Mahalanobis-Taguchi system-based criteria selection for strategy formulation: A case in a training institution

The increasing complexity of decision making in a severely dynamic competitive environment of the universe has urged the wise managers to have relevant strategic plans for their firms. Strategy is not formulated from one criterion but from multiple criteria in environmental scanning, and often, considering all of them is not possible. A list of criteria utilizing Delphi was selected by consultation with company experts. By reviewing the literature and strategy experts' proposals, the list is then classified into five categories, namely, human resource, equipment, market, supply chain, and rules. Since all the criteria may not be necessary for the decision process, as they are eliminated in the early stage traditionally, it is important to identify the prime set of criteria, which is a subset of the original criteria and affects decision making. Utilizing these criteria, a Mahalanobis-Taguchi System-based tool was developed to facilitate the selection of a prime set of criteria, which is a subset of the original criteria for ensuring that only ineffective subcriteria are eliminated and the conditions are prepared for relevant strategy formulation. Mahalanobis distance was used for making a measurement scale to distinguish ineffective subcriteria from significant criteria in the environmental scanning stage. The principles of the Taguchi method were used for screening the important criteria in the system and generate the prime set of criteria for each category. One can use these criteria within each category instead of all criteria for the identification of a suitable institution in training. To validate the proposed approach, a case study has been conducted for 38 educational institutions in Iran. The results demonstrated the usefulness of the proposed approach

Using Reinforcement Learning Methods to Price a Perishable Product, Case Study: Orange

‎Determining the optimal selling price for different commodities has always been one of the main topics of scientific and industrial research‎. ‎Perishable products have a short life and due to their deterioration over time‎, ‎they cause great damage if not managed‎. ‎Many industries‎, ‎retailers‎, ‎and service providers have the opportunity to increase their revenue through optimal pricing of perishable products that must be sold within a certain period‎. ‎In the pricing issue‎, ‎a seller must determine the price of several units of a perishable or seasonal product to be sold for a limited time‎. ‎This article examines pricing policies that increase revenue for the sale of a given inventory with an expiration date‎. ‎Booster learning algorithms are used to analyze how companies can simultaneously learn and optimize pricing strategy in response to buyers‎. ‎It is also shown that using reinforcement learning we can model a demand-dependent problem‎. ‎This paper presents an optimization method in a model-independent environment in which demand is learned and pricing decisions are updated at the moment‎. ‎We compare the performance of learning algorithms using Monte Carlo simulations‎

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

Developing a novel quantitative framework for business continuity planning

<p>Today’s competitive and turbulent environment persuades every organisation to implement a business continuity management system (BCMS) for dealing with disruptive incidents such as earthquake, flood, and terrorist attacks. Within a BCMS, effective and efficient business continuity plans (BCPs) must be provided to ensure the continuity of organisation’s key products. This study develops a novel approach to select the most appropriate BCPs which can meet the business continuity key measures. First, a risk assessment process is conducted to define the disruptive incidents for which the organisation should have suitable BCPs. Then, two different possibilistic programming models including hard and soft BCP selection models are developed to determine appropriate BCPs under epistemic uncertainty of input data. These models aim to maximise the resilience level of the organisation while minimising the establishment cost of selected BCPs.‏ Finally, a real case study is provided whose results demonstrate the applicability and usefulness of the proposed approach.</p