Giada La Scalia

A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding

This paper proposes a new multi objective genetic algorithm (MOGA) for solving unequal area facility layout problems (UA-FLPs). The genetic algorithm suggested is based upon the slicing structure where the relative locations of the facilities on the floor are represented by a location matrix encoded in two chromosomes. A block layout is constructed by partitioning the floor into a set of rectangular blocks using guillotine cuts satisfying the areas requirements of the departments. The procedure takes into account four objective functions (material handling costs, aspect ratio, closeness and distance requests) by means of a Pareto based evolutionary approach. The main advantage of the proposed formulation, with respect to existing referenced approaches (e.g. bay structure), is that the search space is considerably wide and the practicability of the layout designs is preserved, thus improving the quality of the solutions obtained.

Simulation analysis of cold chain performance based on time–temperature data

Perishable goods are a fundamental source of revenue for the retail sector; their management, however, constitutes a severe challenge for retailers and supply chain partners. A significant cost in particular is the fraction of products perished through the supply chain, which also constitutes an ethical and environmental concern. Supply chain organisation and operative characteristics have a significant influence on this matter, as in fact ensuring suitable temperature conditions for the stock-keeping units throughout the supply chain is mandatory for perishable products. Recent developments in sensing and communication technologies allow detailed monitoring and control of cold chain; however, depending on the characteristics of the supply chain, an inherent risk of perished products is often inevitable, even in the hypothesis of perfect control. This article proposes a methodology to evaluate the performance of a cold chain in terms of expected product quality at the retail store, and to estimate the expected fraction of perished products, according to the supply chain configuration. The approach is based on Monte Carlo simulation, and implements referenced shelf-life models. A real application is also presented, involving a preliminary analysis and mapping of the supply chain activities based on time–temperature data, in order to demonstrate the practicability of the approach proposed.

A non dominated ranking Multi Objective Genetic Algorithm and electre method for unequal area facility layout problems

The unequal area facility layout problem (UA-FLP) comprises a class of extremely difficult and widely applicable optimization problems arising in diverse areas and meeting the requirements for real-world applications. Genetic Algorithms (GAs) have recently proven their effectiveness in finding (sub) optimal solutions to many NP-hard problems such as UA-FLP. A main issue in such approach is related to the genetic encoding and to the evolutionary mechanism implemented, which must allow the efficient exploration of a wide solution space, preserving the feasibility of the solutions and ensuring the convergence towards the optimum. In addition, in realistic situations where several design issues must be taken into account, the layout problem falls in the broader framework of multi-objective optimization problems. To date, there are only a few multi-objective FLP approaches, and most of them employ over-simplified optimization techniques which eventually influence the quality of the solutions obtained and the performance of the optimization procedure. In this paper, this difficulty is overcome by approaching the problem in two subsequent steps: in the first step, the Pareto-optimal solutions are determined by employing Multi Objective Genetic Algorithm (MOGA) implementing four separate fitness functions within a Pareto evolutionary procedure, following the general structure of Non-dominated Ranking Genetic Algorithm (NRGA) and the subsequent selection of the optimal solution is carried out by means of the multi-criteria decision-making procedure Electre. This procedure allows the decision maker to express his preferences on the basis of the knowledge of candidate solution set. Quantitative and qualitative objectives are considered referring to the slicing-tree layout representation scheme. The numerical results obtained outperform previous referenced approaches, thus confirming the effectiveness of the procedure proposed.

Multi-Criteria Decision Making support system for pancreatic islet transplantation

Pancreatic islet transplantation consists of replacing insulin-producing cells to restore normal glycemia in diabetic patients. This is a minimal invasive procedure that has been proved successful. Unfortunately unpredictability of islet transplant outcome remains a frustrating and costly issue limiting the clinical implementation of this procedure. Multiple variables are involved in the procedure and assessment is subjective to individual operators. The aim of this study was to generate a system expressing the probability of transplant success in relation to four classes of identified variables (donor, organ, isolation and recipient). We have proposed the utilization of Multi-Criteria Decision Making methods (MCDM) as a powerful tool for evaluating pancreatic islet transplant-related information with the goal to achieve optimal decision. Technique Ordered Preference by Similarity to the Ideal Solution (TOPSIS), one of the most widely used MCDM methods in decision support systems, was here utilized with modification to fit better in a medical system. In our modified method, we have utilized fuzzy logic in order to consider uncertain and vague data.

