Volume 22, Issue 1 - Special Issue: Selected papers from the International Food Operations & Processing Simulation Workshop – Eighth edition; Guest Editors: Eleonora Bottani, Francesco Longo, and Giuseppe Vignali

As shared manufacturing emerges as a transformative paradigm in the industrial sector, it introduces complex resource allocation and scheduling challenges that traditional approaches struggle to address. Current platforms focus primarily on transaction management, lacking adequate mechanisms for evaluating production scenarios in multi-stakeholder environments. This research presents a novel simulation platform designed to support decision-making in shared manufacturing systems by enabling stakeholders to validate and optimize their production schedules through what-if analysis. The innovation lies in our adaptive simulation approach that dynamically reconfigures to represent diverse manufacturing scenarios without specialized modeling expertise. We demonstrate its application in the food manufacturing sector, where complex operational conditions create significant challenges for production planning and resource optimization. The results show that our platform effectively supports complex scheduling decisions while maintaining operational efficiency. This work contributes to the advancement of shared manufacturing by showing how simulation technology can facilitate the transformation of traditional manufacturing systems into flexible shared resources.

Virgin Coconut Oil (VCO) is an integral edible oil whose quality is heavily influenced by production methods. This study assessed the quality and safety of 12 VCO samples from various processors using a novel methodology. Samples underwent comprehensive sensory, chemical, microbial, and contaminant analyses at three separate laboratories. To identify the best VCO, an Analytic Hierarchy Process (AHP) was employed. This multi-criteria decision-making model used standards from the Asian and Pacific Coconut Community (APCC) as its benchmark. The evaluation ranked each producer across four categories: sensory attributes, food safety, composition and quality factors, and free fatty acids. The developed AHP framework provides a robust method for selectors, buyers, and consumers to make informed decisions. Furthermore, it allows high-quality producers to leverage this strategy for marketing, while encouraging others to identify areas for product improvement.

The study develops an empirically validated simulation framework to optimize urban food delivery through vehicle differentiation, multi-order management, and shortest-path routing. Using an empirical dataset from a medium-sized European city, three models are compared: single-order, greedy multi-order, and optimized multi-order delivery. Routing combines Dijkstra and Traveling Salesman Problem heuristics, with travel times predicted by a k-Nearest Neighbors model. The simulation closely reproduces observed operational patterns and shows that multiple-order delivery reduces average delivery time by 15 %–20 %, while the optimized model achieves an additional 8 % improvement. Outcomes indicate higher operational efficiency, better vehicle utilization, and reduced idle time. Overall, the approach supports both economic and environmental sustainability in last-mile logistics, offering a realistic benchmark for algorithmic coordination in heterogeneous delivery fleets offering better customer satisfaction.

In the short-food supply chain, planning the orders that the retailer places with the farmer is essential to ensure the satisfaction of the clients and the minimization of the costs for the retailer. Some of these retailers face a distinct set of challenges that, to the best of our knowledge, we are the first to tailor a model for. We integrate a mathematical model within a rolling horizon framework to address the supplier selection and order allocation problem, accounting for dynamic demand, production, and inventory capacities, as well as unit purchasing, ordering, and holding costs. Additionally, we perform a sensitivity analysis to understand the behavior of the model. The model is able to generate an ordering schedule given the demand of the clients and different parameters of suppliers. When integrated into the rolling horizon framework, the model could adapt to new information and modify previously planned, ordered, stored, and delivered quantities to meet the demand of the clients with minimal costs. When tested on instances of thirty suppliers with planning windows of length fifty time periods, the model had an average execution time of 3.5 seconds which we deemed acceptable.