Biomass is the largest source of renewable energy carrier in the European Union (EU) contributing to over 60% of renewable energy, with the majority of supply coming from domestic sources. However, an increasing significant amount of feedstock is imported, either due to domestic undersupply or higher production costs within the country. This article provides an up-to-date view of bioenergy supply, demand and trade in Northwest Europe to 2030. Projections of the energy system model Green-X are compared to recent national studies concerning bioenergy imports. The results show that there is a sizeable gap of the projection bandwidths after the 2020 horizon. Projections might under- or overestimate biomass potential in certain cases, depending on whether they are derived from national reports or regional models, whether future policy developments were taken into account etc. The ranges of biomass consumption are multiple times apart by 2020 already, and the gap increases by 2030. Total biomass imports in the region can range between 14 and 44.3 Mt by 2020 and 18.5–60 Mt by 2030.@en

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 (PNLP), 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 (PPWA). 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 (PTSPO) 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 (PTSPO) 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.@en

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.@en

This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.@en

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 PNLP problem (NLP: nonlinear programming), the PPWA problem (PWA: piecewise affine), and the PTSPO 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 PTSPO problem and approximated by using a piecewise constant function for the PNLP and PPWA 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 PNLP approach and the PTSPO approach only and compare their performance with the inclusion of the energy-related aspects; the PPWA approach is neglected due to its bad performance, as evaluated in Part 1. According to the experimental results, the PTSPO 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.@en

Innovations of information and communication technology (ICT) open plenty opportunities to promote internal operation efficiency and external service level in logistics. As current logistics developments tend to be more complex in operation and large in scale, recent practices start to pay more attentions on improving asset (e.g. equipment and infrastructure) management performance with new ICT development. One of the primary concern is to improve system robustness and reliability. It not only requires the supervision system be capable of diagnosing the condition of the system, but also proficient to find the intrinsic relationship between different conditions and resources thus lead to an integrated decision making process. Moreover, recent ICT innovations, such as WSN and IOT, could record and deliver system descriptors (physical measurements, virtual resources, operational configurations) in real time. Such large-stream and heterogeneous data requires an integrated framework to process and management. To address such challenges, in this paper, a novel concept of context-aware supervision is proposed. An intelligent system with integration of semantic web and agent technology is developed, which aims at providing condition-monitoring and maintenance decisions to relevant user. A generic ontology-agent based framework will be illustrated. The developed system will be applied for the supervision of a large-scale material handling system-belt conveying system as a proof-of-concept.@en

Modern technologies have enabled approaches to estimate freshness of perishable products during production and distribution. Nevertheless, the loss of perishable goods is still high due to the deteriorating nature and inefficiencies in supply chains. This research focuses on improving the scheduling of banana logistics using real-time quality information. Bananas are typically shipped from tropical production sites to other places in the world. With temperature controlled reefer containers and sensor technologies, bananas can be monitored during transport and situations like early ripening can be predicted. In order to minimize spoilage, we propose a mathematical model for scheduling logistics activities with the consideration of both the biological process and the logistics procedure of bananas. Results of simulation experiments indicate that the method could reduce spoilage using real-time monitoring and scheduling.@en

This paper presents a new approach to determine the bending stiffness of a pipe conveyor belt that is sufficient to form a stable pipe shape based on its throughability performance. The paper describes the mathematical model that determines pipe conveyor contact forces and introduces two numerical models solved using FEM in ANSYS. Results agree with the experimental data obtained using a six-point stiffness device. The mathematical model proposed can be used as a uniform validation technique for any numerical model. Appearance of one of the contact forces that equals zero is considered as a criterion for insufficient bending stiffness of belt to form a stable pipe shape. Effective modulus of elasticity quantified from the throughability parameter becomes a link to express belt pipe-ability. Impact of belt line mass and bending stiffness is investigated: for the same belt geometry, heavier belts require higher bending stiffness for the correct pipe shape formation.@en

Large-scale belt conveyor systems are widely involved in medium and long distance transport of bulk material. The spatially distributed idler rolls are a great challenge to maintain the reliability of the conveyor systems. Automation of maintenance in intelligent ways provides a promising solution to replace traditional corrective maintenance. This paper further elaborates the concept of intelligent maintenance introduced by Lodewijks in 2004. We review state-of-the-art idler roll data acquisition, decision-making techniques and recent development of robotic roll replacement. A novel SD model based maintenance logistics control is proposed. The viability of robotic idler roll replacement has been demonstrated by case studies. Opportunities in the intelligent maintenance of idler rolls are discussed. Cloud-based intelligent maintenance is considered an emerging trend in the near future.@en

