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Magnetic fluid hyperthermia experiment requires a uniform magnetic field in order to control the heating rate of a magnetic nanoparticle fluid for laboratory tests. The automated optimal design of a real-life device able to generate a uniform magnetic field suitable to heat cells in a Petri dish is presented. The paper aims to discuss these issues.

The inductor for tests has been designed using finite element analysis and evolutionary computing coupled to design of experiments technique in order to take into account sensitivity of solutions.

The geometry of the inductor has been designed and a laboratory prototype has been built. Results of preliminary tests, using a previously synthesized and characterized magneto fluid, are presented.

Design of experiment approach combined with evolutionary computing has been used to compute the solution sensitivity and approximate a 3D Pareto front. The designed inductor has been tested in an experimental set-up.

In electrochemotherapy, flexible electrodes, composed by an array of needles, are applied to human tissues to treat large surface tumors. The positioning of the needles in the tissue depends on the surface curvature. The parallel needle case is preferred, as their relative inclinations strongly affect the actual distribution of electric field. Nevertheless, in some case, small inclinations are unavoidable. The purpose of this paper is to study the electric field distribution for non-parallel needles.

The effect of electrode position is evaluated systematically by means of numerical models and experiments on phantoms for two different angles (5° and 30°) and compared with the case of parallel needles. Potato model was used as phantom, as this tissue becomes dark after few hours from electroporation. The electroporation degree was gauged from the color changings on the potatoes.

The distribution of electric field in different needle configuration is found by means of finite element analysis (FEA) and experiments on potatoes. The electric field level of inclined needles was compared with parallel needle case. In particular, the electric field distribution in the case of inclined needles could be very different with respect to the one in the case of parallel needles. The degree of enhancement for different inclinations is visualized by potato color intensity. The FEA suggested that the needle parallelism has to be maintained as possible as if the tips are closer to each other, the electric field intensity could be different with respect to the one in the case of parallel needles.

This paper analyzes the effect of inclined electrodes considering also the non-linearity of tissues.

This paper aims to present the optimal design of an inductor used to heat a magnetic nanoparticle fluid injected in a cell culture inside a Petri dish.

The inductor design is driven by means of a multi-objective optimization algorithm that generalizes the migration-non-dominated sorting genetic algorithm (NSGA); it is called self-adapting migration-NSGA.

The optimized device is able to synthesize a uniform magnetic field in a nanoparticle fluid, substantially helping its heating capability. The ultimate scope is to assist the cancer therapy based on magnetic fluid hyperthermia (MFH).

The optimal design of an inductor for MFH applications has been carried out by applying an improved version of migration-based NSGA-II algorithm including automatic stop and a self-adapting concept. The modified optimization algorithm is suitable to find better optimal solutions with respect to a standard version of NSGA-II.

The purpose of this paper is to present a modified version of the non-dominated sorted genetic algorithm with an application in the design optimization of a power inductor for magneto-fluid hyperthermia (MFH).

The proposed evolutionary algorithm is a modified version of migration-non-dominated sorting genetic algorithms (M-NSGA) that now includes the self-adaption of migration events- non-dominated sorting genetic algorithms (SA-M-NSGA). Moreover, a criterion based on the evolution of the approximated Pareto front has been activated for the automatic stop of the computation. Numerical experiments have been based on both an analytical benchmark and a real-life case study; the latter, which deals with the design of a class of power inductors for tests of MFH, is characterized by finite element analysis of the magnetic field.

The SA-M-NSGA substantially varies the genetic heritage of the population during the optimization process and allows for a faster convergence.

The proposed SA-M-NSGA is able to find a wider Pareto front with a computational effort comparable to a standard NSGA-II implementation.

The purpose of this paper is to develop a quantitative model to assess probability of errors and errors correction costs in parts feeding systems for assembly lines.

Event trees are adopted to model errors in the picking-handling-delivery-utilization of materials containers from the warehouse to assembly stations. Error probabilities and quality costs functions are developed to compare alternative feeding policies including kitting, line stocking and just-in-time delivery. A numerical case study is included.

