Francisco Salas-Molina

A multi-objective approach to the cash management problem

Cash management is concerned with optimizing costs of short-term cash policies of a company. Different optimization models have been proposed in the literature whose focus has been only placed on a single objective, namely, on minimizing costs. However, cash managers may also be interested in risk associated to cash policies. In this paper, we propose a multi-objective cash management model based on compromise programming that allows cash managers to select the best policies, in terms of cost and risk, according to their risk preferences. The model is illustrated through several examples using real data from an industrial company, alternative cost scenarios and two different measures of risk. As a result, we provide cash managers with a new tool to allow them deciding on the level of risk to take in daily decision-making.

Empowering Cash Managers Through Compromise Programming

Typically, the cash management literature focuses on optimizing cost, hence neglecting risk analysis. In this chapter, we address the cash management problem from a multiobjective perspective by considering not only the cost but also the risk of cash policies. We propose novel measures to incorporate risk analysis as an additional goal in cash management. Next, we rely on compromise programming as a method to minimize the sum of weighted distances to an ideal point where both cost and risk are minimum. These weights reflect the particular preferences of cash managers when selecting the best policies that solve the multiobjective cash management problem. As a result, we suggest three alternative solvers to cover a wide range of possible situations: Monte Carlo methods, linear programming, and quadratic programming. We also provide a Python software library with an implementation of the proposed solvers ready to be embedded in cash management decision support systems. We finally describe a framework to assess the utility of cash management models when considering multiple objectives.

Boundless multiobjective models for cash management

ABSTRACT Cash management models are usually based on a set of bounds that complicate the selection of the optimal policies due to nonlinearity. We here propose to linearize cash management models to guarantee optimality through linear-quadratic multiobjective compromise programming models. We illustrate our approach through a reformulation of the suboptimal state-of-the-art Gormley-Meade’s model to achieve optimality. Furthermore, we introduce a much simpler formulation that we call the boundless model that also provides optimal solutions without using bounds. Results from a sensitivity analysis using real data sets from 54 different companies show that our boundless model is highly robust to cash flow prediction errors.

Selecting cash management models from a multiobjective perspective

This paper addresses the problem of selecting cash management models under different operating conditions from a multiobjective perspective considering not only cost but also risk. A number of models have been proposed to optimize corporate cash management policies. The impact on model performance of different operating conditions becomes an important issue. Here, we provide a range of visual and quantitative tools imported from Receiver Operating Characteristic (ROC) analysis. More precisely, we show the utility of ROC analysis from a triple perspective as a tool for: (1) showing model performance; (2) choosing models; and (3) assessing the impact of operating conditions on model performance. We illustrate the selection of cash management models by means of a numerical example.

On the use of multiple criteria distance indexes to find robust cash management policies

ABSTRACT Cash management decision-making can be handled from a multiobjective perspective by optimizing not only cost but also risk. Nevertheless, choosing the best policies under a changing context is by no means straightforward. To this end, we rely on compromise programming to incorporate robustness as an additional goal to cost and risk within a multiobjective framework. As a result, we propose to calculate robustness as a multiple criteria distance index that is able to identify the best compromise policies in terms of cost and risk. Such policies are also robust to cash flow regime changes. We show its utility by transforming the Miller and Orrs cash management model into its robust counterpart using real data from an industrial company.