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Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO₂ emission reduction of highways.

To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO₂ emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility.

The results show that through the application of the scheduling strategy to the HPWIS, the flexibility index of the Lianhuo Expressway increased by 29.17%, promoting a corresponding decrease in CO₂ emissions.

This paper proposed a new model to capture the evolution of the HESS, which provides highway transportation vehicles and service facilities with a clean electricity supply and achieves energy transfer aided by an energy storage system, thus forming a new model of a transportation energy system with source-grid-load-storage-charge synergy. An evaluation method is proposed to improve the air quality index through the coordination of new energy generation and environmental conditions, and dynamic configuration and dispatch are achieved with the master–slave game model.

Agri-food supply chains are facing a number of challenges, which cause inefficiencies resulting in the waste of natural and economic resources, and in negative environmental and social impacts. Food waste (FW) is a result of such inefficiencies and supply chain actors search for economically viable innovations to prevent and reduce it. This study aims to analyse the drivers and the barriers that affect the decision of supply chain operators to adopt innovations (technological – TI, organisational – OI and marketing – MI) to reduce FW.

The analysis was carried out using a four-step approach that included: a literature review to identify factors affecting the decision to adopt innovations; analysis of FW drivers and reduction possibilities along agri-food supply chains through innovations; mapping the results of Steps 1 and 2 and deriving conclusions regarding the factors affecting the adoption of innovations to reduce and prevent FW.

Results show that different types of innovations have a high potential in reducing and preventing FW along the supply chain; however, they still must be economically feasible to be adopted by decision makers in the food supply chain. TI, OI and MI are often interrelated and can trigger each other. When it comes to a combination of different types of innovation to reduce and prevent FW, a good example of combining TI, OI and MI may be observed in the retail sector in Europe. Here, innovative smartphone apps (TI) to promote the sale of products nearing their expiration dates (OI in terms of organising the sales differently and MI in terms of marketing it differently) were developed and adopted via different retailing channels, leading to the creation of a new business model.

This study analyses the drivers of FW generation together with the factors affecting the decision to adopt innovations to reduce it and provides solutions to supply chain operators to prevent and reduce FW through different types of innovations.

Literature has not systematically addressed innovations aiming at the reduction of FW yet. This paper provides a comprehensive literature review of the determinants of innovation adoption and offers a novel view on the problem of FW reduction by means of innovation, by linking factors affecting the decision to innovate with FW drivers.

Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT.

A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands.

The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s.

Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic.

The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time.

A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost.

A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search.

Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space.

This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured.

The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases.

The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.

This paper explores innovation adoption in inter-organizational healthcare networks. The authors develop theoretical speculations to investigate better the role of artificial intelligence (AI) as an innovative tool to improve buyer-supplier relationships, creating better performance outcomes.

The research is based on a theoretical investigation aiming at exploring the role of AI-based solutions for managing buyer-supplier relationships. The conceptual approach allows us to identify some research streams (e.g. co-working collaborations in supply chain management) by proposing a matrix that helps clarify the analysis's directions.

The results show the importance of AI that can help the operator in accessing supplier information, including current prices, available stocks, and delivery status, thereby reducing the risk of information asymmetry. AI is intended not only as a technology tool but also as an innovative solution to promote business relationships and support vertical alliances through the value chain between buyer and supplier.

This paper can help healthcare actors examine the choices behind their operational strategies by providing transparency of the activities and availability of information in real-time. Finally, our study reflects the future directions to enhance the cooperation and innovation adoption among healthcare operators.

Recently, the entry of new players has prompted significant restructuring in the Australian coal market with value migrating away from the existing fragmented, traditional production/export model characterised by competing operators generally using 'common user' infrastructure facilities to new, fully integrated supply chains creating a multi-tiered production-consumer framework.

This paper argues that not only are coal markets restructuring but they are doing so within the framework of a significant paradigm shift towards efficiency-seeking and efficiency-driven mechanisms. Value innovation and a deregulated market are enabling operators to enter the industry seeking and implementing end-to-end control of the supply chain - and, in so doing, capturing the significant gains of integration.

