This paper investigates a dynamic and stochastic shipment matching problem faced by network operators in hinterland synchromodal transportation. We consider a platform that receives contractual and spot shipment requests from shippers, and receives multimodal services from carriers. The platform aims to provide optimal matches between shipment requests and multimodal services within a finite horizon under spot request uncertainty. Due to the capacity limitation of multimodal services, the matching decisions made for current requests will affect the ability to make good matches for future requests. To solve the problem, this paper proposes an anticipatory approach which consists of a rolling horizon framework that handles dynamic events, a sample average approximation method that addresses uncertainties, and a progressive hedging algorithm that generates solutions at each decision epoch. Compared with the greedy approach which is commonly used in practice, the anticipatory approach has total cost savings up to 8.18% under realistic instances. The experimental results highlight the benefits of incorporating stochastic information in dynamic decision making processes of the synchromodal matching system.

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This paper investigates a dynamic and stochastic shipment matching problem, in which a platform aims to provide online decisions on accepting or rejecting newly received shipment requests and decisions on shipment-to-service matches in global synchromodal transportation. The problem is considered dynamic since the platform receives requests and travel times continuously in real time. The problem is considered stochastic since the information of requests and travel times is not known with certainty. To solve the problem, we develop a rolling horizon framework to handle dynamic events, a hybrid stochastic approach to address uncertainties, and a preprocessing-based heuristic algorithm to generate timely solutions at each decision epoch. The experimental results indicate that for instances with above 50% degrees of dynamism, the hybrid stochastic approach that considers shipment request and travel time uncertainties simultaneously outperforms the approaches that do not consider any uncertainty or just consider one type of uncertainties in terms of total profits, the number of infeasible transshipments, and delay in deliveries.

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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.

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Global intermodal transportation involves the movement of shipments between inland terminals located in different continents by using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of shipment requests with transport services in an integrated global network. The characteristics of the global intermodal shipment matching problem include acceptance and matching decisions, soft time windows, capacitated services, and transshipments between multimodal services. The objective of the problem is to maximize the total profits which consist of revenues, travel costs, transfer costs, storage costs, delay costs, and carbon tax. Travel time uncertainty has significant effects on the feasibility and profitability of matching plans. However, travel time uncertainty has not been considered in global intermodal transport yet leading to significant delays and infeasible transshipments. To fill in this gap, this paper proposes a chance-constrained programming model in which travel times are assumed stochastic. We conduct numerical experiments to validate the performance of the stochastic model in comparison to a deterministic model and a robust model. The experiment results show that the stochastic model outperforms the benchmarks in total profits.

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Multimodal transportation, as an efficient and sustainable alternative to unimodal transportation, refers to the utilization of multiple modes, the utilization of standard loading units, and flexibility in planning. The complexity of multimodal transportation at the operational level lies in being able to deal with dynamic events that are unknown before their realization. However, stochastic information on some of the events might be available from historical data. This paper proposes an anticipatory optimization approach to handle dynamic shipment requests in multimodal transportation by incorporating stochastic information of requests’ origin, destination, volume, announce time, release time and due time. The experimental results show that the anticipatory approach outperforms a myopic approach in which decisions are made only based on deterministic information in reducing total costs under various scenarios of the multimodal matching system.@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 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.

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Hinterland intermodal transportation is the movement of containers between deep-sea ports and inland terminals by using trucks, trains, barges, or any combination of them. Synchromodal transportation, as an extension of intermodal transportation, refers to transport systems with dynamic updating of plans by incorporating real-time information. The trend towards spot markets and digitalization in hinterland intermodal transportation gives rise to online synchromodal transportation problems. This paper investigates a dynamic shipment matching problem in which a centralized platform provides online matches between shipment requests and transport services. We propose a rolling horizon approach to handle newly arrived shipment requests and develop a heuristic algorithm to generate timely solutions at each decision epoch. The experiment results demonstrate the solution accuracy and computational efficiency of the heuristic algorithm in comparison to an exact algorithm. The proposed rolling horizon approach outperforms a greedy approach from practice in total costs under various scenarios of the system.

