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Assignment problem

About: Assignment problem is a research topic. Over the lifetime, 7588 publications have been published within this topic receiving 172820 citations. The topic is also known as: marriage problem.


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Journal ArticleDOI
TL;DR: Numerical examples show the superiority of a joint treatment of all assignment variables, including those specifying the routes taken around the barrier polyhedra, over a separate iterative solution of the assignment problem and the single-facility location problems in the presence of barriers.

51 citations

Journal ArticleDOI
TL;DR: The first polynomial-time algorithm that returns an optimal solution for any instance of the linear case is provided, which works in O(h|N|2) time.

51 citations

Journal ArticleDOI
TL;DR: This paper presents a simple algorithm to obtain mechanically SDP relaxations for any quadratic or linear program with bivalent variables, starting from an existing linear relaxation of the considered combinatorial problem.
Abstract: In this paper, we present a simple algorithm to obtain mechanically SDP relaxations for any quadratic or linear program with bivalent variables, starting from an existing linear relaxation of the considered combinatorial problem. A significant advantage of our approach is that we obtain an improvement on the linear relaxation we start from. Moreover, we can take into account all the existing theoretical and practical experience accumulated in the linear approach. After presenting the rules to treat each type of constraint, we describe our algorithm, and then apply it to obtain semidefinite relaxations for three classical combinatorial problems: the K-CLUSTER problem, the Quadratic Assignment Problem, and the Constrained-Memory Allocation Problem. We show that we obtain better SDP relaxations than the previous ones, and we report computational experiments for the three problems.

51 citations

Journal ArticleDOI
TL;DR: This paper provides a polynomial-time algorithm to find the optimal job sequence,Due date values, and resource allocations that minimize an integrated objective function, which includes earliness, tardiness, due date assignment, and total resource consumption costs.
Abstract: In this paper, we consider a single-machine earliness-tardiness scheduling problem with due-date assignment, in which the processing time of a job is a function of its position in a sequence and its resource allocation. The due date assignment methods studied include the common due date, and the slack due date, which reflects equal waiting time allowance for the jobs. For each combination of due date assignment method and processing time function, we provide a polynomial-time algorithm to find the optimal job sequence, due date values, and resource allocations that minimize an integrated objective function, which includes earliness, tardiness, due date assignment, and total resource consumption costs.

51 citations

Journal ArticleDOI
TL;DR: A novel approach that is based on artificial bee colony algorithm (ABC) to address dynamic task assignment problems in multi-agent cooperative systems and shows that ABC improves these two criteria significantly with respect to the other approaches.
Abstract: The task assignment problem is an important topic in multi-agent systems research. Distributed real-time systems must accommodate a number of communication tasks, and the difficulty in building such systems lies in task assignment (i.e., where to place the tasks). This paper presents a novel approach that is based on artificial bee colony algorithm (ABC) to address dynamic task assignment problems in multi-agent cooperative systems. The initial bee population (solution) is constructed by the initial task assignment algorithm through a greedy heuristic. Each bee is formed by the number of tasks and agents, and the number of employed bees is equal to the number of onlooker bees. After being generated, the solution is improved through a local search process called greedy selection. This process is implemented by onlooker and employed bees. In greedy selection, if the fitness value of the candidate source is greater than that of the current source, the bee forgets the current source and memorizes the new candidate source. Experiments are performed with two test suites (TIG representing real-life tree and Fork---Join problems and randomly generated TIGs). Results are compared with other nature-inspired approaches, such as genetic and particle swarm optimization algorithms, in terms of CPU time and communication cost. The findings show that ABC improves these two criteria significantly with respect to the other approaches.

51 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202331
202298
2021303
2020339
2019342
2018326