This paper explores these changes within the framework of integrative efficiency - a product of end-to-end control by a single party, derived from a number of companies, or chain elements, working cooperatively rather than competitively, or a single operator vertically integrating the chain from point of production to point of consumption to capture and deliver significantly higher value. The paper focuses attention on this paradigmatic shift in a brief though detailed case study of a major new industry entrant into export coal chains from the rapidly developing Galilee Basin in northern Queensland. It examines the dynamics and implications of this shift in the context of chain efficiency and value innovation

This paper focusses on the aftermath of disruptions and the importance of the two largest canals (Suez and Panama), commenting on how during the pandemic the canal fees were lowered. Considering the ongoing efforts to decarbonize shipping, some of the ongoing disruptions will help reach these objectives faster.

Following a literature review of route choice in shipping, and a presentation of significant disruptions in recent years, the author deploys a simplified fuel consumption model and conduct case study analyses to compare different routes environmentally and economically.

The results explain why at times of low fuel prices as in 2020, canals provided discounts to entice ship operators to keep transiting these, instead of opting for longer routes. Considering the ongoing repercussions of the pandemic in supply chains, as well as the potential introduction of market-based measures in shipping, the value of transiting canals will be much higher in the coming years.

The main limitation in this work is that the author used the publicly available information on canal tolls, for the different ship types examined.

The envisioned model is simple, and it can be readily used for any ship and route (port to port) combination available, if ship data are available to researchers.

It is possible that canal tolls will increase, to account for the additional environmental benefits brought to ship operators.

The methodology is simple and transferable, and the author proposes several interesting research questions for follow-up work.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

This paper aims to examine the factors affecting the implementation of corporate social responsibility (CSR) sustainably in mobile operators in the kingdom of Bahrain.

The research relies on the existing literature as a secondary data source. The primary data was collected through questionnaires distributed to three leading mobile operators in the Kingdom of Bahrain. The research's population numbered 1,689, and the sample size was 313; the simple random sampling method was used for data collection with a response rate of 87.2% out of a total sample size of 273 respondents. In addition, the responses were analyzed using statistical package for the social sciences (SPSS) software version 24.0; specifically, Spearman's rank correlation was used to test the hypotheses. The research design was quantitative, so a nonparametric procedure was applied to test the hypotheses.

The research produced positive relationships between the independent variables (economic, legal, ethical, philanthropic and environmental responsibilities) and the dependent variable: CSR implementation by Bahrain mobile operators; therefore, the five proposed hypotheses were accepted; furthermore, the highest positive correlation coefficient was 0.735 for environmental responsibility, and the lowest correlation coefficient was 0.533 for economic responsibility.

The research produced positive relationships between the independent variables (economic responsibility, legal responsibility, ethical responsibility, philanthropic responsibility and environmental responsibility) and the dependent variable: CSR implementation by Bahrain mobile operators; therefore, the five proposed hypotheses were accepted; furthermore, the highest positive correlation coefficient was 0.735 for environmental responsibility, and the lowest correlation coefficient was 0.533 for economic responsibility.

The outcomes of the research mainly suggest that mobile operators assign employees who have been working with the company for more than 7 years to implement CSR; due to their engagement to implement CSR as a sustainable practice more than others with less than 3 years or 3-6 years in Bahrain mobile operators. Besides, the research provides a starting point by which other researchers could investigate CSR in other sectors in the Kingdom of Bahrain.

The research provided a framework for Bahrain mobile operators to assist them in enhancing the implementation of CSR in a sustainable manner, which are economic, legal, ethical, philanthropic and environmental responsibilities.

The purpose of this paper is to build possible future scenarios for indoor connectivity in a venue such as a university campus and build alternative value network configurations (VNCs) defining different local network deployment options, focused on the Finnish telecom market.

In this paper, Schoemaker’s scenario planning method (Schoemaker, 1995) is used to construct future scenarios and the VNC method of Casey et al. (2010) is used to build alternative VNCs. The paper studies the Aalto University campus network for current end-user data usage demand and the existing technology used in meeting the end-user needs and forecasts the demand for the next five years to understand the need for 5G.

This research concludes that with the provision of local spectrum licenses, there is an opportunity for venue owners to take the role of 5G local operator on the venue premises. Furthermore, it enables venue owners to collaborate with the incumbent mobile network operators (MNOs) in a neutral host model and provide venue-specific connectivity services.

A detailed economic assessment for the network deployment in the campus is considered for future study.

Considering the provision for local spectrum licenses, this paper has taken a unique attempt in identifying the future scenarios for local 5G network operations. It provides a strategic direction for the venue owners in adopting 5G technology and whether to make 5G or buy 5G from MNOs.