The production scheduling problem of a real-life food industry is addressed in this work. An efficient MILP-based solution strategy is developed to optimize weekly schedules for a Spanish canned fish production plant. The multi-stage, multi-product facility under study consists of both continuous and batch operations resulting in an extremely complex scheduling problem. In order to reduce its computational complexity, an aggregated approach is cleverly proposed, in which the continuous processes are explicitly modeled, while valid feasibility constraints are introduced for the batch stage. Based on this approach, two MILP models are developed, using a mixed discrete-continuous time representation. All technical, operating and design constraints of the facility are considered, while salient characteristics of the canned-food industry, such as assurance of the end products’ microbiological integrity, are aptly modeled. Both the minimization of makespan and changeovers is studied. In order to meet the computational limits imposed by the industry, an order-based decomposition algorithm is further investigated. The method is successfully applied to real-life case studies, generating near-optimal solutions in short CPU times. The suggested solution strategy can be easily extended to consider other real-life scheduling problems from the process industries sector that share similar production characteristics.

这项工作解决了现实食品行业的生产计划问题。已开发出一种有效的基于MILP的解决方案策略，以优化西班牙罐装鱼类生产厂的每周计划。研究中的多阶段，多产品设施包括连续和批处理操作，这导致了极其复杂的调度问题。为了降低其计算复杂度，巧妙地提出了一种聚合方法，其中对连续过程进行了显式建模，同时为批处理阶段引入了有效的可行性约束。基于这种方法，使用混合离散连续时间表示法开发了两个MILP模型。考虑到该设施的所有技术，运营和设计限制，同时罐头食品行业的显着特征是，如最终产品的微生物完整性保证之类的模型被恰当地建模。研究了最小化制造期和转换。为了满足行业施加的计算限制，进一步研究了基于订单的分解算法。该方法已成功应用于现实生活中的案例研究，可在较短的CPU时间内生成接近最佳的解决方案。可以轻松扩展建议的解决方案策略，以考虑来自过程工业领域的具有相似生产特征的其他实际调度问题。该方法已成功应用于现实生活中的案例研究，可在较短的CPU时间内生成接近最佳的解决方案。可以轻松扩展建议的解决方案策略，以考虑来自共享类似生产特性的过程工业部门的其他实际调度问题。该方法已成功应用于现实生活中的案例研究，可在较短的CPU时间内生成接近最佳的解决方案。可以轻松扩展建议的解决方案策略，以考虑来自过程工业领域的具有相似生产特征的其他实际调度问题。