Purpose: The purpose of this paper is to develop an approach to measuring the performance of motor vehicle manufacturers (MVMs) from economic and environmental (E&E) perspectives. Design/methodology/approach: Eight measures are identified for benchmarking the performance from E&E perspectives. A new company performance index I_MVM is constructed to quantitatively generate the historical data of MVMs’ company performance. Autoregressive integrated moving average (ARIMA) models are built to generate the forecast data of the I_MVM. The minimum Akaike information criteria value is used to identify the model of the best fit. Forecast accuracy of the ARIMA models is tested by the mean absolute percentage error. Findings: The construction of the index I_MVM is benchmarked against three frameworks by six benchmark metrics. The I_MVM satisfies all of its applicable metrics while the three frameworks are incapable to satisfy their applicable metrics. Out of 15, 4 MVMs are excluded for benchmarking future performance due to their non-stationary time series data. Based on the forecast I_MVM data, GM is the best performer among the 15 samples in the FY2018. Originality/value: This research highlights the environmental perspective during vehicles’ production. The development of this approach is based on publicly available data and transparent about the methods it used. The data out of the approach can benefit stakeholders with insights by benchmarking the historical performance of MVMs as well as their future performance.

Composite indicators (CIs) are needed for decision makers to effectively benchmark holistic company performance. Composite indicators at macro levels are inappropriate to be implemented at the company level. By a literature survey, this article identified 29 individual methods for constructing CIs, 17 specific business sectors where CIs have been utilized in practice, and the motor vehicle manufacturing sector as the most studied sector. This article identified nine problems and provided four recommendations for future research.

Purpose: Today, most of the car manufacturers world-wide have embraced the principles of lean manufacturing on strategic and operational level. On strategic level car companies like Toyota (Womack et al., 1990) shifted 63 per cent of the value of the car towards the first, second and third tier suppliers for the co-production and co-development of cars as an effect of lean implementation. However, lean implementation was also followed by for instance Ford and GM in the USA, the latter company faced a sudden disruption in 2009 due to the break-out of the financial crisis in 2008, while Ford survived. Could this be foreseen? The exclusive use of (classic) financial performance indicators may give a false image of a company’s current and future performance. There is a need for a model to identify “the stars and the laggards’ regarding car companies by taking into account non-financial and intangible dimensions as advocated by Neely et al. (2003) regarding the third generation of business performance measurement systems. The purpose of this paper is therefor to propose a method to measure and benchmark car company performance which includes the non-financial R&D dimension as well as supply chain, value creating and employee dimensions. These dimensions are present in the value leverage model (van Blokland et al., 2012a, 2012b) which can serve as a basis for this method. The aim is to contribute to the third generation business performance measurement systems by further development of the value leverage model towards a maturity model for benchmarking car company performance. The proposed method can provide a big picture and give insight regarding company performance and direction of the performance. Design/methodology/approach: Value leverage can be measured by a correlation analysis regarding three dimensions, namely, supply chain, R&D and value creation, all relative to the employee or capita which results in the average value leverage (AVL) factor. This AVL factor can be used to compose a combined relative and absolute ranking. The score regarding the AVL results in a relative ranking expressing the level of stability regarding the car companies value chain and system. For the absolute ranking the car companies receive per variable parameter a score according to their absolute performance relative to the other car companies. The relative and absolute ranking are presented on the vertical and horizontal axes forming a matrix. The matrix is the basis for the stability-value leverage maturity model for measuring and benchmarking company performance. With the proposed method, the following main research question can be answered: “How can company performance be measured and benchmarked from a stability-value leverage perspective?”. Findings: With the proposed method, stability-value leverage performance can be measured. The relative ranking on the vertical axis and the absolute ranking form together a matrix which is presented by a scatterplot. A matrix with four maturity levels emerged from the analysis by introducing the average score of all the car companies together in the data set crossing the matrix vertical and horizontal. The four levels are as follows: Level I, low stability – low value leverage; Level II, low stability – high value leverage; Level III, high stability – low value leverage; and Level IV, high stability – high value leverage. Stability-value leverage performance of car companies can be measured over time which makes it possible to observe to which direction the car company migrates for instance from Level I to Level III, before and after the financial crises in 2008. The car companies BMW, Daimler, Audi, Ford and Honda are the best performing companies in stability-value leverage over the period 2000-2014, as they are situated at Level IV. With the findings, the main research question can be answered. The value leverage indicators can be used for measuring and benchmarking company performance regarding four maturity levels of stability and value leverage. The direction of performance can be observed as well. Research/limitations/implications: This research is limited to the car industry. Further research is devised to test the indicators for instance on the truck manufacturing industry. Further research towards new variables is part of the ongoing research. Practical/implications: With the value leverage maturity model, it is possible to inform stakeholders about stability, value leverage and value creation capability of car companies. Weak performing companies can be identified in an early stage with this method to anticipate for instance on possible discontinuation of a car company effecting in merger an acquisition processes. Social/implications: With the method stakeholders such as employees, users of cars and investors can be informed about how and why car companies perform in an unstable or stable manner. Originality/value: This research towards ranking and classification of car companies aligns with theories regarding lean manufacturing and maturity models, as these models are used to compare companies on their level of perfection or excellence.

