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