A novel hybrid differential evolution approach to scheduling of large-scale zero-wait batch processes with setup times

Optimal Scheduling of Flowshop Batch Process for Zero-Wait and No Intermediate Storage Transfer Policy

“…Four products namely P 1 , P 2 , P 3 and P 4 based on four unit operation S 1 , S 2 , S 3 , and S 4 are produced in a batch process and shown in table 3 {Ming et al, [16]}. Gantt chart observed several possible paths ( Fig.3) that can estimate of makespan and also observed that there is some discontinuity path which refer to the idle time between stages (Table 4) which must estimate this value of idle time before determining of makespan due to the fact of accordance with zero-wait transfer policy so, in determination of makespan it is important to estimate the value of idle time between stages.…”

“…Table 6. The idle time between stages for optimal sequence (P 2 -P 1 -P 3 -P 4 ) of Example 1 The summary of results of the proposed method is shown in table 7 along with those of Ming et al [16] where the total completion time (makespan ) using HPDE of feasible production sequence (P 1 P 2 P 3 P 4 ) with value 260 hr and the optimal production sequence (P 2 P 1 P 3 P 4 ) with value 244 hr which confirmed with results obtained and ensures our strategy.…”

“…Considering setup times have significant influent on batch operations, complex scheduling descriptions with transfer and setup times under ZW Policy are also studied by Jung, Lee, Yang, and Lee [8], Kim, Jung, and Lee [10], Lee et al [15], Shafeeq et al [20] where they proposed a matrix-based completion algorithm for ZWSP in multiproduct batch processes. Recently, M-G Dong et al [16] proposed a novel hybrid permutation based differential evolution for zero-wait scheduling problems of batch plant with high quality solution in short computational time. Most of the currently available methods for determining makespan are based on complex mathematical programming techniques mostly use of mathematical methods such as mixed integer linear programming (MILP) and mixed integer nonlinear programming (MINLP) in all the formulations for determining minimum makespan for batch processes (Jung et al, [8]; Kim et al, [10]; Moon et al, [17]; Biegler et al, [1]; Das et al, [4]; Caraffa et al, [21]; Burkard et al, [3]; Dupont and Dhaenens-Flipo, [14]; Lee et al, [15]; Ryu and Pistikopoulos Efstratios,[19].…”

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

“…It was applied with great success for solving different types of problems from different areas. The problems tackled are both benchmarks [11][12][13][14][15][16][17] and real-life [18][19][20][21]. Some examples for chemical engineering applications include: oxidation processes [22][23][24], energy, fuels and petrol derivatives [25][26][27][28][29], fermentation [30][31][32].…”

Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

“…Senties et al (2010) embedded an artificial neural network embedded into a multiobjective GA for multi-decision scheduling problems in a semiconductor wafer fabrication environment. Dong and Wang (2012) presented a hybrid permutationbased differential evolution for scheduling of large-scale zero-wait batch processes with setup times. Costa (2015) developed a hybrid genetic optimisation approach with two stage encoding and a local search for solving a real-world parallel machines scheduling problem from the pharmaceutical environment.…”