Newest papers

Optimizing Flexibility and Commitments: Partially-Adaptive Multi-Stage Matrix Assembly Layout Design Problem

Baturhan Bayraktar, Merve Bodur, Martin Grunow, Rainer Kolisch · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27401 · cited by 0 works (OpenAlex) · open-access PDF

Korean

매트릭스 패트케이스 조립 레이아웃은 제품 다양성과 수요 변동성에 따라 제조의 유연성을 제공합니다. 정기적 인 재 설계는이 유연성을 활용하지만 기존 모델은 연간 개정만 허용합니다. 핀란드 패트케이스는 유연성 약속 거래를 노출합니다 : 제품 다양성과 수요 변동성에 따라 제품 다양성 지원, 공급자 계약 및 할인. 우리는 레이아웃 개정 타이밍 문제를 도입하여 공동으로 개정 포인트를 선택하고 수요 불확실성에 따라 약속 된 레이아웃 시퀀스를 선택하며 생산이 그 레이아웃 내에 적응합니다. 우리는 부분적으로 적응성 다단계 스토카스틱 프로그램으로 정의합니다. 두 개의 보완 비용 기반 확장에 대한 지원. 우리의 확장에는 목표

Machine translation (Meta M2M100); the English original follows.

Abstract

Matrix assembly layouts provide manufacturing flexibility under product variety and demand volatility through locally reconfigurable production stations. Periodic redesign exploits this flexibility, yet existing models allow only annual revisions. Finer calendars expose a flexibility-commitment trade-off: advance layout commitments support coordination, supplier agreements, and discounts. We introduce the layout revision timing problem, jointly choosing revision points and committed layout sequences under demand uncertainty while production adapts within those layouts. We formulate it as a partially adaptive multistage stochastic program with two complementary extensions. Our cost-based extension places the economic value of advance commitment in the objective rather than prescribing revision budgets; our scenario-adaptive extension adapts revision timing to revealed information. For station-use minimization, we establish a cost decomposition across commitment intervals, yielding an exact dynamic program that reuses station subproblem solutions across model variants and parameter settings. As a more general alternative, we develop an integer L-shaped method strengthened by monotonicity-based cuts for budgeted formulations and enhanced cuts exploiting this decomposition. On automotive instances, including scenario trees with nearly 10,000 nodes, our combined model's average savings in expected station-use cost at the base-case discount reach 4.99% versus full adaptivity and 28.65% versus full commitment. Scenario adaptivity is most valuable when demand paths diverge and discounts justify commitment on some paths but not others, as in electrification-driven scenarios.

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Topics: Flexibility (engineering), Mathematical optimization, Computer science, Benders' decomposition, Production (economics), Exploit

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