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Cyrille Briand

Publications and source records attributed to Cyrille Briand.

3 recordsLinked to original sources

Hierarchical Rectangle Packing Solved by Multi-Level Recursive Logic-based Benders Decomposition

We study the two-dimensional hierarchical rectangle packing problem, motivated by applications in analog integrated circuit layout, facility layout, and logistics. Unlike classical strip or bin packing, the dimensions of the container are not fixed, and the packing is inherently hierarchical: each item is either a rectangle or a block occurrence, whose dimensions are a solution of another packing problem. This recursive structure reflects real-world scenarios in which components, boxes, or modules must be packed within higher-level containers. We formally define the problem and propose exact formulations in Mixed-Integer Linear Programming and Constraint Programming. Given the computational difficulty of solving complex packing instances directly, we propose decomposition heuristics. First, we implement an existing Bottom-Up baseline method that solves subblocks before combining them at higher levels. Building upon this, we introduce a novel multilevel Logic-based Benders Decomposition method. This heuristic method dynamically refines block dimension constraints, eliminating the need for manual selection of candidate widths or aspect ratios. Experiments on synthetic instances with up to seven hierarchy levels, 80 items per block, and limited computation time show that the proposed decomposition significantly outperforms both monolithic formulations and the Bottom-Up method in terms of solution quality and scalability.

cs.CG

An iterative Constraint Programming approach to integrate maximum workload constraints in preemptive jobshop scheduling

Optimizing schedules in real-world settings often requires considering workload constraints, specially for human resources, to ensure regulatory compliance, impose rest periods, or level the workload over the working horizon. This paper focuses on tackling this family of constraints in the context of preemptive jobshop scheduling, as preemption is particularly relevant when human resources are involved (allowing personnel to flexibly switch between tasks). Preemption also offers theoretical insights as a relaxation of non-preemptive problems. The main contribution of this paper is a Constraint Programming approach designed to handle effectively maximum workload constraints in a preemptive setting, without decomposing activities into unit-duration tasks (which may be computationally prohibitive). Since workload constraints introduce significant additional complexity, we further propose a method that iteratively introduces the workload constraints into the problem, along with tailored heuristics specifically designed to guide the search efficiently. The experimental results demonstrate the effectiveness of our approach on a large set of instances, highlighting its performance compared to a well-known industrial solver, IBM's CP Optimizer.

math.OC

Solving a large oral examination timetabling problem using a multidimensional knapsack MILP formulation

This paper addresses the Oral Examination Timetabling Problem (OETP) for France's prestigious engineering schools, an organization managed by the Service des Concours Communs Polytechniques (SCCP). The scheduling is highly complex, involving over 7,000 candidates across a four-week period while accounting for constraints such as exam overlaps, geographic origin, and visa requirements. To manage this scale, the paper shows how to model the problem as a Multidimensional Knapsack Problem (MKP) using a Mixed-Integer Linear Programming (MILP) formulation. Their strategy reduces combinatorial complexity by assigning candidates to a predetermined set of pre-validated schedules rather than individual time slots. Experimental results using the SCIP solver on a real 2025 data instance successfully accommodated 7,796 out of 7,804 candidates within a 20-minute time limit.

math.OC