Papers by Keyword: Flow Shop

Abstract: This paper proposes a mathematical model for production scheduling, whose objective is to maximize the profits or Throughput of a company in the food sector through a Flexible Hybrid Flow, based on the theory of constraints. Considering the company's production configuration, which is a two-stage hybrid flow line, a mixed integer linear model programming (MILP) was formulated and programmed to adequately represent the real situation. The mathematical model developed in this study that is an easy and effective tool that helps to control the production process, by optimizing the quantities of each product to be produced, as well as establishing the sequence in which they must be carried out, which becomes an advantage against its competitors and also obtain a timely response to the needs of demand and compliance with the commitments made to its customers. The results obtained with the MILP, with reasonable computational times, allow for maximizing profits, considering the constraints of the problem.

Abstract: Much of the scheduling theory assumes that machines are always available to process jobs at any time during the scheduling horizon. However, machines may be unavailable for various reasons in realistic practices, such as unexpected failures or variable maintenance activities. This article discusses in depth the works published in the literature of joint scheduling of jobs and variable maintenance activities in the flowshop sequencing problems. Our literature review focuses first on the basic concepts of scheduling problems, and more specifically, the scheduling strategies of production and maintenance that have been identified in the literature. Subsequently, we focus our attention on the principal methods for solving scheduling problems, while presenting in the following the main published works for the aforementioned systems. Lastly, a comparative analysis is carried out to highlight the fundamental ideas leading to the adoption of an effective approach capable of producing an optimal solution in a reasonable calculation time.

Abstract: In the paper a job shop and flow shop scheduling problems with availability time constraint for maintenance are considered. Unavailability time due to maintenance is estimated basing on information about predicted Mean Time To Failure/To First Failure and Mean Time of Repair of a machine. Maintenance actions are introduced into a schedule to keep the machine available in a good operation condition. The efficiency of predictive schedules (PS) is evaluated using criteria: makespan, flow time, total tardiness, idle time. The efficiency of reactive schedules (RSs) is evaluated using criteria: solution and quality robustness. For basic schedule generation Multi Objective Immune Algorithm is applied. For predictive scheduling Minimal Impact of Disturbed Operation on the Schedule is applied. After doing computer simulations for the job shop scheduling problem following question arises: do dominated Pareto optimal basic schedules achieve better PSs Although a single Pareto-optimal solution is achieved on Pareto-optimal frontier three different schedules have the same quality in the flow shop scheduling problem. The question is: which schedule is the most robust solution

Abstract: In this research article aims to develop a heuristic for minimizing the total material processing time and idle time of the critical machine in a flow shop. This heuristic is proposed through the Exponential Distribution (ED) factor which helps in developing a mathematical model with less computational instance. When the idle time of the critical machine is reduced then it is indirectly affects the total material processing time of the flow shop, which makes the objective of critical machine utilization as vital. And a set of constrain had been established to position the critical machines in the flow shop. For several instance, computational experiments had done using the benchmark problems, among other heuristics ED heuristic yields a better result.

Abstract: A mathematical model forlot streaming problem with preventive maintenance was proposed. A mixed-integer linear model for multiple-product lot streaming problems was also developed. Mixed-integer programming formulation was presented which will enable the user to identify optimal sublot sizes and sequences simultaneously. Two situations were considered:1) all machines were available, and 2) all machines needed preventive maintenance tasks. For both situations a new mixed-integer formulation was developed. To demonstrate the practicality of the proposed model, numerical example was used. It showed that the percentage of make span reduction due to lot streaming in permutation flow shop is 54% when compared to consistent sublots with intermingling case.

Abstract: In this note, we consider the machine scheduling problems with the effects of learning and deterioration. In this model, job processing times are defined by functions dependent on their starting times and positions in the sequence. The scheduling objectives are makespan, sum of completion times. It is shown that even with the introduction of learning effect and deterioration jobs to job processing times, several flow shop problems remain polynomially solvable.

Abstract: This note studies the flow shop scheduling problems with the effects of exponential learning and simple linear deterioration. The objective functions are to minimize makespan, total completion, the sum of the th power of completion times and total lateness. We show that several flow shop problems can be solved in polynomial time.

Abstract: The joint optimization problem of product schedule and preventive maintenace in the flow shop is NP hard. In this paper, a joint optimization model was firstly established by abstracting the tobacco production process into a equivalent two-machine flow shop. And then a simplified method was proposed to solve the model according to the characteristic of the problem. Finally, a case was used to verify the validity of the model and the effectiveness of the method.

Abstract: Due to the pressure in a shop floor to complete a set of jobs within due date, all the materials are to be processed within a minimum possible time. This is linked to the problem of finding an optimal sequence in terms of total completion time (Make Span) for processing ‘n’ jobs in ‘m’ processing centers in the shop floor. The problem is NP hard as the total number of sequences is (n!) for a permutation flow shop scheduling problem. In the shop floor, due to the limitation in the computing capabilities and computer knowledge, still the Classical heuristics are popular because of their simplicity. However, in most of the cases, we get only one sequence except in CDS algorithm where the best sequence is selected from (m-1) alternatives. This paper deals with optimization of total material processing time in a manufacturing flow shop using the concept of Dummy Machine in one Heuristics proposed by the authors, based on the Pascal’s triangle. In such cases, more sequences having make spans which may be optimal/ near optimal can be obtained. This results in optimization of total material processing time and enables the shop floor supervisor to complete the jobs within a minimum possible time.

Abstract: For the flow shop scheduling problem which aims to minimize makespan, this paper gives a new derivation about its mathematical definition, and mining characteristics of the problem itself further. By which analysis, the new heuristic method proposed in the paper shorten the waiting time of each job as much as possible on the basis of reduce the processing time of the first machine and last job. The result of simulation experiments shows that, our new heuristic algorithm has good performance, and the average quality and stability of scheduling sequences generated by new method is significantly better than other heuristic algorithm which has the same complexity.