Search results

The aim is to examine the recent trend in empty running by trucks in the UK and assesses the potential for a further reduction in empty running in the food supply chain using a new technique.

Data from the UK Government's main road freight survey and other studies are used to investigate the causes of the decline in empty running. Previous attempts to quantify opportunities for backloading are reviewed. The 2002 KPI Survey in the UK food supply chain created a large multi‐fleet database of over 20,000 trips, which permitted retrospective analysis of backloading opportunities. A method was devised to screen these opportunities against four selection criteria and assess the overall potential for cutting empty truck‐kms.

Suitable backloads were found for only 2.4 per cent of the empty journey legs, representing 2 per cent of empty truck‐kms. The analysis highlights the operational constraints on backloading in a sector characterised by short average trip length, tight scheduling and variable use of refrigeration.

The analysis provides a more accurate and realistic assessment of backloading potential than previous studies, though is still deficient in several respects. The main shortcomings relate to the sampling method and structure of the Transport KPI Survey. The analytical framework requires further development to refine backload search areas, incorporate commercial data and permit sensitivity analysis.

The paper shows how retrospective analysis of road deliveries made over a short period (48 hour) can identify opportunities for backloading at a sectoral level. It combines government statistics and original survey data to provide both a macro‐ and micro‐level perspective on the empty running problem.

Multi-criteria recommender systems (MC-RSs) allow users to express their preference in multiple aspects. Bayesian flexible mixture model (BFMM) is a model-based RS which extends FMM from single-criterion to MC. However, results of BFMM have a preference on different rating pattern problem. In single-criterion, FMM with decoupled normalization and W’s transposed function try to solve this problem. However, these techniques are applied to each criterion separately. Then, the relationship among criteria will be lost. This paper aims to solve different rating pattern problems and loss of the relationship between criteria.

The proposed method is combining between BFMM and rating conversion. First, mean and variance normalization is applied to make MC ratings of an active user and a neighbor lying on the same plane. After that, a pattern of each user is extracted using principal component analysis (PCA). Next, the pattern is used to convert neighbors’ MC ratings to the active user aspect. After that, converted MC ratings of neighbors are aggregated to be overall ratings using multiple linear regression (MLR). Finally, overall rating of the active user toward the target item is predicted using weighted average on the derived neighbors’ overall ratings where the similarity from BFMM acts as a weight.

The experimental results show that the proposed method where all criteria ratings are converted simultaneously can improve the performance of recommendation.

The proposed method predicts overall rating of the active user by converting MC ratings of each neighbor to the active user aspect at the same time, which can reduce the loss of the relationship between criteria.

Traction substations are an integral part of the railway infrastructure. In order to ensure that machinery and equipment is available and utilised at full capacity, intelligent maintenance methods should be implemented. The purpose of this paper is to focus on the development of lean maintenance strategies implemented on traction substations and offer the maintenance manager an alternative maintenance method to be utilised during optimisation initiatives.

The rail company under consideration has various improvement options, the maintenance supply chain is a priority. The existing maintenance process of 3 kV_DC traction substations is examined and all operations and activities directly and indirectly associated with maintenance are analysed with lean methodologies. The analysis of maintenance operations and activities reveals that some of these operations and activities are non-value-adding and, therefore, regarded as waste. These activities only prolong the maintenance process without adding value to the process. Alternative operations and activities are suggested and simulated.

The application of lean indicates that the maintenance process could be optimised to deliver on a more efficient process, which would improve the quality of maintenance by ensuring that maintenance is timeously carried out. Through reducing the downtime caused by maintenance, the availability of the system is preserved and the railway lines could be used to capacity.

The research is limited to 3 kV_DC traction substations and the associated maintenance process, as this was the area of opportunity for the company. The track and overhead traction equipment maintenance is not included.

The practical application of this research is in optimisation of the value chain for maintenance, specifically in the rail industry.

This research could be applied by maintenance managers to effectively implement if a run-to-failure (RTF) maintenance philosophy is being utilised. The research offers maintenance techniques within the RTF maintenance scope which reduces the maintenance cycle and equipment downtime.

The blockchain-based digital twin has been recognized as a prominent technological ecosystem featuring synergies with both established and emergent information management practice. The purpose of this research is to explore the applicability, interoperability and integrability of a blockchain-based digital twin for asset life cycle management and develop a model of framework which positions the digital twin within a broader context of current management practice and technological availability.

A systematic literature review was performed to map use cases of digital twin, IoT, blockchain and smart contract technologies. Surveys of industry professionals and analyses were conducted focussing on the mapped use cases' life cycle–centric applicability, interoperability and integrability with current asset life cycle management practice, exploring decision support capabilities and industry insights. Lastly, a model of framework was developed based on the use case, interoperability and integrability findings.

The results support approaching digitization initiatives with blockchain-based digital twins and the positioning of the concept as both a strategic tool and a multifunctional on-field support application. Integrability enablers include progression towards BIM level 3, decentralized program hubs, modular cross-technological platform interfaces, as well as mergeable and scalable blockchains.

Knowledge of use cases help highlight the functionality of an integrated technological ecosystem and its connection to comprehensive sets of asset life cycle management aspects. Exploring integrability enablers contribute to the development of management practice and solution development as user expectations and technological prerequisites are interlinked.

The research explores asset life cycle management use cases, interoperability and integrability enablers of blockchain-based digital twins and positions the technological ecosystem within current practice and technological availability.