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The vehicle routing problem (VRP) is critical for the successful execution of logistics activities. However, there is strong evidence that efficiently solving the VRP is often complicated and requires more powerful – and possibly intelligent – support tools. In accordance with this necessity, the present study proposes a decision support system (DSS) applicable to the VRP, which includes both initial planning and replanning phases to support the real-time operations.

The proposed DSS lies at the basis of resilience thinking to provide a capacity to absorb and withstand the impact of disruptions, where resilience is connected with the factors of preparedness, flexibility and redundancy. These factors are approached in this study through a number of operational strategies in the reactive and proactive modes. The DSS includes a multi-layer perceptron neural network to predict changes that may arise in dynamic contexts, a modified k-means clustering algorithm to group customers with both static and dynamic attributes and two mixed-integer programming models to produce primary and alternate routing plans.

The research is motivated by the operational challenges faced by a collaborative networked clinical laboratory, which seeks to enhance efficiency and productivity in the daily management of medical sample collection and delivery through the implementation of increased automation. The findings reveal that centralized planning leads to heightened vulnerability in route planning and increased costs for replanning. Furthermore, the effectiveness of resilience-enhancement strategies varies based on the source and level of uncertainty.

The contributions of this paper are as follows: incorporating resilience thinking into the operational planning of logistics services, managing the decision-making of transport and collection companies through a DSS framework to ensure proper support to real-time operations, addressing the clustered VRP in a dynamic setting and adopting forecasting approaches to cover possible sources of dynamism.

A plethora of studies focused on the cause and solutions for the bullwhip effect, and consequently many have successfully experimented to dampen the effect. However, the feasibility of such studies and the actual contribution for supply chain performance are yet up for debate. This paper aims to fill this gap by providing a holistic system-based perspective and proposes a fuzzy logic decision-making implementation for a single-product, three-echelon and multi-period supply chain system to mitigate such effect.

This study uses system dynamics (SD) as the central modeling method for which Vensim® is used as a tool for hybrid simulation. Further, the authors used MATLAB for undertaking fuzzy logic modeling and constructing a fuzzy inference system that is later on incorporated into SD model for interaction with the main supply chain structure.

This research illustrated the usefulness of fuzzy estimations based on experts’ linguistically and logically defined parameters instead of relying merely on the traditional demand forecasting based on time series. Despite the increased complexity of the calculations and structure of the fuzzy model, the bullwhip effect has been considerably decreased resulting in an improved supply chain performance.

This dynamic modeling approach is not only useful in supply chain management but also the model developed for this study can be integrated into a corporate financial planning model. Further, this model enables optimization for an automated system in a company, where decision-makers can adjust the fuzzy variables according to various situations and inventory policies.

This study presents a systemic approach to deal with uncertainty and vagueness in dynamic models, which might be a major cause in generating the bullwhip effect. For this purpose, the combination between fuzzy set theory and system dynamics is a significant step forward.