This paper confirms with quantitative evidence the economic relevance of logistic errors (LEs) in parts feeding processes, a problem neglected in the existing literature. It also points out the most frequent or relevant error types and identifies specific corrective measures.

While the model is general purpose, conclusions are specific to each applicative case and are not generalizable, and some modifications may be required to adapt it to specific industrial cases. When no experimental data are available, human error analysis should be used to estimate event probabilities based on underlying modes and causes of human error.

Production managers are given a quantitative decision tool to assess errors probability and errors correction costs in assembly lines parts feeding systems. This allows better comparing of alternative parts feeding policies and identifying corrective measures.

This is the first paper to develop quantitative models for estimating LEs and related quality cost, allowing a comparison between alternative parts feeding policies.

Artificial intelligence (AI) can help in defining preventive strategies in taking decisions in complex situations. This paper aims to research how workers might deal with intervening AI tools, with the goal of improving their daily working decisions and movements. We contribute to deepening how workers might deal with intervening AI tools aiming at improving their daily working decisions and movements. We investigate these aspects within a field, which is growing in importance due to environmental sustainability issues, i.e. waste management (WM).

This manuscript intends to (1) investigate if AI allows better performance in WM by reducing social security costs and by guaranteeing a better continuity of service and (2) examine which structural change is required to operationalize this predictive risk model in the real working context. To achieve these goals, this study developed a qualitative inquiry based on face-to-face interviews with highly qualified experts.

There is a positive impact of AI schemes in helping to detect critical operating issues. Specifically, AI potentially represents a tool for an alignment of operational behaviours to business strategic goals. Properly elaborated information, obtained through wearable digital infrastructures, allows to take decisions to streamline the work organization, reducing potential loss due to waste of time and/or physical resources.

Being a qualitative study, and the limited extension of data, it is not possible to guarantee its replication and generalizability. Nevertheless, the prestige of the interviewees makes this research an interesting pilot, on such an emerging theme as AI, thus eliciting stimulating insights from a deepening of information coming from respondents’ knowledge, skills and experience for implementing valuable AI schemes able to an align operational behaviours to business strategic goals.

The most critical issue is represented by the “quality” of the feedback provided to employees within the business environment, specifically when there is a transfer of knowledge within the organization.

The study focuses on a less investigated context, the role of AI in internal decision-making, particularly, for what regards the interaction between managers and workers as well as the one among workers. Algorithmically managed workers can be seen as the players of summarized results of complex algorithmic analyses offered through simpleminded interfaces, which they can easily use to take good decisions.

The purpose of this paper is to develop analytical planning models to compare just-in-time (JIT) delivery and line storage (LS) alternatives for a continuous supply of materials to assembly lines.

A mathematical model is developed to size resources and to determine total system costs.

The choice of assembly lines feeding policy requires a thorough economic comparison of alternatives. However, the existing models are often simplistic, neglecting many critical factors which affect the systems’ performances. As a consequence, industries are unsure about which system is best for their environment. This model allows to compare the cost and suitability of two major continuous-supply alternatives in any specific industrial setting. Results of the model application are case-specific and cannot be generalized.

The model is aimed at single-model assembly lines operating in a deterministic environment. Although relevant quantitative cost drivers are included, some context-related qualitative factors are not yet included. The model assumes that the information about product structure and part requirements is known and that a preliminary design of the assembly system has been carried out.

Production managers are given a quantitative decision tool to properly assess the implementation of continuous material supply policies at an early decision stage, and determine which option is the best, also allowing to explore trade-offs between the alternatives.

With respect to previous simplified literature models, this new approach allows to quantify a number of additional factors which are critical for the successful implementation of cost-effective continuous-supply systems, including error costs. No other direct comparison of LS and JIT is available in the literature.

The purpose of this paper is to develop an optimization model allowing the choice of parts feeding policy to assembly lines in order to minimize total cost.