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Purpose: Current literature presents limited measurement methods of quantifying manufacturers' performance with environmental concerns. The purpose of this paper is to construct a company performance index for benchmarking motor vehicle manufacturers (MVMs) with environmental concerns. Design/methodology/approach: Methods of constructing the index include regression analysis, a modified linear method for normalizing variables and a geometric mean for aggregating variables into a single index I_MVM (index for MVMs). A case study is conducted in 12 MVMs from 2008 to 2017. A sensitivity analysis with the simple additive weighting method is performed to analyze how different aggregation methods affect the final value. The index I_MVM is assessed through a benchmark with three existing indices. Findings: Three realistic considerations are identified from MVMs, based on which proper and transparent methods are chosen to construct the I_MVM. The construction of the index I_MVM has been assessed through a benchmark against the methodologies of three other indices. The results indicate that the new measurement is feasible and effective for MVMs to measure their company performance from an environmental perspective. Practical implications: The construction of the index I_MVM can support policymakers with accurate statistics for decision-making. As a response to current imperative climate policies, this paper raises awareness of CO₂ emissions in vehicles' production. For statistical organizations and stakeholders in the investment world, this paper provides available and reliable statistics for trend analysis of different MVMs. Originality/value: A new method is designed for constructing a company performance index for MVMs. Three environmental variables are identified based on literature, their environmental impact as well as their data availability from public documents. A ranking by manufacturer with environmental concerns is generated. This index can contribute with available statistics and useful insights toward decision-making.

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This paper examines the development modes of inland ports based on the economic models and the Chinese empirical cases. After reviewing the recent policies in China, four modes, i.e., the government-driven mode, the seaport-driven, the market-driven mode and the corridor-effect mode, are established to describe the development of Chinese inland ports from the perspective of the driving forces. Moreover, we setup an economic model to compare them and conclude that (1) the seaport-driven mode promotes the larger inland port than the corridor-effect mode and the market-driven mode; (2) if the marginal capacity investment cost is low or the efficiency of the inland port is high enough, the corridor-effect mode leads to higher social welfare than the market-driven mode and the seaport-driven mode; (3) whether the government-driven mode promotes the larger inland port and higher social welfare than the other modes depends on the positive externality from the inland port to the social welfare; (4) The ‘Go west’ policy and the Belt and Road Initiative (B&R) promote the inland port capacity under all modes. Whether the Free Trade Zone (FTZ) and the port integration promote the inland port capacity depends on the port efficiency improvement after the implementation of these policies.

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The Automated People Mover (APM) is an important asset for many airports to transport passengers inside or between terminal and satellite buildings An APM system normally runs on fixed schedules throughout the day, which means that the capacity of the APM is pre-determined and not depending on the actual demand. This at times can cause either an overcapacity, which leads to a waste of resources, or an under capacity, which results in passengers waiting at the station. Especially the latter factor is problematic, as it reduced passenger experience and can negatively affect the transfer process between airport facilities. In order to better match the offered APM capacity with the demand, it is proposed in this paper to use sensor-based predictive control system, which adapts the APM system capacity to real-time demand. By means of sensor data, passenger numbers are determined before they walk onto the stations platforms, and subsequently the APM system capacity is adjusted to the measured demand. In principle there are two methods to change the APM system capacity, i.e.: 1) by changing the APM capacity (i.e. more cars per train) or 2) by changing the frequency. A simulation test case was designed to provide numerical insight in the potential of adaptively changing the capacity of an APM, based on sensor derived real-time demand. The test case was derived from a variety of typical systems used worldwide and represents a complex APM system. From the simulation results it is concluded that an intelligent design of the control system results in significant improvements in terms of passenger experience, operational cost, capital cost and emission footprint. The favourable method of adjusting capacity to demand is by increase train capacity, before reducing the headway between trains.@en

Purpose: The paper provides insights regarding the optimal maintenance strategies for offshore wind farms. The maintenance strategies distinct from each other with location of the operation base and manner of logistics regarding the vessels. Methodology: The methodology for modelling the transport for maintenance operations for offshore wind farms is done with a discrete event simulation with data regarding offshore maintenance operations provided from a large-scale research. Findings: The paper conclude with the optimal maintenance strategy based on the optimal location of the operation base in combination with the best manner of logistics to achieve t he preferred requirements with the least transportation costs withinthe boundaries and limitations of this research. Originality: There are no current papers regarding the maintenance operations for offshore wind farms located far in the Nord Sea.@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.

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This paper addresses the desired performance of the production of sunflower oil from sunflower seeds. This research is performed to improve the stability of the production process by removing the disturbances. After identifying the restrictions on the input and output of the production process, it was found that full meal silos were the largest disturbance. New design solutions were obtained by analysing the current state and subsequently develop new design alternatives. A discrete event simulation model was developed to compare the performance of the new designs through the predefined KPIs. It was found that increasing the available storage space and improving the efficiency of the outbound logistics the disturbance on the oil production could be reduced. The best design solution was creating a by-pass that allows for simultaneously loading of trucks and vessels, which focuses on increasing the available loading hours.@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.

