A ranking-based differential evolution algorithm for hybrid flow shop sustainable scheduling

With the increasing of environmental pressure, the sustainable production for hybrid flow shops (HFS) has attracted more attention due to its broad industrial applications. In implementing the sustainable production of HFS, the selection of parallel machines for various jobs is a vital step. In light of this, a multi-objective mathematical model for minimization of makespan and energy consumption of HFSP was formulated. The sustainability of parallel machines were evaluated and ranked according to fuzzy TOPSIS method. To solve the multi-objective model of HFSP, an improved differential evolution algorithm was presented to assign the job with the ranked parallel machines, which can narrow the search scope and accelerate the convergence speed. Finally, a case study was presented to evaluate the effectiveness of the proposed ranking-based algorithm and to prove the feasibility of the model. The results showed that the proposed improved algorithm outperforms NSGA-II and PSO in searching for non-dominated solutions, which can effectively solve the sustainable production of HFSP.

FLOWSHOP SCHEDULING WITH SEQUENCE DEPENDENT SET-UP TIME USING IMMUNE ALGORITHM TO REDUCE MAKESPAN IN PT. XYZ BANDUNG AS A TEXTILE INDUSTRY

In manufacturing operations, production system is certainly an important aspect. Thus, company should do the planning and production control well in order to have satisfactory system. One of the production planning process is scheduling. This process is one of the main process in production planning. The urge of doing the scheduling process is due to scheduling is the details of the planning process. The production scheduling in textile industry is considered as the industry with the hybrid flow shop scheduling type. Moreover, in this company there are several machine have the characteristic of sequence dependent setup time. Thus, in order to get the optimal solution this research used Immune Algorithm. This algorithm combines an efficient mutation to cooperate the pursuit for a near-optimal solution. Keywords—Hybrid flow shops, Sequence Dependent Setup Times, Makespan, Immune Algorithm

Minimizing The Number of Tardy Jobs in Hybrid Flow Shops with Non-Identical Multiple Processors

Two-stage hybrid flow shops (a.k.a., flow shops with multiple processors (FSMPs)) are studied wherein the multiple processors at a stage are non-identical, but related (a.k.a., uniform) in their processing speeds. The impact of ten different dispatching procedures on a due-date based criterion (specifically, the number of tardy jobs) is analyzed over a set of 1,800 problems of varying configurations wherein the number of jobs per problem is between 20 and 100 and their due dates are randomly assigned. Results indicate that the modified due date (MDD), earliest due date (EDD), slack (SLK), shortest processing time (SPT), and least work remaining (LWR) rules are statistically inseparable but yield superior performance to the other rules included in this study. The longest processing time (LPT) and most work remaining (MWR) rules provide the poorest performance.