Multi-stage hybrid evolutionary algorithm for multiobjective distributed fuzzy flow-shop scheduling problem.

In the current global cooperative production mode, the distributed fuzzy flow-shop scheduling problem (DFFSP) has attracted much attention because it takes the uncertain factors in the actual flow-shop scheduling problem into account. This paper investigates a multi-stage hybrid evolutionary algorithm with sequence difference-based differential evolution (MSHEA-SDDE) for the minimization of fuzzy completion time and fuzzy total flow time. MSHEA-SDDE balances the convergence and distribution performance of the algorithm at different stages.

Applying GA-PSO-TLBO approach to engineering optimization problems.

Under addressing global competition, manufacturing companies strive to produce better and cheaper products more quickly. For a complex production system, the design problem is intrinsically a daunting optimization task often involving multiple disciplines, nonlinear mathematical model, and computation-intensive processes during manufacturing process. Here is a reason to develop a high performance algorithm for finding an optimal solution to the engineering design and/or optimization problems.

Green closed-loop supply chain optimization strategy considering CER and incentive-compatibility theory under uncertainty.

Under the uncertain market demand and quality level, a total profit model of green closed-loop supply chain system (GCL-SCS) considering corporate environmental responsibility (CER) and government differential weight subsidy (GDWS) is constructed. Based on incentive-compatibility theory, the optimal subsidy allocation policy and green investment level were explored. Fuzzy chance-constrained programming (FCCP) is used to clarify the uncertainty factors of this model; while genetic algorithm (GA) and CPLEX are used to find and compare a calculating example's approximate optimal solution about this model.

Multiobjective particle swarm optimization with direction search and differential evolution for distributed flow-shop scheduling problem.

As a classic problem of distributed scheduling, the distributed flow-shop scheduling problem (DFSP) involves both the job allocation and the operation sequence inside the factory, and it has been proved to be an NP-hard problem. Many intelligent algorithms have been proposed to solve the DFSP. However, the efficiency and quality of the solution cannot meet the production requirements.