We focus on ways of combining simple database models with managerial intuition. We present a model and method for isolating managerial intuition. For five different business forecasting situations, our results indicate that a combination of model and manager always outperforms either of these decision inputs in isolation, an average R2 increase of 0.09 (16%) above the best single decision input in cross-validated model analyses. We assess the validity of an equal weighting heuristic, 50% model + 50% manager, and then discuss why our results might differ from previous research on expert judgment.

Database Models And Managerial Intuition: 50% Model + 50% Manager

A great amount of energy is wasted in industry by machines that remain idle due to underutilisation. A way to avoid wasting energy and thus reducing the carbon print of an industrial plant is to consider minimisation of energy consumption objective while making scheduling decisions. To minimise energy consumption, the decision maker has to decide the timing and length of turn off/turn on operation (a setup) and also provide a sequence of jobs that minimises the scheduling objective, assuming that all jobs are not available at the same time. In this paper, a framework to solve a multiobjective optimisation problem that minimises total energy consumption and total tardiness is proposed. Since total tardiness problem with release dates is an NP‐hard problem, a new greedy randomised multiobjective adaptive search metaheuristic is utilised to obtain an approximate pareto front (i.e. an approximate set of non‐dominated solutions). Analytical Hierarchy Process is utilised to determine the ‘best’ alternative amon...

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A framework to minimise total energy consumption and total tardiness on a single machine

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. 
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Forecasting: theory and practice

Forecasting advice from human advisors is often utilized more than advice from automation. There is little understanding of why “algorithm aversion” occurs, or specific conditions that may exaggerate it. This paper first reviews literature from two fields—interpersonal advice and human–automation trust—that can inform our understanding of the underlying causes of the phenomenon. Then, an experiment is conducted to search for these underlying causes. We do not replicate the finding that human advice is generally utilized more than automated advice. However, after receiving bad advice, utilization of automated advice decreased significantly more than advice from humans. We also find that decision makers describe themselves as having much more in common with human than automated advisors despite there being no interpersonal relationship in our study. Results are discussed in relation to other findings from the forecasting and human–automation trust fields and provide a new perspective on what causes and exaggerates algorithm aversion.

Understanding algorithm aversion: When is advice from automation discounted?

Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting

Reliance, trust and heuristics in judgmental forecasting

The location routing problem with stochastic transportation cost and vehicle travel speeds is considered in this paper. A hybrid solution procedure based on Ant Colony Optimisation (ACO) and Discrete-Event Simulation (DES) is proposed. After using a sequential heuristic algorithm to solve the location subproblem, the subsequent capacitated vehicle routing problem is solved using ACO. Finally, a DES model evaluates those vehicle routes in terms of their impact on the expected total costs. The approach is tested using well-known randomly generated datasets. Since no previous works in the literature studied exactly the same SLRP, the proposed procedure is compared against its deterministic version. Numerical results show the efficiency and efficacy of the hybrid ACO-DES approach.

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Simulation-optimization approach for the stochastic location-routing problem

Opinion mining and sentiment analysis in texts from social networks such as Twitter has taken great importance during the last decade. Quality lexicons for the sentiment analysis task are easily found in languages such as English; however, this is not the case in Spanish. For this reason, we propose CSL, a Combined Spanish Lexicon approach for sentiment analysis that uses an ensemble of six lexicons in Spanish and a weighted bag of words strategy. In order to build CSL we used 68,019 tweets previously classified by researchers at the Spanish Society of Natural Language Processing (SEPLN) obtaining a precision of 62.05 and a recall of 60.75 in the validation set, showing improvements in both measurements. Additionally, we compare the results of CSL with a very well-known commercial software for sentiment analysis in Spanish finding an improvement of 10 points in precision and 15 points in recall.

CSL: A Combined Spanish Lexicon - Resource for Polarity Classification and Sentiment Analysis.

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Jorge Andrés Alvarado-Valencia

Papers

Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting

Reliance, trust and heuristics in judgmental forecasting

Simulation-optimization approach for the stochastic location-routing problem

CSL: A Combined Spanish Lexicon - Resource for Polarity Classification and Sentiment Analysis.

Greedy Randomized Adaptive Search Procedure (GRASP)