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Shaanxi, Shanxi province and Inner Mongolia in north China are the nation’s leading producers of coal. In the past decades, a huge amount of coal has been transported from these areas to the central and south China via ports in Hebei Province and Port of Tianjin. Traditionally, coal is transported in bulk shipment. However, in recent years, an interesting phenomenon is that the lump coal is sorted out in the north and then moved in containers to the south. This paper aims to ascertain whether the coal containerization will become an alternative transport mode for moving lump coal from the north to the south of China. Based on extensive field investigation and data analyses, it is found that although the container mode appears to be more expensive than the bulk shipping mode, the cost difference between the two modes is not as significant as our expectation if we also take into factors like fragmented demand, environment pressure, geographic distribution characteristics of customers and so on.

Extensive field investigation, data analysis and economic decision model.

It is found that although the container mode appears to be more expensive than the bulk shipping mode, the cost difference between the two modes is not as significant as the authors’ expectation if they also take into factors like fragmented demand, environment pressure, geographic distribution characteristics of customers and so on.

Although the major focus has been placed on the food containerization, the coal containerization is still a new thing and has attracted only limited attention. This paper is making an initial attempt to profile the phenomenon of coal containerization in China. This attempt is believed to not only enrich the literature of coal containerization but also support the literature of bulk containerization.

When port authorities or terminal operators set the free time or increase storage density, the decision is often made without a clear understanding of their effects on throughput and rehandling productivity. This is partly because practical methods that deal specifically with the effect of dwell time on throughput and productivity are limited in the literature; hence the motivation for this work. This paper introduces simple methods to evaluate the effect of container dwell time and storage policies on import throughput, storage density, and rehandling productivity. The analysis considers two import storage strategies 1) non-mixed - no stacking of new import containers on top of old ones, and 2) mixed - stacking of new import containers on top of old ones. The results highlight the effect dwell time has on throughput and rehandling productivity. For the non-mixed storage policy, the increasing container dwell time lowers throughput and average stack height - resulting in an increase in rehandling productivity. On the other hand, for the mixed storage policy, the increasing container dwell time raises throughput and average stack height - resulting in a decrease in rehandling productivity. Using the presented methods, port authorities and terminal operators are able to assess and quantify the benefits of their decisions regarding container free time and subsequently make an informed decision.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

We consider the problem of assigning ship assembly blocks to storage locations at a ship yard while allowing one-way movement of the blocks. The objective is to minimize the number of blocks obstructing other blocks’ movements. We show that the integer program in a previous study contains errors in identifying obstructive blocks and therefore, suggest another integer program without the error. Also, we suggest construction heuristics and a tabu search algorithm using properties characterizing optimal solutions of subproblems. Computational experiment results indicate that our heuristics outperform the existing algorithm in terms of solution quality.

Cross-docking is a supply chain distribution and logistics strategy for which less-than-truckload shipments are consolidated into full-truckload shipments. Goods are stored up to a maximum of 24 hours in a cross-docking terminal. In this chapter, we build on the literature review by Ladier and Alpan (2016), who reviewed cross-docking research and conducted interviews with cross-docking managers to find research gaps and provide recommendations for future research. We conduct a systematic literature review, following the framework by Ladier and Alpan (2016), on cross-docking literature from 2015 up to 2020. We focus on papers that consider the intersection of research and industry, e.g., case studies or studies presenting real-world data. We investigate whether the research has changed according to the recommendations of Ladier and Alpan (2016). Additionally, we examine the adoption of Industry 4.0 practices in cross-docking research, e.g., related to features of the physical internet, the Internet of Things and cyber-physical systems in cross-docking methodologies or case studies. We conclude that only small adaptations have been done based on the recommendations of Ladier and Alpan (2016), but we see growing attention for Industry 4.0 concepts in cross-docking, especially for physical internet hubs.

The technique of cross-docking is attractive to organisations because of the lower warehousing and transportation (consolidated shipments) costs. This concept is based on the fast movement of products. Accordingly, cross-docking operations should be monitored carefully and accurately. Several factors in cross-docking operations can be impacted by uncertain sources that can lead to inaccuracy and inefficiency of this process. Although many papers have been published on different aspects of cross-docking, there is a need for a comprehensive review to investigate the sources of uncertainties in cross-docking. Therefore, the purpose of this paper is to analyse and categorise sources of uncertainty in cross-docking operations. A systematic review has been undertaken to analyse methods and techniques used in cross-docking research.

A systematic review has been undertaken to analyse methods and techniques used in cross-docking research.

The findings show that existing research has limitations on the applicability of the models developed to solve problems due to unrealistic or impractical assumption. Further research directions have been discussed to fill the gaps identified in the literature review.

There has been an increasing number of papers about cross-docking since 2010, among which three are literature reviews on cross-docking from 2013 to 2016. There is an absence of study in the current literature to critically review and identify the sources of uncertainty related to cross-docking operations. Without the proper identification and discussion of these uncertainties, the optimisation models developed to improve cross-docking operations may be inherently impractical and unrealistic.

Several factors may play critical roles in alterations to product quality during storage of hog plum juice. This study aims to evaluate variations to physicochemical, antioxidant, anti-nutritional properties and microbial stability of hog plum juice during storage.

Juice was produced from hog plum fruits and stored for eight weeks at refrigerated and ambient conditions. Physicochemical, antioxidant properties, antinutritional factors and microbial properties of juices were determined using standard procedures

Degradation of ascorbic acid was higher in juices stored at ambient conditions (64.4%) compared to those stored by refrigeration (44.4%). Trends were similar for total phenolic, total flavonoid and total carotenoid contents. Total phenolic, total carotenoid and lycopene contents of fresh juice were 3.9 mg GAE/mL, 4.0 mg/mL and 1.3 mg/mL, which were not significantly different (at p = 0.08, 0.07 and 0.08, respectively) from the values at two weeks of storage at refrigerated conditions (3.9 mg GAE/mL, 3.9 mg/mL and 1.3 mg/mL). A sharp decrease of more than 40% (p = 0.02) in lycopene was recorded after four weeks, irrespective of storage temperature. Pasteurized hog plum juice showed no microbial growth until after four weeks of refrigerated storage when 1 CFU/mL each of bacterial and fungal growth were recorded. The juices, however, showed higher susceptibility to fungal growth as storage period increased.

Other variables not considered in this study such as nature of packaging materials may have significantly contributed to the observed data set. Further studies may, therefore, widen the scope of discussion to evaluate the associated relationship of these variables. Hog plum juice retained a considerable amount of bioactive components during refrigerated storage, which makes it a viable nutraceutical drink with industrial potentials and possible positive health implications for consumers.

This study provides new information that support the possible classification and use of hog plum juice as a safe functional beverage for human consumption.

Although the effect of storage temperature was significant in most of the properties studied, storage duration seems to have a greater influence on the stability of quality parameters during the storage of hog plum juice.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.