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Driven by motivation of quality enhancement and brand reputation promotion, automotive industries try to improve product quality and customer satisfaction by performing quality pilot programs continuously. The purpose of this paper is to develop a dynamic model to select the improvement quality pilot program from competitive candidates based on dynamic customers’ feedback.

An extended dynamic multi-criteria decision-making method is developed by embedding dynamic triangular fuzzy weighting average operators into fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and the novel evaluation indicator “ζ” is introduced to reflect prioritization performance.

The two evaluation indicators (Q and “ζ” ) assist quality managers to identify the best program with respect to multiple conflicting criteria and the best choice based on these two indexes shows high conformity. Besides, ranking sequences obtained by “ζ” can avoid the dilemma that there are several candidates with top priority calculated by comprehensive group utility value Q.

The dynamic MCDM method has been applied into the quality improvement procedure in Chinese domestic auto factories and contributes to highly efficient promotion.

Few dynamic models on pilot program selection for quality improvement based on dynamic customers’ feedback, this study deals with the dynamic promotion by an extended fuzzy VIKOR method and presents a case application.

The purpose of this paper is to provide a decision support framework for locations identification to address network design in the domain of disaster relief supply chains. The solution approach is then applied to a real-life case about Indonesia.

An approach integrating geographic information system technology and fuzzy analytical hierarchy process has been used.

For the Indonesian case, distribution centers should be located in Pekanbaru, Surabaya, Banjarmasin, Ambon, Timika, and Manado.

The main limitation of this work is that facilities being sited are incapacitated. Inclusion of constraints over capacity would elevate the framework to a further level of sophistication, enabling virtual pool of inventory that can be used to adsorb fluctuation in the demand due to disasters.

The use case provided in this paper shows a practical example of applicability for the proposed framework. This study is able to support worldwide decision makers facing challenges related with disaster relief chains resilience. In order to achieve efficiency and effectiveness in relief operations, strategic logistics planning in preparedness is key. Hence, initiatives in disaster preparedness should be enhanced.

It adds value to the previous literature on humanitarian logistics by providing a real-life case study as use case for the proposed methodology. It can guide decision makers in designing resilient humanitarian response, worldwide. Moreover, a combination of recommendations from humanitarian logistics practitioners with established models in facility location sciences provides an interdisciplinary solution to this complex exercise.

The purpose of this paper is to test the effectiveness of fuzzy time series (FTS) forecasting system in a supply chain experiencing disruptions and also to examine the changes in performance as the authors move across different tiers.

A discrete event simulation based on the popular beer game model is used for these tests. A popular ordering management system is used to emulate the behavior of the system when the game is played with human players.

FTS is tested against some other well-known forecasting systems and it proves to be the best of the lot. It is also shown that it is better to go for higher order FTS for higher tiers, to match auto regressive integrated moving average.

This study fills an important research gap by proving that FTS forecasting system is the best for a supply chain during disruption scenarios. This is important because the forecasting performance deteriorates significantly and the effect is more pronounced in the upstream tiers because of bullwhip effect.

Having a system which works best in all scenarios and also across the tiers in a chain simplifies things for the practitioners. The costs related to acquiring and training comes down significantly.

This study contributes by suggesting a forecasting system which works best for all the tiers and also for every scenario tested and simultaneously significantly improves on the previous studies available in this area.

A major component in managing pandemic outbreaks involves testing the suspected individuals and isolating them to avoid transmission in the community. This requires setting up testing centres for diagnosis of the infected individuals, which usually involves movement of either patient from their residence to the testing centre or personnel visiting the patient, thus aggregating the risk of transmission to localities and testing centres. The purpose of this paper is to investigate and minimize such movements by developing a drone assisted sample collection and diagnostic system.

Effective control of an epidemic outbreak calls for a rapid response and involves testing suspected individuals and isolating them to avoid transmission in the community. This paper presents the problem in a two-phase manner by locating sample collection centres while assigning neighbourhoods to these collection centres and thereafter, assigning collection centres to nearest testing centres. To solve the mathematical model, this study develops a mixed-integer linear programming model and propose an integrated genetic algorithm with a local search-based approach (GA-LS) to solve the problem.

Proposed approach is demonstrated as a case problem in an Indian urban city named Kolkata. Computational results show that the integrated GA-LS approach is capable of producing good quality solutions within a short span of time, which aids to the practicality in the circumstance of a pandemic.

The COVID-19 pandemic has shown that the large-scale outbreak of a transmissible disease may require a restriction of movement to take control of the exponential transmission. This paper proposes a system for the location of clinical sample collection centres in such a way that drones can be used for the transportation of samples from the neighbourhood to the testing centres.

Epidemic outbreaks have been a reason behind a major number of deaths across the world. The present study addresses the critical issue of identifying locations of temporary sample collection centres for drone assisted testing in major cities, which is by its nature unique and has not been considered by any other previous literature. The findings of this study will be of particular interest to the policy-makers to build a more robust epidemic resistance.