Seminars

Upcoming CSAM Events

• 16th July 2024. Yi Liu, PhD student and Professor Fotios Petropoulos, Professor of Management Science, University of Bath  
  • Title: To follow
  • Abstract: To follow

 

• 4th June 2024. Qing Zhu. Visiting PhD student.
  • Title: Optimal contract selection strategies under three different structures
  • Abstract: We explore optimal contract choice under monopoly, retailer competition, and manufacturer competition structures. The impact of interest rates and the substitution rate between two products on manufacturers' and retailers' profits is also considered. We also focus on the similarities and differences between the manufacturers and the retailers under Bertrand and Cournot competition. We identify the contracts the manufacturers wish to adopt, the contracts the retailers prefer manufacturers to choose, and whether there are differences in the reasons for contract selection. Our model employs a two-stage Stackelberg game. The research findings indicate that in the monopoly case,  with retailers engaging in either Bertrand and Cournot competition, the volatility of spot market price affects the manufacturer's contract choice and the retailers' contract preferences in the same manner. We found that under manufacturer competition, whether in Bertrand or Cournot games, the manufacturer's contract choices and equilibrium strategies are the same. However, in this structure, the  retailers' preferences for the type of contract chosen by manufacturers may vary depending on the magnitude of market demand price volatility. Furthermore, the study concludes that under the two game modes of manufacturer and retailer competition, both manufacturer and retailer profits decrease with an increase in the substitutability between the two products, and manufacturer profits decrease with interest rate increases.

 

Recent CSAM Events

• 9th April 2024. Yogendra Singh. PhD student.
  • Title: Reducing Bullwhip and MRP nervousness with propotional future guidance
  • Abstract: Material Requirements Planning (MRP) systems regulate raw material inventory (RMI) levels by periodically issuing call-off orders and order forecasts to suppliers. These orders and forecasts change with each regeneration of the MRP plan, often resulting in a costly effect called MRP nervousness. The manufacturer receives orders from customers, sets production targets, and issues replenishment orders to the supplier. A typical manufacturing echelon of a supply chain comprises of three lead times; the lead time experienced by the customer, the lead time required by the shop floor to produce finished goods inventory (FGI), and the lead time the supplier requires to deliver RMI. If a customer is willing to wait for the product, their advanced demand information (ADI) can lead to lower FGI levels/costs. However, how the ADI affects MRP nervousness is unknown. Using value stream maps and control theory, we model and analyse a manufacturing supply chain echelon to understand how the three lead times influence the MRP nervousness in the future order guidance given to the supplier.

 

• 26th March 2024.  Professor Dong Li. Professor of Operational Research, Lancaster University Management School.  
  • Title: Choice-based availability controls for urban carsharing revenue management
  • Abstract: Urban carsharing services provide flexible, affordable and green mobility options where customers can pick up a car from one station and return it to the same station (round trip) or any other station in the car-sharing network (one-way).  While one-way car-sharing offers a higher degree of mobility flexibility to customers, it may cause operational challenges such as fleet unbalance between multiple rental stations. Carsharing operators have employed various vehicle relocation strategies to ensure the availability of sufficient vehicles across rental stations. In this study, we adopt the user-based relocation strategy where customers are offered incentives (e.g., fare discounts) to drop off the vehicles to alternative destinations. We estimate a Multinomial logit (MNL) choice model based on collated responses from a discrete choice experiment implemented in a survey. We build the choice model into a dynamic program to determine the optimal destination-fare discount combinations offered to each arriving customer, which maximises the total expected revenue over a finite time horizon. To address the computational complexity of the dynamic program, we propose two approximation approaches: a choice-based deterministic linear program and a decomposition method. An extensive numerical study shows the effectiveness and efficiency of the proposed approaches, especially the latter.

