Sloan Working Papers
http://hdl.handle.net/1721.1/1792
2018-06-21T18:41:29ZPull for Knowledge Work
http://hdl.handle.net/1721.1/115364
Pull for Knowledge Work
Dogde, Sheila; De Smet, Timothy; Meldrim, James; Lennon, Niall; Perrin, Danielle; Ferriera, Steve; Leber, Zachary; Friedrich, Dennis; Gabriel, Stacey; Lander, Eric S.; Kieffer, Don; Repenning, Nelson
2018-05-14T00:00:00ZAgile for Everyone Else: Using Triggers and Checks to Create Agility Outside of Software Development
http://hdl.handle.net/1721.1/110325
Agile for Everyone Else: Using Triggers and Checks to Create Agility Outside of Software Development
Repenning, James; Kieffer, Donald; Repenning, Nelson
2017-06-27T00:00:00ZA new 0-1 formulation of the restricted container relocation problem based on a binary encoding of congurations
http://hdl.handle.net/1721.1/109978
A new 0-1 formulation of the restricted container relocation problem based on a binary encoding of congurations
Galle, Virgile; Barnhart, Cynthia; Jaillet, Patrick
The Container Relocation Problem (CRP), also called Block Relocation Problem (BRP), is concerned with finding a sequence of moves of containers that minimizes the number of relocations needed to retrieve all containers, while respecting a given order of retrieval. The restricted CRP enforces that only containers blocking the target container can be relocated. We improve upon and enhance an existing binary encoding and using it, formulate the restricted CRP as a binary integer programming problem in which we exploit structural properties of the optimal solution. This integer programming formulation reduces significantly the number of variables and constraints compared to existing formulations. Its efficiency is shown through computational results on small and medium sized instances taken from the literature.
Submitted to EJOR June 2017
2017-06-16T00:00:00ZThe Stochastic Container Relocation Problem
http://hdl.handle.net/1721.1/107762
The Stochastic Container Relocation Problem
Galle, V.; Borjian Boroujeni, S.; Manshadi, V.H.; Barnhart, C.; Jaillet, P.
The Container Relocation Problem (CRP) is concerned with finding a sequence of moves of containers that minimizes the number of relocations needed to retrieve all containers, while respecting a given order of retrieval. However, the assumption of knowing the full retrieval order of containers
is particularly unrealistic in real operations. This paper studies the stochastic CRP (SCRP), which relaxes this assumption. A new multi-stage stochastic model, called the batch model, is introduced, motivated, and compared with an existing model (the online model). The two main contributions are an
optimal algorithm called Pruning-Best-First-Search (PBFS) and a randomized approximate algorithm called PBFS-Approximate with a bounded average error. Both algorithms, applicable in the batch and online models, are based on a new family of lower bounds for which we show some theoretical properties. Moreover, we introduce two new heuristics outperforming the best existing heuristics. Algorithms, bounds and heuristics are tested in an extensive computational section. Finally, based on strong computational evidence, we conjecture the optimality of the “Leveling” heuristic in a special “no information” case, where at any retrieval stage, any of the remaining containers is equally likely to be retrieved next.
2017-03-28T00:00:00Z