Debora Di Caprio

An application of an integrated ANP–QFD framework for sustainable supplier selection

ABSTRACT This study provides a novel integrated multi-criteria decision-making approach to sustainable supplier selection problems. Despite the large supply chain management literature on green performance measurement, the need for a systematic analysis of how specific sustainable variables develop and affect each other remains mostly overlooked. The proposed integrated framework allows for such an analysis. By combining analytic network process and quality function deployment, our model identifies a clear hierarchical structure for all the relevant sustainable factors and sub-factors while weighting the decision criteria based on the importance given to customer requirements. Finally, suppliers are ranked using a multi-objective optimisation procedure based on ratio analysis and weighted aggregated sum product assessment. The proposed framework is used to analyse a case study of a dairy company, but it can be easily implemented for supplier selection by any other company with similar features.

Modeling synergies in multi-criteria supplier selection and order allocation: An application to commodity trading

We propose a novel meta-approach to support collaborative multi-objective supplier selection and order allocation (SSOA) decisions by integrating multi-criteria decision analysis and linear programming (LP). The proposed model accounts for suppliers’ performance synergy effects within a hierarchical decision structure. It incorporates both heterogeneous objective data and subjective judgments of the decision makers (DMs) representing various groups with different voting powers (VPs). We maximize the total value of purchasing (TVP) by optimizing order quantity assignment to suppliers and taking into consideration their synergies encountered in different time horizons. We apply the proposed model to a contractor selection and order quantity assignment problem in an agricultural commodity trading (ACT) company. We maximize the strategic effectiveness of both the customers and the suppliers, minimize risks, increase the degree of cooperation between trading partners on all levels of supply chain integration, enhance transparent knowledge sharing and aggregation, and support collaborative decision making.

An Optimal Information Gathering Algorithm

The current paper defines the optimal sequential information gathering structure of a rational utility maximizer decision maker in the simplest non-trivial theoretical scenario, where the decision maker is allowed to acquire only two pieces of information from a set of multidimensional goods. We show how this problem, hardly ever considered in the literature, does not admit a simple or intuitive solution. Indeed, while the standard sequential search and information gathering algorithms presented in the literature are identified with optimal stopping rules, we analyze explicitly the behavior of the decision maker when choosing which piece of information to acquire. We show that the decision of how to optimally allocate the second available piece of information depends on two well-defined real-valued expected utility functions. The crossing points between the graphs of both functions correspond to optimal thresholds for the information gathering process that define the dynamic behavior of the algorithmic search structure. We characterize explicitly the behavior and the value of these thresholds through the properties of the utility functions and probability densities inherent to the decision maker. The results are illustrated numerically for a variety of utility functions commonly used in decision theory.

An optimal information acquisition model for competitive advantage in complex multiperspective environments

Abstract The optimal information acquisition process is a major strategic task for sustaining a firm’s competitive advantage. We define the optimal sequential information acquisition behavior of a rational decision maker (DM) when allowed to acquire two pieces of information from and observe positive credible signals on a set of multidimensional products. We illustrate how firm reputation affects the continuity of the expected utilities derived from a given search and may generate reversals in the information acquisition incentives of DMs when deciding whether or not to shift their search processes between different signal-induced markets. This study makes a number of important contributions to our understanding of a firm’s information acquisition. First, it provides a formal analysis of the information acquisition process when the characteristics defining a product have a continuous set of variants. Second, it allows for the study of risk-averse DMs, while most of the literature concentrates on risk-neutral DMs. Third, it opens the way for strategic scenarios to be considered when analyzing the information acquisition processes of firms and creates a direct link to the game theoretical literature on strategic reporting. Fourth, it can be easily implemented within multicriteria decision making methods such as the analytic hierarchy process (AHP) to study the information acquisition behavior of DMs when the characteristics of the products are unknown.

An extended stochastic VIKOR model with decision maker's attitude towards risk

Abstract We propose a risk-based stochastic VIKOR (RB-VIKOR) model that accounts for differences in the risk attitudes of the decision makers (DMs) when ranking stochastic alternatives. Our proposed RB-VIKOR model is designed to solve multi-criteria problems characterized by stochastic data and DMs categorized by their risk averse or risk seeking behavior. These differences in risk attitudes determine the subjective beliefs of the DMs regarding the evaluation of each alternative per decision criterion and the resulting rankings. We present a case study in the banking industry to illustrate how differences in the risk attitudes of the DMs condition the rankings obtained. Moreover, we compare our results with those derived from a stochastic super-efficiency data envelopment analysis (DEA) model to demonstrate the applicability and efficacy of RB-VIKOR. The proposed method has a considerable amount of potential applications to diverse research areas ranging from economics to knowledge based and decision support systems.