Purpose: The development of bulk material handling equipment can be accelerated and made less expensive when testing of virtual prototypes is adopted. However, the modelling of a grab unloader requires a large volume (77 m3) of iron ore pellets, making the computational costs prohibitive. This paper investigates the extent to which the original particles can be substituted by larger, coarser grains. It is crucial that this particle upscaling does not alter the realistic behaviour of the simulated bulk material, nor its interaction with the bulk handling equipment. Approach: First, our coarse graining technique is explained and set out for the particle system at hand. The material behaviour is then characterized using three laboratory experiments (two angle of repose tests and a penetration test). Next, the results of simulations using two contact models with and without coarse graining with different scale factors are compared with the measured material behaviour and material-equipment interaction. This includes a comparison of the macrobehaviour of the bulk material and the tool interaction of coarser grains in a cutting and sliding process. After reaching a satisfactory verified solution on the laboratory scale, the material behaviour and interaction behaviour of a large-scale experiment are modelled. A simulation model of a grab unloader was used for validation of the chosen coarse graining approach. Findings: Using the scaling method presented, the macroscopic tests indicated consistent material behaviour, regardless of the chosen particle scale for two contactmodels. Scaling of the tool interaction process produced mixed results: the sliding process scaled consistently but the penetration process did not, most likely because it is significantly harder for coarser grains to move since they have to move further to the sides before the tool can pass, leading to higher normal forces and frictional forces on the tip. This inconsistency was compensated for by adjusting the wall friction coefficient in the tip of the penetration tool. Once this adapted coarse graining scheme was applied to the industrial-scale simulation of a grab unloader, it produced consistent particle-scale invariant results. Originality/value: This research is the first to show how coarse graining schemes for DEM simulations can be applied to large-scale bulk handling equipment involving dominance of material equipment interaction through penetration of the bulk material.

Belt conveyor systems are widely utilized for continuous transport of bulk materials. Maintenance activities are essential to ensure the reliability of belt conveyor systems. Conventional diagnosis decision is achieved based on empirical constant thresholds. The Challenge of this study is to propose a framework of integrated maintenance decision making for belt conveyor idlers. Information from operational conditions, reliability estimation of idlers and condition monitoring data are integrated for accurate decision making. Innovatively, in the proposed framework threshold of the monitoring parameter can vary according to real time operational conditions and reliability estimation results. A simulation study is presented to demonstrate the effectiveness of framework. Simulation results show that the framework can result in more accurate maintenance decision making compared to conventional approaches.

The ultimate strength of metallic pipelines will be inevitably affected when they have suffered from structural damage after mechanical interference. The present experiments aim to investigate the residual ultimate bending strength of metallic pipes with structural damage based on large-scale pipe tests. Artificial damage, such as a dent, metal loss, a crack, and combinations thereof, is introduced to the pipe surface in advance. Four-point bending tests are performed to investigate the structural behavior of metallic pipes in terms of bending moment-curvature diagrams, failure modes, bending capacity, and critical bending curvatures. Test results show that the occurrence of structural damage on the pipe compression side reduces the bending capacity significantly. Only a slight effect has been observed for pipes with damage on the tensile side as long as no fracture failure appears. The possible causes that have introduced experimental errors are presented and discussed. The test data obtained in this paper can be used to further quantify damage effects on bending capacity of seamless pipes with similar D/t ratios. The comparison results in this paper can facilitate the structural integrity design as well as the maintenance of damaged pipes when mechanical interference happens during the service life of pipelines.

