Large-sized Problems Research Articles

Multi-level lot-sizing and job shop scheduling with lot-streaming: Reformulation and solution approaches

This paper addresses a multi-level lot-sizing and job shop scheduling problem with lot-streaming. In the multi-level production system, workstations receive materials from the lower level, and after some operation, materials are shipped to the next level. Hence, establishing a material balance between the different levels is the most challenging part of multi-level production planning and scheduling. The material balance can be performed with or without lot-streaming. Lot-streaming effectively enables consecutive operations to overlap by splitting a processing lot into several sub-lots. In small-bucket time models, this capability is taken into account by establishing the material balance in each small unit of time (micro-period), which makes the models computationally expensive. In the present work, a novel and much less complicated big-bucket time formulation has been developed, which incorporates lot-streaming considering sequence-dependent setup times and capacitated machines. Computational experiments affirm the promising results of the proposed model compared to the well-known models in the literature. Moreover, two efficient heuristics have been developed for solving larger-size problems. First, the fix-and-relax algorithm as a constructive heuristic is combined with the fix-and-optimize algorithm as an improvement heuristic. Next, a decomposition heuristic is proposed using mixed-integer programming (MIP) and constraint programming (CP) in the master and sub-problem, respectively. The computational results show that the proposed heuristics are very efficient, even in solving large-sized problems.

An efficient lexicographic approach to solve multi-objective multi-port fabric dyeing machine planning problem

In this study, the fabric dyeing process in a towel manufacturing factory is investigated, focusing on the batching of towel fabric bolts with different quantities and features belonging to different orders in multi-port dyeing machines. This research differs from previous studies in several aspects. Firstly, it is considered three different objectives: total tardiness, total number of washes (sequence-dependent setups), and total machine fixed cost. These objectives have not been simultaneously optimized in previous studies, making our approach unique. Secondly, we adopt a lexicographic optimization approach to prioritize these objectives according to the company’s requirements. This allows to find solutions that are optimal in terms of one objective while satisfying the constraints imposed by the other objectives. Lastly, it is proposed a novel lexicographic multi-objective genetic algorithm, which extends the traditional single-objective genetic algorithm, to effectively solve large-sized problems. Overall, our study not only addresses a real-life problem in the textile industry but also introduces innovative techniques and approaches for optimizing multiple objectives in a scheduling context.

A Hybridization Approach to Solve the Capacitated Asymmetric Allocation Hub Location Problem

This paper deals with a capacitated asymmetric allocation hub location problem (CAAHLP). We determine the number of hubs, the locations of hubs, and asymmetric allocation of non-hub nodes to hub with the objective of minimum total transportation costs satisfying the required service level. To solve the problem optimally, we present a 0-1 integer programming model and find an optimal solution using CPLEX. As the CAAHLP has impractically demanding for the large-sized problem, a solution method based on combined ant colony optimization algorithm and genetic algorithm is developed which solve hub location problem and node allocation problem respectively. We investigate performance of the proposed solution method through the comparative study. Key Words: Hub Location, Asymmetric Allocation, Ant Colony Optimization, Genetic Algorithm

A Structured Recognition Method for Invoices Based on StrucTexT Model

Invoice recognition has long been an active research direction in the field of image recognition. Existing invoice recognition methods suffer from a low recognition rate for structured invoices, a slow recognition speed, and difficulty in mobile deployment. To address these issues, we propose an invoice-structured recognition method based on the StrucTexT model. This method uses the idea of knowledge distillation to speed up the recognition speed and compress the model size without reducing the model recognition rate; this is achieved using the teacher model StrucTexT to guide the student model StrucTexT_slim. The method can effectively solve the problems of slow model recognition speed and large model size that make mobile deployment difficult with traditional methods. Experimental results show that the proposed model achieves an accuracy rate of over 94% on the SROIE and FUNSD public datasets and over 95% on the self-built structured invoice dataset. In addition, the method is 30% faster than other models (YOLOv4, LeNet-5, and Tesseract-OCR) in terms of recognition speed, while the model size is compressed by about 20%.

Design and Research of Missile launcher Drive System for Anti-aircraft Gun and Missile Weapon System

Aiming at the problems of large size and weight of electric cylinder, low efficiency and large pressure fluctuation of centralized hydraulic drive system, a drive system of anti-aircraft gun and missile weapon system based on electro-hydrostatic actuator is designed. The working principle of the hydraulic system of electro-hydrostatic actuator is discussed, its components are selected and designed, the micro cartridge hydraulic control check valve is designed and the electro-hydrostatic actuator is analyzed and integrated structure is designed;The simulation model is established by AMEsim software. The simulation results show that the electro-hydrostatic actuator can move the missile driving device to the highest position within 2s, meet the working requirements, and have certain guiding significance for the research and development of missile launcher driving.