Modeling Sequential Information Acquisition Behavior in Rational Decision Making

Most real-life decisions are made with less than perfect information and there is often some opportunity to acquire additional information to increase the quality of the decision. In this article, we define and study the sequential information acquisition process of a rational decision maker (DM) when allowed to acquire any finite amount of information from a set of products defined by vectors of characteristics. The information acquisition process of the DM depends both on the values of the characteristics observed previously and the number and potential realizations of the remaining characteristics. Each time an observation is acquired, the DM modifies the probability of improving upon the products already observed with the number of observations available. We construct two real-valued functions whose crossing points determine the decision of how to allocate each available piece of information. We provide several numerical simulations to illustrate the information acquisition incentives defining the behavior of the DM. Applications to knowledge management and decision support systems follow immediately from our results, particularly when considering the introduction and acceptance of new technological products and when formalizing online search environments.

An optimal sequential information acquisition model subject to a heuristic assimilation constraint

Purpose – The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set. Design/methodology/approach – The authors incorporate a heuristic mechanism that makes the n-observation scenario faced by a DM tractable. This heuristic allows the DM to assimilate substantial amounts of information and define an acquisition strategy within a coherent analytical framework. Numerical simulations are introduced to illustrate the main results obtained. Findings – The information acquisition behavior modeled in this paper corresponds to that of a perfectly rational DM, i.e. endowed with complete and transitive preferences, whose objective is to choose optimally among the products available subject to a heuristic assimilation constraint. The current paper opens the way for additional research on heuristic information acquisitio...

Information acquisition processes and their continuity: Transforming uncertainty into risk

We propose a formal approach to the problem of transforming uncertainty into risk via information revelation processes. Abstractions and formalizations regarding information acquisition processes are common in different areas of information sciences. We investigate the relationships between the way information is acquired and the continuity properties of revelation processes. A class of revelation processes whose continuity is characterized by how information is transmitted is introduced. This allows us to provide normative results regarding the continuity of the information acquisition processes of decision makers (DMs) and their ability to formulate probabilistic predictions within a given confidence range.

A novel two-stage DEA production model with freely distributed initial inputs and shared intermediate outputs

Abstract Conventional data envelopment analysis (DEA) models treat the decision-making units (DMUs) as black-boxes: inputs enter the system and outputs exit the system, with no consideration for the intermediate steps characterizing the DMUs. As a result, intermediate measures are lost in the process of changing the inputs to outputs and it becomes difficult, if not impossible, to provide individual DMU managers with specific information on what part of a DMU is responsible for the overall inefficiency. This study defines a two-stage DEA model, where each DMU is composed of two sub-DMUs in series, the intermediate products by the sub-DMU in Stage 1 are partly consumed by the sub-DMU in Stage 2, and the initial inputs of the DMU can be freely allocated in both stages. Also, there are additional inputs directly consumed in Stage 2 while part of the outputs of Stage 1 are final outputs. We develop four new linear models to determine the upper and lower bounds of the efficiencies of the two sub-DMUs in a non-cooperative setting and a linear model to calculate the overall efficiency of DMU in a cooperative setting. That is, the overall efficiency of a DMU is modelled in a cooperative setting via upper and lower bounds obtained in the non-cooperative one. The proposed two-stage DEA method allows for important applications to several management areas. A case study in the banking industry is presented to demonstrate the applicability and exhibit the efficacy of the proposed models.

A logit-based model for measuring the effects of transportation infrastructure on land value

ABSTRACT Mutual interactions between transportation and land use have long been debated. Despite progress made in computational technology, the study of these interactions is not adequately developed. The most important aspect of such interactions is given by the changes in land values due to changes in transportation infrastructures. We consider the behavioural features of these interactions along with the constraints on the land and/or zoning restrictions and propose a reliable model for the first time to predict land value changes with respect to changes in transportation facilities and accessibility. The proposed model is a logit-based mathematical programming methodology where the relative price of land is predicted with respect to transportation accessibility, neighbourhood amenities, location premium, availability of land, and zoning regulations. A real-world case study is used to exhibit the applicability of the proposed methodology and demonstrate the efficacy of the algorithms and procedures.