Scheduling a dynamic flexible flow line with sequence-dependent setup times: a simulation analysis

This paper presents a simulation-based experimental study of scheduling rules for scheduling a dynamic flexible flow line problem considering sequence-dependent setup times. A discrete-event simulation model is presented as well as eight adapted heuristic algorithms, including seven dispatching rules and one constructive heuristic, from the literature. In addition, six new proposed heuristics are implemented in the simulation model. Simulation experiments are conducted under various conditions such as setup time ratio and shop utilisation percentage. One of the proposed rules performs better for the mean flow time measure and another one performs better for the mean tardiness measure. Finally, multiple linear regression based meta-models are developed for the best performing scheduling rules.     

Simulation-based optimisation approach for the stochastic two-echelon logistics problem

This work proposes a simulation-based optimisation approach for the two-echelon vehicle routing problem with stochastic demands (2E-VRPSD). In the proposed 2E-VRPSD, freight delivery from the depot to the customers is managed by shipping the freight through intermediate satellites, while each customer has a stochastic demand. The 2E-VRPSD is an extension of the famous capacitated vehicle routing problem with stochastic demands and the two-echelon vehicle routing problem (2E-VRP). A tabu search algorithm is designed to solve the 2E-VRPSD, in which Monte Carlo sampling is adopted to tackle the issue of stochastic demands. Modified two-echelon vehicle routing problem benchmark instances are used in the numerical experiments. The computational results show the advantage of the proposed simulation-based approach.     

Scheduling dynamic block assembly in shipbuilding through hybrid simulation and spatial optimisation

Scheduling block assembly in shipyard production poses great difficulties regarding the accurate prediction of the required spatial resource and effective production control for achieving managerial objectives due to the dynamic spatial layout and the stochastic nature of the production system. In this study, this dynamic space-constrained problem is viewed as two sequential decisions, namely rule-based dispatching and a static spatial configuration. A novel hybrid planning method is developed to employ discrete-event simulation as look-ahead scheduling to evaluate the system performance under various control policies. To rationalise block placement and improve long-term area utilisation, a discrete spatial optimisation problem is formulated and solved using an enumeration-based search algorithm, followed by the application of a series of heuristic positioning strategies. By imitation of the dynamic dispatching and spatial operation, a statistical analysis of the resultant performance can be conducted to select the best-performing priority rules. A case study with an experimental investigation is performed for a local shipyard to demonstrate the applicability of the proposed method.     

Dynamic scheduling of production-assembly networks in a distributed environment

In complex manufacturing environments, meeting the due dates of the jobs and minimizing in process inventories are important performance metrics. One of the common characteristics of complex production systems is production-assembly network of operations. This paper presents an auction-based algorithm for simultaneous scheduling of all manufactured and assembled jobs in a dynamic environment, where the objective function is to minimize both the due date penalties associated with the final products and the inventory cost of the work in process. An auction-based approach using a Mixed-Integer Linear Programming (MILP) model to construct and evaluate the bids so that the auction mechanism mimics a Lagrangian relaxation-based subgradient optimization to ensure global optimality is proposed. The inner structure of the problem enables very efficient calculation of bids for each job or assembly. Using a full factorial experimental design the properties of the proposed algorithm are analyzed. Results show that the proposed auction based algorithm performs better than the popular dispatching rules and is more scalable than the MILP model or direct implementations of the subgradient algorithm. Furthermore, the proposed algorithm is designed to work in a dynamic environment.     

