In recent years stock exchange has become one of the most attractive and growing businesses in respect of investment and profitability. But applying a scientific approach in this field is really troublesome because of variety and complexity of decision making factors in the field. This paper tries to deliver a new solution for portfolio selection based on multi criteria decision making literature and clustering approach. The paper results reveal that four basic indexes (including: leverage indices, efficiency ratio, liquidity ratio, and market value ratio) besides twenty four secondary indexes were effective in portfolio selection.Multi attribute decision making METHOD (MADM) and clustering were used as analytical tools. The cases of this study embrace pharmaceutical companies accepted in Tehran Stock Exchange. For clustering companies, they were divided into two clusters and finally the companies which located in optimum cluster were ranked via SD HEURISTICS METHOD. This METHOD eliminates considerable amount of cases and by doing so it helps the experts to sort and rank the alternatives more effectively. It's recommended by researchers to apply qualitative METHODs in addition to quantitative ones.

Quadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic METHODs, which are presented for QAP in the literature. In this paper, we applied 2-opt, greedy 2-opt, 3-opt, greedy 3-opt, and VNZ as heuristic METHODs and tabu search (TS), simulated annealing, and particle swarm optimization as meta-heuristic METHODs for the QAP. This research is dedicated to compare the relative percentage deviation of these solution qualities from the best known solution which is introduced in QAPLIB. Furthermore, a tuning METHOD is applied for meta-heuristic parameters. Results indicate that TS is the best in 31% of QAPs, and the IFLS METHOD, which is in the literature, is the best in 58 % of QAPs; these two METHODs are the same in 11% of test problems. Also, TS has a better computational time among heuristic and meta-heuristic METHODs.

The concept of graph burning and burning number (bn(G)) of a graph G was introduced recently [4]. Graph burning models the spread of contagion (, re) in a graph in discrete time steps. bn(G) is the minimum time needed to burn a graph G. The problem is NP-complete. In this paper, we develop , rst HEURISTICS to solve the problem in general (connected) graphs. In order to test the performance of our algorithms, we applied them on some graph classes with known burning number such as ,-graphs. We tested our algorithms on DIMACS and BHOSLIB that are known benchmarks for NP-hard problems in graph theory. We also improved the upper bound for burning number on general graphs in terms of their distance to cluster. Then we generated a data set of 1000 random graphs with known distance to cluster and tested our HEURISTICS on them.

Two major research programs on the use of HEURISTICS in decision-making exist, and each has its own set of followers. The first program was initiated by Kahneman and Tversky in the 70s. They primarily concentrated on the errors caused by using HEURISTICS. This has now grown into many HEURISTICS named after their associated biases. The second program was initiated by Gigerenzer and colleagues in Germany. Gigerenzer argues that although simple HEURISTICS sometimes leads to “, biased”,decisions, they can deliver better answers in some situations. This is particularly true for uncertain or complex environments, where there is only a small data sample or there is no time to formally seek an optimized decision. Gigerenzer and colleagues have generated a substantial body of evidence that humans use simple HEURISTICS, often with great results. Like Kahneman and Tversky, Gigerenzer’, s work has attracted researchers exploring the power of “, Fast and Frugal”,HEURISTICS, and how they are used by humans. This part, of the four-part paper, discusses Kahneman and Gigerenzer's findings concerning engineering decision making. Avoiding errors when using simple HEURISTICS is discussed in Part IV.