Managing the resource allocation for the COVID-19 pandemic in healthcare institutions : a pluralistic perspective

vital:16949Purpose: As COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective. Design/methodology/approach: The present study has combined data analytics with the K-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By using K-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. After K-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions. Findings: After successful iterative training, the model is programmed using R functions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated. Research limitations/implications: Using probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition. Practical implications: The study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference. Originality/value: Further, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization. © 2021, Emerald Publishing Limited

Dynamic lot-sizing with short-term financing and external deposits for a capital-constrained manufacturer

The interface between operations and finance has recently been of interest to both practitioners and academics, who recognize that coordinating decisions from both functions provides a great opportunity to improve the performance of firms. However, the link between dynamic lot-sizing and financing decisions remains largely unexplored in the literature. This paper investigates a dynamic lot-sizing problem with short-term financing and external deposits for a capital-constrained manufacturer. In each period, the manufacturer finances its operations using a combination of internal cash, short-term loans, and short-term capital subscriptions from shareholders. Furthermore, the manufacturer decides on an amount to send to an external depository to take into consideration the opportunity cost of carrying cash. Two formulations for this integrated problem are presented, structural properties of the optimal solution are derived, and a dynamic programming algorithm is developed to solve the problem. Subsequently, the integrated problem, analysis, and solution procedure are extended to consider the case of a per-period loan limit. A numerical study is conducted to derive insights on the effect of financial and operational parameters, which are then validated using extensive computational experiments. Our results show that integration leads to significant savings, smoother production, and smaller capital subscriptions and external deposits. In addition, while the discount factor affects the number of setups, inventory levels, capital subscriptions, and external deposits, the short-term interest rate determines the choice between using internal cash or short-term loans from the lender

Hybrid manufacturing/remanufacturing lot-sizing and supplier selection with returns, under carbon emission constraint

International audienceThis paper addresses a lot-sizing problem in manufacturing/remanufacturing systems. The studied system is a single manufacturing line where both regular manufacturing and returns remanufacturing processes are carried out, with different set-up costs for each process. We consider also a returns collection phase from customers/distributors with deterministic returns quantities at each period of the planning horizon. The environmental aspect is assumed in this study by considering a carbon emission constraint for the manufacturing, remanufacturing and transportation activities. A mixed integer programming model to minimise the management cost and meet the customer’s needs under different manufacturing constraints is proposed. Otherwise, An adaptation of the well-known Silver and Meal (SM) heuristic and two hybrid method approaches (HM1 and HM2) providing approximates solutions are developed. The mixed integer model was tested on Cplex (Software optimizer), and the obtained results were compared with the ones provided by the adapted heuristic SM and the hybrid methods. The numerical analyses show that hybrid methods provide good-quality solutions in a moderate computational time. The proposed model establishes a collegial and an integrated process that sets values, goals, decisions and priorities along the considered supply chain while taking into account the environmental aspect

A quay crane productivity predictive model for building accurate quay crane schedules

The efficient management of maritime logistics operations improves the performance of global supply chains. An important issue in container terminal operations is the scheduling of quay cranes (QCs), which is affected by the productivity rate of QCs. This productivity rate depends on the type of tasks to be completed by QCs on any given vessel. In this paper, we propose an artificial neural network (ANN) model with a variable neighbourhood search (VNS) as a training algorithm to build a productivity rate predictive model. This model considers several predictors depending on the type of containers in the vessel and the expected equipment downtime. We also study how QC scheduling is impacted by the productivity rate of QCs. The proposed predictive model and a moving average model are used to estimate the productivity rate, which is then taken as input to the QC scheduling optimisation model. Results show that when the proposed predictive model is used, QC schedules are closer to those generated using real data obtained from our container terminal partner

Berth and quay crane allocation and scheduling with worker performance variability and yard truck deployment in container terminals