ROBUST DESIGN FOR IMPROVED VEHICLE HANDLING UNDER A RANGE OF MANEUVER CONDITIONS

In this article, a robust design procedure is applied to achieve improved vehicle handling performance as an integral part of simulation-based vehicle design. Recent developments in the field of robust design optimization and the techniques for creating global approximations of design behaviors are applied to improve the computational efficiency of robust vehicle design built upon sophisticated vehicle dynamic simulations. The approach is applied to the design of a M916A1 6-wheel tractor/M870A2 3-axle semi-trailer. The results illustrate that the proposed procedure is effective for preventing the rollover of ground vehicles as well as for identifying a design that is not only optimal against the worst maneuver condition but is also robust with respect to a range of maneuver inputs. Furthermore, a comparison is made between a statistical approach and a bi-level optimization approach in terms of their effectiveness in solving robust design problems     

Optimisation of geometrically non-linear composite structures based on load–displacement control

A design optimisation methodology for beam reinforced composite structures with non-linear geometric behaviour is proposed. The formulation involves displacement, stresses, buckling and size constraints. The Newton–Raphson iterative procedure and the arc-length method are used for tracing equilibrium path and later updating the buckling load and the first ply failure load. The proposed sensitivity analysis model is based on an approach of the adjoint variable method for structures with non-linear geometric behaviour. The optimal design performs on a multilevel scheme based on structural efficiency maximisation exploring the anisotropic properties of the composites and weight minimisation using the ply thickness and the cross-section variables of the stiffeners. To demonstrate the applicability of the proposed developments, optimisation problems considering first ply failure and buckling conditions are presented.     

In‐Field Measurement of Forces and Deformations at the Rear End of a Motorcycle and Structural Optimisation: Experimental–Numerical Approach Aimed at Structural Optimisation

Abstract: The present study concerns the analysis of the asymmetric displacement behaviour of the rear part of a motorcycle. Stylistic reasons led to the design of a vehicle with only one suspension located on the left‐hand side. Experimental tests performed on a circuit with seven obstacles along a straight line confirmed that the bending displacement is higher on the right‐hand side than on the left. This study aimed to perform a structural optimisation of the components involved at the rear end of the motorcycle, to find a solution to the problem. A hybrid approach is applied: the force acting on the suspension and bending displacements at the rear end were simultaneously measured in working conditions; a finite‐element method model was then set up, validated and applied for design optimisation purposes. Both methodological aspects and applicative results are presented and discussed. Finally, a solution in accordance with design specifics is proposed.     

Stochastic modelling and simulation of production lines: a computer-based approach towards model validation

This study employs a simulation-based design methodology to investigate the performance of two models of manufacturing systems. In the first model, the dynamic behaviour of a single parallel-machine stage with unreliable work stations is modelled as a Markov process. A similar analytical method for evaluating the performance of a buffered production line is presented in the second model. A simple approach towards coding and simulating the models is presented, and numerical examples based on these simulation models indicate that the approach is viable.     

The study of real time scheduling by an intelligent multi-controller approach

Earlier studies indicated that using multiple dispatching rules (MDRs) for the various zones in the system can enhance the production performance to a greater extent than using a single dispatching rule (SDR) over a given scheduling interval for all the machines in the system, since MDRs employ the multi-pass simulation approach for real-time scheduling (RTS). However, if a classical machine learning approach is used, an RTS knowledge base (KB) can be developed by using the appropriate MDRs strategy (this method is called an intelligent multi-controller in this paper) as obtained from training examples. The main disadvantage of using MDRs is that the classes (scheduling decision variables) to which training examples are assigned must be provided. Hence, developing an RTS KB using the intelligent multi-controller approach becomes an intolerably time-consuming task because MDRs for the next scheduling period must be determined. To address this issue, we proposed an intelligent multi-controller incorporating three main mechanisms: (1) simulation-based training example generation mechanism, (2) data pre-processing mechanism and (3) SOM-based real time MDRs selection mechanism. Under various performance criteria over a long period, the proposed approach yields better system performance than the machine learning-based RTS using the SDR approach and heuristic individual dispatching rules.     

Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach

The purpose of this paper is to develop a data-mining-based dynamic dispatching rule selection mechanism for a shop floor control system to make real-time scheduling decisions. In data mining processes, data transformations (including data normalisation and feature selection) and data mining algorithms greatly influence the predictive accuracy of data mining tasks. Here, the z-scores data normalisation mechanism and genetic-algorithm-based feature selection mechanism are used for data transformation tasks, then support vector machines (SVMs) is applied for the dynamic dispatching rule selection classifier. The simulation experiments demonstrate that the proposed data-mining-based approach is more generalisable than approaches that do not employ a data-mining-based approach, in terms of accurately assigning the best dispatching strategy for the next scheduling period. Moreover, the proposed SVM classifier using the data-mining-based approach yields a better system performance than obtained with a classical SVM-based dynamic dispatching rule selection mechanism and heuristic individual dispatching rules under various performance criteria over a long period.     

Stochastic two-sided U-type assembly line balancing: a genetic algorithm approach

In this paper, a novel stochastic two-sided U-type assembly line balancing (STUALB) procedure, an algorithm based on the genetic algorithm and a heuristic priority rule-based procedure to solve STUALB problem are proposed. With this new proposed assembly line design, all advantages of both two-sided assembly lines and U-type assembly lines are combined. Due to the variability of the real-life conditions, stochastic task times are also considered in the study. The proposed approach aims to minimise the number of positions (i.e. the U-type assembly line length) as the primary objective and to minimise the number of stations (i.e. the number of operators) as a secondary objective for a given cycle time. An example problem is solved to illustrate the proposed approach. In order to evaluate the efficiency of the proposed algorithm, test problems taken from the literature are used. The experimental results show that the proposed approach performs well.     

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed in the literature. The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent-based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent-based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles are explained and discussed.     

Concurrent manual-order-picking warehouse design: a simulation-based design of experiments approach

The design of manual-order-picking warehouses is a combination of interdependent decisions with enormous possible varieties in design components. The strong interrelationship between these components, in addition to the dynamic and interconnected stochastic nature of the problem; necessitate the utilisation of a simultaneous simulation-based approach. This study proposes a concurrent simulation-based design of experiments approach for the design of manual-order-picking warehouses. The proposed approach can investigate all possible warehousing design combinations with their stochastic nature and interactions; hence, widening the search for performance improvement. The examined design components include warehouse throughput, size, layout, operational policies and manpower/carts. Furthermore, the presented approach captures the probabilistic nature of all the key warehouse functions of receiving, unloading, put away, storage, preparation and picking and shipping; and evaluates the performance of the studied designs using the cycle time for a stock keeping unit in the warehouse. Statistical analysis of the simulation results showed several interesting findings; horizontal layout was preferable over all other types of layouts and small size warehouses perform better than other large sizes. The study has also recommended using high throughput for traditional layout-small size warehouses.     

Effective overhead hoist transport dispatching based on the Hungarian algorithm for a large semiconductor FAB

This paper considers the vehicle dispatching problem in large-scale overhead hoist transport (OHT) systems of semiconductor fabrication lines. We propose a Hungarian algorithm based OHT reassignment approach named HABOR. HABOR attempts to take advantage of simultaneous vehicle reassignment based on up-to-date system status using the formulation of the assignment problem. The effectiveness of HABOR is demonstrated using a sample OHT system of a semiconductor fabrication line with more than 130 vehicles, where the flow path of the line allows direct delivery so that an inter-bay wafer movement can be accomplished by a single vehicle without an intermediate storage step at a stocker. HABOR compares favorably with the shortest travel distance first rule and with the reassignment-based rule recently proposed by the authors.     

