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1. Solving Bin Packing Problems Using VRPSolver Models

Author

Ruslan Sadykov, Eduardo Uchoa, Artur Alves Pessoa, Universidade Federal Fluminense [Rio de Janeiro] (UFF), Reformulations based algorithms for Combinatorial Optimization (Realopt), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Experiments presented in this paper were carried out using the PlaFRIM (Federative Platform for Research in Computer Science and Mathematics), created under the Inria PlaFRIM development action with support from Bordeaux INP, LABRI and IMB and other entities: Conseil Régional d’Aquitaine, Université de Bordeaux, CNRS and ANR in accordance to the 'Programme d’Investissements d’Avenir'. The research was partially supported by the following grants: CNPq 313601/2018-6, Faperj E-26/202.887/2017, and CAPES PrInt UFF n° 88881., Plafrim, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Institut de Mathématiques de Bordeaux (IMB), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
Mathematical optimization, 021103 operations research, Bin packing problem, Computer science, 0211 other engineering and technologies, 0102 computer and information sciences, 02 engineering and technology, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Solver, 01 natural sciences, Variable (computer science), 010201 computation theory & mathematics, Vehicle routing problem, Column generation, Heuristics, Integer programming, Smoothing
Abstract

International audience; We propose branch-cut-and-price algorithms for the classic bin packing problem and also for the following related problems: vector packing, variable sized bin packing and variable sized bin packing with optional items. The algorithms are defined as models for VRPSolver, a generic solver for vehicle routing problems. In that way, a simple parameterization enables the use of several branch-cut-and-price advanced elements: automatic stabilization by smoothing, limited-memory rank-1 cuts, enumeration, hierarchical strong branching and limited discrepancy search diving heuristics. As an original theoretical contribution, we prove that the branching over accumulated resource consumption (Gélinas et al. 1995), that does not increase the difficulty of the pricing subproblem, is sufficient for those bin packing models. Extensive computational results on instances from the literature show that the VRPSolver models have a performance that is very robust over all those problems, being often superior to the existing exact algorithms on the hardest instances. Several instances could be solved to optimality for the first time.

Published
2021

2. Intergenerational Risk Sharing in Life Insurance: Evidence from France

Author

Johan Hombert, Victor Lyonnet, Ecole des Hautes Etudes Commerciales (HEC Paris), and HEC Paris Research Paper Series

Subjects
Actuarial science, intergenerational risk-sharing, Redistribution (cultural anthropology), Monetary economics, JEL: G - Financial Economics/G.G2 - Financial Institutions and Services/G.G2.G22 - Insurance • Insurance Companies • Actuarial Studies, Asset return, Life insurance, JEL: G - Financial Economics/G.G2 - Financial Institutions and Services/G.G2.G20 - General, Transfer (computing), Value (economics), Economics, Risk sharing, [SHS.GESTION]Humanities and Social Sciences/Business administration, Survey data collection, Smoothing
Abstract

We study intergenerational risk sharing taking place in one of the most common retail investment products in Europe---life insurance savings contracts---focusing on the 1.4 trillion euro French market. Using regulatory and survey data, we show that contract returns are an order of magnitude less volatile than the returns of assets backing the contracts. Contract return smoothing is achieved using reserves that absorb fluctuations in asset returns and that generate intertemporal transfers across generations of investors. We estimate the average annual amount of intergenerational transfer at 1.4% of contract value, i.e., 17 billion euros per year or 0.8% of GDP. While theory asserts that intergenerational risk sharing cannot take place in competitive markets because it relies on non-exploited return predictability, we show that: (a)~contracts returns are indeed predictable; (b)~investor flows barely react to predictable returns; (c)~observed fees offset the estimated gain from exploiting contract return predictability.

Published
2017