SRCON: A Data-Driven Network Performance Simulator for Real-World Wireless Networks

Optimizing the performance of a real-world wireless network is extremely challenging because of the difficulty to predict the network performance as a function of network parameters, and the prohibitively large problem size. The conventional network optimization approach relies on drive tests, trial-and-error and engineering experience, which is labor-intensive, error-prone and far from optimal. In this article, we introduce a new wireless network simulator, named simulated reality of communication networks (SRCON), that relies on a variety of white-box and machine-learning models to accurately reproduce the stochastic behavior (e.g., traffic arrival, user association, and signal propagation) of real-world 4G/5G mobile networks. We describe the key challenges, including the gigantic problem size, network coupling, stochasticity and many others, to develop SRCON. We also illustrate how SRCON can be used to optimize mobile network performance effectively. Notable performance gains were achieved after applying SRCON-enabled optimization to “conventionally-optimized” commercial wireless networks.

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.

Sustainable hierarchical multi-modal hub network design problem: bi-objective formulations and solution algorithms

This paper presents a bi-objective model for the design and optimization of a sustainable hierarchical multi-modal hub network. The proposed model focuses on sustainability by considering economic, environmental, and social aspects of the decisions in a hierarchical network. A case of Turkish network for freight transportation is used to validate the proposed model. To solve the small-sized problems, the augmented epsilon constraint method version 2 (AUGMECON2) is applied. It can be inferred from the Pareto-optimal set obtained by AUGMECON2 that the effect of increasing the number of hubs after a threshold is marginal. The current contribution proposes two multi-objective genetic algorithms (NSGA-II and NRGA), which incorporate LP solving and Dijkstra algorithm. The results show the superiority of NRGA compared to NSGA-II in terms of solution time. Also, we present an alternative, more efficient formulation to the problem. Based on the alternative formulation, in addition to AUGMECON2, we use two exact methods, including Torabi and Hassini (TH) method and augmented weighted Tchebycheff procedure (AWTP), to find Pareto-optimal solutions for small, medium, and large-sized problems (including the case study). The performance of the proposed solution methods is measured using some multi-objective indicators. The results show the superiority of AUGMECON2.

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.

Ultra-precision time-controlled grinding for flat mechanical parts with weak stiffness

High-precision flat mechanical parts are widely used, such as in aerostatic bearing, which requires a rather high machining precision. Due to large size, low stiffness and other machining problems, traditional ultra-precision grinding machine tool cannot directly achieve the final precision, so manual grinding is necessary to meet the manufacturing requirements, resulting in low efficiency. Based on the computer-controlled optical surfacing (CCOS) method and the flexible grinding process, the time-controlled grinding method (TCG) is proposed to achieve quantitative material removal at each position on the workpiece. Combined with the material removal mechanism, the removal function with good linearity and stability is extracted by controlling corresponding parameters. By applying a novel measuring and processing method of surface data with high precision and high efficiency, the flatness error and parallelism error are accurately obtained and used as grinding reference to carry out the time-controlled grinding experiment on one spacer ring with a diameter of 190 mm. The final flatness error converges from 1.5–2 μm to 0.6–0.7 μm and parallelism error converges from ~2 μm to ~0.6 μm, while the roughness converges from 273 nm Ra to 65 nm Ra, achieving the goal of ultra-precision digital machining of submicron level. The machining precision is better than traditional ultra-precision grinding machine tool, and the potential for time-controlled grinding method of ultra-precision machining in flat mechanical parts especially with low stiffness is verified.

A study of a slim compact piezo inertia actuator

Most previously reported inertia actuators suffer from the problems of low speed and large size. To overcome these shortcomings, this study proposes a slim compact piezo inertia actuator based on the principle of stick–slip drive. Actuated by the transverse motion of a cantilever beam forming part of a monolithic elastomer, this actuator achieves a high velocity. The construction and basic operating principle of the actuator are discussed in detail. Commercial finite element analysis software is employed to determine the appropriate geometry for the monolithic elastomer. To study the actuator’s mechanical characteristics, a prototype is fabricated and a series of experimental tests are performed. According to the results of these tests, the maximum velocity and maximum load force are about 24.03 mm/s and 1.96 N, respectively, and the minimum step size is about 0.47 μm. It is shown that the inertia actuator based on a monolithic elastomer with a cantilever beam not only has a slim compact structure, but also exhibits good output characteristics.

