Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems

• An emerging AI metaheuristic referred to as Cohort Intelligence is discussed.
• Cohort Intelligence method is applied for solving three combinatorial problems.
• They are assignment; cross-border shipper selection and Sea-Cargo mix problem.
• The CI results are compared with the CPLEX, LP relaxation.
• A multi-random-start-local search (MRSLS) method is developed for all the three problems.
• The MRSLS results are compared with the Cohort Intelligence method.

The real world problems in the supply-chain domain are generally constrained and combinatorial in nature. Several nature-/bio-/socio-inspired metaheuristic methods have been proposed so far solving such problems. An emerging metaheuristic methodology referred to as Cohort Intelligence (CI) in the socio-inspired optimization domain is applied in order to solve three selected combinatorial optimization problems. The problems considered include a new variant of the assignment problem which has applications in healthcare and inventory management, a sea-cargo mix problem and a cross-border shipper selection problem. In each case, we use two benchmarks for evaluating the effectiveness of the CI method in identifying optimal solutions. To assess the quality of solutions obtained by using CI, we do comparative testing of its performance against solutions generated by using CPLEX. Furthermore, we also compare the performance of the CI method to that of specialized multi-random-start local search optimization methods that can be used to find solutions to these problems. The results are robust with a reasonable computational time and accuracy.