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 IMVM 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 IMVM. 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 IMVM is benchmarked against three frameworks by six benchmark metrics. The IMVM 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 IMVM 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.@en

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 IMVM 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 IMVM. 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 IMVM is benchmarked against three frameworks by six benchmark metrics. The IMVM 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 IMVM 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.@en

To enhance the transportation ability of biomass and improve its material properties such as energy density and hydrophobicity, biomass is modified by torrefaction. This process, counting as a mild pyrolysis, takes place between 200 C and 300 C. The densified torrefied product is usable as a biofuel for heat and energy production. Over the past years, research on the torrefaction process itself has been intensified and has led to a further understanding of the main chemical mechanisms and their effects. However, the logistics and handling aspects for the supply chain have hardly been covered yet in research. Similar to the use of wood pellets, factors such as dust generation and the risk of dust explosions could pose a serious issue for transport and storage of densified torrefied products and should therefore be examined. The aim of this paper is to present the state of the art on torrefied biomass and biomass logistics as a basis for the research into the handling characteristics of the supply chain. Firstly, densification and torrefaction are introduced. Following, an overview on literature of the handling aspects of torrefied material is presented. Conclusions will be given on the approach to identify key factors for further research on the handling of torrefied biomass.@en

To enhance the transportation ability of biomass and improve its material properties such as energy density and hydrophobicity, biomass is modified by torrefaction. This process, counting as a mild pyrolysis, takes place between 200 C and 300 C. The densified torrefied product is usable as a biofuel for heat and energy production. Over the past years, research on the torrefaction process itself has been intensified and has led to a further understanding of the main chemical mechanisms and their effects. However, the logistics and handling aspects for the supply chain have hardly been covered yet in research. Similar to the use of wood pellets, factors such as dust generation and the risk of dust explosions could pose a serious issue for transport and storage of densified torrefied products and should therefore be examined. The aim of this paper is to present the state of the art on torrefied biomass and biomass logistics as a basis for the research into the handling characteristics of the supply chain. Firstly, densification and torrefaction are introduced. Following, an overview on literature of the handling aspects of torrefied material is presented. Conclusions will be given on the approach to identify key factors for further research on the handling of torrefied biomass.@en

To enhance the transportation ability of biomass and improve its material properties such as energy density and hydrophobicity, biomass is modified by torrefaction. This process, counting as a mild pyrolysis, takes place between 200 C and 300 C. The densified torrefied product is usable as a biofuel for heat and energy production. Over the past years, research on the torrefaction process itself has been intensified and has led to a further understanding of the main chemical mechanisms and their effects. However, the logistics and handling aspects for the supply chain have hardly been covered yet in research. Similar to the use of wood pellets, factors such as dust generation and the risk of dust explosions could pose a serious issue for transport and storage of densified torrefied products and should therefore be examined. The aim of this paper is to present the state of the art on torrefied biomass and biomass logistics as a basis for the research into the handling characteristics of the supply chain. Firstly, densification and torrefaction are introduced. Following, an overview on literature of the handling aspects of torrefied material is presented. Conclusions will be given on the approach to identify key factors for further research on the handling of torrefied biomass.@en

To enhance the transportation ability of biomass and improve its material properties such as energy density and hydrophobicity, biomass is modified by torrefaction. This process, counting as a mild pyrolysis, takes place between 200 C and 300 C. The densified torrefied product is usable as a biofuel for heat and energy production. Over the past years, research on the torrefaction process itself has been intensified and has led to a further understanding of the main chemical mechanisms and their effects. However, the logistics and handling aspects for the supply chain have hardly been covered yet in research. Similar to the use of wood pellets, factors such as dust generation and the risk of dust explosions could pose a serious issue for transport and storage of densified torrefied products and should therefore be examined. The aim of this paper is to present the state of the art on torrefied biomass and biomass logistics as a basis for the research into the handling characteristics of the supply chain. Firstly, densification and torrefaction are introduced. Following, an overview on literature of the handling aspects of torrefied material is presented. Conclusions will be given on the approach to identify key factors for further research on the handling of torrefied biomass.@en

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.@en

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.@en