This article deals with the cost-oriented assembly line balancing problem with sequence-dependent set-up times. To this end, a mixed-integer linear programming (MILP) model is proposed for time- and cost-oriented assembly line balancing problems with sequence-dependent set-up times between tasks. The problem is computationally intractable; therefore, a Benders’ decomposition algorithm is developed

Cell formation is a crucial problem in the design of cellular manufacturing systems and has a decisive effect on productivity and cost. The similarity coefficient method is the most widely used to solve this problem because of its flexibility and computational efficiency. Although various similarity coefficient methods have been developed in the literature, few of these methods consider the factor

The auxiliary converter cabinet (ACC) of electric multiple unit vehicles guarantees the regular operation of electrical equipment. This article develops and optimizes a parametric model of the ACC based on lightweight composite pyramidal lattice materials to improve its performance. To make the sequential optimization procedure automatically closed-loop, an ACC model with parameter interfaces is built

To study the design method of openings on the web of an I-beam, three I-beams with different circular holes were designed using the traditional truss mechanism, and the circular holes were reinforced by ring stirrups. A shear test was performed to study the failure modes of the I-beams and efficiency of the ring stirrups. A new method for establishing the truss mechanism was proposed, which used the

With the increasing attention on environmental issues, green scheduling in manufacturing industries has become a hot research topic. As a typical scheduling problem, the flexible job-shop scheduling problem (FJSP) has received increasing attention, but research on the FJSP considering set-up and transportation times simultaneously is still rare. To address the energy-efficient FJSP with sequence-dependent

ABSTRACT Global optimization is one of the most difficult research topics in the optimization area. In the past few decades, many stochastic methods have been proposed that proved to be effective. In this article, a new multistart global optimization method, using tunnelling and an evolutionary strategy and supervised by a martingale (MGO-TEM) is proposed, which also belongs to the stochastic family

Abstract In practice, allocating tasks to resources is often tackled in (near) real-time due to the latency of the task information and sudden task arrivals into a system. Therefore, the problem must be solved within a very short time budget, when tasks are urgent or idle resources are critical to the system's performance. Local search algorithms could be a good solution to this issue. These algorithms

The input power to a centrifugal pump is optimized by changing the impeller blade exit angle. In the optimization, blade exit angles of three meridional profile layers are selected as the design variables, while pump shaft power and head developed are the multi-objective optimization functions obtained through a surrogate model. A multi-objective genetic algorithm is used to find the optimum design

This study addresses a dynamic lot-sizing problem for remanufacturing systems in which end-of-use/life products are disassembled into their parts on a single disassembly workstation and then each part is reprocessed on one of the parallel reprocessing workstations. The problem, which integrates disassembly and reprocessing lot-sizing for multi-level disassembly structures, determines disassembly and

Open pit mines contain ore to be extracted from the surface downwards to obtain a profit. This is achieved using large pieces of equipment with significant space requirements to extract the material from the surface. Unfortunately, the industry currently lacks a standard methodology for optimizing the designs of these spaces; current practice may lead to inconsistent, subjective and suboptimal results

An important generalization of the classic 0-1 knapsack problem is the multi-demand multidimensional knapsack problem (MDMKP). In addition to being theoretically difficult to solve (it is NP-hard), it can be in practice difficult to solve because of its conflicting knapsack and demand constraints. Since there are significant large-scale applications of the MDMKP, approximate solution approaches are

Multi-objective integer or mixed-integer programming problems typically have disconnected feasible domains, making the task of constructing an approximation of the Pareto front challenging. The present article shows that certain algorithms that were originally devised for continuous problems can be successfully adapted to approximate the Pareto front for integer, and mixed-integer, multi-objective

This article presents an algorithm for solving a very common water resources management problem. This involves finding the optimal solution to the problem of minimizing the cost of energy consumed during pumping and transporting water from a group of wells to a reservoir to satisfy the water needs of a residential area. This algorithm addresses the problem of minimizing the cost of the energy consumed

In recent decades, surrogate-based optimization (SBO) has been developed to replace costly models with cheap surrogates to improve efficiency. In this article, an adaptive parallel infill strategy is proposed to balance exploration and exploitation over the design space during the optimization process of SBO. Within this method, an inaccurate search strategy is adopted to optimize the surrogate models

The Stope Boundary Optimization (SBO) problem refers to the layout design of production areas (stopes) in an underground mining operation. It falls within the domain of strategic mine planning and a model that addresses this problem aims to maximize profitability, offers optimal management of mineral resources and facilitates decision making around capital investments. The SBO problem is computationally

