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Nowadays, countering the complexity of waste collection path optimization problems (POP) are reliably solved through evolutionary computation (EC). However, a sustainable collection strategy requires a specific rule and outcome for a given route network. Most waste managers are concerned with expenditure, work hours, and ecological footprints, combined with model flexibility to mimic real-life execution for the specific study case. Contemporary stochastic and deterministic POP methods are every so often confronted with optimality and scalability. Beyond its predecessor, namely the A* and the Dijkstra algorithm, a fundamental nature-based deterministic algorithm called a Ripple-Spreading Algorithm (RSA) had performed a straightforward heuristic evaluation. This study investigates prospects and future challenges in developing the RSA simulation based on Indonesia’s waste management scope. Specific overseas theme literature was employed to explore the possible research setting.

Economy and corporate activities are significant elements relating to sustainable drawbacks. The 2030 agenda is a supremely transformational vision ensuring no one is left behind and guarantees full benefits for today and future generations. However, there is a limited understanding of their relationship and how their critical root causes them. Therefore, this study explores the complicated history of Indonesia’s sustainability within a systematic qualitative review from primeval documents to recent trends data. As a result, the pattern of nature degradation concerning nation growth has been interpreted. A corporate sustainability perspective has been designed as a problem solver. The knowledge management of sustainability and carbon management hierarchy tools have also been introduced. Hopefully, this study expresses extensive reasoning for Indonesia’s road map to a sustainable society.

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