Supplier Portfolio Selection and Optimum Volume Allocation: A Knowledge Based Method

Selection of suppliers and allocation of optimum volumes to suppliers is a strategic business decision. This paper presents a decision support method for supplier selection and the optimal allocation of volumes in a supplier portfolio. The requirements for the method were gathered during a case study that was conducted within the logistics unit of Shell Chemicals Europe. The proposed method is based on the classical view by Sprague and Carlson of sequence and interaction of the different phases of decision making in a decision support system and supports Kraljic’s portfolio approach for supplier management. This method aims to help the managers in making decisions on the allocation of volumes to suppliers while simultaneously trying to satisfy conflicting objectives of improvement in benefit and reduction in risk. A mathematical model to struc-ture the problem is presented, knowledge elicited from the managers is used to parameterize the mathemati-cal model and a multi-objective, hierarchical optimization procedure produces ‘trade-off’ outputs. The man-agers can also conduct interactive post optimization ‘what-if’ analysi

Web-based AHP system

This chapter discusses about the development of a web-based multi criteria decision making system which implements Analytical Hierarchical Process (AHP) method in order to give the best decision/choice to decision makers. It is believed that such system will be able to offer more accurate and acceptable result based on a number of criteria and alternatives that the user has provided. The main objective of this chapter is to provide an understanding of how such a system is build and some idea on how actually Analytical Hierarchical Process is being implemented in the system. It is hoped that after reading this chapter, readers will get the general concept and idea on how similar systems work and function in the future

Abstractions in Reasoning for Long-Term Autonomy

The path to building adaptive, robust, intelligent agents has led researchers to develop a suite of powerful models and algorithms for agents with a single objective. However, in recent years, attempts to use this monolithic approach to solve an ever-expanding set of complex real-world problems, which increasingly include long-term autonomous deployments, have illuminated challenges in its ability to scale. Consequently, a fragmented collection of hierarchical and multi-objective models were developed. This trend continues into the algorithms as well, as each approximates an optimal solution in a different manner for scalability. These models and algorithms represent an attempt to solve pieces of an overarching problem: how can an agent explicitly model and integrate the necessary aspects of reasoning required to achieve long-term autonomy? This thesis presents a general hierarchical and multi-objective model called a policy network that unifies prior fragmented solutions into a single graphical decision-making structure. Policy networks are broadly useful to solve numerous real-world problems. This thesis focuses on autonomous vehicle (AV) problems: (1) route-planning with multiple objectives; (2) semi-autonomy with proactive transfer of control; and (3) intersection decision-making for reasoning online about any number of other vehicles and pedestrians. Formal models are presented for each of the distinct problems. Solutions are evaluated using real-world map data in simulation and demonstrated on a fully operational AV prototype driving on real public roads. Policy networks serve as a shared underlying framework for all three, enabling their seamless integration as parts of an overall solution for rich, real-world, scalable decision-making in agents with long-term autonomy

Multi-test Decision Tree and its Application to Microarray Data Classification

Objective: The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. Methods: We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Results: Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on $14$ datasets by an average $6$ percent. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. Conclusion: This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts

A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization

Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies. Decision-making in this domain generally takes multiple criteria into consideration, which usually arouses more complexity. To address this issue, the present paper proposes a decision support system that combines the intelligent data-based random forest (RF) models and a human knowledge based analytical hierarchical process (AHP) multi-criteria structure in accordance to the objective and the subjective factors of the textile manufacturing process. More importantly, the textile manufacturing process is described as the Markov decision process (MDP) paradigm, and a deep reinforcement learning scheme, the Deep Q-networks (DQN), is employed to optimize it. The effectiveness of this system has been validated in a case study of optimizing a textile ozonation process, showing that it can better master the challenging decision-making tasks in textile manufacturing processes.Comment: arXiv admin note: text overlap with arXiv:2012.0110

Optimizing the Managerial Decision in Energetic Industry

Making a decision is a complex process which must be based upon a method that is able to establish the optimum criteria in choosing an alternative, in evaluating the main effects of implementing the decision which was taken and in estimating the risks involved. The optimizing methods and techniques fall into several groups. Thus, judging by the number of criteria that was taken into consideration when making decisions, the optimization methods and techniques can be identified as uni-criterial decisions and multi-criterial decisions; considering the objective condition state which affects the problem that needs decisional solution, there can be decisional methods and techniques used in optimizing decisions in conditions of certainty, decisional methods and techniques used in optimizing decisions in conditions of uncertainty and decisional methods and techniques used in optimizing decisions in risky conditions. The continuous improvement of the decisional subsystem - an important component of the firm’s management - represents a necessity under the circumstances that the latest decades reveal a development of the decisional elements, both in the theoretic-methodological field and in the application field. The decisional methods and techniques must be found in the managers’ decisional processes at different hierarchical levels (individual managers or group managers), so that a high scientific materialization level of the methods should be ensured.decision; variant; optimizing methods and techniques; decisional tree; certainty; uncertainty; risk

Interactive Bi-Level Multi-Objective Integer Non-linear Programming Problem

Abstract This paper presents a bi-level multi-objective integer non-linear programming (BLMINP) problem with linear or non-linear constraints and an interactive algorithm for solving such model. At the first phase of the solution algorithm to avoid the complexity of non convexity of this problem, we begin by finding the convex hull of its original set of constraints using the cutting-plane algorithm to convert the BLMINP problem to an equivalent bi-level multi-objective non-linear programming (BLMNP) problem. At the second phase the algorithm simplifies an equivalent (BLMNP) problem by transforming it into separate multi-objective decision-making problems with hierarchical structure, and solving it by using ε -constraint method to avoid the difficulty associated with non-convex mathematical programming. In addition, the author put forward the satisfactoriness concept as the first-level decision-maker preference. Finally, an illustrative numerical example is given to demonstrate the obtained results. Mathematics Subject Classification: 90C29; 90C30; 41A58; 90C1

A multicriteria hierarchical discrimination approach for credit risk problems

Recently, banks and credit institutions have shown an increased interest in developing and implementing credit-scoring systems for taking corporate and consumer credit granting decisions. The objective of such systems is to analyze the characteristics of each applicant (firm or individual) and support the decision making process regarding the acceptance or the rejection of the credit application. This paper addresses this problem through the use of a multicriteria classi - fication technique, the M.H.DIS method (Multi-group Hierarchical DIScrimination). M.H.DIS is applied to real-world case studies regarding the assessment of corporate credit risk and the evaluation of credit card applications. The results obtained through the M.H.DIS method are compared to the results of three wellknown statistical techniques, namely linear and quadratic discriminant analysis, as well as logit analysis.peer-reviewe