The shortest path from S to S ′ is found in PATH list as S−3−D−D ′−1−S ′ which has the least distance by using Algorithm 2. Ultimately, a pair of disjoint paths are found as S−3−D and S−1−D, their multi-path SHM is 0 and C is 1, which meets the requirement of Problem 1. Fig. 5 The example of algorithm.
These cards were provided to the learners as a requirement of problem-posing.
It included producing aesthetically pleasing products that met the specific needs, cost, and functional requirements of the problem at hand.
Such conflicts usually develop either because the designer tries to carry out an aesthetic concept that ignores the technical and functional requirements of the problem or because the program is so demanding technically, functionally, or economically that it eliminates aesthetic considerations from the design process.
The algorithm was designed carefully by considering three major technological requirements of the problem: dual visibility, reliability, and efficiency.
These criteria should be identified under each of the k criteria (C1, C2,…, C h, Ch+1,…, C k ) considering the specific requirements of the problem.
The fuzzy requirements of the problem (3.1) can be quantified by electing a membership function μ ( f ( x ) ) (Figure 2) which is differentiable in the open interval f ( x 1 ) < f ( x ) < f ( x 0 ) where μ ( f ( x ) ) is defined by μ ( f ( x ) ) = { 1, f ( x ) ≤ f ( x 1 ), f ( x ) − f ( x 1 ) f ( x ¯ 0 ) − f ( x ¯ 1 ), f ( x 1 ) ≤ f ( x ) ≤ f ( x 0 ), 0, f ( x ) ≥ f ( x 0 ), (4.6).
Namely, we adapt the multiple plan approach (MPA, see Algorithm 8) and the multiple scenario approach (MSA, see Algorithm 9) to the requirements of the problem at hand.
The first solution method we adapt to the requirements of our problem is a dynamic variable neighborhood search (VNS), presented for the DSDARP by Schilde et al. (2011).
