Abstract
The most important topic for researchers is supply chain, that takes into account environmental factors and social aspects. That is why top managers prefer taking into account key performance indicators currently. Harmonization of social, environmental and economic components makes development of supply chains sustainable. This document is based on environmental protection; it details the main features of sustainable supply chain. It presents supporting tools of collaboration in sustainable supply chains. The main examined areas: system identification, order picking, inventory control systems, city logistics, intermodal logistics processes, routing, and logistics processes of earthwork. The tools: neural network, simulation, genetic algorithm, ant colony algorithm. The paper is structured as follows: First chapter defines sustainability and Sustainable Supply Chain (SSC). The second chapter presents supporting tools of collaboration in sustainable supply chains.
Highlights
The paper is structured as follows: First chapter defines sustainability and Sustainable Supply Chain (SSC)
In the article [15] the analysis reveals that six enablers ‘Commitment from top management’, ‘Eco-literacy amongst supply chain partners’, ‘Corporate social responsibility’, ‘High level of supply chain integration’, ‘Waste management’ and ‘Logistics organisation ensuring goods safety and consumer health’ are ranked as Independent enablers as they possess the maximum driver power
The learning and intuitive capability of the artificial neural network is usable on any fields, where a prompt decision must be done with the support of previously acquired knowledge
Summary
The main reason is that the artificial intelligent methods are the mathematical models of human thinking and natural laws, a human-made decision support system (DSS) can behave similar way as an intelligent living being. With this ability the commonly used logistics methods can be developed in different fields such as planning and operation. The learning and intuitive capability of the artificial neural network is usable on any fields, where a prompt decision must be done with the support of previously acquired knowledge It is effectively applicable solving inventory, scheduling, shortest route problems. If the attention is made the result is a robust, fast and flexible system wherein the unknown and random events can be treated effectively
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