SOLUTION APPROACH TO P-MEDIAN FACILITY LOCATION PROBLEM WITH INTEGRATED PROGRAMMING AND GENETIC ALGORITHM

In the study, the P-median problem that Hakimi brought to science was discussed. With this problem, in short, it is ensured that the locations for the supply of n demand points of p facilities with the lowest cost are determined and the demand points to be served are allocated to these locations. The solution of the p-median problem with the integer programming approach is in the NP (Non-Polynominal) difficult class, and the solution time increases exponentially as the size of the problem increases. For this, the use of heuristic approaches in the P-median problem significantly shortens the solution time of large-sized problems. The data used in the application were taken from the Operation Research-Library site. In practice, the P-median is considered as capacity-constrained and unconstrained. Euclidean distances were calculated in Excel. The solution of mixed integer programming is obtained by using the CPLEX program. In addition to mixed integer programming, genetic algorithm, which is a meta-heuristic method, is applied to the capacity-constrained P-median problem. Different solutions were obtained by changing the population size, maximum number of iterations, crossover and mutation probability values. It has been observed that there is more than one optimum solution in repeated studies. When working with larger data sets, this algorithm will be less likely to find the global optimum. However, when compared to mixed integer programming, the genetic algorithm gave results very close to the optimum result. The use of heuristic algorithms in real-world complex problems will both shorten the solution time considerably compared to the integer programming method and provide results that are very close to the optimum result.

The new approaches for solving hierarchical Chinese postman problem with stochastic travel times

The hierarchical Chinese postman problem (HCPP) aims to find the shortest tour or tours by passing through the arcs classified according to precedence relationship. HCPP, which has a wide application area in real-life problems such as shovel snow and routing patrol vehicles where precedence relations are important, belongs to the NP-hard problem class. In real-life problems, travel time between the two locations in city traffic varies due to reasons such as traffic jam, weather conditions, etc. Therefore, travel times are uncertain. In this study, HCPP was handled with the chance-constrained stochastic programming approach, and a new type of problem, the hierarchical Chinese postman problem with stochastic travel times, was introduced. Due to the NP-hard nature of the problem, the developed mathematical model with stochastic parameter values cannot find proper solutions in large-size problems within the appropriate time interval. Therefore, two new solution approaches, a heuristic method based on the Greedy Search algorithm and a meta-heuristic method based on ant colony optimization were proposed in this study. These new algorithms were tested on modified benchmark instances and randomly generated problem instances with 817 edges. The performance of algorithms was compared in terms of solution quality and computational time.

A Reinforcement Learning Approach for Scheduling Problems with Improved Generalization through Order Swapping

The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy, but also for increasing the overall efficiency. Among the different job scheduling problems, the Job Shop Scheduling Problem (JSSP) is addressed in this work. JSSP falls into the category of NP-hard Combinatorial Optimization Problem (COP), in which solving the problem through exhaustive search becomes unfeasible. Simple heuristics such as First-In, First-Out, Largest Processing Time First and metaheuristics such as taboo search are often adopted to solve the problem by truncating the search space. The viability of the methods becomes inefficient for large problem sizes as it is either far from the optimum or time consuming. In recent years, the research towards using Deep Reinforcement Learning (DRL) to solve COPs has gained interest and has shown promising results in terms of solution quality and computational efficiency. In this work, we provide an novel approach to solve the JSSP examining the objectives generalization and solution effectiveness using DRL. In particular, we employ the Proximal Policy Optimization (PPO) algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs. We incorporated a new method called Order Swapping Mechanism (OSM) in the environment to achieve better generalized learning of the problem. The performance of the presented approach is analyzed in depth by using a set of available benchmark instances and comparing our results with the work of other groups.

A learning-based solution method for practical slab allocation problem in multiple hot rolling lines

Steel slabs are key in-process products in the steel production process. The slab allocation problem is to allocate slabs to suitable orders by considering complicated technical restrictions and multiple management requirements. In practice, thousands of slabs in multiple production lines should be allocated to orders. Due to the complexity and large scale of the problem, it takes planners a long time to make decisions, and the obtained allocation schemes are usually ineffective because of the myopic nature of the rule-based methods. In this article, we present a learning-based solution method for solving a practical slab allocation problem in multiple hot rolling lines. We formulate the problem as an integer programming model, then a data-based method is adopted to evaluate the mismatching cost between slabs and orders. To effectively solve the large-sized problem, a learning-based decomposition strategy is proposed to decompose the original problem into several small-sized subproblems. Then, a branch-and-price algorithm is proposed to optimally solve the subproblems. To further speed up the solution process, a primal heuristics based on column generation is designed to solve the large-sized subproblems. The solution methods have been implemented in a steel company and effectively increased slab utilization and reduced production cost.

