Assessing aviation e-marketplaces:a multi-criteria approach

After a proliferation of logistics e-Marketplaces during the dot.com boom of 1998-2000, there has been a high rate of failure and survivals are developing much more slowly than expected. This is the case in the aviation industry where a large number of B2B e-Marketplaces emerged according to the focus of aviation companies’ strategies on electronic B2B in the late 1990s. However, the current use of e-Marketplaces in the industry is low and many of them have ceased trading. The traditional e-Marketplaces model has been characterised by poor quality portals and a lack of technical standards. Such an approach is unsustainable in today’s competitive scenario. Improvements in website quality attributes may strongly contribute to the simplification of website functionality by users and speed up communication with all supply chain partners. In this context, it appears critical to develop models for the evaluation of e-Marketplace web sites. This chapter, after a discussion about the development of e-Marketplaces in the transport and logistics service industry and its application in the aviation industry, proposes a multi-criteria model for assessing different types of aeronautic B2B e-Marketplaces.

Critical noise levels for low-density parity check decoding

We determine the critical noise level for decoding low-density parity check error-correcting codes based on the magnetization enumerator (M), rather than on the weight enumerator (W) employed in the information theory literature. The interpretation of our method is appealingly simple, and the relation between the different decoding schemes such as typical pairs decoding, MAP, and finite temperature decoding (MPM) becomes clear. In addition, our analysis provides an explanation for the difference in performance between MN and Gallager codes. Our results are more optimistic than those derived using the methods of information theory and are in excellent agreement with recent results from another statistical physics approach.

Systematic financial resources allocation processes:a model for critical resources mobilization and deployment for nigerian universities

Purpose – The purpose of this research is to study the perceived impact of some factors on the resources allocation processes of the Nigerian universities and to suggest a framework that will help practitioners and academics to understand and improve such processes. Design/methodology/approach – The study adopted the interpretive qualitative approach aimed at an ‘in-depth’ understanding of the resource allocation experiences of key university personnel and their perceived impact of the contextual factors affecting such processes. The analysis of individual narratives from each university established the conditions and factors impacting the resources allocation processes within each institution. Findings – The resources allocation process issues in the Nigerian universities may be categorised into people (core and peripheral units’ challenge, and politics and power); process (resources allocation processes); and resources (critical financial shortage and resources dependence response). The study also provides insight that resourcing efficiency in Nigerian universities appears strongly constrained by the rivalry among the resource managers. The efficient resources allocation process (ERAP) model is proposed to resolve the identified resourcing deficiencies. Research limitations/implications – The research is not focused to provide generalizable observations but ‘in-depth’ perceived factors and their impact on the resources allocation processes in Nigerian universities. The study is limited to the internal resources allocation issues within the universities and excludes the external funding factors. The resource managers’ responses to the identified factors may affect their internal resourcing efficiency. Further research using more empirical samples is required to obtain more widespread results and the implications for all universities. Originality/value – This study contributes a fresh literature framework to resources allocation processes focusing at ‘people’, ‘process’ and ‘resources’. Also a middle range theory triangulation is developed in relation to better understanding of resourcing process management. The study will be of interest to university managers and policy makers.

A knowledge driven approach towards the validation of externally acquired traceability datasets in supply chain business processes

The sharing of near real-time traceability knowledge in supply chains plays a central role in coordinating business operations and is a key driver for their success. However before traceability datasets received from external partners can be integrated with datasets generated internally within an organisation, they need to be validated against information recorded for the physical goods received as well as against bespoke rules defined to ensure uniformity, consistency and completeness within the supply chain. In this paper, we present a knowledge driven framework for the runtime validation of critical constraints on incoming traceability datasets encapuslated as EPCIS event-based linked pedigrees. Our constraints are defined using SPARQL queries and SPIN rules. We present a novel validation architecture based on the integration of Apache Storm framework for real time, distributed computation with popular Semantic Web/Linked data libraries and exemplify our methodology on an abstraction of the pharmaceutical supply chain.

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW.

An incentivized auction based group-selling approach for demand response management in V2G systems

Vehicle-to-Grid (V2G) system with efficient Demand Response Management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at EVs. An incentivilized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling based auction. In the proposed approach, a number of aggregators sit on the first level auction responsible to communicate with a group of EVs. EVs as bidders consider Quality of Energy (QoE) requirements and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.

A multi-organisational approach for disaster preparedness and response:the use of optimisation and GIS for facility location, stock pre-positioning, resource allocation and relief distribution

From 1992 to 2012 4.4 billion people were affected by disasters with almost 2 trillion USD in damages and 1.3 million people killed worldwide. The increasing threat of disasters stresses the need to provide solutions for the challenges faced by disaster managers, such as the logistical deployment of resources required to provide relief to victims. The location of emergency facilities, stock prepositioning, evacuation, inventory management, resource allocation, and relief distribution have been identified to directly impact the relief provided to victims during the disaster. Managing appropriately these factors is critical to reduce suffering. Disaster management commonly attracts several organisations working alongside each other and sharing resources to cope with the emergency. Coordinating these agencies is a complex task but there is little research considering multiple organisations, and none actually optimising the number of actors required to avoid shortages and convergence. The aim of the this research is to develop a system for disaster management based on a combination of optimisation techniques and geographical information systems (GIS) to aid multi-organisational decision-making. An integrated decision system was created comprising a cartographic model implemented in GIS to discard floodable facilities, combined with two models focused on optimising the decisions regarding location of emergency facilities, stock prepositioning, the allocation of resources and relief distribution, along with the number of actors required to perform these activities. Three in-depth case studies in Mexico were studied gathering information from different organisations. The cartographic model proved to reduce the risk to select unsuitable facilities. The preparedness and response models showed the capacity to optimise the decisions and the number of organisations required for logistical activities, pointing towards an excess of actors involved in all cases. The system as a whole demonstrated its capacity to provide integrated support for disaster preparedness and response, along with the existence of room for improvement for Mexican organisations in flood management.

QoE-aware power management in vehicle-to-grid networks:a matching-theoretic approach

Frequency, time and places of charging and discharging have critical impact on the Quality of Experience (QoE) of using Electric Vehicles (EVs). EV charging and discharging scheduling schemes should consider both the QoE of using EV and the load capacity of the power grid. In this paper, we design a traveling plan-aware scheduling scheme for EV charging in driving pattern and a cooperative EV charging and discharging scheme in parking pattern to improve the QoE of using EV and enhance the reliability of the power grid. For traveling planaware scheduling, the assignment of EVs to Charging Stations (CSs) is modeled as a many-to-one matching game and the Stable Matching Algorithm (SMA) is proposed. For cooperative EV charging and discharging in parking pattern, the electricity exchange between charging EVs and discharging EVs in the same parking lot is formulated as a many-to-many matching model with ties, and we develop the Pareto Optimal Matching Algorithm (POMA). Simulation results indicates that the SMA can significantly improve the average system utility for EV charging in driving pattern, and the POMA can increase the amount of electricity offloaded from the grid which is helpful to enhance the reliability of the power grid.