Joint Decision-Making Model for Production Planning and Maintenance of Fully Mechanized Mining Equipment

Abstract

With the development of complex and intelligent sizeable mechanical equipment, the conflict between maintenance and production is becoming increasingly severe, restricting coal mine enterprises' production and development. This paper proposes a joint decision-making model for production planning and maintenance of fully mechanized mining equipment to solve the conflict between production and maintenance. Taking fully mechanized mining equipment as the research object and minimizing the total cost as the joint decision-making objective, we use the Hybrid Genetic Whale Optimization Algorithm (HGWOA) to optimize the production planning and maintenance process of fully mechanized mining equipment. We compared the joint decision-making model of production planning and maintenance with the independent decision-making model by setting different parameters. The result shows that the joint decision-making model reduces the cost by 36.06% and the downtime rate by 40.76%. It can better balance maintenance and production, which is of great significance to the coal mine and related enterprises. INDEX TERMS Fully mechanized mining equipment, predictive opportunity maintenance, production planning, joint decision-making, hybrid genetic whale optimization algorithm.

Empirical Analysis of Decision Recommendation Models for Various Processes from A Pragmatic Perspective

“…Based on this strategy, comparison of these models is tabulated in table 1 Based on this evaluation, it can be observed that Ens. [2], RL DTW [8], TDM [17], 3WD MA DM [20], TOP SIS GRA [28], IT2F DP [35], RNN Bi LSTM [36], RAD DRL [38], HG WOA [43], DPF CDT [46], and DM GAN [49] showcase higher contextual accuracy, which makes them highly useful for accurate decision recommendation use cases. It can also be observed that TF ADM [5], HG WOA [43], and DPF CDT [46] showcase lower computational complexity, thereby making them useful for low complexity decision recommendation scenarios.…”

“…In terms of computational delay or response time, TF ADM [5], RL DTW [8], TOP SIS GRA [28], IT2F DP [35], HG WOA [43], CRP [48], and DM GAN [49] are observed to have faster response, thus can be deployed for real-time use cases. While, LTBP STDP [12], TOP SIS GRA [28], IT2F DP [35], RNN Bi LSTM [36], RAD DRL [38], DEMA TEL [47], and DM GAN [49] showcase high recommendation efficiency, which makes them suitable for critical decision recommendation scenarios.…”

“…Based on this evaluation and figure 1, it can be observed that HG WOA [43], TOP SIS GRA [28], IT2F DP [35], DM GAN [49], DPF CDT [46], TF ADM [5], RNN Bi LSTM [36], and RAD DRL [38] showcase better overall performance, thus must be selected for high-performance decision recommendation use cases.…”

“…Large, complicated, and expensive mechanical equipment makes the maintenance-output conflict worse, and it also hinders coal mines from expanding and becoming more productive, according to developers of soft computing models in [43]. This research suggests a collaborative decision-making strategy for production planning and maintenance of fully automated mining equipment in order to overcome production-maintenance conflicts.…”

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

“…For example, Adam et al (2020) proposed the binary WOA (bWOA) to solve user-base station (BS) association and sub-channel assignment problems. Cao et al (2021) proposed a hybrid genetic WOA (HGWOA) that optimizes purchased equipment’s production planning and maintenance processes. El-Kenawy et al (2020) proposed a stochastic fractal search (SFS)-based guided WOA (SFS-Guided WOA) and performed feature classification balancing experiments on it based on COVID-19 images to achieve high accuracy classification prediction of COVID-19 diseases.…”