Scholarlink

<p>We present an intelligent decision framework for sustainable manufacturing in a high throughput perishable meat factory. We address this challenge of food waste in a facility with a two-week turnover exceeding €1 million. We develop a risk aware stochastic linear program with hierarchical objectives: (i) satisfy demand, (ii) balance daily workload to limit labour volatility, and (iii) minimise environmental and economic waste using a Conditional Value-at-Risk (CVaR) term calibrated from historical data. We analyse age-agnostic retrieval and age-aware retrieval warehouses and the impact on the production schedule. On retrospective industrial data, the proposed system reduces CVaR risk by <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\approx\)</EquationSource></InlineEquation>59% under ageaware retrieval and <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(\approx\)</EquationSource></InlineEquation>18% under age-agnostic retrieval at unchanged throughput. We find that analysing sensitivity to the CVaR confidence level yields practical guidelines for sustainable production planning in the factory. We discuss the system’s deployment implications for digital transformation in factories handling short-lived SKUs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Intelligent Decision System for Sustainable Food Production: Mitigating Waste via CVaR Stochastic Optimisation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

查看全文
  • Josep Maria Salvia Hornos,
  • Cèsar Fernández Camón,
  • Carles Mateu Piñol,
  • Ivet Rafegas Fonoll

摘要

We present an intelligent decision framework for sustainable manufacturing in a high throughput perishable meat factory. We address this challenge of food waste in a facility with a two-week turnover exceeding €1 million. We develop a risk aware stochastic linear program with hierarchical objectives: (i) satisfy demand, (ii) balance daily workload to limit labour volatility, and (iii) minimise environmental and economic waste using a Conditional Value-at-Risk (CVaR) term calibrated from historical data. We analyse age-agnostic retrieval and age-aware retrieval warehouses and the impact on the production schedule. On retrospective industrial data, the proposed system reduces CVaR risk by \(\approx\)59% under ageaware retrieval and \(\approx\)18% under age-agnostic retrieval at unchanged throughput. We find that analysing sensitivity to the CVaR confidence level yields practical guidelines for sustainable production planning in the factory. We discuss the system’s deployment implications for digital transformation in factories handling short-lived SKUs.