Production Planning by Mixed Integer Programming

By Y. Pochet, Laurence A. Wolsey. 2010

1,029 citations recorded by the source corpus.

Subfields: Management Science and Operations Research

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Citations in the corpus

Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches (2019, methodology): “Mixed-Integer Programing is the most successful technique to optimally solve NP-Hard problems and has been applied to a large number of problems, from production planning (Pochet and Wolsey, 2006) to prediction of protein structures (Zhu, 2007).”
Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network (2018, background): “Without consider the resource allocation, the task duration minimization problem (7) can be proved NP-hard according to [45].”
Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks (2017, background): “finding the optimal solution usually requires exponential time complexity [36].”
User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, background): “Hence, we must establish the implied trade-off through a robust study for the effect of distributing (or co-locating) the available antennas on”
Integration of production planning and scheduling: Overview, challenges and opportunities (2009, neutral): “For example, Wan, Pekny, and Reklaitis (2006) proposed a simulation-based optimization approach to create a support vector for use as a surrogate model.”
Uncapacitated lot-sizing problem with production time windows, early productions, backlogs and lost sales (2011, neutral): “1 2 3 4 5 6 [10] [40] [20]”
Redundant Observability PMU Placement in the Presence of Flow Measurements Considering Contingencies (2013, background): “The optimality gap, or duality gap, is defined as the relative distance of the best upper bound and best lower bound in the branch-and-bound algorithm [8].”
Exact Combinatorial Optimization with Graph Convolutional Neural Networks (2019, neutral): “tative set of MILP instances [40]. However, in many contexts it is common to repeatedly solve similar combinatorial optimization problems, e.g., day-to-day production planning and lot-sizing problems [43], which may signiﬁcantly differ from the set of instances on which B&B algorithms are typica”
Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds (2017, background): “Accordingly, we can rewrite the MINLP problem P as P : max s max f ,p ∑ i∈S ρivi(si, fi, pi) s.t. C1, C2, C3, C4, and C5.”
MIRPLib - A library of maritime inventory routing problem instances: Survey, core model, and benchmark results (2014, background): “Consider the standard capacitated lot-sizing set (see, e.g., Pochet and Wolsey [42], whose notation we use here) in which one must decide in what periods to produce an item and how much to produce, given demand data d t , initial inventory s 0 , constant storage capacity s max , and capacities C t o”