Decentralised energy and its performance assessment models

Energy development concerns not only the development of renewable energies but also the shift from centralised to clean,decentralised power generation.The development of decentralised energy(DE)is a core part of the energy and economic strategies being adopted around the world that drives the progress toward a highly sustainable future.This paper reviews the concepts,development status,trends,benefits and challenges of DE systems and analyses the existing models and methods for assessing the performance of these systems.A hierarchical decision model for evaluating the performance of DE systems is also constructed based on the framework of multiple criteria decision analysis,which considers the identification,definition and assessment grade of decision criteria.The evidential reasoning approach is applied to aggregate assessment information in a case study of the implementation of an intelligent decision system.Sensitivity and trade-off analyses are also conducted to show how the proposed model can be used to support decision making in DE systems.

Overall Efficiency and its Decomposition in a Two-Stage Network DEA Model

This paper aims to propose a new approach to decompose an overall data envelopment analysis model into equivalent two-stage models.In this approach,we use a minimax reference point method to set the weights and reliabilities of the two stage models so that the combined efficiency of the two stages is equal to the overall efficiency.The equivalent multi-stage models are useful to support planning for performance improvement.An illustrative example is first explored to compare the results from the new approach with those of four other existing approaches.The main finding from the comparisons is that the new decomposition approach of this paper satisfies the proposed assumptions.A case study is then conducted on a two-stage process of steel manufacturing to illustrate the validity and applicability of the proposed approach.

Using Two-layer Minimax Optimization and DEA to Determine Attribute Weights

This study aims to explore a novel method for determining attribute weights,which is a key issue in constructing and analyzing multiple-attribute decision-making(MADM)problems.To this end,a hybrid approach combining the data envelopment analysis(DEA)model without explicit inputs(DEA-WEI)and a two-layer minimax optimization scheme is developed.It is demonstrated that in this approach,the most favorable set of weights is first considered for each alternative or decision-making unit(DMU)and these weight sets are then aggregated to determine the best compromise weights for attributes,with the interests of all DMUs simultaneously considered in a fair manner.This approach is particularly suitable for situations where the preferences of decision-makers(DMs)are either unclear or difficult to acquire.Two case studies are conducted to illustrate the proposed approach and its use for determining weights for attributes in practice.The first case concerns the assessment of research strengths of 24 selected countries using certain measures,and the second concerns the analysis of the performance of 64 selected Chinese universities,where the preferences of DMs are either unknown or ambiguous,but the weights of the attributes should be assigned in a fair and unbiased manner.

Inventory Policy and Heuristic for Long-Term Multi-product Perishable Inventory Routing Problem with Static Demand

This work considers a long-term Perishable Inventory Routing Problem with multiple products,static demand,and single vehicle,in the setting of Vendor Managed Inventory.By analyzing the optimal solutions of long-term cases that can be solved in Python+Gurobi within 2 h,we capture some patterns of optimal solutions.Utilizing these patterns,experiments show that under certain conditions,the mathematical models of multi-product problems could be simplified to single-product problems,which have the same optimal solutions while taking less time to solve.Managerial insights were generated that for products with static demand in the long term,delivery should be arranged at the store level rather than at the product level.Products in the same store should have the same delivery pattern,no matter how different the unit holding costs are.By further analyzing the optimal solutions of the simplified models,we find that optimal value will stabilize in the long term,and the optimal solution is very close to the solution point where total inventory holding cost and transportation cost are close.Based on these findings,we have developed a heuristic that always provides optimal or close-to-optimal solutions with far less computational time,compared with Python+Gurobi.

Decision Support System for Evaluating Impact of Product Carbon Labeling Scheme

In this paper a decision support system for systematically evaluating the impact of labeling products with their carbon footprints is developed and applied to prioritize products for carbon labeling in a large supermarket chain in the UK.Carbon labels may change consumers’ behavior and encourage suppliers to implement carbon-reduction solutions.Those changes may,however,lead to unintended risks.To handle the challenges of uncertainties in the evaluation,the Evidential Reasoning approach and the Intelligent Decision System software for multi-criteria decision analysis are applied to support the process.The system developed can be applied to assessing the impact of sustainable development policies to maximize their benefits and minimize their risks.