Scheduling parallel-batching processing machines problem with learning and deterioration effect in fuzzy environment

In this paper, a problem of scheduling jobs with different sizes and fuzzy processing times (FPT) on non-identical parallel batch machines to minimize makespan is investigated. Moreover, the processing time (PT) of each batch is subject to the location-based learning and total-PT-based deterioration effect. Since this is an NP-hard combinatorial optimization problem, an improved intelligent algorithm based on fruit fly optimization algorithm (IFOA) is proposed. To verify the performance of the algorithm, the IFOA is compared with three state-of-the-art algorithms. The comparative results demonstrate that the proposed IFOA outperforms the other compared algorithms.

A genetic algorithm for fuzzy order acceptance and scheduling problem

In light of the imprecise and fuzzy nature of real production environments, the order acceptance and scheduling (OAS) problem is associated with fuzzy processing times, fuzzy sequence dependent set up time and fuzzy due dates. In this study, a genetic algorithm (GA) which uses fuzzy ranking methods is proposed to solve the fuzzy OAS problem. The proposed algorithm is illustrated and analyzed using examples with different order sizes. As illustrative numerical examples, fuzzy OAS problems with 10, 15, 20, 25, 30 and 100 orders are considered. The feasibility and effectiveness of the proposed method are demonstrated. Due to the NP-hard nature of the problem, the developed GA has great importance to obtain a solution even for big scale fuzzy OAS problem. Also, the proposed GA can be utilized easily by all practitioners via the developed user interface.

Fuzzy Parallel Machine Scheduling Problem Under Fuzzy Job Deterioration and Learning Effects With Fuzzy Processing Times

This chapter presents a mixed integer non-linear programming (MINLP) model for a fuzzy parallel machine scheduling problem under fuzzy job deterioration and learning effects with fuzzy processing times in order to minimize fuzzy makespan. The uncertainty of parameters such as learning/deterioration effects and processing times in a scheduling problem makes the solution of the problem uncertain. Fuzzy sets can be used to encode uncertainty in parameters. In this chapter, possibilistic distributions of fuzzy parameters and possibilistic linear programming approaches are used in order to create a solution method for MINLP model of fuzzy parallel machine scheduling problem.

Genetic-Based Optimization of the Manufacturing Process of a Robotic Arm under Fuzziness

Fuzziness is a key concern in modern industry and, thus, its implementation in manufacturing process modeling is of high practical importance for a wide industrial audience. The scientific contribution of the present attempt lies on the fact that the assembly line balancing problem of type 2 (SALBP-2) is approached for a real manufacturing process by introducing fuzzy processing times. The main scope of this work is the solution of the SALBP-2, which is an NP-hard problem, for a real manufacturing process considering fuzziness in the processing times. Since the data obtained from realistic situations are imprecise and uncertain, the consideration of fuzziness for the solution of SALBP-2 is of great interest. Thus, real data values for the processing times are gathered and estimated with uncertainty. Then, fuzzy processing times are used for finding the optimum cycle time. The optimization tool for the solution of the fuzzy SALBP-2 is a Genetic Algorithm (GA). The validity of the proposed approach is tested on the construction process of a metallic robotic arm. The experimental results demonstrate the effectiveness and efficiency of the proposed GA in determining the optimum sequence of the tasks assigned to workstations which provides the optimum fuzzy cycle time.

MODELING, OPTIMISATION AND ANALYSIS OF RE-ENTRANT FLOWSHOP JOB SCHEDULING WITH FUZZY PROCESSING TIMES

This paper presents a makespan minimization of -jobs -machines re-entrant flow shop scheduling problem (RFSP) under fuzzy uncertainties using Genetic Algorithm. The RFSP objective is formulated as a mathematical programme constrained by number of jobs and resources availability with traditional scheduling policies of First Come First Serve (FCFS) and the First Buffer First Serve (FBFS). Jobs processing times were specified by fuzzy numbers and modelled using triangular membership function representations. The modified centroid defuzzification technique was used at different alpha-cuts to obtain fuzzy processing times (FPT) of jobs to explore the importance of uncertainty. The traditional GA schemes and operators were used together with roulette wheel algorithm without elitism in the selection process based on job fuzzy completion times. A test problem of five jobs with specified Job Processing and Transit Times between service centres, Job Start Times and Job Due times was posed. Results obtained using the deterministic and fuzzy processing times were compared for the two different scheduling policies, FCFS and FBFS. The deterministic optimal makespan for FBFS schedule was 61.2% in excess of the FCFS policy schedule.  The results also show that schedules with fuzzy uncertainty processing times provides shorter makespans than those for deterministic processing times and those under FCFS performing better than those under FBFS policy for early jobs while on the long run the FBFS policy performs better. The results underscore the need to take account of comprehensive fuzzy uncertainties in job processing times as a trade-off between time and costs influenced by production makespan. http://dx.doi.org/10.4314/njt.v36i3.21