ABSTRACT
The crucial role of sustainable development and resiliency strategies is undeniable in today's competitive market space, especially after the Coronavirus outbreak. Hence, this research develops a multistage decision-making framework to investigate the supply chain network design problem considering the sustainability and resiliency dimensions. In this way, the scores of the potential suppliers based on the sustainability and resilience dimensions were calculated using the MADM methods, and then, these scores were applied as inputs in the proposed mathematical model (the second stage), which determined which supplier should be selected. The proposed model aims to minimize the total costs, maximize the suppliers' sustainability and resiliency, and maximize the distribution centers' resiliency. Then, the proposed model is solved by the preemptive fuzzy goal programming method. Overall, the main objectives and aims of the current work are to present a comprehensive decision-making model that can incorporate the sustainability and resilience dimensions into the supplier selection and supply chain configuration processes. In general, the main contributions and advantages of this work can be summarized as follows: (i) this research simultaneously investigates the sustainability and resiliency concepts in the dairy supply chain, (ii) the current work develops an efficient multistage decision-making model that can evaluate the suppliers based on the resilience and sustainability dimensions and configure the supply chain network, simultaneously. Based on the obtained results, the responsiveness and facilities reinforcement indicators are the most important indicators for the resilient aspect. On the other hand, reliability and quality are the most important indicators of sustainability aspect. Also, the results show that a large percentage of supply chain costs are related to purchasing and production costs. Besides, according to the outputs, the total cost of supply chain increases by enhancing the demand. Graphical Supplementary Information The online version contains supplementary material available at 10.1007/s10098-023-02538-8.
ABSTRACT
Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants.
ABSTRACT
The recent COVID-19 pandemic revealed that healthcare networks must have a flexible and effective structure. In this study, we develop a viable healthcare network design for a pandemic using a multi-stage stochastic approach. We propose a multi-level network that includes health centers, computed tomography scan centers, hospitals, and clinics. Patients have conditions to returning to normal life or quarantining at home. Three objectives are defined: maximizing the probability of patient recovery, minimizing the costs of all centers in the network, and minimizing the Coronavirus death rate. We investigate a real case study in Iran to demonstrate the model's applicability. Finally, we compare the healthcare supply chain network design in a pandemic with a normal situation to advise how the network can continue to remain viable.
ABSTRACT
The coronavirus (COVID-19) disease has caused serious and irreversible damage to the ecotourism industry, posing serious challenges to all parts of the ecotourism supply chain. The ecotourism supply chain is made up of various components, the most important of which are ecotourism centers. During these pandemic times, the primary concerns of these centers are to improve their deplorable economic conditions and retain customers for the post-coronavirus era. As a result, an investigation should be conducted to address these concerns and provide appropriate solutions to help them overcome the challenges that have emerged. To achieve the research goal, a bi-objective mathematical model for the ecotourism supply chain in an uncertain environment is developed, accounting for the effects of COVID-19. The first objective function minimizes the total cost of the supply chain, while the second maximizes customer satisfaction. The proposed mathematical model is solved using a fuzzy goal programming (FGP) method. A sensitivity analysis study is also carried out to examine the performance of some basic parameters. Furthermore, the model is tested in a real case study to determine its efficacy. Finally, some effective managerial insights are proposed to improve the situation of the centers during the pandemic.
ABSTRACT
The global epidemic caused by novel coronavirus continues to be a crisis in the world and a matter of concern. The way the epidemic has wreaked havoc on the international level has become difficult for the healthcare systems to supply adequately personal protection equipment for medical personnel all over the globe. In this paper, considering the COVID-19 outbreak, a multi-objective, multi-product, and multi-period model for the personal protection equipment demands satisfaction aiming to optimize total cost and shortage, simultaneously, is developed. The model is embedded with instances and validated by both modern and classic multi-objective metaheuristic algorithms. Moreover, the Taguchi method is exploited to set the metaheuristic into their best performances by finding their parameters' optimum level. Furthermore, fifteen test examples are designed to prove the established PPE supply chain model and tuned algorithms' applicability. Among the test examples, one is related to a real case study in Iran. Finally, metaheuristics are evaluated by a series of related metrics through different statistical analyses. It can be concluded from the obtained results that solution methods are practical and valuable to achieve the efficient shortage level and cost.
ABSTRACT
Obviously, the Covid-19 pandemic has huge impact on most businesses and has caused serious and countless problems for them. Therefore, providing solutions for affected businesses to recover and improve their activities during pandemic times is inevitable. In this regard, ecotourism centers are one of the businesses that went through this problem and have faced significant dilemmas in their activities. Also, reportedly, there is no related research focusing on the recovery approaches to address these obstacles relating to these kinds of businesses during the pandemic. Therefore, all of these exhorted us to do the current research. In this paper, some practical and useful action plans for ecotourism centers are firstly developed to help these businesses. To obtain the action plans, some brainstorming sessions were held consisting of tourism experts, university professors, managers, owners, and some personnel of eco-tourism centers. In order to prioritize the defined action plans, four criteria are considered. Firstly, we compute the weights of the considered criteria by the Fuzzy DEMATEL and then they are prioritized using the Fuzzy VIKOR. The findings of the current study divulge that the AP2 "Standardization of the centers" and AP3 "Estimating demand number and increasing the capacity" and AP7 "Identifying other natural tourist attractions of the region" have the highest and lowest priority to be implemented.
ABSTRACT
The rapid growth of the COVID-19 pandemic in the world and the importance of controlling it in all regions have made managing this crisis a great challenge for all countries. In addition to imposing various monetary costs on countries, this pandemic has left many serious damages and casualties. Proper control of this crisis will provide better medical services. Controlling travel and tourists in this crisis is also an effective factor. Hence, the proposed model wants to control the crisis by controlling the volume of incoming tourists to each city and region by closing the entry points of that region, which reduces the inpatients. The proposed multi-objective model is designed to aim at minimizing total costs, minimizing the tourist patients, and maximizing the number of city patients. The Improved Multi-choice Goal programming (IMCGP) method has been used to solve the multi-objective problem. The model examines the results by considering a case study. Sensitivity analyses and managerial insight are also provided. According to the results obtained from the model and case study, two medical centers with the capacity of 300 and 700 should be opened if the entry points are not closed.
ABSTRACT
The recent pandemic triggered by the outbreak of the novel coronavirus boosted the demand for medical services and protective equipment, causing the generation rate of infectious medical waste (IMW) to increase rapidly. Designing an efficient and reliable IMW reverse logistics network in this situation can help to control the spread of the virus. Studies on this issue are limited, and minimization of costs and the risks associated with the operations of this network consisting of different types of medical waste generation centers (MWGC) are rarely considered. In this research, a linear programming model with three objective functions is developed to minimize the total costs, the risk associated with the transportation and treatment of IMW, and the maximum amount of uncollected waste in MWGCs. Also, multiple functions that calculate the amount of generated waste according to the parameters of the current epidemic outbreak are proposed. Revised Multi-Choice Goal Programming method is employed to solve the multi-objective model, and a real case study from Iran is examined to illustrate the validation of the proposed model. The final results show that the model can create a balance between three considered objectives by determining the flow between centers, deciding to install two new temporary treatment centers, and allowing the network to only have uncollected waste in the first two periods in some MWGCs. Also, managerial insights for health organization authorities extracted from the final results and sensitivity analyses are presented for adequately handling the IMW network.