リスクマネジメント研究部会（2022年2月21日）開催のお知らせ
2022年2月11日
研究部会主査： 早稲田大学 森戸晋
幹事： 静岡大学 呉 偉
- 日時：2022年 2月 21日（月）17:00 — 18:15
- 会場：Zoom でのオンライン開催
- 参加申込：下記のリンクから参加申込を行ってください．登録後に，登録のメールアドレスへ Zoom ミーティングの情報をお送りします． 参加申込フォーム
- 参加申込締切：2月19日（土）23:59
- 講演者：Andrea PIZZUTI（Marche Polytechnic University）
- 題目：Cutting plan optimization: A general framework for cut-and-schedule and the case with variable pattern processing time
- 概要：
- Cutting and packing problems (C&Ps) are a prominent family of optimization problems typically arising in manufacturing environments, well-known to both practitioners and academics for the practical and theoretical relevance. A solution of C&Ps is in general a non-ordered set of patterns with run lengths, which usually lacks several aspects that are meaningful for the operations, among which the specific schedule of operations and technological details. A subsequent effort is hence required to implement solutions, often leading to poor or unfeasible overall solutions. Enriched C&Ps are then conceived to overcome the limits of classical C&P, making use of MILPs that integrates beforehand technological and scheduling features from real environments.
According to an integrated cut-and-schedule perspective, we provide a general framework for the cutting plan optimization, which can be specialized at convenience into particular MILPs for specific optimization problems. As detailed example, we discuss the one-dimensional bin packing where patterns require a variable processing time, dependent by the number of items cut. The minimization of the combined number of employed patterns and delays of operations is solved by a price-and-branch approach, that makes use of a column generation procedure and a sequential value correction heuristic.
- Cutting and packing problems (C&Ps) are a prominent family of optimization problems typically arising in manufacturing environments, well-known to both practitioners and academics for the practical and theoretical relevance. A solution of C&Ps is in general a non-ordered set of patterns with run lengths, which usually lacks several aspects that are meaningful for the operations, among which the specific schedule of operations and technological details. A subsequent effort is hence required to implement solutions, often leading to poor or unfeasible overall solutions. Enriched C&Ps are then conceived to overcome the limits of classical C&P, making use of MILPs that integrates beforehand technological and scheduling features from real environments.