Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency

Agricultural investment project selection is a complex multi-criteria decision-making problem,as agricultural projects are easily influenced by various risk factors,and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency.Existing literature primarily employed direct preference elicitation methods to address such issues,necessitating a great cognitive effort on the part of decision-makers during evaluation,specifically,determining the weights of criteria.In this study,we propose an indirect preference elicitation method,known as a preference disaggregation method,to learn decision-maker preference models fromdecision examples.To enhance evaluation ease,decision-makers merely need to compare pairs of alternatives with which they are familiar,also known as reference alternatives.Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons.To address the inconsistency among a group of decision-makers,we develop a pair of 0-1mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers.Finally,we conduct a case study and comparative analysis.Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information.

A DEA-TOPSIS METHOD FOR MULTIPLE CRITERIA DECISION ANALYSIS IN EMERGENCY MANAGEMENT

A hybrid approach of DEA （data envelopment analysis） and TOPSIS （technique for order performance （preference） by similarity to ideal solution） is proposed for multiple criteria decision analysis in emergency management. Two DEA-based optimization models are constructed to facilitate identifying parameter information regarding criterion weights and quantifying qualitative criteria in TOPSIS. An emergency management case study utilizing data from the Emergency Management Australia （EMA） Disasters Database is provided to demonstrate the feasibility of the proposed analysis procedure.

An Improvement to Multiple Criteria ABC Inventory Classification Using Shannon Entropy

This paper extends the Ng-model [Ng, 2007] for multiple criteria ABC inventory classification based upon Shannon entropy. The proposed approach determines the common weights associated with all criteria importance rankings, and provides a comprehensive scoring scheme by aggregating all rankings of the criteria importance. A numerical illustration is presented to compare the model with previous studies.

A HIERARCHICAL MULTIPLE CRITERIA MODEL FOR ELICITING RELATIVE PREFERENCES IN CONFLICT SITUATIONS

A multiple criteria decision analysis （MCDA） approach is designed for capturing the relative preference information of a decision maker involved in ＇a conflict. More specifically, an MCDA approach based on the outranking method, ELECTRE III, is employed for ranking states or possible scenarios in the conflict from most to least preferred, where ties are allowed, for a decision maker according to his or her value system. To demonstrate how this preference elicitation methodology can be conveniently implemented in practice within the framework of the Graph Model for Conflict Resolution, it is applied to a real world water supply crisis which occurred in the town of North Battleford, located in the Canadian province of Saskatchewan.

An in-depth review of theory of the TOPSIS method:An experimental analysis

Decision-making is an important part of daily and business life for both individuals and organizations.Although the multi-criteria decision-making methods provide decision makers the necessary tools,they have differences in terms of the assumptions and fundamental theory.Hence,selecting the right decision-making method is at least as important as making the decision.TOPSIS(Technique for Order Performance by Similarity to Ideal Solution)method,which is one of the most widely used multi-criteria decision-making methods,has gained attention of researchers and thus various improved versions of the method have been proposed.This study considers the conventional TOPSIS method and experimentally displays the underlying reasons of the lacks of the conventional TOPSIS method by using a simulation technique.Detailed experimental analysis based on simulation with an application is used to reveal theoretical fundamentals of the TOPSIS method to better understand it and contribute to its improvement.

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.