 

• 20th March 2024.  Marta Staff. PhD student.
  • Title: Human Donor Milk-Potential Demand Estimation.
  • Abstract: Using donor human milk (DHM) for preterm infants, where the mother’s milk is unavailable, has multiple health benefits. Understanding the relationship between clinical choices for DHM provision and the resulting demand is important for multiple stakeholders. For policymakers, it informs decision-making around the provision of DHM based on cost-benefit analyses. For milk banks, it helps plan for required capacity, donor recruitment and supply-side collections. Beyond sharing the “latest and greatest” framework and model (and case study results) for demand estimation I will also share, with the audience, how the manuscript has evolved over time.

 

• 27th February 2024.  Geng Cui. Project Research Associate, University of Tokyo.
  • Title: Jamology: Analysis and solution for various kind of jams
  • Abstract: Traffic jams are not only observed in vehicles but also in pedestrian flow and logistics. "Jamology" analyzes these phenomena using various approaches in a multidisciplinary manner. Eliminating traffic jams offers numerous benefits: it reduces traffic congestion and waiting times in vehicle transportation systems, lowers the risk of crowd accidents in areas with high pedestrian traffic, and decreases waste and improves production efficiency in logistics systems. In this presentation, I will introduce several research topics covered within the scope of Jamology, including vehicle traffic, pedestrian dynamics, and logistics. The approaches utilized in Jamology studies encompass statistical processes, fluid dynamics, and artificial intelligence. I will begin by describing the application of cellular automata to vehicular transportation research, then transition to pedestrian dynamics related research, and finally describe my study in the field of logistics.

• 13th February 2024. Dr Alison Harper. Lecturer in Operations & Analytics, Exeter Business School.

  • Title: Open-source Modeling for Orthopaedic Elective Capacity Planning using Discrete-event Simulation

  • Abstract:  Increases in elective surgical waiting lists over the last few years are creating significant consequences for health services.  The allocation of NHS capital funds to increase capacity for managing elective waits has created planning and operational challenges for health services. This presentation describes the development and deployment of an interactive web-based discrete-event simulation model for supporting capacity planning of surgical activity and ward stay in a proposed new ring-fenced orthopaedic facility in a UK health service. The model is free and open-source and developed to be generic and applicable for new capacity planning of elective recovery in orthopedics in other regions.

 

• 30th January 2024.  Dr Xuan Vinh Doan. Reader at Warwick Business School.
  • Title: Operations research games under uncertainty: a robust chance constrained approach
  • Abstract: Operations research games such as linear production games concern the cooperation among players who would face a joint optimization problem and share benefits/costs together if they agree to cooperate. In this talk, we aim to address a fundamental challenge of incorporating uncertainty into these games. We introduce a new solution concept of (robust) least chance decisions under uncertainty and distributional ambiguity, which is motivated by the concept of least core solutions for deterministic games. We develop a framework to find those decisions and compute their (robust) least chance dissatisfaction for games under normally distributed uncertainty and moment-based distributional ambiguity. We demonstrate how the framework can be applied to operations research games such as linear production games with detailed analytical results.

• 5th December 2023.  Dr Avril Sun. Lecturer in Operations and Supply Chain Management, University of Exeter.
  

  • Title: Supply Chain Resilience: The Case of Chinese Pig Industry. 

  • Abstract. The focus on lean production in turbulent business environment and increasing unforeseen adverse events have resulted a higher likelihood of severe supply chain disruptions, which drives the need for supply chain resilience (SCR) building as a mean for organizations to achieve business continuity through adapting, responding, and recovering from unexpected risks. Responding to the call to better understand the mechanism of SCR building and framed by the contextual issues arising from the agricultural supply chain, the overarching purpose of this study is to advance knowledge in supply chain resilience beyond the traditional risk management and static resilience approach. More specifically, this study explores how organizations make decisions on resources investment and build SCR capabilities during risk recovery and discovered how supply chain co-evolves with its environment in terms of SCR building through analysing organization’s resources investment configuration strategies. This study presents empirical findings drawn from multiple case studies within four Chinese leading pig production organisations, focusing on their risk mitigation processes. Building on conservation of resources theory and ambidexterity theory, a novel resilience building framework was revealed, and a theory of Resilient Resource Based View (RRBV) was developed.

