type of parallel programs, have the characteristic of non-determinism, which is reflected by the non-deterministic execution under different scheduling sequences against the same program input. Previous studies have shown that different difficulties are raised in generating test data under different scheduling sequences, suggesting that selecting appropriate scheduling sequences is beneficial to a high efficiency. Objective: We propose a method of selecting a superior and feasible scheduling sequence for generating test data in the criterion of path coverage against each target path of an MPI program. Method: In the proposed method, a number of program inputs are first sampled by Latin hypercube sampling. Then, the program is executed against each sample under each scheduling sequence, and all the scheduling sequences are sorted according to the similarities between the paths traversed by these samples and the target one. Finally, the feasibility of a scheduling sequence with the best quality is investigated based on the symbolic execution. Results: We apply the proposed method to seven typical MPI programs and compare it with the random one. The experimental results show that test data covering the target path can be generated under the selected scheduling sequence with high success rate and low time consumption. Conclusion: The proposed method takes the influence of scheduling sequences on generating test data into consideration, thus providing a competent way to test parallel programs.

Phase-contrast magnetic resonance imaging for analyzing hemodynamic parameters and wall shear stress of pulmonary arteries in patients with pulmonary arterial hypertension

To investigate flow-related parameters in pulmonary arteries of patients with pulmonary arterial hypertension (PAH). Eleven PAH patients and twelve control participants were recruited. PAH and controls had similar age and gender distribution. 2D phase-contrast MRI (PC-MRI) was performed in the main, right, and left pulmonary artery (MPA, RPA, and LPA). The flow velocity, wall shear stress (WSS), and oscillatory shear index (OSI) were measured. PAH patients displayed prolonged acceleration time (Tacce) and increased ratio of flow change to acceleration volume in pulmonary arteries (both P < 0.001). The temporally averaged WSS values of MPA, RPA, and LPA in PAH patients were significantly lower than those of control participants (P < 0.001). The OSI in the pulmonary arteries was higher in PAH patients than control participants (P < 0.05). The ROC analysis indicated the ratio of maximum flow change to acceleration volume, WSS, and Tacce exhibited sufficient sensitivity and specificity to detect patients with PAH. The WSS demonstrated strong correlations with Tacce and the ratio value in the two groups (R2 = 0.78-0.96). We used a clinically feasible 2D PC-MRI sequence with a reasonable scanning time to compute aforementioned indices. The quantitative parameters provided sufficient information to differentiate PAH patients from control participants.

Freeform Assembly Planning

3-D printing enables the fabrication of complex architectures at multiple length scales by automating large sequences of additive steps. The increasing sophistication of printers, materials, and generative design promise to make geometric complexity a nonissue in manufacturing; however, this complexity can only be realized if a design can be translated into a physically executable sequence of printing operations. We investigate this planning problem for freeform direct-write assembly, in which filaments of material are deposited through a nozzle translating along a 3-D path to create sparse, frame-like structures. We enumerate the process constraints for different variants of the freeform assembly process and show that, in the case where material stiffens via a glass transition, determining whether a feasible sequence exists is NP-complete. Nonetheless, for topologies typically encountered in real-world applications, finding a feasible or even optimal sequence is a tractable problem. We develop a sequencing algorithm that maximizes the fidelity of the printed part and minimizes the probability of print failure by modeling the assembly as a linear, elastic frame. We implement the algorithm and validate our approach experimentally, printing objects composed of thousands of large-aspect-ratio sugar alcohol filaments with diameters of 100– $200~\mu \text{m}$ . Note to Practitioners —Extrusion-style 3-D printers typically pattern material in a series of 2-D layers, but they can be also be programmed to deposit material along 3-D paths in a “freeform” fashion. However, programming a printer to operate in this way requires consideration of constraints related to collision and stability. For large designs, finding an optimal or even feasible plan with respect to these constraints requires automated planning. We address a hard version of this problem in which any joint in the frame can only support one cantilever at a time. We develop an exact algorithm that maximizes the robustness of the printing plan and validate it by printing complex freeform designs. The assembly planner allows the freeform process to be applied to arbitrarily complex parts, with applications ranging from tissue engineering and microfluidics at the micrometer scale, to vascularized functional materials and soft robots at the millimeter scale, to structural components at the meter scale. This approach removes a major bottleneck in the workflow for freeform assembly, allowing scientists and engineers to automatically translate complex freeform designs into optimal printing plans.

Optimization of cutter orientation for multi-axis NC machining based on minimum energy consumption of motion axes

A cutter axis optimization method for multi-axis machining based on minimum energy consumption of motion axes is presented in this paper to avoid the unstable running status of the machine tool induced by unreasonable cutter directions in multi-axis machining of sculptured surfaces. Firstly, a mathematic model based on minimum energy consumption of motion axes of the machine tool is established to achieve minimum energy consumption of the machine tool feed system. Secondly, the feasible space meeting the requirements of collision avoidance of the cutter orientation and the travel limit of each movement axis are constructed in the machine tool coordinate system, and the change of rotation axis angle of the machine tool is restrained by introducing the rule curve of sinusoidal acceleration. Then, the smooth feasible cutter axis sequences are obtained with no rigid impact or flexible impact on the machine tool. Finally, a machining experiment is carried out, and the results show that the presented method can save the energy consumption of the feed system by an average of 3.36% as well as smoothing the cutting process.

Hybrid bidirectional ant colony optimization (hybrid BACO): An algorithm for disassembly sequence planning

In traditional disassembly sequence planning (DSP), a disassembly sequence is planned based on the description of the product design and personal experience. Research on disassembly sequence planning needs to take into account the relationship between the parts and related factors, such as changes in orientation and tools, so as to optimize the order of the disassembly sequence. Among the numerous feasible disassembly sequences, it is important for researchers to find the most economical DSP, which becomes more difficult as the number of parts increases. In this study, a hybrid bidirectional ant colony optimization (Hybrid BACO) algorithm is proposed and compared with four related algorithms. Simulated cases show that the hybrid BACO algorithm provides a better solution than other ant algorithms. The five ant algorithms are also compared from the viewpoint of a reverse assembly sequence, and the results again show that the Hybrid BACO algorithm provides the best solution quality.