Wheel impact load detectors are widespread railway systems used for measuring the wheel–rail contact force. They usually measure the rail strain and convert it to force in order to detect high impact forces and corresponding detrimental wheels. The measured strain signal can also be used to identify the defect type and its severity. The strain sensors have a limited effective zone that leads to partial observation from the wheels. Therefore, wheel impact load detectors exploit multiple sensors to collect samples from different portions of the wheels. The discrete measurement by multiple sensors provides the magnitude of the force; however, it does not provide the much richer variation pattern of the contact force signal. Therefore, this paper proposes a fusion method to associate the collected samples to their positions over the wheel circumferential coordinate. This process reconstructs an informative signal from the discrete samples collected by multiple sensors. To validate the proposed method, the multiple sensors have been simulated by an ad hoc multibody dynamic software (VI-Rail), and the outputs have been fed to the fusion model. The reconstructed signal represents the contact force and consequently the wheel defect. The obtained results demonstrate considerable similarity between the contact force and the reconstructed defect signal that can be used for further defect identification.

We study the integration of real-time traffic management and train control by using mixed-integer nonlinear programming (MINLP) and mixed-integer linear programming (MILP) approaches. Three innovative integrated optimization approaches for real-time traffic management that inherently include train control are developed to deliver both a train dispatching solution (including train routes, orders, departure and arrival times at passing stations) and a train control solution (i.e., train speed trajectories). Train speed is considered variable, and the blocking time of a train on a block section dynamically depends on its real speed. To formulate the integrated problem, we first propose an MINLP problem (P_NLP), which is solved by a two-level approach. This MINLP problem is then reformulated by approximating the nonlinear terms with piecewise affine functions, resulting in an MILP problem (P_PWA). Moreover, we consider a preprocessing method to generate the possible speed profile options for each train on each block section, one of which is further selected by a proposed MILP problem (P_TSPO) with respect to safety, capacity, and speed consistency constraints. This problem is solved by means of a custom-designed two-step approach, in order to speed up the solving procedure. Numerical experiments are conducted using data from the Dutch railway network to comparatively evaluate the effectiveness and efficiency of the three proposed approaches with heterogeneous traffic. According to the experimental results, the MILP approach (P_TSPO) yields the best overall performance within the required computation time. The experimental results demonstrate the benefits of the integration, i.e., train delays can be reduced by managing train speed.

We introduce a distributed optimization method for improving the computational efficiency of real-time traffic management approaches for large-scale railway networks. We first decompose the whole network into a pre-defined number of regions by using an integer linear optimization approach. For each resulting region, a mixed-integer linear programming approach is used to address the traffic management problem, with micro details of the network and incorporated with the train control problem. For handling the interactions among regions, an alternating direction method of multipliers (ADMM) algorithm based solution approach is developed to solve the subproblem of each region through coordination with the other regions in an iterative manner. A priority rule based solution approach is proposed to generate feasible suboptimal solutions, in case of lack of convergence. Numerical experiments are conducted based on the Dutch railway network to show the performance of the proposed solution approaches, in terms of effectiveness and efficiency. We also show the trade-off between solution quality and computational efficiency.

Shuttle based storage and retrieval systems (SBS/RS) attract continuous research attention because of their ability to achieve a high throughput. In an SBS/RS system, lifts are regarded as the bottleneck that hinder reaching higher throughput and therefore require subtle control polices. In this paper, the scheduling of two non-passing lifts on a common rail SBS/RS has been studied with consideration of the acceleration and deceleration of the lifts. Lift scheduling includes storage and retrieval requests sequencing, assignment of lifts, and collision avoidance. The main objective of the lift scheduling is minimizing the makespan of the moves. Different with the traditional constant velocity lift scheduling approach is that new collisions emerge when the acceleration/deceleration of the lifts are taken into consideration. This makes the scheduling different. In this paper a collision free lifts trajectory predicting approach with acceleration/deceleration is presented. Combined with the collision-free method, request sequencing and assignment are carried out by a proposed genetic algorithm. Experimental results with several SBS/RS practical working scenarios provide evidence that the proposed scheduling approach achieved on average 12.2% and 6.4% improvement in makespan compared with the constant velocity approach when the maximum velocity of the lifts is 1.5 m/s and 2 m/s respectively.

