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Modified cuckoo optimization algorithm (MCOA) to solve precedence constrained sequencing problem (PCSP)
Maadi M., Javidnia M., Ramezani R. Applied Intelligence48 (6):1407-1422,2018.Type:Article
Date Reviewed: Jan 7 2019

The precedence constrained sequencing problem (PCSP) is a general problem:

Consider a set of jobs where an arbitrary precedence relationship exists among the jobs and a cost is associated with every permutation of the jobs. The objective is to find a minimum-cost permutation which is consistent with the precedence relations. [1]

One of the earliest works in this field is by Monma [1]. It is one of the many problems “related to locating the optimal sequence with the shortest traveling time among all feasible sequences.” Maadi et al. go on define the problem’s motivation: “applications in network[ing], scheduling, project management, logistics, assembly flow, and routing.” Due to its various practical applications, PCSP is considered “a useful tool for a variety of industrial planning and scheduling problems.” As the authors explain, “new metaheuristic algorithms have been developed to solve optimization problems,” for example, Rajabioun’s cuckoo optimization algorithm (COA) [2].

In this paper, Maadi et al. attempt to modify COA and apply the approach to PCSP. One aspect that they handle is the meaning of distance in the discrete solutions space, since “the concept of operators [in] COA is based on space and geometrical laws of solutions.”

The authors compare their proposed modified cuckoo optimization algorithm (MCOA) with other existing methods, including a genetic algorithm and a forest optimization algorithm. Empirical results are presented for two types of problem instances of PCSP. In the first instance, multiple algorithms achieve the same optimal result, so the results themselves are not particularly indicative. In the second instance, however, the proposed MCOA approach performs better than the other algorithms.

While the algorithm itself is clearly defined, the importance of the computation results is not immediately clear. It appears that more empirical tests are needed--on different problem instances and of different sizes--to fully understand the merits of this approach.

Reviewer:  Amrinder Arora Review #: CR146372 (1904-0135)
1) Monma, C. L. Sequencing with general precedence constraints. Discrete Applied Mathematics 3, 2(1981), 137–150.
2) Rajabioun, R. Cuckoo optimization algorithm. Applied Soft Computing 11, 8(2011), 5508–5518.
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