Machine Flow Shop Scheduling Research Articles

Comparison of Firefly algorithm and Artificial Immune System algorithm for lot streaming in -machine flow shop scheduling

Abstract Lot streaming is a technique used to split the processing of lots into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production time (makespan). The objective of this paper is to minimize the makespan and total flow time of n-job, m-machine lot streaming problem in a flow shop with equal and variable size sublots and also to determine the optimal sublot size. In recent times researchers are concentrating and applying intelligent heuristics to solve flow shop problems with lot streaming. In this research, Firefly Algorithm (FA) and Artificial Immune System (AIS) algorithms are used to solve the problem. The results obtained by the proposed algorithms are also compared with the performance of other worked out traditional heuristics. The computational results shows that the identified algorithms are more efficient, effective and better than the algorithms already tested for this problem.

A Flow Shop Scheduling Problem with Transportation Time and Separated Setup Time of Jobs

A flowshop scheduling problem has been one of the classical problems in production scheduling since Johnson proposed the well-known Johnson's rule in the two-stage flow-shop make-span scheduling problem. Mitten and Johnson (1959) separately gave solution algorithm of obtaining an optimal sequence for an ′n-job, 2-machine′ flow shop scheduling problem in which each job involves arbitrary time lags (start-lag, stop-lag). Also Maggu and Das (1980) gives solution algorithm of obtaining optimal sequence for a ′ n-job, 2-machine′ flow shop scheduling problem where in each job involves transportation time. Johnson (1959) presented a solution to the n-job, 2-machine flow-shop problem with an algorithm that produces an ordered sequence with minimum total elapsed time. Kim and Bobrowski (1994) present a computer simulation model for a limited machine job shop scheduling problem with sequence-dependent setup times. They study the influence of setup times and due date's information in priority rules performance for job-shop problem with setup times. S. M. Johnson (1954) presents optimal two and three stage production schedules with setup time included. Wlodzimierz Szwarc (1986) discussed the flow shop problem with time lags and separated setup times. C.T. Ng et al. (2007) presents the three-machine flow-shop scheduling problem to minimize maximum lateness with separate setup times. W. H. Yang et al. (1999) addressed a survey of scheduling research involving setup times. An overall view on set up times in sequencing problems is presented. In this paper, we addressed the three machine flow shop scheduling problem to minimize total completion time where setup times are treated as separate from processing times. Two cases are studied, taking into consideration these factors and the set up times separated from the processing times. This new algorithm provides an optimal scheduling sequence for flow-shop scheduling problems of 5-jobs on 3-machines and is proposed by using separated setup times of a job.The present paper is a modest attempt to investigate how the setup time is helpful for the production.

Proposed method for solving the flow shop scheduling problem for n-jobs on m-machines

This research suggested a new heuristic method for solving the n-jobs, m- machines flow shop scheduling problem to minimize total completion time. This method was compared with The (NEH) method for data generated by using (matlab) and showed high efficiency and easy to use.

Two machine flow shop scheduling problem with weighted WIP costs

This paper considers two new machine flow shop scheduling problems where the objective is to minimize the total WIP (work-in-process) cost. In this type of problems, the unit time WIP cost increases or decreases as a job passes through a series of stages in the production process. We first introduce this relatively new class of flow shop scheduling problems. Then, we establish the complexity of several problems with different types of job restrictions and weighted cost characteristics. For some tractable cases, we propose optimal solution procedures. Finally, a heuristic is introduced for a class of problem instances, and we find upper bounds on the relative errors.

Polynomial algorithms for single machine scheduling problems with financial constraints

This paper deals with the single machine scheduling problem with multiple financial resource constraints. It is shown that the problem can be reduced to the two machine flow shop scheduling problem if the financial resources arrive uniformly over time, and it is also shown that the LPT (Largest Processing Time) rule generates an optimal solution to the problem if the financial resources are consumed uniformly by all the kobs. Hence there exist polynomial algorithms for these two special cases of the problem.

Two machine flow shop scheduling problem with no wait in process: controllable machine speeds

The paper considers a two machine flow shop scheduling problem with no wait in process. The objective is to find the optimal values of machine speeds and to determine the time-optimal schedule. There are two machines, A and B, and a set of n jobs each consisting of two operations. The first operation of each job is to be processed on machine A and the second on machine B. For each feasible schedule, the second operation of each job starts at the same time the first operation of this job is completed (the no wait condition). Machine speeds are supposed to be controllable and should be selected in order to minimize a certain penalty function. The objective function depends on both profits resulting from the reduction of the makespan and expenditures for increasing the machine speeds. An O( n 3) algorithm is described to solve the problem.

An efficient heuristic based on machine workload for the flowshop scheduling problem with setup and removal

We are concerned in this paper with solving ann jobs,M machines flowshop scheduling problem where the objective function is the minimization of the makespan. We take into account setup, processing and removal times separately. After drawing up a synthesis of existing work which addresses this type of problems, we propose a new heuristic algorithm which is based on the machine workload to find an efficient permutation schedule. Computational experiences show that our algorithm yields excellent results, particularly when bottleneck machines are present.

Managing a two-bottleneck job shop with a two-machine flow shop algorithm

Abstract This work studies the possibility of using three new sequencing rules in a simple hybrid assembly shop with two bottleneck machines in order to study the conjecture that a job shop may be characterized by its bottleneck structure. A simulation model for a nine-machine job shop is developed and used to compare these rules. The total makespan is the performance measure. The study is made under a combination of three different factors. The effect of the bottleneck machines location is also studied. The results show that some job shops can be managed by simply managing the bottleneck machines, that the three new rules can be used to manage a job shop with two bottlenecks, and that the location of the bottleneck machines is of importance. The results indicate new management rules may be inferred for job shops by studying the application of single and multiple machine flow shop scheduling theory.

On non-permutation solutions to some two machine flow shop scheduling problems

In this paper, we study two versions of the two machine flow shop scheduling problem, where schedule length is to be minimized. First, we consider the two machine flow shop with setup, processing, and removal times separated. It is shown that an optimal solution need not be a permutation schedule, and that the problem isNP-hard in the strong sense, which contradicts some known results. The tight worst-case bound for an optimal permutation solution in proportion to a global optimal solution is shown to be 3/2. An O(n) approximation algorithm with this bound is presented. Secondly, we consider the two machine flow shop with finite storage capacity. Again, it is shown that there may not exist an optimal solution that is a permutation schedule, and that the problem isNP-hard in the strong sense.

Two machine flow shop scheduling problems with sequence dependent setup times: A dynamic programming approach

AbstractThis paper considers the two different flow shop scheduling problems that arise when, in a two machine problem, one machine is characterized by sequence dependent setup times. The objective is to determine a schedule that minimizes makespan. After establishing the optimally of permutation schedules for both of these problems, an efficient dynamic programming formulation is developed for each of them. Each of these formulations is shown to be comparable, from a computational standpoint, to the corresponding formulation of the traveling salesman problem. Then, the relative merits of the dynamic programming and branch and bound approaches to these two scheduling problems are discussed.