Verifying the Integrity of Hyperlinked Information Using Linked Data and Smart Contracts

We present an approach to verify off-chained information using Linked Data, Smart Contracts, and RDF graph hashes stored on a Distributed Ledger. We use the notion of a Linked Pedigree, i.e. a decentralised dataset for storing hyperlinked information, as modelling foundation. We evaluate our approach by comparing different ways to build the Smart Contract. We develop a cost model and show, based on our implementation, that for managing multiple Linked Pedigree instances, a single larger Smart Contract is superior to multiple smaller Smart Contracts for supply chains shorter than 50 participants

Driving Innovation through Big Open Linked Data (BOLD): Exploring Antecedents using Interpretive Structural Modelling

YesInnovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions

Logistics service provider selection for disaster preparation: a socio-technical systems perspective

Since 1990s, the world has seen a lot of advances in providing humanitarian aid through sophisticated logistics operations. The current consensus seems to be that humanitarian relief organizations (HROs) can improve their relief operations by collaborating with logistics service providers (CLSPs) in the commercial sector. The question remains: how can HROs select the most appropriate CLSP for disaster preparation? Despite its practical significance, no explicit effort has been done to identify the criteria/factors in prioritising and selecting a CLSP for disaster relief. The present study aims to address this gap by consolidating the list of criteria from a socio-technical systems (STS) perspective. Then, to handle the interdependence among the criteria derived from the STS, we develop a hybrid multi-criteria decision making model for CLSP selection in the disaster preparedness stage. The proposed model is then evaluated by a real-life case study, providing insights into the decision-makers in both HROs and CLSPs

Dynamic temporary blood facility location-allocation during and post-disaster periods

The key objective of this study is to develop a tool (hybridization or integration of different techniques) for locating the temporary blood banks during and post-disaster conditions that could serve the hospitals with minimum response time. We have used temporary blood centers, which must be located in such a way that it is able to serve the demand of hospitals in nearby region within a shorter duration. We are locating the temporary blood centres for which we are minimizing the maximum distance with hospitals. We have used Tabu search heuristic method to calculate the optimal number of temporary blood centres considering cost components. In addition, we employ Bayesian belief network to prioritize the factors for locating the temporary blood facilities. Workability of our model and methodology is illustrated using a case study including blood centres and hospitals surrounding Jamshedpur city. Our results shows that at-least 6 temporary blood facilities are required to satisfy the demand of blood during and post-disaster periods in Jamshedpur. The results also show that that past disaster conditions, response time and convenience for access are the most important factors for locating the temporary blood facilities during and post-disaster periods

Applicability of ARIMA Models in Wholesale Vegetable Market: An Investigation.

To investigate the applicability of ARIMA models in wholesale vegetable market models are built taking sales data of one perishable vegetable from Ahmedabad wholesales market in India. It is found that these models can be applied to forecast the demand with Mean Absolute Percentage Error (MAPE) in the range of 20%. This error is acceptable in fresh produce market where the demand and prices are highly unstable. The model is successfully validated using sales data of another vegetable from the same market. This model can facilitate the farmers and wholesalers in effective decision making

ARIMA Models to Forecast Demand in Fresh Supply Chains.

This paper presents the application of autoregressive integrated moving average (ARIMA) models to forecast the demand of fresh produce (fruits and vegetables) on a daily basis. Models were built using 25 months sales data of onion from Ahmedabad market in India. Results show that the model can be used to forecast the demand with mean absolute percentage error (MAPE) of 43.14%. This error is within the acceptable limit for fruits and vegetable markets with highly fluctuating demand pattern. The model was validated taking sales data for the same commodity from a different vegetable market. The proposed forecasting model can be used to assist the farmers in determining the volume of daily harvesting for fruits and vegetables

An Inventory Model for Continuously Deteriorating Agri-Fresh Produce: An Artificial Immune System Based Solution Approach.

This paper presents an inventory model for managing the continuously deteriorating agri-fresh produce in unorganized wholesale market. Replenishment policy is proposed assuming stochastic demand, periodic review and lost sales. Three inventory retrieval options namely first-in-first-out (FIFO), last-in-first-out (LIFO), and random retrieval (RR) are compared for the proposed replenishment policy. Considering the high problem complexity, artificial immune system (AIS)-based solution methodology is applied and tested on a new dataset generated from real life problem scenario. Results show that the proposed model can be used by wholesalers to efficiently manage the inventory of agri-fresh produce. Results also show that LIFO may be a better policy for produce which are highly perishable and have lower margins. Additionally, RR policy proved to be satisfactory irrespective of the produce characteristics. AIS outperformed when compared with the results obtained by commonly used algorithms such as genetic algorithm (GA) and simulated annealing (SA) for same problem instances

Harvest scheduling to reduce waste in agri-fresh produce supply chains: An Artificial Immune System-based solution approach.

This paper presents a mathematical model to maximise the overall profit by reducing the waste of agri-fresh produce. This is achieved by synchronising demand with supply through an optimal harvest schedule. The proposed model is complex in nature, and obtaining an optimal solution in practical time limits is extremely difficult. Therefore, we applied a meta-heuristics, artificial immune system (AIS) to obtain (near) optimal solutions. The proposed model was tested on a dataset generated from real-life scenario of Azadpur wholesale market, New Delhi (India). The result shows that the proposed model, when solved with AIS, provides better results as compared to the base policy, which assumes the plantations are harvested as soon as they attain maturity. Performance of the applied algorithm, AIS, is tested by comparing the results obtained by solving the same problem instances with other established algorithms such as simulated annealing (SA) and genetic algorithm (GA)

Artificial Immune System-based algorithm for vehicle routing problem with time window constraint for the delivery of agri-fresh produce.

This paper addresses the problem of delivering continuously deteriorating agri-fresh produce from a wholesaler to a number of retailers, within specific time windows. The prime objective is to decide the routes in such a way that the overall cost incurred in transportation, deterioration and penalty is minimised. To model these conflicting objectives a mathematical modelling approach is proposed. The Vehicle Routing Problem with Time Windows (VRPTW) is a Non-deterministic Polynomial-time hard (NP-hard) problem, without considering the business constraints, and becomes computationally prohibitive with the increase in number of retailers. To solve the VRPTW within feasible time limits, Artificial Immune System (AIS)-based solution methodology is proposed. The algorithm is tested on real-life instances generated from Azadpur wholesale market, New Delhi (India). An experiment is performed on the same problems with other algorithms, such as Genetic Algorithm (GA) and Simulated Annealing (SA), to compare the effectiveness and efficiency of the proposed approach. It is found from the quality of solution and rate of convergence that AIS performed better compared to the other applied approaches