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Author (up) Letelier, O.R.; Clautiaux, F.; Sadykov, R. doi  openurl
  Title Bin Packing Problem with Time Lags Type
  Year 2022 Publication Informs Journal On Computing Abbreviated Journal INFORMS J. Comput.  
  Volume Early Access Issue Pages  
  Keywords integer programming; algorithms; cutting planes; branch and bound; production-scheduling; cutting stock; relaxation  
  Abstract We introduce and motivate several variants of the bin packing problem where bins are assigned to time slots, and minimum and maximum lags are required between some pairs of items. We suggest two integer programming formulations for the general problem: a compact one and a stronger formulation with an exponential number of variables and constraints. We propose a branch-cut-and-price approach that exploits the latter formulation. For this purpose, we devise separation algorithms based on a mathematical characterization of feasible assignments for two important special cases of the problem: when the number of possible bins available at each period is infinite and when this number is limited to one and time lags are nonnegative. Computational experiments are reported for instances inspired from a real-case application of chemical treatment planning in vineyards, as well as for literature instances for special cases of the problem. The experimental results show the efficiency of our branch-cutand-price approach, as it outperforms the compact formulation on newly proposed instances and is able to obtain improved lower and upper bounds for literature instances. Summary of Contribution: The paper considers a new variant of the bin packing problem, which is one of the most important problems in operations research. A motivation for introducing this variant is given, as well as a real-life application. We present a novel and original exact branch-cut-and-price algorithm for the problem. We implement this algorithm, and we present the results of extensive computational experiments. The results show a very good performance of our algorithm. We give several research directions that can be followed by subsequent researchers to extend our contribution to more complex and generic problems.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1091-9856 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000804384300001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1588  
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Author (up) Letelier, O.R.; Espinoza, D.; Goycoolea, M.; Moreno, E.; Munoz, G. doi  openurl
  Title Production Scheduling for Strategic Open Pit Mine Planning: A Mixed-Integer Programming Approach Type
  Year 2020 Publication Operations Research Abbreviated Journal Oper. Res.  
  Volume 68 Issue 5 Pages 1425-1444  
  Keywords open pit mining; production scheduling; column generation; heuristics; cutting planes; integer programming applications  
  Abstract Given a discretized representation of an ore body known as a block model, the open pit mining production scheduling problem that we consider consists of defining which blocks to extract, when to extract them, and how or whether to process them, in such a way as to comply with operational constraints and maximize net present value. Although it has been established that this problem can be modeled with mixed-integer programming, the number of blocks used to represent real-world mines (millions) has made solving large instances nearly impossible in practice. In this article, we introduce a new methodology for tackling this problem and conduct computational tests using real problem sets ranging in size from 20,000 to 5,000,000 blocks and spanning 20 to 50 time periods. We consider both direct block scheduling and bench-phase scheduling problems, with capacity, blending, and minimum production constraints. Using new preprocessing and cutting planes techniques, we are able to reduce the linear programming relaxation value by up to 33%, depending on the instance. Then, using new heuristics, we are able to compute feasible solutions with an average gap of 1.52% relative to the previously computed bound. Moreover, after four hours of running a customized branch-and-bound algorithm on the problems with larger gaps, we are able to further reduce the average from 1.52% to 0.71%.  
  Address [Rivera Letelier, Orlando] Univ Adolfo Ibanez, Doctoral Program Ind Engn & Operat Res, Santiago 7941169, Chile, Email: orlando.rivera@uai.cl;  
  Corporate Author Thesis  
  Publisher Informs Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0030-364x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000574409100008 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1250  
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