An Introduction to the Special Issue "Recent advances on supply chain network design"

Discussions of the resiliency, sustainability, and agility of supply chains are important in the research and management of supply chains in these difficult times, considering the ongoing pandemic of COVID-19. A viable supply chain is often characterized by resiliency, sustainability, and agility in its network design. Resiliency is essential because disruption and demand fluctuations are forced upon SCs, and the effects of these for many managerial supply chains are unknown. In addition, applying novel technology in the supply chain, such as blockchain, Internet-of-Things (IoT), and artificial intelligence (AI) as agility tools can assist and enable the transition to lean production. This special issue of the Foundations of Computing and Decision Sciences is dedicated to advancements in this fields. Besides, the special issue covers instructional information about OR techniques which are useful for addressing real-world applications on such challenges.

Robust optimization of risk-aware, resilient and sustainable closed-loop supply chain network design with Lagrange relaxation and fix-and-optimize

This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 pandemic. A two-stage robust stochastic multiobjective programming model serves to express the proposed problems in formulae. The objective functions include minimising costs, CO2 emissions, energy consumption, and maximising employment by applying Conditional Value at Risk (CVaR) to achieve reliability through risk reduction. The Entropic Value at Risk (EVaR) and Minimax method are used to compare with the proposed model. We utilise the Lp-Metric method to solve the multiobjective problem. Since this model is complex, the Lagrange relaxation and Fix-and-Optimise algorithm are applied to find lower and upper bounds in large-scale, respectively. The results confirm the superior power of the model offered in estimating costs, energy consumption, environmental pollution, and employment level. This model and algorithms are applicable for other CLSC problems.

On Building Two-Player Games with Treatment Schedules for the SIR Model

In this study, a treatment argument is provided as a discrete two-player game related to an epidemiological dynamics, so-called, Susceptible- Infectious-Recovered (SIR) model. Here, a simple discrete version of the dynamics of SIR model is considered within a treatment structure in such a way to control the behaviour of each candidate: population of the susceptible, infected and recovered people, respectively. In this regard, several two-player game models are proposed, where one player follows its own existed policy where as the other tries to track its opponent's treatment schedule as close as possible. In this regard, different strategies are built for one player to catch the other in a two-player game environment, where one player determines the total number of susceptible or infected people at a given period, in the meantime, the other tries to build its corresponding treatment policy to get closer to its opponent's counting schedule. The main contribution of this work is to build a better treatment schedule by using a game theoretical point of view to cure the population suffered from an infectious disease. At the end, the work is related to pursuer-evasion discrete games and the idea could be implemented on compartmental models like COVID-19 and transportation problems.

On building two-player games with treatment schedules for the sir model

In this study, a treatment argument is provided as a discrete two-player game related to an epidemiological dynamics, so-called, Susceptible-Infectious-Recovered (SIR) model. Here, a simple discrete version of the dynamics of SIR model is considered within a treatment structure in such a way to control the behaviour of each candidate: population of the susceptible, infected and recovered people, respectively. In this regard, several two-player game models are proposed, where one player follows its own existed policy where as the other tries to track its opponent’s treatment schedule as close as possible. In this regard, different strategies are built for one player to catch the other in a two-player game environment, where one player determines the total number of susceptible or infected people at a given period, in the meantime, the other tries to build its corresponding treatment policy to get closer to its opponent’s counting schedule. The main contribution of this work is to build a better treatment schedule by using a game theoretical point of view to cure the population suffered from an infectious disease. At the end, the work is related to pursuer-evasion discrete games and the idea could be implemented on compartmental models like COVID-19 and transportation problems. © 2010 AZJM All rights reserved.

Editorial – Preface to the special issue on numerical techniques meet with OR

This special issue of the Foundations of Computing and Decision Sciences, titled ”Numerical Techniques Meet with OR”, is devoted to the numerical techniques and their applications in real-world phenomena. The special issue and its editorial present numerical algorithms as they meet with different research topics such as, e.g., from operational research, supply chain management, geometrical structures and Covid-19 effects on financial applications. Besides, the special issue covers instructional information about numerical techniques which are useful for OR research problems and real-world applications on such issues. © 2021 Sciendo. All rights reserved.