 

• 7th November 2023. Dr Justyna Rybicka, Technology Specialist at the Manufacturing Technology Centre.
  • Title: From mathematical model to digital twin in manufacturing– best practice for modelling and simulation.
  • Abstract: This session aims, through lens of best practice in development of the operational simulation models, showcase use cases from the cross-sector manufacturing applications. It will cover the modelling simulation capabilities, model development best practice and practical use cases showcasing different simulation examples for application at different level of technology readiness levels.

 

• 25th October 2023. Professor Nenad Jukic, Quinlan School of Business, Loyola University Chicago. 
  • Title: Prevalence of Conceptual Modeling in Database Development Projects
  • Abstract: Conceptual database modeling is widely accepted in academia as an essential and integral part of the database development process, essential for minimizing the risk of information systems project failures.  It is described in detail in database textbooks, and it is taught at most university level database courses at the undergraduate and graduate levels.  However, there has not yet been a research study published that examines the prevalence of conceptual database modeling in real world database projects.  The ongoing research that will be described in this talk aims to conduct such a study.  In the contemporary world, daily interaction with databases is a fact of life. It occurs in many everyday situations, such as when we make purchases, view content on the internet, or engage in banking transactions. The purpose of a database is to organize the data in a way that facilitates straightforward access to the information captured in the data. The first step in the development of databases is requirements collection, definition, and visualization.  This step results in the end user requirements specifying which data the future database system will hold, and in what fashion, and what the capabilities and functionalities of the database system will be. The requirements are used to model and implement the database. If this step is successful, the remaining steps have a great chance of success. However, if this step is done incorrectly, all the remaining steps, and consequently the entire project will fail.  The database requirements step has three parts: requirement collection, requirement definition, and requirements visualization (also known as conceptual modeling).  Requirement collection refers to the process of eliciting requirements from the clients and/or future users of the database (typically via conversations and interviews).  Requirement definition refers to recording the collected requirements into a written document.  Requirement visualization/conceptual modeling refers to creating a blueprint for the database which is a direct reflection of the requirements.  To illustrate conceptual modeling, in this talk a brief demonstration will be given using a small example of developing a database. The example will be conducted using ERDPlus, a free web-based database modeling suite created by Prof. Jukic, used daily by thousands of academic and industry users worldwide. Following the demonstration, the ongoing survey of database professionals will be presented.  The survey identifies the levels of use of conceptual database modeling, and it investigates the reasons for not using conceptual database modeling among the practitioners who do not typically use it. The survey also examines how usage (or lack thereof) of conceptual modeling affects the overall outcome of the database development process. The initial results of this survey, based on the responses of several hundred database practitioners, will be shared.   

 

• 11th October 2023. Professor Justin Tumlinson. Associate Professor in Business Analytics, University of Exeter.

 

• 27th September 2023. Professor Nav Mustafee. Professor of Analytics and Operations Management, University of Exeter.
  • Title: Hybrid models with real-time data: Characterising real-time simulation and digital twins
  • Abstract: Real-time Simulation (RtS) and Digital Twins (DT) are terms generally associated with hybrid models that use real-time data to drive computational models. Additionally, in the case of DTs, real-time data is often used to create virtual replicas of the physical system as it progresses through real-time. There is an increasing volume of literature on RtS and DT; however, the field of OR/MS is yet to coalesce on accepted definitions and conceptualisations. This has arguably led to the cascading usage of these terms. The objective of the paper is threefold: (1) distinguish between RtS and DT, (2) present RtS-DT conceptualisation in four dimensions, and (3) present methodological and technical insights on developing RtS with limited data. We argue that the evolution of conventional simulation models to fully-fledged hybrid DTs may necessitate a focus on a transitional stage; namely, RtS models primarily driven using historical distributions with limited real-time data feeds.