An integer linear programming mathematical model is developed to assign the optimal material feeding policy to each part type. The model allows choice between kitting, line stocking and just in time delivery policies.

The choice of assembly lines feeding policy is not trivial and requires a thorough economic comparison of alternatives. It is found that a proper mix of parts feeding policies may be better that adopting a single material delivery policy for all parts.

The model is aimed at single-model assembly lines operating in a deterministic environment, but can be extended to the multi-model line case. While relevant quantitative cost drivers are included, some context-related qualitative factors are not included yet. The model assumes that information about product structure and part requirements are known and that a preliminary design of the assembly system has been carried out.

Production managers are given a quantitative-decision tool to determine the optimal mix of material supply policies at an early decision stage.

Respect previous simplified literature models, this approach allows to quantify a number of additional factors which are critical for successful implementation of cost-effective parts feeding systems, allowing comparison of alternative policies on a consistent basis.

The aim of this paper is to develop a detailed descriptive model for kitting operations, allowing resources sizing and computation of systems’ economic performances.

A mathematical model allows to size resources, given product characteristics and production mix, and determines total system costs by assessing relevant cost items including investment costs (vehicles, containers, storage racks), direct operating costs (transport and kitting workforce, vehicles energy consumption and maintenance, quality costs), indirect operating costs (space requirements, work in process (WIP) and safety stock holding costs, administration and control).

The choice of parts delivery supply to assembly lines requires a thorough economic comparison of alternatives. However, existing models are often simplistic and neglect many critical factors which affect the systems’ performances. As a consequence, industries are unsure about which system is best for their environment. This model allows assessment of the cost and suitability of kitting in any specific industrial setting. Results of the model application are case-specific and cannot be generalized, but the major impact of labour and error correction cost has been highlighted.

The model at present focusses on the in-house kitting systems based on travelling kits concept only. Although all quantitative cost drivers are included, some context-related qualitative decision factors are not yet included. The model assumes that the information about product structure and part requirements is known and that a preliminary design of the assembly system (i.e. line balancing) has been carried out.

Production managers are given a quantitative decision tool to properly assess the implementation of kitting policies at an early decision stage. This allows exploring the trade-offs between the alternatives and properly planning the adoption of kitting systems, as well as comparing kitting with alternative material supply methods.

With respect to previous simplified literature models, this new approach allows quantification of a number of additional factors which are critical for successful implementation of cost-effective kitting systems, including kitting errors. An exhaustive cost estimation of kitting systems in multiple, mixed-model assembly lines is thus permitted.

While humanitarian supply chains (HSCs) inherently contribute to social sustainability by alleviating the suffering of afflicted communities, their unintended adverse environmental impact has been overlooked hitherto. This paper draws upon contingency theory to synthesize green practices for HSCs, identify the contingency factors that impact on greening HSCs and explore how focal humanitarian organizations (HOs) can cope with such contingency factors.

Deploying an action research methodology, two-and-a-half cycles of collaboration between researchers and a United Nations agency were completed. The first half-cycle developed a deductive greening framework, synthesizing extant green practices from the literature. In the second and third cycles, green practices were adopted/customized/developed reflecting organizational and contextual contingency factors. Action steps were implemented in the HSC for prophylactics, involving an operational mix of disaster relief and development programs.

First, the study presents a greening framework that synthesizes extant green practices in a suitable form for HOs. Second, it identifies the contingency factors associated with greening HSCs regarding funding environment, stakeholders, field of activity and organizational management. Third, it outlines the mechanisms for coping with the contingency factors identified, inter alia, improving the visibility of headquarters over field operations, promoting collaboration and resource sharing with other HOs as well as among different implementing partners in each country, and working with suppliers for greener packaging. The study advances a set of actionable propositions for greening HSCs.

Using an action research methodology, the study makes strong practical contributions. Humanitarian practitioners can adopt the greening framework and the lessons learnt from the implementation cycles presented in this study.

This is one of the first empirical studies to integrate environmental sustainability and HSCs using an action research methodology.