Band 16, Heft 5-6 - Selected papers from the International Food Operations & Processing Simulation Workshop; Guest Editors: Eleonora Bottani, Francesco Longo, Giuseppe Vignali

This paper presents the development and adoption of a discrete event simulation model of a pig meat-packing plant located in Navarre (Spain). The simulation model was developed to represent all the tasks and pig meat cuts production performed in the plant and implemented in ExtendSim ™ 9.2. The development was incremental as the whole model was made of different sub-models focused in different products as for example ham, ribbon or sirloin. The main utility of the proposed model was the economic assessment of pig meat processing and cutting production. Pietrain breed presented more homogeneity and a better performance than Large White breed at equal price of the same products. In addition, even the ham is the most important cut, the loin and the bacon showed the best relative economic value with 52–53 % and 44–45 %, respectively, depending on the breed.

This study proposes an advanced discrete-event simulation-based tool to support decision-making in the internal logistic design of a packaging line of a multinational brewery company. The selected software, Simio, allows emulating, advising and predicting the behavior of complex real-world systems. The simulation model provides a 3D interface that facilitates verification and validation. In this work, the designed model is used to understand the dynamic interactions between multiple factors and performance measures including both material-handling and inventory systems and to define necessary quantities and/or capacities of resources for a future can packaging line. Based on the proposed model, a what-if analysis is performed to determine inventory threshold values and other critical variables in order to optimize the configuration of internal logistics in potential scenarios.

This paper presents the first results of a large-scale-Agent-Based Simulation devoted to simulate individual behaviour inside a medium sized city (600,000 inhabitants). Humans are simulated as Intelligent Individual entities characterized by several attributes created from the Open Data available by means of a multi-layer approach. The work presented is divided into two main parts: the first part aims to describe the multi-layer approach adopted with the inclusion of the social network layer devoted to capture how social networks can be correlated with human activities and how an “Individual Opinion” can changes based on social interactions. The second part is devoted to present a preliminary case study for simulating the propagation dynamics of the individual opinion in the form of an ethical value function. The basic idea is to capture the changes in the individual opinion based on the social interactions predicted by the simulation. Finally, a food choice model for predicting individual choices based on the individual opinion function is presented; the model is based on three parameters: accessibility of ethical shops, price difference with standard products, and ethical value propagation.

The aim of this research paper is to develop a new framework for an information fractal to improve the food distribution network sustainability through two variables; Greenfield service constraints and minimum vehicle weight fill level on board. This paper applies the proposed framework to a hypothetical distribution network. Further, Supply Chain GURU Software is adapted to implement Greenfield analysis to identify the optimal number and location for setting up the new facilities through different Greenfield service constraints. A new Green Split Delivery-Vehicle Routing Problem also is developed to minimise CO 2 emission and implemented using the simulated annealing algorithm. The results revealed that the proposed dynamic control system has led to an enhancement in both collaboration and integration to decide upon the optimal number and location of distribution facilities as well as optimal vehicle weight fill levels to improve the sustainability throughout the food distribution chain.

Food supply chains are benefiting from blockchain technology, as it establishes a shared, secure record of information flows, thus reducing food safety risks, increasing consumers’ trust in products’ provenance and enhancing supply chain efficiency. However, despite some embryonic applications, systematic literature review reports very few investigation studies. This article proposes a potential design and update frequency of relevant data to be stored in the Ethereum blockchain for monitoring and traceability purposes and explores the cost connected to every transaction in the case of a fresh milk processing industry and supply chain, from dairy farms to the end consumers. Results show that (i) investments are limited for the supply chain actors; (ii) the benefits of a blockchain-enabled supply chain can be achieved with a minimal impact on the product’s consumer price, and (iii) the costs of operating the blockchain increases as we move down along the tiers of the supply chain.

Novel algorithm to determine the least cost formulation of a surimi blend was developed using linear programming (LP). Texture properties and the unit cost of surimi blend at the target moisture content were used as constraint functions and the objective function, respectively. The mathematical models to describe the moisture content dependence of the ring tensile properties were developed using critical moisture content, and the model parameters were used for the least cost LP (LCLP) model. The LCLP model successfully predicted the quality of surimi blend. Sensitivity analysis was used to obtain an additional information when the perturbations of design variables are provided. A standard procedure to determine the least cost formulation for blending surimi with varied moisture contents was systematically developed.

Alaska pollock (AP) surimi paste was prepared (0–3% salt and 76–84% moisture). The density, specific heat, and thermal conductivity were measured and modelled in temperatures between 25 and 90 °C ( R 2  > 0.92). The thermal diffusivity (α) function showed a strong dependence on the moisture content and a unique salt dependence at 84% of the moisture content and applied to the heat transfer simulation of surimi paste. The simulation model coupled with the empirical thermal properties accurately predicted the heat penetration curves during heating with RMSE values ranging from 0.43 to 1.22 °C. The salt dependence on thermal diffusivity was identified and modeled only at 84% moisture content. With a model for 84% moisture content, the RMSE value of 3% salt content decreased from 1.11 °C to 0.56 °C. This study demonstrated that an accurate prediction of the heat transfer of the surimi paste needs to be coupled with the nonlinear thermal diffusivity functions.

This study focuses on the convective drying of apple rings by air at ambient temperature. The lumped reaction engineering approach (REA) model has been employed to determine the rate of drying for apple rings under forced convection considered as a normal processing regime. The algorithm implies origination of the activation energy curve from accurate ‘reference’ measurements of the drying rate under natural convection conditions. Mass transfer coefficients for the apple ring samples required for the model implementation were obtained in a series of experiments under natural and forced conditions. These were compared to the values of mass transfer coefficients obtained from numerical simulation using COMSOL Multiphysics® software. The study extends the application of REA for the limiting simplest case of convective drying of thick samples under ambient temperature conditions.

The aim of this work is to evaluate several packaging and treatment processes for ricotta cheese in thermoformed trays. Shelf life has been evaluated considering microbiological aspects, colour variation and the quantity of Volatile Organic Compound (VOC). The first solution tested was Modified Atmosphere Packaging (MAP). Experimental tests were performed to evaluate not only the effect of a mixture of CO 2 , O 2 and N 2 in different percentages but also the deep of the trays (20, 30 or 40 mm). As a comparison, High Pressure Processing (HPP) at 6000 MPa for 3 min was applied to a second series of packaged ricottas. Microbiological results show that MAP preserves the product for 10 days, while HPP extends its shelf life for up to a month due to the lower levels of yeasts, moulds and lactic bacteria. In addition, the treatment preserves the ricotta's colour and VOCs.