Formulating the single machine sequencing problem with release dates as a mixed integer program
Discrete Applied Mathematics - Southampton conference on combinatorial optimization, April 1987
A time indexed formulation of non-preemptive single machine scheduling problems
Mathematical Programming: Series A and B
A branch-and-bound algorithm for the single machine earliness and tardiness scheduling problem
Computers and Operations Research
Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing
Time-Indexed Formulations for Machine Scheduling Problems: Column Generation
INFORMS Journal on Computing
An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing
Mathematical Programming: Series A and B
New Exact Algorithms for One-Machine Earliness-Tardiness Scheduling
INFORMS Journal on Computing
Operations Research Letters
Computers and Operations Research
Computers and Industrial Engineering
An exact approach for scheduling jobs with regular step cost functions on a single machine
Computers and Operations Research
Iterated local search and very large neighborhoods for the parallel-machines total tardiness problem
Computers and Operations Research
Computers and Operations Research
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This study proposes an exact algorithm for the general single-machine scheduling problem without machine idle time to minimize the total job completion cost. Our algorithm is based on the Successive Sublimation Dynamic Programming (SSDP) method. Its major drawback is heavy memory usage to store dynamic programming states, although unnecessary states are eliminated in the course of the algorithm. To reduce both memory usage and computational efforts, several improvements to the previous algorithm based on the SSDP method are proposed. Numerical experiments show that our algorithm can optimally solve 300 jobs instances of the total weighted tardiness problem and the total weighted earliness-tardiness problem, and that it outperforms the previous algorithms specialized for these problems.