Stochastic optimization of operational production planning for fisheries

• A stochastic operational production planning model for fisheries is developed.
• The model incorporates raw material supply and product market price uncertainties.
• The economic value of super-chilling and incorporated uncertainties is estimated.
• The stochastic formulation improves robustness of the obtained production plans.
• Super-chilling yields greater flexibility and profitability for the facility.

The fishing industry is one of the main contributors to the national economy, value creation, and employment in Norway. Furthermore, it is a significant source of export incomes. The fishing industry is also a well-known arena for applying operations research methodology. Traditionally divided in three main parts, the works within this area have dealt with fish stock and harvesting, fish processing, and marketing. Recently, the focus has shifted to integrated planning, where fishing fleet operations are combined with plant processing. Currently, a broader view of the supply chain needs to be adopted as many companies in this industrial sector are striving to improve their capacity utilizations, operational efficiency, and profitability. Thus, both upstream and downstream uncertainties have to be handled. While it has been recognized that decision flexibility can be used to manage supply chain uncertainty, no known stochastic modeling formulations have explicitly accounted for it in fish processing.To address the described planning challenges, this paper develops an integral stochastic model, incorporating both upstream (raw material quantities) and downstream (finished goods market prices) uncertainties, while accounting for fish quality deterioration and shelf-life restrictions. It is then tested, estimating the potential economic value of flexibility in the supply chain provided by the introduction of super-chilling technologies and application of the described stochastic formulation. This way, it reflects a triangulation of technological development, operational efficiency, and market profitability. Thus, it is a unique opportunity to address the real-world complexity and enhance the body of knowledge in operations research.