Nowadays, sustainability and Industry 4.0 (I4.0) are trending concepts used in the literature on industrial processes. Industry 4.0 has been mainly addressed by the current literature from a technological perspective, overlooking sustainability challenges regarding this recent paradigm. The objective of this paper is to evaluate the state of the art of relations between sustainability and I4.0. The goal will be met by (1) mapping and summarizing existing research efforts, (2) identifying research agendas, (3) examining gaps and opportunities for further research. Web of Science, Scopus, and a set of specific keywords were used to select peer-reviewed papers presenting evidence on the relationship between sustainability and I4.0. To achieve this goal, it was decided to use a dynamic methodology called “systematic literature network analysis”. This methodology combines a systematic literature review approach with the analysis of bibliographic networks. Selected papers were used to build a reference framework formed by I4.0 technologies and sustainability issues. The paper contributes to the Sustainable Industry 4.0 reference framework with application procedures. It aims to show how I4.0 can support ideas of sustainability. The results showed that apart from a huge contribution to both concepts, many papers do not provide an insight into realization of initiatives to introduce Sustainable Industry 4.0.

https://www.mdpi.com/2071-1050/12/14/5650/pdf

Impact of Industry 4.0 on Sustainability—Bibliometric Literature Review

The concept of Industry 4.0 has been mainly addressed by the current literature from a technological perspective, overlooking the organisational and even ethical challenges related to this recent paradigm. In order to become ‘4.0 compliant’, an enterprise must adapt its organisation and business approaches, and these changes may lead to a significant impact on sustainability. Therefore, we performed a systematic literature review to investigate the most recent Industry 4.0 research streams by adopting a multi-perspective approach. This analysis led to collect insights on the key traits of an Enterprise 4.0: integration, decomposed hierarchy, flexibility, and autonomy. Each of these keywords involves work environments, business and organisational models, and educational approaches, which constitute the key traits of the novel framework proposed in this study.

https://www.mdpi.com/2071-1050/11/22/6363/pdf

How Can Organisations and Business Models Lead to a More Sustainable Society? A Framework from a Systematic Review of the Industry 4.0

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Fuzzy Logic Intelligence Control And Information

Switching transport modes to meet voluntary carbon emission targets

The economic lot-sizing problem with an emission capacity constraint

This paper presents the implementation of simulated annealing (SA) method, an artificial intelligence technique, to solve the optimization problem known as the school bus routing problem (SBRP). A specific challenge in all artificial intelligence optimization techniques is the selection of appropriate value parameters. One contribution of this paper is the implementation of a design of experiments technique to provide statistical support for parameter selection. The SBRP is formulated as a 0–1 integer linear programming model, where the objective function is to minimize the total cost. Because this problem is combinatorial in nature, it is not possible to find exact solutions in an adequate time, calling for the use of an artificial intelligence optimization technique. The proposed technique is SA due to its modeling flexibility and processing speed. To demonstrate the performance of the proposed algorithm, several experiments with real instances were carried out, showing that the metaheuristic algorithm performs better in quality and time than the classic routing method.

Artificial intelligence and DOE: an application to school bus routing problems

This work presents a comparative analysis of specific, rather than general, mathematical programming implementation techniques of the quadratic optimization problem (QP) based on Support Vector Machines (SVM) learning process. Considering the Karush-Kuhn-Tucker (KKT) optimality conditions, we present a strategy of implementation of the SVM-QP following three classical approaches: (i) active set, also divided in primal and dual spaces, methods, (ii) interior point methods and (iii) linearization strategies. We also present the general extension to treat large-scale applications consisting in a general decomposition of the QP problem into smaller ones, conserving the exact solution approach. In the same manner, we propose a set of heuristics to take into account for a better than a random selection process for the initialization of the decomposition strategy. We compare the performances of the optimization strategies using some well-known benchmark databases.

Quadratic optimization fine tuning for the Support Vector Machines learning phase

The University Timetabling problem (UTT) is about finding an optimal distribution of classrooms and faculty during a fixed period of time. UTT is a common problem for Educational Institutions. Genetic algorithms (GAs) are used to obtain high quality results for UTT. GAs are a general purpose metaheuristics that generate very good results when population diversity is correctly handled. This paper presents a new GA for UTT that incorporates a set of rules to guarantee the population diversity.

http://www.wseas.us/e-library/conferences/2006cscc/papers/594-163.pdf

Genetic algorithm with forced diversity for the university timetabling problem

Police Iteration (PI) is a widely used traditional method for solving Markov Decision Processes (MDPs). In this paper, the cosine policy iteration (CPI) method for solving complex problems formulated as infinite-horizon MDPs is proposed. CPI combines the advantages of two methods: i) Cosine Simplex Method (CSM) which is based on the Karush, Kuhn, and Tucker (KKT) optimality conditions and finds rapidly an initial policy close to the optimal solution and ii) PI which is able to achieve the global optimum. In order to apply CSM to this kind of problems, a well- known LP formulation is applied and particular features are derived in this paper. Obtained results show that the application of CPI solves MDPs in a lower number of iterations that the traditional PI.

Jaime Mora-Vargas

Papers

Artificial intelligence and DOE: an application to school bus routing problems

Quadratic optimization fine tuning for the Support Vector Machines learning phase

Genetic algorithm with forced diversity for the university timetabling problem

Cosine Policy Iteration for Solving Infinite-Horizon Markov Decision Processes

Drowsy Driver Detection Using Wavelets and Support Vector Machines