A webGIS-based system for real time shelf life prediction

Technologies to acquire and monitor changes in the product shelf life were described.Four different SL predictive models were considered.A simulated transport using a prototype of Smart Logistic Unit was analysed.Real time shelf life prediction system was implemented in a webGIS platform. Shelf-life (SL) prediction and Least Shelf-life First Out (LSFO) stock strategy are important factors in perishable food supply chain in order to reduce quality and economic losses.In particular, distribution represents one of the main critical phases in logistic chain management and only the introduction of monitoring procedure can allow a reduction in food losses. Literature shows several mathematical models for analysing the changes in food quality using environmental/product parameters. However, these models can be very useful decision support tools only if the abovementioned parameters can be processed in real time.This paper focuses on technologies and applications to acquire and monitor changes in product shelf life using different SL predictive models. The paper includes a case study on a simulated transportation of strawberries, using a prototype of Smart Logistic Unit equipped with a GPS module for real time positioning of the truck, a 3G connection as communication system for remote quality supervision, and a shelf life prediction algorithm based on four different models and implemented on a webGIS platform.

Solving type-2 assembly line balancing problem with fuzzy binary linear programming

This paper deals with the use of fuzzy set theory as a viable alternative method for modelling and solving the stochastic assembly line balancing problem. This paper presents a fuzzy extension of the simple assembly line balancing problem of type 2 SALBP-2 with fuzzy job processing times since uncertainty, variability, and imprecision are often occurred in real-world production systems. The job processing times are formulated by triangular fuzzy membership functions using their statistical distributions. This study proposes to solve a Fuzzy Binary Linear Problem FBLP with fuzzy coefficients in the objective function and in a constraint. Finally, the effect of the unbalancing of a station in a real case study has been investigated in order to consider different scenarios. In particular, four different scenarios were studied considering the balanced and unbalanced line with deterministic and stochastic job processing times. The results obtained showed that the use of a fuzzy approach allows to take in account the effect of the job processing time variability demonstrating the validity of the proposed model.

An expert system for vineyard management based upon ubiquitous network technologies

Vineyard operations for quality wines production are currently based upon costly and time-consuming manual sampling operations required to assess the maturity phases of grapevines. The ripening process however is significantly influenced by the environmental parameters which nowadays can be effectively monitored by means of ubiquitous computing technologies. Besides the possibility of gathering data, hence, suitable tools are required to support the vineyard management process. The present research concerns the development of an expert system to effectively manage the vineyard operations. The methodology is based on the analysis of the time series of indices related to the maturation phases by means of referenced growth models, and on the prediction of the achievement of maturation thresholds. The paper also reports the results of an experimental study on a Sicilian vineyard.

Predictive shelf life model based on RF technology for improving the management of food supply chain: A case study

The aim of this paper was the development of a Smart Logistic Unit (SLU) based on RF technology to support the management of the food supply chain, in order to guarantee the shelf life of products in agreement with logistic efficiency and system sustainability. For this purpose, the main parameters that influence the quality of perishable products were determined and a shelf life equation based on Volatile Organic Compounds (VOCs) was modelled. The levels of VOCs were gathered by the sensors allocated inside the SLU, which configures as the remote element of a system for identification and data transmission. The proposed model was then validated through an experimental test, simulating the real life transport conditions.

Preliminary analysis of warehouse localization systems based on RFID technology

In the industrial warehouse design domain, todays computerized information systems have gained a relevant interest due to the issues related to asset tracking and traceability. In such context, the employment of new information technologies on warehouse management systems has opened new business opportunities due to the sensible price reduction in the last years. This paper focuses on the technical issues related to the realization of wireless localization systems for warehouses, which are necessary for the implementation of random allocation policies, and in particular investigates the opportunities offered by RFID technology in such context. The enforcement of random allocation policies allows to increase the utilization coefficient of warehouses which is a critical issue, for example in refrigerated warehouses for perishable products. In particular, the development of localization systems based on trilateration is here considered, and an experimental model which links the received signal strength to the reading distance has been determined in order to take into account the specific features of the technology employed. A methodology is hence proposed to overcome the simplifying assumptions theoretical attenuation models rely on, by fitting a mathematical model on experimental observations. The problem of the accuracy level required to locate Stock Keeping Units (SKUs) of fixed dimensions is also considered, resulting in the evaluation of the maximum allowed reading distance. On the basis of the obtained results, the optimal configuration of the fixed infrastructure in terms of the number of antennas and their position is finally defined.

Standardization of islet isolation outcome - A new automatic system to determine pancreatic islet viability

Pancreatic islet transplantation is emerging as a therapeutic approach for patients affected by diabetes. This approach consists of a minimally invasive procedure replacing insulin-producing cells (pancreatic islets). The technique has been proven successful, but limitations have been identified. One of the major challenges of the procedure is the counting of the isolated pancreatic islets, which is currently jeopardized by subjectivity and inaccuracy. Determination of the accurate islet number is a crucial factor in determining the correlation between the isolation product and clinical outcome. In the proposed study, we have developed software capable of objectively evaluating islet numbers and other viability variables by image analysis. This software is based on image processing and feature extraction algorithms for recognition of the area of interest. This is the first step toward standardization of the isolation outcome and potential clinical success predictability.