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Assembly is the last step in manufacturing processes. The two-sided assembly line balancing problem (TALBP) is a typical research focus in the field of combinatorial optimization. This paper aims to study a multi-constraint TALBP-I (MC-TALBP-I) that involves positional constraints, zoning constraints and synchronism constraints to make TALBP more in line with real production. For enhancing quality of assembly solution, an improved imperialist competitive algorithm (ICA) is designed for solving the problem.

A mathematical model for minimizing the weighted sum of the number of mated-stations and stations is established. An improved ICA is designed based on a priority value encoding structure for solving MC-TALBP-I.

The proposed ICA was tested by several benchmarks involving positional constraints, zoning constraints and synchronism constraints. This algorithm was compared with the late acceptance hill-climbing (LAHC) algorithm in several instances. The results demonstrated that the ICA provides much better performance than the LAHC algorithm.

The best solution obtained by solving MC-TALBP-I is more feasible for determining the real assembly solution than the best solution obtained by solving based TALBP-I only.

A novel ICA based on priority value encoding is proposed in this paper. Initial countries are generated by a heuristic method. An imperialist development strategy is designed to improve the qualities of countries. The effectiveness of the ICA is indicated through a set of benchmarks.

Airports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. This study aims to develop a model that acts as a tactical runway scheduling methodology for reducing delays while managing runway usage.

By considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, this study presents a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. The approach transforms the planning problem into an assignment problem with side constraints. The coupled landing/take-off problem is solved to optimality by exploiting a time-indexed (0, 1) formulation for the problem. The Binary Integer Linear Programming approach allows to include multi-criteria and multi-constraints levels and, even with some major simplifications, provides fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, the use of robust optimization leads to a protection against tactical uncertainties, reduces delays and achieves more stable operations.

This model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach: the proposed algorithm significantly reduces weighted aircraft delay and computes efficient runway schedule solutions within a few seconds and with little computational effort. It can be adopted as a decision-making tool in the tactical stage. Furthermore, this study presents operational insights regarding demand and delay management based on the results of this work.

Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management.

Maintaining a balanced neutral point, reducing power loss, execution time are important criteria for the controlling of neutral point clamped (NPC) inverter. However, it is tough to meet all the challenges and also supplying the load current within the harmonic limit. This paper aims to maintain load current quality within the Institute of Electrical and Electronics Engineers 519 standard and meet the above-mentioned challenges.

The output load current of a three-level simplified neutral point clamped (3 L-SNPC) inverter is controlled in this paper using model predictive control (MPC). The 3 L-SNPC inverters is considered because fewer semiconductor devices are used in this topology; this will enhance the reliability of the system. MPC is used as a controller because it can handle the direct current-link capacitors’ voltage balancing problem in a very intuitive way. The proposed 3 L-SNPC yields similar current total harmonic distortion (THD), transient and steady-state responses, voltage stress and over current protection capability as the conventional NPC inverter. To reduce the computational burden of the proposed SNPC system, two simplified MPC strategies are proposed, namely, single voltage vector prediction-based MPC and selective voltage vector prediction-based MPC.

The system shows a current THD of 2.33% at 8.96 kHz. The overall loss of the system is reduced significantly to be useful in medium power applications. The required execution times for the simplified MPC strategies are tested on the hardware dSPACE 1104 platform. It is found that the single voltage vector prediction-based MPC and the selective voltage vector prediction-based MPC are computationally efficient by 8.28% and 62.9%, respectively, in comparison with the conventional MPC-based conventional NPC system.

Multiple system constraints are considered throughout the paper and also compare the SNPC to the conventional NPC inverter. Proper current tracking, over-current protection, overall power loss reduction especially switching loss and maintaining capacitor voltages balance at a neutral point are achieved. The improvement of execution time has also been verified and calculated using hardware-in-loop of the dSPACE DS1104 platform.

Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems.

This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions.

Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively.

This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

The purpose of this paper is to optimize the downrange for hypersonic boost‐glide (HBG) missile under near‐real condition, and to validate the suitability of proposed wall cooling materials.

The trajectory optimization problem is characterized by a boost phase followed by a glide phase. A multi‐phase trajectory optimization tool is adopted to optimize the downrange. The associated optimal control problem has been solved by selecting a direct shooting method. The dynamics has been transcribed to a set of nonlinear constraints and the arising nonlinear programming problem has been solved through a sequential quadratic programming solver. An aerothermodynamics analysis method is introduced to calculate the aerodynamic heating at nose, leading edge, and ventral centerline regions.

HBG missile is suitable for long‐range attack, and the optimal trajectory solved is a novel boost‐glide‐skip trajectory, i.e. boost firstly, glide secondly, and skip at last. The proposed wall materials are valid.

This paper provides further study on the methods of trajectory design and aerothermodynamics analysis for HBG missile.

The purpose of this paper is to propose an assembly coordination modelling approach based on measured data for assembly quality control of multi-constrained objects in aircraft assembly. This approach aims to establish a high-precision digital mirror of physical assembly system in the virtual environment, with which the assembly process in the virtual environment can be performed synchronously with that in the physical world.

