Order Assignment and Scheduling for Personal Protective Equipment Production During the Outbreak of Epidemics

This paper investigates a new multi-objective order assignment and scheduling problem for personal protective equipment (PPE) production and distribution during the outbreak of epidemics like COVID-19. The objective is to simultaneously minimize the total cost and maximize the PPE supply timeliness. For the problem, we first develop a bi-objective mixed-integer linear program (MILP). Then an epsilon-constraint combined with logic-based Benders decomposition method is proposed based on some explored properties. We then extend the proposed model to handle dynamics and randomness. In particular, we design a predictive reactive rescheduling approach to address random order arrivals and manufacturer disruptions. Computational experiments on a real case from China and 100 randomly generated instances are conducted. Results show that the proposed algorithm significantly outperforms an adapted epsilon-constraint method combined with the proposed MILP and the widely used non-dominated sorting genetic algorithm II (NSGA-II) in obtaining high-quality Pareto solutions. Note to Practitioners-The unprecedented outbreak of COVID-19 and its rapid spread caught numerous national and local governments unprepared. Healthcare systems faced a vital scarcity of PPEs. The urgency of producing and delivering PPEs increases as the number of infected cases rapidly increases. A key challenge in response to the epidemic is effectively and efficiently matching the demands and needs. Performing practical and efficient order assignment and scheduling for PPE production during the COVID-19 outbreak is critical to curbing the COVID-19 pandemic. This work first proposes a bi-objective mixed-integer linear program for optimal order assignment and scheduling for PPE production. The aim is to achieve an economical and timely PPE production and supply. A novel method that combines the epsilon-constraint framework and the logic-based Benders decomposition is proposed to yield high-quality Pareto solutions for practical-sized problems. Computational results indicate that the proposed approaches are practical and feasible, which can help decision-makers to perform acceptable order assignment and scheduling decisions.

Bi-objective Model for the Distribution of COVID-19 Vaccines

It is important to define optimal supply chain strategies that can respond to real vaccination needs in different disasters, especially in the event of a pandemic. The distribution of medicines and vaccines is more critical when they can decay and must arrive at their final destination as fast as possible. In this paper, to overcome these problems and respond to the pandemic of COVID-19 needs, we introduced a bi-objective model for the distribution of COVID-19 vaccines. The objectives are to minimize cost function and to minimize the maximum traveling time of the vaccines to treat targeted populations in different time phases. The bi-objective model is solved with the well-known multi-objective augmented epsilon-constraint method. Besides, we bring numerical results and the appliance of our proposed model. By solving the proposed model, we can find the optimal network of the vaccines and open needed facilities in several locations. Finally, we give the decision-maker several possible answers to choose according to his preferences. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.