Abstract
In this study, a multi-resource agent bottleneck generalized assignment problem (MRBGAP) is addressed. In the bottleneck generalized assignment problem (BGAP), more than one job can be assigned to an agent, and the objective function is to minimize the maximum load over all agents. In this problem, multiple resources are considered and the capacity of the agents is dependent on these resources and it has minimum two indices. In addition, agent qualifications are taken into account. In other words, not every job can be assignable to every agent. The problem is defined by considering the problem of assigning jobs to employees in a firm. BGAP has been shown to be NP- hard. Consequently, a multi-start iterated tabu search (MITS) algorithm has been proposed for the solution of large-scale problems. The results of the proposed algorithm are compared by the results of the tabu search (TS) algorithm and mixed integer linear programming (MILP) model.
Highlights
Assignment problems (AP) are an important topic which is frequently studied in the literature
The multi-resource agent bottleneck generalized assignment problem (MRBGAP) problem is the generalized version of generalized assignment problem (GAP) and is a more difficult problem to solve
Due to the NP hardness of the problem, a multi- start iterated tabu search algorithm is proposed for the solution of the problem
Summary
Assignment problems (AP) are an important topic which is frequently studied in the literature. Each agent should be assigned a job (oneto-one) Another class of the AP is generalized assignment problem (GAP). Chakravarthy et al [24], proposed a heuristic algorithm for bottleneck generalized assignment problem. Yagiura et al [27], proposed a new algorithm for multi-resource generalized quadratic assignment problem. Many studies in the literature propose an heuristic algorithm for GAP. Wu et al [10], Souza et al [20], Sethanan and Pitakaso [21], and Moussavi et al [31] proposed an heuristic algorithm for the generalized assignment problem. Difference from the literature, in this study an heuristic algorithm is proposed for the multi-resource agent bottleneck generalized assignment problem with agent qualifications. Experimental results are given and conclusions are given in the final section
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