This paper presents a realistic geometrical representation model based on measured point cloud, as well as the multiple constraints modelling methods for local and global constraints with the proposed representation model. For the assembly target optimization, a novel optimization method based on the evaluation of multi-dimensional tolerance zone is proposed, where the particle swarm optimization and simulated annealing algorithm are combined to calculate the optimal solutions.

As shown in the validation results, the minimum easiness value for easiness model in global optimization is 3.01, while the best value for weighting model by adjusting weights for more than 10 times is 1.94. The results verify that the proposed coordination modelling approach is effective to the assembly of multi-constrained objects, and the optimization model has an obvious advantage over the traditional weighting method.

This paper provides a new idea for the fine control of assembly quality of non-ideal components by introducing the measured data into the on-line assembly process. Besides, a novel optimization method based on the evaluation of multi-dimensional tolerance zone is proposed, which overcomes the problem of traditional weighting model wherein the weightings are difficult to determine.

The purpose of this paper is to present a novel guidance law for hypervelocity descent to a stationary target such that the impact angle and impact velocity can be constrained.

The proposed method is based on inverse dynamics and is designed using a third-order Bézier curve approximation to the reference trajectory.

Simulations indicate that the proposed law is able to satisfy impact angle and impact velocity constraints as well as follow control and path limitations in the case of guidance under perturbations. Comparisons with other methods also indicate better performance.

The onboard implementation requires an offline selection of Bézier parameters.

The presented scheme could be extremely important for further research on automated onboard control of impact angle and velocity for both re-entry and terminal guidance laws.

This paper presents an innovative method for the solution of an inverse dynamics-based guidance law using Bézier curve approximation.

The aim of this paper is to develop and demonstrate an outsourcing decision model in which constraint resource prevents the throughput of the organization.

An integrated model is proposed by incorporating the weighted product model (WPM) of AHP in estimating the priority of each product in a multi‐product constraint resource environment. A numerical example is presented to demonstrate the effectiveness of this model. The outsourcing decision model compares four alternatives: Standard cost accounting, standard theory‐of‐constraints (TOC), LP analysis and an original solution.

The numerical results show that the proposed model is superior and more realistically optimizes resource allocation and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This research is applicable to the companies which produce multi‐products in a situation in which market demand exceed the company's production capacity.

This is the first time that the WPM of AHP/TOC has been used to maximize the product throughput. Instead of calculating $return per constraint minute, it decides the priority of product that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing organization where one can compare the financial performance of the organization by selecting the right decision model.

Vision‐based inverted robot control exhibits a complex and a multi‐parameter estimation task. Compromises over speed and accuracy must be made to reduce the cost of the system, if high profile techniques are not utilized. The purpose of this paper is to present such a technique where many parameter estimation problems admit partitioning process. The process eliminates the complexity by dividing such a high dimension task into several reduced dimension problems. The partition procedure is defined by human understanding of the task. Here a mechanical setup is designed that handles the pose parameters estimations as a set of sub‐problems. The estimated pose parameters are applied to an inverted robot approaching a target on the floor.

A novel technique that helps the inverted robot to approach its target is detailed here. The new methodology is based upon the modified versions of existing and proven techniques of pure translations and rotations estimations. Providing valid conditions, the parameters of the two vectors of the camera's pose are isolated. In the first pass, the roll angle is adjusted. A subsequent pass uses modified 2‐point algorithm to estimate partial translational vector. Introducing 4‐point algorithm; an extension of pure rotational vector estimation technique, to estimate partial rotational vector. Lastly, visual depth is estimated to complete the task. For simplicity, the robot dynamics are not detailed here. It is assumed that the robot can possibly achieve any position if the desired pose parameters are known.

It is found that the isolated vectors estimation process reduces the complexity of the system and so reduces the computational cost and processing time. The proposed technique is applied to a prototype inverted Cartesian robot having 3D rotary wrist. Through analysis, it is observed that the estimated parameters are very close to the actual pose parameters.

The proposed technique can aid CNC vertical milling machines to countercheck the exact position and orientation of the tool w.r.t. the job. Similar systems in practice are DECKEL MAHO, DMU 60/80/100, 5‐axis package and DMC 35 V series.

The dynamic nature of the method proposed in the paper makes it more efficacious for mechanical/robotic systems with vertically downward tool under gravity effect.

This paper aims to present a model of a multi‐phase multi‐product manufacturing system considering a CONstant work‐in‐process (CONWIP) control mechanism and using continuous‐time Markov chain modelling approach.

The model includes defining a state space then constructing the rate matrix, which contains the transition rates, followed by formulating the transition matrix. The time‐dependent probabilities that a product is in a particular state at a certain time are characterized. Performance measures related to the statistics on the waiting time and average number of work‐in‐process in the production system have been determined. Consequently, a numerical example is presented to illustrate the computations of different model aspects.

The analyses explain a foundation needed for analyzing the steady state behavior of manufacturing systems. Results have shown how production data can be easily modified for what‐if analyses by the use of Excel add‐in tool.

The multi‐level model outlines a framework that provides a practical tool for production engineers seeking to enhance the performance of their production system by selecting the best order release mechanism.

A novel aspect of the work reported in this paper is the application of Chapman‐Kolmogrov mathematics and CONWIP ordering theory, which is developed for evaluating and managing CONWIP controlled production systems.