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A bi-objective robust optimization approach for the management of infectious wastes with demand uncertainty during a pandemic

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JOURNAL OF CLEANER PRODUCTION 2021年 314卷 127922-127922页

作者： Zhao, Jiahong Wu, biaohua Ke, Ginger Y. Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou Peoples R China Mem Univ Newfoundland Fac Business Adm St John NF Canada

The current global COVID-19 pandemic attracts public attention to the management of waste generated by health-care activities. Due to the hazardous nature, infectious waste requires the design of a multi-tiered system to provide cost-efficient and eco-friendly services of waste collection, transportation, treatment, and final disposal. However, the impact of uncertainties has not been well studied in the existing literature. Considering the presence of random waste generation during a pandemic, we aim to answer the following questions: 1) where to locate temporary transfer stations and temporary treatment centers;2) how to plan collection tours among the small generation nodes and temporary transfer stations;3) how to plan the direct transportation from large generation nodes to treatment centers;4) how to transport waste from temporary transfer stations to treatment centers, and 5) how to transport wastes from treatment centers to disposal facilities. The relevant cost and associated risk are respectively formulated and assessed using a scenario-based bi-objective robust approach. The complexity of the resulting mathematical model motivated the adaption and comparison of three multi-objective optimization approaches, including the goal programming method, a lexicographic weighted Tchebycheff approach, and an augmented epsilon-constraint solution technique. A case study based on the real situation in Wuhan, China, during the COVID-19 outbreak is conducted to demonstrate the workability of the proposed model and provide managerial insights for infectious waste management. The computational results show that our proposed model can more than double the demand fulfillment rate at an approximately 40% lower cost when facing a distinctively high increment in the amount of infectious waste.

关键词： Infectious waste Uncertain waste generation bi-objective robust optimization COVID-19 Location-routing Non-dominated solutions

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bi-objective scenario-guided swarm intelligent algorithms based on reinforcement learning for robust unrelated parallel machines scheduling with setup times

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 80卷

作者： Wang, bing Feng, Kai Wang, Xiaozhi Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

This paper addresses an uncertain unrelated parallel machine scheduling problem (UPMSP) with setup times, which is referred to the scenario UPMSP since uncertain processing times are described by a set of discrete scenarios. The bi-objective robust optimization formulation is established. Two objectives are to minimize the mean makespan and the worst-case makespan across all scenarios, which reflect solution optimality and solution robustness respectively. The contribution of this paper is three-fold. First, we propose the bi-objective robust optimization formulation under discrete scenarios for uncertain UPMSP. Second, two versions of swarm intelligent algorithms are developed by combining fruit fly optimization algorithm (FOA) framework and scenarioguided local search, which are performed based on two problem-specific neighborhood structures. The learning-scenario neighborhood structure is constructed by selecting single scenario using reinforcement learning. The united-scenario neighborhood structure is constructed by collecting all discrete scenarios. Third, an experiment was conducted to compare two developed algorithms with the state-of-the-art alternative algorithms. The computational results show that the developed algorithms are identically better than possible alternatives in terms of multi-objective metrics. Moreover, it is shown that the FOA algorithm with learning-scenarioneighborhood smell search is advantageous for the discussed problem.

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