Reliability-based multidisciplinary design optimization (RBMDO) has been considered as an efficient technology for addressing complex engineering design optimization problems under uncertainties. Current RBMDO methods, however, perform design optimization either in a sequential manner or in an all-at-once manner, which does not support design autonomy in various disciplines and has low computational

The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal ‘climate action’ stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed

This article proposes the individual data privacy during collaborative computation in data mining method using an optimization model. The privacy problem is solved using different methodologies. The solution for individual privacy is considered as a multi-objective optimization model. Practically, the requirement for privacy varies from user to user. Therefore, it generates inherent vagueness for individual

This article develops a multiscale topology optimization model for the free damping structure to optimize the microstructure of the damping material and its macro layout. The objective is to minimize structural response in a broad frequency gap subject to volume constraints on both macro and micro levels. To overcome the convergence difficulty caused by the sharp jumping of the harmonic response around

Ballistic missiles often require the terminal velocity and impact angle to be confined to a certain region around a desired value considering a wide variety of uncertain initial conditions and operational ranges. This study presents a design framework to determine optimum waypoints that satisfy constraints for given launch conditions and mission profiles. As a systematic approach to this kinodynamic

In this study, a novel multi-objective reliability-based design optimization (MORBDO) method considering the maximum allowable deviation range of design variables is proposed for the reducer housing of electric vehicles. First, the numerical model of the reducer housing is established by ABAQUS and verified by experiments. A radial basis function (RBF) neural network model is used to construct the

In this study, the U-type assembly line balancing problem (UALBP) with assignment restrictions (AR-UALBP) is considered. Linked and incompatible distance and station restrictions are taken into account. New mathematical programming (MP) and constraint programming (CP) models are proposed. The objective function of the models is minimizing the cycle time for a given number of workstations. In addition

In a bike sharing system (BSS), for various reasons, such as truck fuel load, truck capacity and high customer demand, the number of trucks deployed by the BSS operator is usually insufficient and they cannot balance all of the stations in time. To incentivize users to relocate some of the excess bikes from stations with surplus bikes to bike deficient stations, a mixed rebalancing strategy is proposed

This article presents the optimization of the design of a subscale effusion-cooled rocket engine inner liner made of porous composite material, with the aim of improving the ability of the coolant film to protect the combustion chamber wall from thermochemical attacks. The optimization problem is defined as a global minimization problem and solved using response surface methodology (RSM). Recommendations

Lattice recursive least-squares (LRLS) adaptive filters are well known for their fast convergence to achieve the minimum least-squares error. This accelerated decaying error property, however, comes with a high cost filter structure. Using a variable tap-length technique known as the fractional tap-length (FT) algorithm, this article provides an adaptive method to search for the optimum length of LRLS

The functionality of the Active Suspension System (ASS) concerning stability and ride quality offers scope in modelling and simulation of automobile active suspension model. This work explores the design and simulation of a five degree of freedom ASS based driver integrated half car model (i.e.) Active Half Car Driver (AHCD) model. A novel design approach which assesses the Fuzzy-Fractional Order PID

Vehicle fleets support a diverse array of functions and are increasing rapidly in the world of today. For a vehicle fleet, maintenance plays a critical role. In this article, an evolutionary algorithm is proposed to optimize the vehicle fleet maintenance schedule based on the predicted remaining useful lifetime (RUL) of vehicle components to reduce the costs of repairs, decrease maintenance downtime

This study examines the capability of various optimization algorithms and proposes novel hybrid algorithms for more precise prediction of open-edge cracks in cantilever beams. The natural frequencies of the beam with a crack are obtained by modal analysis and experimentally validated by impact testing. The performance of Harris hawk optimization (HHO), electrostatic discharge algorithm (ESDA), pathfinder

A method for predicting deformation during the excavation of a foundation pit in composite formation is proposed. The artificial bee colony algorithm (ABC) is introduced to optimize the back-propagation (BP) neural network with the input variables filtered. This method is applied to predict the deformation of a foundation pit project. The prediction results are verified by comparing the results with

This article presents day-in-advance microgrid heat and electric power scheduling with and without demand-side management. The objective is to minimize the cost of heat and electric power generation, considering solar and wind power uncertainty. This scheduling problem is solved using a social group entropy optimization (SGEO) technique. The proposed microgrid comprises diesel generators, biomass-fuel-fired