We study the integration of real-time traffic management and train control by using mixed-integer nonlinear programming (MINLP) and mixed-integer linear programming (MILP) approaches. In Part 1 of the paper (Luan et al., 2018), three integrated optimization problems, namely the P_NLP problem (NLP: nonlinear programming), the P_PWA problem (PWA: piecewise affine), and the P_TSPO problem (TSPO: train speed profile option), have been developed for real-time traffic management that inherently include train control. A two-level approach and a custom-designed two-step approach have been proposed to solve these optimization problems. In Part 2 of the paper, aiming at energy-efficient train operation, we extend the three proposed optimization problems by introducing energy-related formulations. We first evaluate the energy consumption of a train motion. A set of nonlinear constraints is first proposed to calculate the energy consumption, which is further reformulated as a set of linear constraints for the P_TSPO problem and approximated by using a piecewise constant function for the P_NLP and P_PWA problems. Moreover, we consider the option of regenerative braking and present linear formulations to calculate the utilization of the regenerative energy obtained through braking trains. We focus on two objectives, i.e., delay recovery and energy efficiency, through using a weighted-sum formulation and an ε-constraint formulation. With these energy-related extensions, the nature of the three optimization problems remains same to Part 1. In numerical experiments conducted based on the Dutch test case, we consider the P_NLP approach and the P_TSPO approach only and compare their performance with the inclusion of the energy-related aspects; the P_PWA approach is neglected due to its bad performance, as evaluated in Part 1. According to the experimental results, the P_TSPO approach still yields a better performance within the required computation time. The trade-off between train delay and energy consumption is investigated. The results show the possibility of reducing train delay and saving energy at the same time through managing train speed, by up to 4.0% and 5.6% respectively. In our case study, applying regenerative braking leads to a 22.9% reduction of the total energy consumption.

Current research in the field of performance measurement hasn't presented a rigorous composite indicator for quantifying company performance, with environmental indicators for automobile companies. This paper aims to construct this missing composite indicator. A new approach is developed, including techniques of fuzzy logic, analytic network process, the entropy theory and a geometric mean with unequal weights. The method is transparent, and the composite indicator derived can serve as a statistical tool for benchmarking. A case study is conducted in six leading automobile companies with data from the fiscal year 2016.

Purpose: Bulk material-handling equipment development can be accelerated and is less expensive when testing of virtual prototypes can be adopted. However, often the complexity of the interaction between particulate material and handling equipment cannot be handled by a single computational solver. This paper aims to establish a framework for the development, verification and application of a co-simulation of discrete element method (DEM) and multibody dynamics (MBD). Design/methodology/approach: The two methods have been coupled in two directions, which consists of coupling the load data on the geometry from DEM to MBD and the position data from MBD to DEM. The coupling has been validated thoroughly in several scenarios, and the stability and robustness have been investigated. Findings: All tests clearly demonstrated that the co-simulation is successful in predicting particle–equipment interaction. Examples are provided describing the effects of a coupling that is too tight, as well as a coupling that is too loose. A guideline has been developed for achieving stable and efficient co-simulations. Originality/value: This framework shows how to achieve realistic co-simulations of particulate material and equipment interaction of a dynamic nature.

This paper investigates the optimization of biomass terminal equipment deployment. A mixed integer linear programming model is developed and applied to minimize the terminal's investment and operational costs related to dedicated and partially used or shared equipment between a terminal's operational steps. The results minimize annual terminal costs through equipment and infrastructure selection and utilization. Tipping points where the technology and equipment type or size change in relation to the increasing throughput are highlighted. Analytical results emphasize the importance of storage costs in all biomass terminals, as well as the critical influence of operational costs in larger facilities.