Abstract

AbstractNumerous application problems are expressed as nonlinear binary programming models, which make it challenging to address them using precise methods, especially in cases where the dimensions are enormous. A new problem in network optimization known as the Traveling Advisor Problem (TAP) is one of these practical applications. It is defined as an advisor who wants to select a subset of candidate workplaces that comprise the most profitable route within the time constraints of daily working hours to maximize profitability.To address binary optimization issues, this article suggests a novel binary variant of the Gaining-Sharing knowledge-based optimization technique (GSK). The GSK algorithm is built on the idea of how people learn and impart knowledge throughout their lives. The binary stages of binary junior gaining-sharing stage and binary senior gaining-sharing stage with knowledge factor 1 are the fundamental components of the sharing knowledge-based optimization method (BGSK). These two phases give BGSallow BGSKciently and effectively explore and utilize the search space to address issues in binary space.A Nonlinear Binary Model is introduced with a detailed real application example; the example is solved using the novel Binary Gaining-Sharing knowledge-based optimization algorithm (BGSK). The obtained optimal solution is better than that provided by the health and safety agency management in utilizing the available time and profitability.KeywordsTraveling advisor problemNonlinear binary modelOccupational health and safetyOR in health servicesGaining-sharing knowledge-based optimization algorithm

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