A branch, bound, and remember algorithm for the 1|r i |∑t i scheduling problem

Kao, Gio K.; Sewell, Edward C.; Jacobson, Sheldon H.

doi:10.1007/s10951-008-0087-3

Cited by 20 publications

(10 citation statements)

References 13 publications

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“…The BB&R algorithm has also been effectively applied to other scheduling problems, including the total tardiness scheduling problem, 1|r i | t i [12] and the single machine scheduling problem with sequence dependent setup times, where the objective is to find a schedule with minimum total tardiness, 1|ST sd | t i [29]. There are also practical military applications of the 1|r i | U i scheduling problem that involve limited, highly valued assets that process certain tasks within a given time window (e.g., a surveillance satellite that must photograph as many locations as possible within its overpass time window.)…”

Section: Resultsmentioning

confidence: 99%

“…(CBFS was formerly called distributed best first search in [12].) In CBFS, subproblems are explored based on the length of the sequence of on-time jobs and a best-measure, a heuristic function that evaluates the potential of a subproblem leading to an optimal solution.…”

Section: Branching and Dominance Schemementioning

confidence: 99%

“…4). A BB&R algorithm using the CBFS exploration strategy [12] is described and compared to the traditional depth-first search (DFS) and best-first search (BFS) exploration strategies. The computational results reported indicate that the BB&R algorithm outperforms the current best known algorithms.…”

Section: Introductionmentioning

confidence: 99%

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New dominance rules and exploration strategies for the 1|r i |∑U i scheduling problem

et al. 2010

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The paper proposes a new exact approach, based on a Branch, Bound, and Remember (BB&R) algorithm that uses the Cyclic Best First Search (CBFS) strategy, for the 1|r i | U i scheduling problem, a single machine scheduling problem, where the objective is to find a schedule with the minimum number of tardy jobs. The search space is reduced using new and improved dominance properties and tighter upper bounds, based on a new dynamic programming algorithm. Computational results establish the effectiveness of the BB&R algorithm with CBFS for a broad spectrum of problem instances. In particular, this algorithm was able to solve all problems instances, up to 300 jobs, while existing best known algorithms only solve problems instances up to 200 jobs. Furthermore, the BB&R algorithm with CBFS runs one to two orders of magnitude faster than the current best known algorithm on comparable instances.

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Section: Resultsmentioning

confidence: 99%

Section: Branching and Dominance Schemementioning

confidence: 99%

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New dominance rules and exploration strategies for the 1|r i |∑U i scheduling problem

et al. 2010

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“…There is no need to repeatedly solve the same subproblem because its solution can be retrieved from the database. This method has been successfully applied to several other problems-see Hall et al (2008) and Kao et al (2009).…”

Section: Boundsmentioning

confidence: 99%

“…The cyclic best-first search (CBFS) is a new search strategy (Kao et al , 2011Sewell et al 2009) that is a hybrid between DFS and BFS (note that CBFS was formerly called distributed best-first search in Kao et al 2009). CBFS begins by choosing the best subproblem at depth 1.…”

Section: Search Strategymentioning

confidence: 99%

A Branch, Bound, and Remember Algorithm for the Simple Assembly Line Balancing Problem

Sewell

Jacobson

2012

INFORMS Journal on Computing

104

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W e present a new exact algorithm for the assembly line balancing problem. The algorithm finds and verifies the optimal solution for every problem in the combined benchmarks of Hoffmann, Talbot, and Scholl in less than one-half second per problem, on average, including one problem that has remained open for over 10 years. The previous best-known algorithm is able to solve 257 of the 269 benchmarks. The new algorithm is based on a branch-and-bound method that uses memory to eliminate redundant subproblems.

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Resequencing with parallel queues to minimize the maximum number of items in the overflow area

Boywitz

Boysen

Briskorn

2016

Naval Research Logistics

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This article treats an elementary optimization problem, where an inbound stream of successive items is to be resequenced with the help of multiple parallel queues in order to restore an intended target sequence. Whenever early items block the one item to be currently released into the target sequence, they are withdrawn from their queue and intermediately stored in an overflow area until their actual release is reached. We aim to minimize the maximum number of items simultaneously stored in the overflow area during the complete resequencing process. We met this problem in industry practice at a large German automobile producer, who has to resequence containers with car seats prior to the assembly process. We formalize the resulting resequencing problem and provide suited exact and heuristic solution algorithms. In our computational study, we also address managerial aspects such as how to properly avoid the negative effects of sequence alterations. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 401–415, 2016

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A branch, bound, and remember algorithm for the 1|r i |∑t i scheduling problem

Cited by 20 publications

References 13 publications

New dominance rules and exploration strategies for the 1|r i |∑U i scheduling problem

New dominance rules and exploration strategies for the 1|r i |∑U i scheduling problem

A Branch, Bound, and Remember Algorithm for the Simple Assembly Line Balancing Problem

Resequencing with parallel queues to minimize the maximum number of items in the overflow area

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