In a container terminal, the quay cranes (QCs) are the main equipment involved in the core activities. According to a bottleneck principle, the productivity rate of QCs is related to both worker productivity and deployed yard trucks. Yet, in the literature, the berth and quay cranes allocation and scheduling problems were addressed separately from these two parameters. In contrast, practitioners confirm their important impact on cranes productivity and efficiency. This paper proposes a new extension of the berth and quay cranes allocation and scheduling problems considering worker performance variability and yard truck deployment constraints. First, we formulate the problem as a mixed-integer linear program to minimize the vessels departure time under many practical regulations involving the work roster constraints specific to container terminals and the trucks utilization congestion targets. Additionally, the study provides a computationally efficient method to find a lower bound. We also show that integrative planning minimizes the vessels departure time compared to the separated decision making process. Second, to build good solutions in a reasonable time, we propose a heuristic and a Variable Neighborhood Search (VNS) with a new architecture and settings designed to suit the novel issues of the tackled problem. The algorithm is tested on real time data sets and outperforms a commercial solver. This study originates from our experience with a multinational company managing a container terminal. Thus, we have embedded the proposed resolving methods in a decision-support system for our industrial partner

Hybrid resolution approaches for dynamic assignment problem of reusable containers

In this study, we are interested in the reusing activities of reverse logistics. We focus on the dynamic assignment of reusable containers problem (e.g. gas bottles, beverages, pallets, maritime containers, etc.). The objective is to minimize the collect, reloading, storage and redistribution operations costs over a fixed planning horizon taking into account the greenhouse gas emissions. We present a new generic Mixed Integer Programming (MIP) model for the problem. The proposed model was solved using the IBM ILOG CPLEX optimization software; this method yield exact solutions, but it is very time consuming. So we adapted two hybrid approaches using a genetic algorithm to solve the problem at a reduced time (The second hybrid approach is enhanced with a local search procedure based on the Variable Neighborhood Search VNS). The numerical results show that both developed hybrid approaches generate high-quality solutions in a moderate computational time, especially the second hybrid method

Coupling the ILS optimisation algorithm and a simulation process to solve the travelling quay-crane worker assignment and balancing problem

In container terminals (CTs), the performance of quay cranes (QCs) is extremely impacted by their operators' productivity, which makes the QC worker assignment a relevant element in the QC scheduling. Nevertheless, in the literature, this problem was tackled without considering the objectives of balancing operators' workload and minimising the distance required to move between QCs, even though practitioners underline their importance in maintaining an efficient working environment. Accordingly, and based on a real-case study raised by a CT partner, this paper addresses a novel problem referred to as the travelling QC worker assignment and balancing problem. First, we propose a novel mathematical model with original constraints faced by the company to assign operators to QCs on a daily basis. Second, to find approximate solutions in a reasonable time, a constructive heuristic and an iterative local search (ILS) are proposed and tested on real datasets. We also propose a simulation-optimisation mechanism to evaluate the robustness of the solutions under real-time probabilistic perturbations. The proposed algorithms are integrated into a decision support tool, a network-linked application allowing users in a real CT to generate automatic assignments. The proposed simulation-optimisation procedure helps obtain balanced planning, avoid workload conflicts, and increase productivity

Dynamic Planning of Reusable Containers in a Close-loop Supply Chain under Carbon Emission Constrain

Nowadays, Companies need to collect and to deliver goods from and to their depots and their customers. Reusable containers are considered as a greener choice and a cost saving strategy. This paper addresses a dynamic management of reusable containers (e.g gases bottles, wood pallets, maritime containers, etc.) in a Closed-loop supply chain. The aim of the study is to find an optimal lot sizing and assignment strategy that minimizes the cost of reusable containers management under environmental constraint. In this contribution, a new integer-linear-programming model and two hybrid approaches based on the genetic algorithm are proposed to solve the problem. The second hybrid method is enhanced with a local search based on the VNS (variable neighborhood search). The numerical results show the performance of the two hybrid approaches in terms of solution quality and response time

Return multi sourcing lot-sizing problem in a hybrid manufacturing / remanufacturing system under carbon emission constraint

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