This study focuses on a real-world case that is defined as an extension of the container loading problem (CLP) with weakly heterogeneous types of boxes. A company producing plastic cups wants to increase its efficiency in container loading operations. To solve the problem, a hybrid approach including a static rule to rank orders and integer nonlinear programming (INLP) models is developed. INLP models

In the injection moulding process, a set-up containing positioning moulds, a cleaning raw material hopper and machine settings is required. The set-up is uncertain owing to random factors, such as the crew’s skill levels and unexpected breakdowns. The general version of this problem is known as the single-machine scheduling problem with stochastic sequence-dependent set-up times. In this specialized

A stochastic bilevel programming problem with multiple followers is presented in this article. Such kinds of problem are computationally difficult and efficient algorithms are lacking thanks to the randomness properties in the problem setting, its hierarchical structure and the expected simultaneous decision at the followers' level for each strategy of the leader. This article proposes a systematic

ABSTRACT This article considers single-machine problems in which the actual processing time of a job is a function of its position in a sequence (i.e. position-dependent deterioration effects). In this model, a job is either accepted or rejected. If the job is accepted, it is processed on a single machine, but if the job is rejected, a penalty (cost) is imposed. The goal is to minimize the sum of the

Design optimization and performance analysis of natural gas hydrates (NGH) coring tool is a complex engineering system that involves several disciplines, such as structural, hydraulics, thermology and mechanism kinematics. An adaptive metamodel-based analytical target cascading (A-ATC) method is developed to efficiently deal with the NGH coring tool design problem. The NGH coring tool design problem

In this article, a deep reinforcement learning-based strategy for the optimization of gain parameters of a cross-coupled controller is proposed. First, to compensate the contour error caused by servo lag, a proportional–integral (PI)-type cross-coupled controller is designed, with its gain parameters being determined using the cut-off frequency of the contour error transfer function. Next, on the basis

This study presents a new approach to find the optimal operating parameters of a tractor disc plough, to minimize the fuel used for the land preparation of sugarcane fields. The new approach is composed of three steps: (1) construct the regression model; (2) use modified differential evolution (MDE) to find the optimal parameters; and (3) verify the optimal parameters by performing a real experiment

Structural optimization for performance-based seismic designs is aimed at finding a minimum objective function with constraints on performance requirements. In this research, a hybrid algorithm is introduced based on big bang–big crunch (BB-BC) optimization, harmony search (HS) algorithm, multi-design variable configuration (multi-DVC) cascade optimization and upper bound strategy (UBS). This new scheme

Multi-speed transmissions can enhance the comprehensive performance of electric vehicles (EVs), while exhibiting potential to further improve their shift quality. In this study, a gearshift control architecture combined with multi-objective trajectory planning was proposed for EVs with two-speed transmission. Three indices, namely shifting duration, jerk and friction work, were selected as optimization

This article presents a multi-component topology optimization method for thin-walled structures, which can be applied to two-dimensional plate components and three-dimensional tube components. Based on the theory of multi-component topology optimization for stamped sheet metal assemblies, an accurate calculation method for the Young’s modulus of joint elements is provided. To avoid the discontinuous

ABSTRACT This article presents a flexible job-shop scheduling problem with a parallel assembly stage and lot streaming. Suppose that several products of different kinds are ordered to be produced. Each product consists of several specific parts. The components (parts) of products are manufactured in a flexible job shop, then assembled into products in parallel lines or parallel stations. The objective

Solving Multi-Objective Mixed Integer NonLinear Programming (MO-MINLP) problems is a point of interest for many researchers as they appear in several real-world applications, especially in the mechanical engineering field. Many researchers have proposed using hybrids of metaheuristics with mono-objective branch and bound. Others have suggested using heuristics with Multi-Criteria Branch and Bound (MCBB)

ABSTRACT With the growing social and environmental concerns, the integration of sustainability in supplier selection and order allocation is of paramount importance. This research presents a holistic multi-phase decision-support framework to solve the sustainable supplier selection and order allocation problem for the multi-echelon supply chain. The framework comprises a multi-objective mixed-integer

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator’s strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer

This article proposes a deep convolutional neural network with perceptible generalization ability for structural topology optimization. The architecture of the neural network is made up of encoding and decoding parts, which provide down- and up-sampling operations. The popular U-Net was adopted to improve the performance of the proposed neural network. To train the neural network, a large dataset is

