AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Application of simulation and modelling in managing unplanned healthcare demand

Patients who attend Accident and Emergency (A & E) departments with problems that could be dealt with by their general practitioners (GPs) use time and resources of the department that could be otherwise used for patients with more appropriate needs. Hospital managers throughout the world are facing increasing pressure to introduce measures and initiatives to significantly ease the problem of such inappropriate attendances at A&E departments. This study looks at an initiative in which primary care clinicians are used to help deflect patients with non-urgent needs away from A&E. Simulation and modelling was used to assess the impact that this initiative would have on A&E workflow. The results suggest that the deflection of patients attending A&E with non-urgent needs may reduce the time spent in A&E by all patients attending A&E

A multi-faceted approach to optimising a complex unplanned healthcare system

Unscheduled and urgent health care represents the largest area of activity and cost for the UK’s National Health Service (NHS). Like typical complex systems unplanned care has the features of interdependence and having structures at different scales which requires modelling at different levels. The aim of this paper is to discuss the development of a multifaceted approach to study and optimise this complex system. We aim to integrate four different methodologies to gain better understanding of the nature of the system and to develop ways to enhance its performance. These methodologies are: (a) Lean/ Flow theory to look at the process and patients and other flows; (b) Simulation/ System Dynamics to undertake analytical analysis and multi-level modelling; (c) stakeholder consultation and use of system thinking to analyse the system and identify options, barriers and good practice; and (d) visual analytic modelling to facilitate effective decision making in this complex environment. Of particular concern are the boundary issues i.e. how changes in unplanned care will impact on the adjacent facilities and ultimately on the whole Healthcare system

Artificial Intelligence in Supply Chain Operations Planning: Collaboration and Digital Perspectives

[EN] Digital transformation provide supply chains (SCs) with extensive accurate data that should be combined with analytical techniques to improve their management. Among these techniques Artificial Intelligence (AI) has proved their suitability, memory and ability to manage uncertain and constantly changing information. Despite the fact that a number of AI literature reviews exist, no comprehensive review of reviews for the SC operations planning has yet been conducted. This paper aims to provide a comprehensive review of AI literature reviews in a structured manner to gain insights into their evolution in incorporating new ICTs and collaboration. Results show that hybrization man-machine and collaboration and ethical aspects are understudied.This research has been funded by the project entitled NIOTOME (Ref. RTI2018-102020-B-I00) (MCI/AEI/FEDER, UE). 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Analysis and design of returns policies from a supplier's perspective.

This paper considers the problem of designing a returns policy in a supply chain from a supplier's perspective. The supply chain considered here is assumed to have one supplier and one retailer who serves a random demand of a product with a short life cycle. The retailer can return all the unsold products to the supplier with a partial refund. We found that if the retailer behaviour is rational, that is, ordering the optimal quantity to maximize its expected profit, then both retailer and supplier could benefit from the returns policy. Furthermore, we established that the optimal buyback price is independent of the mean of the random demand, but the variance of the demand has a significant impact on setting the optimal buyback price. The higher the variance the higher the optimal buyback price and the larger the profit gain of both parties. Numerical studies are employed to help understand the benefits of returns policies for the supplier, the retailer, and the whole supply chain

A holistic, multi-level analysis identifying the impact of classroom design on pupils’ learning

The aim of this study was to explore if there is any evidence for demonstrable impacts of school building design on the learning rates of pupils in primary schools. Hypotheses as to positive impacts on learning were developed for 10 design parameters within a neuroscience framework of three design principles. These were tested using data collected on 751 pupils from 34 varied classrooms in seven different schools in the UK. The multi-level model developed explained 51% of the variability in the learning improvements of the pupils, over the course of a year. However, within this a high level of explanation (73%) was identified at the “class” level, linked entirely to six built environment design parameters, namely: colour, choice, connection, complexity, flexibility and light. The model was used to predict the impact of the six design parameters on pupil’s learning progression. Comparing the “worst” and “best” classrooms in the sample, these factors alone were found to have an impact that equates to the typical progress of a pupil over one year. It was also possible to estimate the proportionate impact of these built environment factors on learning progression, in the context of all influences together. This scaled at a 25% contribution on average. This clear evidence of the significant impact of the built environment on pupils’ learning progression highlights the importance of this aspect for policy makers, designers and users. The wide range of factors involved in this holistic approach still leaves a significant design challeng