A data-driven decision support framework for DEA target setting:an explainable AI approach

The intention of target setting for Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA) is to perform better than their peers or reach a reference efficiency level. However, most of the time, the logic behind the target setting is based on mathematical models, which are not achievable in practice. Besides, these models are based on decreasing/increasing inputs/outputs that might not be feasible based on DMU's potential in the real world. We propose a data-driven decision support framework to set actionable and feasible targets based on vital inputs-outputs for target setting. To do so, DMUs are classified in their corresponding Efficiency Frontier (EF) levels based on multiple EFs approach and a machine learning classifier. Then, the vital inputs-outputs are determined using an Explainable Artificial Intelligence (XAI) method. Finally, a Multi-Objective Counterfactual Explanation is developed based on DEA (MOCE-DEA) to lead DMU in reaching the reference EF by adjusting actionable and feasible inputs-outputs. We studied Iranian hospitals to evaluate the proposed framework and presented two cases to demonstrate its mechanism. The results show that the performance of the DMUs is improved to reach the reference EF for studied cases. Then, a validation was conducted with the primal DEA model to show the robust improvement of DMUs after adjusting their original value based on the generated solutions by the proposed framework. It demonstrates that the adjusted values can also improve DMUs' performance in the primal DEA model.</p

A data-driven decision support framework for DEA target setting:an explainable AI approach

The intention of target setting for Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA) is to perform better than their peers or reach a reference efficiency level. However, most of the time, the logic behind the target setting is based on mathematical models, which are not achievable in practice. Besides, these models are based on decreasing/increasing inputs/outputs that might not be feasible based on DMU's potential in the real world. We propose a data-driven decision support framework to set actionable and feasible targets based on vital inputs-outputs for target setting. To do so, DMUs are classified in their corresponding Efficiency Frontier (EF) levels based on multiple EFs approach and a machine learning classifier. Then, the vital inputs-outputs are determined using an Explainable Artificial Intelligence (XAI) method. Finally, a Multi-Objective Counterfactual Explanation is developed based on DEA (MOCE-DEA) to lead DMU in reaching the reference EF by adjusting actionable and feasible inputs-outputs. We studied Iranian hospitals to evaluate the proposed framework and presented two cases to demonstrate its mechanism. The results show that the performance of the DMUs is improved to reach the reference EF for studied cases. Then, a validation was conducted with the primal DEA model to show the robust improvement of DMUs after adjusting their original value based on the generated solutions by the proposed framework. It demonstrates that the adjusted values can also improve DMUs' performance in the primal DEA model.</p

An integrated model for selecting efficient suppliers in a competitive environment under uncertain demand

One of the most important issues in the supply chain is supplier selection with the aim of optimizing expenditures on uncertain demand. On the other hand, due to today's competitive environment, rising customer expectations for high quality and affordable products purchased, lead to development the long-term relationship of supply chain members including buyer and supplier. Therefore, selection of an appropriate set of efficient supplier and allocating orders to theirs, is one of the most important strategic decisions to create effective and efficient supply chain system in competitive environment that characterized by uncertainty. This study, at first, is attempted to select a set of efficient suppliers in a non-competitive environment with uncertain demand through the presenting an integrated model of multi objective programming including data envelopment analysis (DEA) and single buyer-multi vendor (supplier) coordination. Afterwards, by presenting a DEA model based on Nash bargaining game, the competitive environment among suppliers is simulated. The results of the two models shows that competitive environment has led to improved efficiency

Implementing bargaining game-based fuzzy cognitive map and mixed-motive games for group decisions in the healthcare supplier selection

Evaluating and selecting proper suppliers in the healthcare centers due to their high impact on the financial situation and citizens’ satisfaction is vital. The abundance of various criteria affecting the Supplier Selection (SS) problem makes it a decision-making problem. To this end, an approach according to the Bargaining Game-based Fuzzy Cognitive Map (BG-FCM) and mixed-motive games has been proposed for simultaneously modeling the SS complexity and suppliers’ competition in the market. First, according to the BG-FCM, the causal relationships between SS criteria have been determined. Then, by implementing Particle Swarm Optimization and the S-shaped transfer function (PSO-STF) and scenario-making, the BG-FCM is executed to extract robust payoffs for suppliers to compete. The competition between suppliers is modeled by mixed-motive games and their robust payoffs to determine their best strategies in the competition. Finally, suppliers compete with each other two by two, and suppliers with the most wins will have higher priority. The proposed approach has been applied in a general hospital to evaluate major suppliers for purchasing necessities. Then, it is compared with two well-known Multi-Criteria Decision Making (MCDM) approaches, showing a better performance in modeling the complexity and competition in the problem. The proposed approach can help the hospital select the most appropriate suppliers according to its preferences and avoid cooperating with inappropriate suppliers, which may cause a low-quality Supply Chain (SC) system or financial calamities.</p

A hybrid method using fuzzy cognitive map- DEA to study the delays in construction projects

Delay is a common occurrence in the country's construction projects. Identifying delay factors in these projects and determining the influence of these factors is necessary to achieve the objectives of management. In this study, the effective delay factors on construction projects are identified by using previous studies, project documents and experts opinions. Since these factors affect on each other, the fuzzy cognitive map has drawn for effective factors and assessment factors or management objectives. Then, the effect of each factor on the assessment factors are evaluated by using hybrid learning algorithm and prioritization factors are done by using fuzzy data envelopment analysis. The results of the survey in West Azerbaijan province show that “supervision technical weaknesses for overcoming technical and executive workshop problems”, “inaccurate estimate of workload, required equipments and project time” and “the multiplicity of decision centers on the doing of projects” are the most important delay factors in construction project

Evaluation and selection of sustainable suppliers in supply chain using new GP-DEA model with imprecise data

Abstract Nowadays, with respect to knowledge growth about enterprise sustainability, sustainable supplier selection is considered a vital factor in sustainable supply chain management. On the other hand, usually in real problems, the data are imprecise. One method that is helpful for the evaluation and selection of the sustainable supplier and has the ability to use a variety of data types is data envelopment analysis (DEA). In the present article, first, the supplier efficiency is measured with respect to all economic, social and environmental dimensions using DEA and applying imprecise data. Then, to have a general evaluation of the suppliers, the DEA model is developed using imprecise data based on goal programming (GP). Integrating the set of criteria changes the new model into a coherent framework for sustainable supplier selection. Moreover, employing this model in a multilateral sustainable supplier selection can be an incentive for the suppliers to move towards environmental, social and economic activities. Improving environmental, economic and social performance will mean improving the supply chain performance. Finally, the application of the proposed approach is presented with a real dataset

Enhancing risk assessment of manufacturing production process integrating failure modes and sequential fuzzy cognitive map

When a risk occurs in a stage of the production process, it can be due to the risks of the previous stages, or it is effective in causing the risks in the later stages. The current paper proposes an intelligent approach based on cause-and-effect relationships to assess and prioritize a manufacturing unit’s risks. Sequential multi-stage fuzzy cognitive maps (MSFCMs) are used for drawing the map of risks. Then, the learning algorithm is implemented for learning the MSFCM and finalizing the risks score. A case study on an auto-parts manufacturing unit is applied to demonstrate the capabilities of the proposed approach