Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
A survey of algorithms for the single machine total weighted tardiness scheduling problem
Discrete Applied Mathematics - Southampton conference on combinatorial optimization, April 1987
Computers and Operations Research
Improved heuristics for the n-job single-machine weighted tardiness problem
Computers and Operations Research
Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing
An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing
A tabu search algorithm for parallel machine total tardiness problem
Computers and Operations Research
Block approach: tabu search algorithm for single machine total weighted tardiness problem
Computers and Industrial Engineering
Operations Research Letters
Threshold accepting framework for discrete and continuous search spaces
International Journal of Innovative Computing and Applications
Computers and Operations Research
Expert Systems with Applications: An International Journal
Guided restarting local search for production planning
Engineering Applications of Artificial Intelligence
Computers and Operations Research
Journal of Intelligent Manufacturing
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This paper investigates the single machine total weighted tardiness problem, in which a set of independent jobs with distinct processing times, weights, and due dates are to be scheduled on a single machine to minimize the sum of weighted tardiness of all jobs. This problem is known to be strongly NP-hard, and thus provides a challenging area for metaheuristics. A population-based variable neighborhood search (PVNS) algorithm is developed to solve it. This algorithm differs from the basic variable neighborhood search (VNS). First, the PVNS consists of a number of iterations of the basic VNS, and in each iteration a population of solutions is used to simultaneously generate multiple trial solutions in a neighborhood so as to improve the search diversification. Second, the PVNS adopts a combination of path-relinking, variable depth search and tabu search to act as the local search procedure so as to improve the search intensification. Computational experiments show that the proposed PVNS algorithm can obtain the optimal or best known solutions within a reasonable computation time for all standard benchmark problem instances from the literature.