ABSTRACT The Bi-Level Optimization Problem (BLOP) is defined as a mathematical program with two nested optimization tasks. Although many applications fit the bi-level framework, however, existing resolution methods were most proposed to solve single-objective bi-level problems. Regarding Multi-objective BLOPs (MBLOPs), there do not exist too many previous studies because of the difficulties associated

ABSTRACT In this article, a two-objective procedure is proposed to optimize the topology of wide horns. These two objectives are: the longitudinal natural frequency of the horn should be similar to the frequency of the driven transducer–booster, and the output face of the horn should vibrate uniformly. The weighted sum method is used to calculate the two-objective sensitivity number. Based on the proposed

Cell Formation (CF) and Group Layout (GL) are two important problems in designing a cellular manufacturing system. This article addresses an integrated CF and GL model as a mixed-integer program, considering energy consumption, assembly aspects and process routing. As a novel feature, machines are assumed to be capable of processing operations with different energy consumption rates and processing

The spatial distribution of bus garages determines the total vehicular dead mileage of the transit system because buses must travel between bus garages and terminals at the start or conclusion of a day. By contrast, the size of the garages determines the queuing status when buses enter or leave the garages. Thus, a bus garage system with reasonable distribution and size is required to address these

A structural multi-objective topology optimization method integrating the solid isotropic material with penalization (SIMP) approach, compromise programming method and criteria importance through intercriteria correlation (CRITIC) method is proposed. The SIMP approach performs topology optimization. The compromise programming method builds a comprehensive objective function to handle the multi-objective

The compound kriging-based importance sampling (IS) strategy is proposed for the efficient estimation of failure probability of systems with multiple failure modes. The proposed method is based on the IS probability density function of each failure mode constructed by the kriging model, where the probabilistic classification function is treated as a surrogate model for the actual failure indicator

Abstract In this article, an improved Gaussian distribution based quantum-behaved particle swarm optimization (IG-QPSO) algorithm is proposed to solve engineering shape design problems with multiple constraints. In this algorithm, the Gaussian distribution is employed to generate the sequence of random numbers in the QPSO algorithm. By decreasing the variance of the Gaussian distribution linearly,

Optimization problems are considered that involve the multiplication of variable matrices to be selected from a given family, which might be a discrete set, a continuous set or a combination of both. Such nonlinear, and possibly discrete, optimization problems arise in applications from biology and materials science among others, and are known to be NP-hard for a special case of interest. The underlying

Constrained layer damping (CLD) is a typical application known to exhibit high damping. However, the damping ratio generally attains its maximum value when the applied surface is fully covered with the damping material. In this study, a method is proposed of topology optimization in the thickness direction using viscoelastic and constrained materials based on CLD in order to obtain a higher damping

ABSTRACT The present study presents the application of a structural optimization scheme for a Double Split Hollow Sphere (DSHS) based acoustic cloaking structure for irregular surfaces. For acoustic wave cloaking, many scientific devices utilizing phase differences have been developed. Considering the irregular surfaces of objects to be concealed, acoustic wave reflections become complex with random

This article introduces a novel strategy for determining the optimal control parameters of particle swarm optimization (PSO) for the shortest convergence time and lowest failure rate of photovoltaic (PV) maximum power point tracker (MPPT) systems. This strategy is used offline to determine these parameters and then the control system uses them in the online MPPT. The strategy uses two nested particle

Competition among companies leads to a race in order to improve the management system of their production with respect to the delivery time. In the last few decades, the optimization of production scheduling has attracted the interest of numerous researchers. This article deals with the Flexible Jobshop Scheduling Problem (FJSP) as one of the most challenging combinatorial optimization problems. FJSP

Very few studies have considered modelling the statuses of loading and component combinations (LC) in chiller systems. This study uses a decision tree (DT) and a neural network (NN) to model accurately six output levels of LC in terms of 11 predictor variables. The NN model gave a higher correct prediction of 89.52% than 84.44% in the DT model. A variable dropout analysis from the DT and NN models

The mathematical model of thermomechanical coupling multi-objective topology optimization for anisotropic structures was established using the element-free Galerkin (EFG) method, and the weighted function of compliance and heat dissipation was defined as the objective function. The proposed model and procedure were verified by the topological results based on the finite element method. The multi-objective

This article proposes a novel biogeography-based optimization (NBBO) algorithm to solve the distributed assembly permutation flow-shop scheduling problem with sequence-dependent set-up times (DAPFSP-SDST). The optimization objective of this problem is minimizing the maximum completion time (makespan). In the initialization phase, NBBO generates two kinds of feasible solutions. Secondly, the linear