Robust fixture layout design for multi-station sheet metal assembly processes using a genetic algorithm

The optimal fixture layout is crucial to product quality assurance in the multi-station sheet metal assembly processes. Poor fixture layout may lead to product variation during the assembly processes. In this paper, a genetic algorithm (GA)-based optimisation approach has been presented for the robust fixture layout design in the multi-station assembly processes. The robust fixture layout is developed to minimise the sensitivity of product variation to fixture errors by selecting the appropriate coordinate locations of pins and slot orientations. In this paper, a modified state space model for variation propagation in the multi-station sheet metal assembly is developed for the first time, which is the mathematical foundation of optimal algorithm. An e-optimal is applied as the robust design criteria. Based on the state space model and design criteria, a genetic algorithm is used to find the optimal fixture layout design. The proposed method can greatly reduce the sensitivity level of product variation. A four-station assembly process of an inner-panel complete for a station wagon (estate car) is used to illustrate this method.     

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.     

Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

In vehicle ad-hoc networks (VANETs), the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes significant. Motivated by the self-adaptive paradigm of cellular attractors, this paper regards an individual communication as a cell, so that we can apply the revised attractor selection model to induce each connected vehicle. Aiming at improving the Quality of Service (QoS), we presented the bio-inspired handover decision-making mechanism. In addition, we employ the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for any vehicle to choose an access network. This paper proposes a novel framework where the bio-inspired mechanism is combined with TOPSIS. In a dynamic and random mobility environment, our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global sense. The experimental results confirm that the proposed method performs better when compared with conventional schemes.     

Time estimation method for manual assembly using MODAPTS technique in the product design stage

This paper aims to propose an accurate and quick assembly time estimation method using the modular arrangement of predetermined time standards in the product design stage. It describes a classification of 2382 assembly operations that are incurred in manually assembling consumer electronics such as air conditioners, washing machines and refrigerators, and a method of choosing representative motions comprising work elements by examining the frequency distribution of the assembly operation’s motions. It then presents criteria for assigning time values associated with the movement of the representative motions using the design factors employed in design for assembly and the layout factors of an assembly line. A case study then presents the practicality of the method, the statistical results of which indicate that the proposed method would be accurate enough for practical purposes.     

A hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem for TFT-LCD manufacturing

This research addresses a hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem. It optimises three performance criteria simultaneously, namely: cycle time, slack time, and throughput. A case study is adopted to illustrate the performance of applying the methodology. Thin film transistor-liquid crystal display (TFT-LCD) is a high-technology industry, with a growing market. The manufacturing process is complex. It involves multi-products, sequence-dependent set-ups, random breakdowns, and multiple-objectives, with bias-weighted optimisation problems. To determine appropriate dispatching strategies, under various system conditions, is a non-trivial challenge to control the complex systems. There has been little research on these problems aimed at solving them simultaneously. This paper presents an event-triggered dynamic dispatching system that combines artificial intelligence methods to archive optimum dispatching strategies under diverse shop-floor conditions. Results show this system to be superior to previous researches.     

城轨车辆车体多重动力吸振器减振方法研究

为了让动力吸振器在降低轨道车辆车体振动的同时能够更好的适应车下剩余空间,根据多重动力吸振器原理,针对城轨车辆运行的特点,建立了包含多重动力吸振器的轨道车辆垂向振动模型,提出了适用于城市轨道车辆车体多重动力吸振器的设计方法。①讨论了载客量和速度变化对多重动力吸振器的减振性能的影响,指出了传统多重动力吸振器的局限性;②针对轨道车辆振动频率变化频繁的特点,提出了多重动力吸振器的目标频率的优化方法,从而避免了增振的情况出现;③以四条典型城市轨道线路为算例,利用DVA减振指标进行评价,分别获得了不同线路的车体多重动力吸振器的最优目标频率,并验证了该优化方法的有效性。研究结果表明:相同附加质量下,多重动力吸振器对车体的吸振能力要优于单个动力吸振器,考虑到实际的应用,在车体安装四重动力吸振器是较为适宜的选择;经过优化的多重动力吸振器在整个速度区间都能起到很好的减振效果,能够有效避免增振现象的发生;多重动力吸振器的目标频率的设计要针对不同的线路进行调整,特定线路需要特定设计才能发挥出最佳减振能力。该研究的工作为车体多重动力吸振器的研究和应用提供了参考依据。