Covid 19 vaccine order allocation: an optimization model with substitution

PurposeThis paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.Design/methodology/approachThe weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.FindingsCovid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.Originality/valueThis paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors' knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.

COVID-19 and Beyond: Implications on Supply Chain Network Design

[...]the longevity and relentlessness of the pandemic disruptions are much more damaging than other disruptions we have endured since the Second World War. [...]while the typical disruption affects only the supply side, the pandemic is simultaneously affecting the demand side. [...]how should firms reshape their supply chain networks and operations strategies to deal with uncertainties through the pandemic and beyond? [...]the impact of pandemic can be different from other typical disruptions as the global supply chain is obstructed upstream, downstream, and midstream.

Intelli-GATS: Dynamic Selection of the Wi-Fi Multicast Transmission Policy Using Interpretable-AI

COVID-19 has changed the way we use networks, as multimedia content now represents an even more significant portion of the traffic due to the rise in remote education and telecommuting. In this context, in which Wi-Fi is the predominant radio access technology (RAT), multicast transmissions have become a way to reduce overhead in the network when many users access the same content. However, Wi-Fi lacks a versatile multicast transmission method for ensuring efficiency, scalability, and reliability. Although the IEEE 802.11aa amendment defines different multicast operation modes, these perform well only in particular situations and do not adapt to different channel conditions. Moreover, methods for dynamically adapting them to the situation do not exist. In view of these shortcomings, artificial intelligence (AI) and machine learning (ML) have emerged as solutions to automating network management. However, the most accurate models usually operate as black boxes, triggering mistrust among human experts. Accordingly, research efforts have moved towards using Interpretable-AI models that humans can easily track. Thus, this work presents an Interpretable-AI solution designed to dynamically select the best multicast operation mode to improve the scalability and efficiency of this kind of transmission. The evaluation shows that our approach outperforms the standard by up to 38%.

Consumption Coupons, Consumption Probability and Inventory Optimization: An Improved Minimum-Cost Maximum-Flow Approach

The issuance of consumption coupons during the epidemic period to stimulate the economy must take full account of the level of probabilistic consumption and inventory optimization. In this paper, an improved minimum-cost maximum-flow model is constructed to dynamically adjust the inventory capacity of node enterprises with the change of probabilistic consumption level, and three scenarios are simulated by numerical assumptions. The results show that: (1) The model can better solve the problem of consumption coupons, probabilistic consumption and inventory optimization;(2) Consumer welfare remains unchanged, the largest number of government consumption coupons is issued, and the number of enterprise inventories reaches the lowest;(3) Enterprise inventories are minimized with different decisions on consumer probability consumption, and the government’s issuance of consumption coupons and the satisfaction of consumer demand have reached a dynamic balance. Corresponding suggestions are put forward, hoping to better help the government to implement the consumption coupons policy to stimulate the economy.

The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction

Different material supply-related decisions intensively affect the efficiency of manufacturers. To obtain a suitable supply-related protocol, this study proposes a supply selection model which considers both manufacturers’ development orientation and material ordering. In contrast to traditional approaches that rely on expert opinions, the proposed approach in this study allows the time series analysis (ARIMA) to forecast the trend in manufacturers’ development during the execution of the plan. Based on the predicted trend, taking the minimum of total material management cost as the objective function, the control function and optimization conditions are constructed to select the appropriate protocol. The dynamic prediction protocol is obtained by considering the variation in production and material costs by an evolutionary algorithm. The model enables users to determine material supply protocol in continuous time and autonomously adapt to changes in the manufacturers’ production goals within a lower convergence time.

A prescriptive framework to support express delivery supply chain expansions in highly urbanized environments

Purpose>With the proliferation of e-commerce companies, express delivery companies must increasingly maintain the efficient expansion of their networks in accordance with growing demands and lower margins in a highly uncertain environment. This paper provides a framework for leveraging demand data to determine sustainable network expansion to fulfill the increasing needs of startups in the express delivery industry.Design/methodology/approach>While the literature points out several hub assignment methods, the authors propose an alternative spherical-clustering algorithm for densely urbanized population environments to strengthen the accuracy and robustness of current models. The authors complement this approach with straightforward mathematical optimization and simulation models to generate and test designs that effectively align environmentally sustainable solutions.Findings>To examine the effects of different degrees of demand variability, the authors analyzed this approach's performance by solving a real-world case study from an express delivery company's primary market. The authors structured a four-stage implementation framework to facilitate practitioners applying the proposed model.Originality/value>Previous investigations explored driving distances on a spherical surface for facility location. The work considers densely urbanized population and traffic data to simultaneously capture demand patterns and other road dynamics. The inclusion of different population densities and sustainability data in current models is lacking;this paper bridges this gap by posing a novel framework that increases the accuracy of spherical-clustering methods.

Self-Organizing Networks for 5G and Beyond: A View from the Top

We describe self-organizing network (SON) concepts and architectures and their potential to play a central role in 5G deployment and next-generation networks. Our focus is on the basic SON use case applied to radio access networks (RAN), which is self-optimization. We analyze SON applications’ rationale and operation, the design and dimensioning of SON systems, possible deficiencies and conflicts that occur through the parallel operation of functions, and describe the strong reliance on machine learning (ML) and artificial intelligence (AI). Moreover, we present and comment on very recent proposals for SON deployment in 5G networks. Typical examples include the binding of SON systems with techniques such as Network Function Virtualization (NFV), Cloud RAN (C-RAN), Ultra-Reliable Low Latency Communications (URLLC), massive Machine-Type Communication (mMTC) for IoT, and automated backhauling, which lead the way towards the adoption of SON techniques in Beyond 5G (B5G) networks.

Manufacturing for the Masses: A Novel Concept for Consumer 3D Printer Networks in the Context of Crisis Relief

Local or national crises, such as natural disasters, major infrastructure failures, and pandemics, pose dire threats to manufacturing. The concept of a rideshare‐like distributed network of consumer‐type 3D printers is proposed to address the limited ability of the industrial base to quickly respond to abrupt changes in critical product demand or to disruptions in manufacturing and supply‐chain capacity. The technical challenges that prevent the implementation of such a network are discussed, including 1) remote qualification of 3D printers, 2) dynamic routing algorithms with reactive and predictive components, which take advantage of real‐time information about current events that may affect the network, and 3) performance evaluation of the network. Furthermore, a cyber‐infrastructure that enables autonomous operation and reconfiguration of the network to render it “crisis‐proof” by minimizing human involvement is introduced. The concept of a distributed network of consumer‐type 3D printers allows anyone with a 3D printer and access to the internet to manufacture critical supplies, triggered by actual and predicted customer demand. Beyond crisis relief, distributed networks of manufacturing assets have broad relevance, and they can establish a virtual marketplace to exchange manufacturing capacity. Thus, this future manufacturing platform has the potential to transform how to manufacture for the masses.