A conceptual fuzzy-genetic algorithm framework for assessing the potential risks in supply chain management

Supply chain risk management and artificial intelligence: state of the art and future research directions

“…Selim and Ozkarahan (2008) employ a simple feedback loop to modify coefficient values until an acceptable solution is reached. Tang, Lau, and Ho (2008) propose a two-phase knowledge framework for risk assessment, with experts formulating the initial input to genetic algorithms. Micheli, Mogre, and Perego (2014) propose a decision support system that involves the decision maker throughout the SCRM process, from the identification of risks and corresponding mitigation measures and budget to the definition of risk profiles in terms of likelihood and impact.…”

“…Micheli, Mogre, and Perego (2014) propose a decision support system that involves the decision maker throughout the SCRM process, from the identification of risks and corresponding mitigation measures and budget to the definition of risk profiles in terms of likelihood and impact. It should also be noted that the work of Tang, Lau, and Ho (2008) is the only fuzzy programming study that focuses on risk assessment, rather than risk mitigation, as is the case for all others. Table B5 in Appendix 2 includes all reviewed studies that fall under the fuzzy programming category.…”

Enterprise Risk Management in Supply Chains

“…Tang et al applied a fuzzy genetic algorithm approach to evaluate logistics strategies to lower supply chain risks. 32 As mentioned in the cases section, Hewlett-Packard applies regression models as the basis of forecasting, and optimization models to select how much to purchase from each available source. 33 Bogataj and Bogataj used parametric linear programming based on net present value to estimate supply chain vulnerability.…”

A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

“…They used a greedy heuristic, which constructs a schedule by sequentially selecting shifts, from a very large set of pre-generated legal potential shifts, to cover the remaining work. Tang et al 33 proposed using a fuzzygenetic algorithm (GA) intelligent framework embedded with performance measurement. A fuzzy-GA approach was developed to include fuzzy rule sets with the associated membership functions in one chromosome.…”

“…For our example, 3-jobs × 2-machines, the following operations will take place in order in the rows and columns of the table. Operations: 11,12,13,21,22,23,31,32,33; in this series, the term 11 is the first work's first operation, 12 is the first work's second operation, and similarly 33 is the third work's third operation. Write the percent deviation values of each operation to the related row and column of the table considering the second operation series or chromosome.…”