ABSTRACT
In recent times, COVID-19 pandemic has posed certain challenges to transportation companies due to the restrictions imposed by different countries during the lockdown. These restrictions cause delay and/ or reduction in the number of trips of vehicles, especially, to the regions with higher restrictions. In a pandemic scenario, regions are categorized into different groups based on the levels of restrictions imposed on the movement of vehicles based on the number of active cases (i.e., number of people infected by COVID-19), number of deaths, population, number of COVID-19 hospitals, etc. The aim of this study is to formulate and solve a fixed-charge transportation problem (FCTP) during this pandemic scenario and to obtain transportation scheme with minimum transportation cost in minimum number of trips of vehicles moving between regions with higher levels of restrictions. For this, a penalty is imposed in the objective function based on the category of the region(s) where the origin and destination are situated. However, reduction in the number of trips of vehicles may increase the transportation cost to unrealistic bounds and so, to keep the transportation cost within limits, a constraint is imposed on the proposed model. To solve the problem, the Genetic Algorithm (GA) has been modified accordingly. For this purpose, we have designed a new crossover operator and a new mutation operator to handle multiple trips and capacity constraints of vehicles. For numerical illustration, in this study, we have solved five example problems considering three levels of restrictions, for which the datasets are generated artificially. To show the effectiveness of the constraint imposed for reducing the transportation cost, the same example problems are then solved without the constraint and the results are analyzed. A comparison of results with existing algorithms proves that our algorithm is effective. Finally, some future research directions are discussed.
ABSTRACT
Logistics problems play a significant role in an emergency situation. During and after a critical circumstance (like pandemic COVID-19), it is an important task to active the opened- and closed-loop system through an efficient and resilient supply chain network. This paper considers a multi-objective multi-product multi-period two-stage sustainable opened- and closed-loop supply chain planning to maintain supply among production centers and various hospitals during COVID-19 pandemic situation. To build a less contagious network, transportation problem and pick-up-delivery vehicle routing problem are designed as two stages, respectively to carry out distribution. We allow a mixed uncertain environment by considering uncertain-random parameters in the proposed model to express ambiguity in real-life data. A multi-attribute decision making approach is suggested to determine the priorities of affected areas, according to their urgency in terms of entropy weights. Moreover, a robust optimization approach for uncertain-random parameter is developed to cope with uncertainty in different scenarios, and thereafter augmented weighted Tchebycheff method is applied to solve the model. To demonstrate the practicability of the proposed model and solving approach, three test problems with reasonable sizes are considered and results are discussed through some sensitivity analyses.