INVESTIGATING EFL TEACHERS’ PERCEPTIONS OF IMPLEMENTING ACTIVE LEARNING TECHNIQUES IN TEACHING SPEAKING

Active learning (AL) is widely implemented in Asia including Vietnam. However, to date, research on this method seems to be scarce, particularly in the context of Vietnam. This study aims to investigate teachers' perceptions of AL and its principles in teaching English speaking. In addition, the study also explores teachers’ attitudes about AL implementation and their concerns about applying this method to their teaching context. Ten teachers of English teaching at a university in the Mekong Delta, South of Vietnam participated in the study. A semi-structured interview activity was employed to collect data. The results showed that the teachers generally had a high level of understanding of AL teaching principles and showed positive attitudes toward the implementation of it. The results also revealed teachers’ concerns about implementing AL. In particular, three problems of large class size, students’ mixed language proficiency and time limitation were addressed. Accordingly, possible suggestions for a better implementation of AL in teaching English speaking in Vietnam and similar contexts elsewhere will be provided.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0006/a.php" alt="Hit counter" /></p>

Accurate coarse mesh simulation of delamination in composites using a novel hp-adaptive cohesive element

Cohesive elements are routinely used to simulate delamination between plies in composites. However, the allowable maximum size of these elements is limited, owing to the presence of a very small process zone ahead of the cohesive crack-tip, which needs to be discretised with multiple elements for obtaining an accurate failure response. This restricts their applicability beyond analysing delamination in coupon scale problems, due to the need for a very fine mesh and associated large problem size. In this paper, a novel hp cohesive element is developed to address this problem. This element is capable of simultaneous mesh refinement and elevation of interpolation order, locally and adaptively around the propagating cohesive crack tip. Additional degrees of freedom associated with the hp refinement are handled by extra nodes already supplied in the initial mesh file, that are selectively activated by the program as required. Several example problems are presented showing the ability of this method to improve the prediction of delamination onset and growth in extremely coarse meshes with a reduced computational time.

The current status and development of DC/AC inverter technology

The advantages, applications, and development trends of DC/AC inverter technology are compared with conventional inverter technology. The traditional DC/AC inverter technology of the low-frequency link inverter process has been gradually replaced by the high-frequency band inverter process. The replacement of the industrial frequency transformer in the low-frequency link inverter process technology with a high-frequency transformer further optimizes and improves the low-frequency link inverter process technology and significantly enhances the characteristics of the inverter. In this paper, some new inverters are highlighted. The focus is on a new high-frequency chain inverter using a unipolar SPWM control method to design different drive logic control switch tubes. A single-stage single-phase high-to-low voltage converter is widely used to interchange solar and wind turbine power supplies. They all belong to PV inverters with high reliability and a wide input voltage range. These completely solve the problems of large size, high ratio, and poor dynamic response of low-frequency transformers. Future trends in inverters at this stage are mainly characterized by high frequency, high performance, and high input power factor.

Development of a Low-Power Automatic Monitoring System for Spodoptera frugiperda (J. E. Smith)

Traditional traps for Spodoptera frugiperda (J. E. Smith) monitoring require manual counting, which is time-consuming and laborious. Automatic monitoring devices based on machine vision for pests captured by sex pheromone lures have the problems of large size, high power consumption, and high cost. In this study, we developed a micro- and low-power pest monitoring device based on machine vision, in which the pest image was acquired timely and processed using the MATLAB algorithm. The minimum and maximum power consumption of an image was 6.68 mWh and 78.93 mWh, respectively. The minimum and maximum days of monitoring device captured image at different resolutions were 7 and 1486, respectively. The optimal image resolutions and capture periods could be determined according to field application requirements, and a micro-solar panel for battery charging was added to further extend the field life of the device. The results of the automatic counting showed that the counting accuracy of S. frugiperda was 94.10%. The automatic monitoring device had the advantages of low-power consumption and high recognition accuracy, and real-time information on S. frugiperda could be obtained. It is suitable for large-scale and long-term pest monitoring and provides an important reference for pest control.

Solution approaches for the bi-objective Skiving Stock Problem

The Skiving Stock Problem (SSP) aims to determine an optimal plan for producing as many large objects as possible by combining small items. The skiving process may need different considerations depending on the production environment and the product characteristics. In this study, we address bi-objective 1D-SSP with two conflicting objectives. One common objective is to minimize the trim loss remaining after skiving, as removing the excess width is an extra procedure. When welding is an element of the skiving process, increasing the number of items for each product indicates compromised quality. Therefore, minimizing the number of small items for each product becomes a primary objective in such cases. To solve this bi-objective version of the NP-hard problem, we implement a Lexicographic Method (LM) in which the importance of the objectives imposes their preference orders. We propose two methodologies within the LM framework. The first methodology integrates Column Generation (CG) and Branch & Bound (B&B) to search for an exact solution. Given the excessive computational time an exact solver may require for tight or large-sized problems, we propose a heuristic method integrating the Dragonfly Algorithm (DA) and a Constructive Heuristic (CH). Real-world application results validate the exact solver and demonstrate comparable results for the heuristic solver in terms of solution quality and computational time. The efficiency of the solution methodologies for a preemptive multi-objective SSP aims to support decision-makers with make-or-buy decisions.