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This paper aims to develop a quantitative model of company performance from an inventory perspective for truck manufacturers. With the inventory performance as a new dimension, fourteen indicators are identified to form a conceptual framework for truck manufacturers to measure their company performance. Accordingly, techniques of the fuzzy logic and the analytic network process (ANP) are used to generate the quantitative model, considering the interdependency between the indicators and the uncertainty arising from human qualitative judgments. A case study is conducted in nine truck manufacturers, with time series data from the fiscal year 2004 to 2015. The ranking result out of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used as a validation, which proves the higher accuracy of the model developed.@en

This paper investigates the multi-commodity multi-service matching problem of synchromodal hinterland container transportation. To improve the computational efficiency, this paper proposes a many-to-one algorithm to solve the many-to-many matching problem. We assess the performance of the proposed method with 51 instances of the problem, and perform sensitivity analysis to analyze the influence of different demand patterns. The computational results indicate that the algorithm is suitable for large-scale instances of the problem.@en

Synchromodal transportation comprises the promising idea of integrated planning and real time switching while considering cost efficiency, time effectiveness and environmental impact. As the foundation of real time switching, integrated planning has been researched at the operational level. The intermodal routing choice problem is the major problem at the operational level. From an integrated planning perspective, it is typically defined as a multi-commodity multimodal routing choice problem. This paper proposes to consider the routing choice problem as a multi-commodity multi-service matching problem with cost sharing characteristic of self-operated intermodal services among commodities. The multi-commodity multi-service matching problem belongs to a many-to-many matching problem in which a commodity can be matched with multiple services by transferring at terminals, and multiple commodities can be matched with one service by considering cost sharing and capacity limitations. The objective of the matching model is the minimization of generalized cost combining transit cost, transfer cost, inventory cost and carbon tax. Finally, this paper designs a numerical experiment to verify the feasibility of the proposed model. In order to test the influence of the new aspects of the model, four different scenarios are considered. The computational results indicate that the many-to-many matching model is suitable for container transportation systems.@en

Transportation of perishables such as fruits and vegetables with short shelf life in international, long distance and cooled condition, plays a key role in global cold chains. Compared with truck transportation, intermodal transportation largely reduces logistics cost and emissions, however, has less flexibility for disturbances. Another aspect is that truck transportation occupies the largest share in inland transportation, which causes traffic congestion and environmental pollutions. Synchromodal transportation is a known method to study the effectiveness, efficiency and sustainability of transportation by using real-time information. However, limited articles can be found about the cold chain perspective, an integral analysis is missing. Our objective is to thoroughly analyze the characteristics and challenges of synchromodal transportation in global cold chains. The critical successful factors are analyzed at first. After that, we survey on planning problems in strategic, tactical and operational level, respectively. Finally, we conclude by suggesting further research directions.@en

Purpose – Implementation of lean manufacturing is currently performed in the production industry; however, for the airline maintenance service industry, it is still in its infancy. Indicators such as work in process, cycle time, on-time performance and inventory are useful indicators to measure lean implementation; however, a financial economic perspective taking fixed assets into consideration is still missing. Hence, the purpose of this paper is to propose a method to measure lean implementation from a fixed asset perspective for this type of industry. With the indicators, continuous improvement scenarios can be explored by value stream discrete event simulation. Design/methodology/approach – From literature, indicators regarding asset specificity to measure lean implementation are found. These indicators are analysed by a linear least square method to know if variables are interrelated to form a preliminary model. The indicators are tested by value stream-based discrete event simulation regarding continuous improvement scenarios. Findings – With the new found lean transaction cost efficiency indicators, namely, turnover, gross margin and inventory pre-fixed asset (T/FA, GM/FA and I/FA, respectively), it is possible to measure operation performance from an asset specificity perspective under the influence of lean implementation. Secondly, the results of implementing continuous improvement scenarios are measured with the new indicators by a discrete event simulation. Research limitations/implications – This research is limited to the airline maintenance, repair and overhaul (MRO) service industry regarding component repair. Further research is necessary to test the indicators regarding other airline MRO service companies and other sectors of complex service industries like health care. Practical implications – The lean transaction cost efficiency model provides the capability for a maintenance service company to simulate the effects of process improvements on operation performance for service-based companies prior to implementation. Social/implications – Simulation of a Greenfield process can involve employees with possible changes in processes. This approach supports the adoption of anticipated changes. Originality/value – The found indicators form a preliminary model, which contributes to the usage and linkage of theories on lean manufacturing and transaction cost theory – asset specificity.@en