Selecting Equipment for Flexible Flow Shops

The International Journal of Advanced Manufacturing Technology, 2016

The focus of this paper is on the treatment of a reentrant and flexible flow shop problem in which the processing times of the jobs at some stage may depend on the decisions made for the jobs at stages before and after the current stage, that is, they may depend on the machine sequence the jobs take in the processing flow. The problem was encountered in a cutting stock application embedded in the context of a virtual organisation. A mathematical model capturing the issues of reentrancy and machine sequence dependency is given. Solution procedures using a mixedinteger programming (MIP) solver and two metaheuristics, simulated annealing and tabu search are presented. The feasibility of the approach is established by computational tests with 30 randomly generated problem instances. The optimal results were obtained for all instances up to ten clients and five service providers and one instance with 15 clients and five service providers. The rest of the results were within the limits provided by the MIP solver.

International Journal of Advanced Manufacturing Technology, Vol. 37, 2008, 354 - 370

In textile industries, production facilities are established as multi-stage production flow shop facilities where a production stage may be made up of parallel machines. It is known as flexible or hybrid flow shop environment. This paper considers the problem of scheduling n independent jobs in such an environment. In addition, we also consider the general case in which parallel machines in each stage may be unrelated. Each job is processed in ordered operations on a machine of each stage. Its release date and due date are given. Preemption of jobs is not permitted. We consider both sequence- and machine-dependent setup times. The problem is to determine a schedule that minimizes a convex combination of makespan and the number of tardy jobs. A 0-1 mixed integer program of the problem is formulated. Since this problem is NP-hard in the strong sense, we develop heuristic algorithms to solve it approximately. Firstly, several basic dispatching rules and well-known constructive heuristics for flow shop makespan scheduling problems are generalized to the problem under consideration. We sketch how from a job sequence a complete schedule for the flexible flow shop problem with unrelated parallel machines can be constructed. To improve the solutions, polynomial heuristic improvement methods based on shift moved of jobs are applied. Then genetic algorithms are suggested. We discuss the components of these algorithms and test their parameters. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.

Computers and Operations Research, Vol. 36, No.2, 2009, 358 - 378

This paper considers a flexible flow shop scheduling problem, where at least one production stage is made up of unrelated parallel machines. Moreover, sequence- and machine-dependent setup times are given. The objective is to find a schedule that minimizes a convex sum of makespan and the number of tardy jobs in a static flexible flow shop environment. For this problem, a 0-1 mixed integer program is formulated. The problem is however a combinatorial optimization problem which is too difficult to be solved optimally for large problem sizes, and hence heuristics are used to obtain good solutions in a reasonable time. The proposed constructive heuristics for sequencing the jobs start with the generation of the representatives of the operation time for each operation. Then some dispatching rules and flow shop makespan heuristics are developed. To improve the solutions obtained by the constructive algorithms, fast polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges of jobs are applied. In addition, metaheuristics are suggested, namely simulated annealing, tabu search and genetic algorithms. The basic parameters of each metaheuristic are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages and with an optimal solution for small-size problems. We have found that among the constructive algorithms the insertion based approach is superior to the others, whereas the proposed simulated annealing algorithms are better than tabu search and genetic algorithms among the iterative metaheuristic algorithms.

Informaticasi, 2008

This article describes the development of a new intelligent heuristic search algorithm (IHSA*) which guarantees an optimal solution for flow-shop problems with an arbitrary number of jobs and machinesprovided the job sequence is constrained to be the same on each machine. The development is described in terms of 3 modifications made to the initial version of IHSA*. The first modification concerns thechoice of an admissible heuristic function. The second concerns the calculation of heuristic estimates as the search for an optimal solution progresses, and the third determines multiple optimal solutions whenthey exist. The first 2 modifications improve performance characteristics of the algorithm and experimental evidence of these improvements is presented as well as instructive examples which illustrate the use of initial and final versions of IHSA*.

The goal of this paper is to investigate scheduling heuristics to seek the minimum of a positively weighted convex sum of makespan and the number of tardy jobs in a static hybrid flow shop environment where at least one production stage is made up of unrelated parallel machines. In addition, sequence -and machine -dependent setup times are considered. The problem is a combinatorial optimization problem which is too difficult to be solved optimally for large problem sizes, and hence heuristics are used to obtain good solutions in a reasonable time. Some dispatching rules and flow shop makespan heuristics are developed. Then this solution may be improved by fast polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges. In addition, metaheuristic proposed is a tabu search algorithm. Three basic parameters (i.e., number of neighbors, neighborhood structure, and size of tabu list in each iteration) of a tabu search algorithm are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.

International Journal of Management Science and Engineering Management

The no-wait two stage flexible manufacturing problem is an important problem in flow shops. This paper investigates the no-wait two stage flexible flow shop with a minimizing mean flow time performance measure. Six meta-heuristic algorithms are developed to solve the problem. In addition some numerical experiments are established to compare the efficiency of the proposed algorithms to each other. The results of the simulation study are illustrated to testify to the performance of the proposed algorithms. This is followed by proposing the most efficient algorithm and giving concluding remarks and potential areas for further research.

American Journal of Applied Sciences, 2007

Scheduling is an important process widely used in manufacturing, production, management, computer science, and so on. Appropriate scheduling can reduce material handling costs and time. Finding good schedules for given sets of jobs can thus help factory supervisors effectively control job flows and provide solutions for job sequencing. In simple flow shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow-shop problem. Flexible flow shops are thus generalization of simple flow shops. In this paper, we propose three algorithms to solve flexible flow-shop problems of more than two machine centers. The first one extends Sriskandarajah and Sethi's method by combining both the LPT and the search-and-prune approaches to get a nearly optimal makespan. It is suitable for a medium-sized number of jobs. The second one is an optimal algorithm, entirely using the search-and-prune technique. It can work only when the job number is small. The third one is similar to the first one, except that it uses Petrov's approach (PT) to deal with job sequencing instead of searchand-prune. It can get a polynomial time complexity, thus being more suitable for real applications than the other two. Experiments are also made to compare the three proposed algorithms. A trade-off can thus be achieved between accuracy and time complexity.

Proceedings of the 15th …, 2010

The main problem of establishing equipment replacement decisions rules under specific conditions is to find decision variables that minimize total incurred costs over a planning horizon. Basically, the rules differ depending on what type of production type is used. For batch production organization methods are suitable criteria built on the principle of economies of scale. Proposed models in this chapter are focused on a multiple machine replacement problem in flexible manufacturing cells that is characterized as a flow shop problem.

Computers, Materials & Continua

Planning and scheduling is one of the most important activity in supply chain operation management. Over the years, there have been multiple researches regarding planning and scheduling which are applied to improve a variety of supply chains. This includes two commonly used methods which are mathematical programming models and heuristics algorithms. Flowshop manufacturing systems are seen normally in industrial environments but few have considered certain constraints such as transportation capacity and transportation time within their supply chain. A two-stage flowshop of a single processing machine and a batch processing machine are considered with their capacity and transportation time between two machines. The objectives of this research are to build a suitable mathematical model capable of minimizing the maximum completion time, to propose a heuristic optimization algorithm to solve the problem, and to develop an applicable program of the heuristics algorithm. A Mixed Integer Programming (MIP) model and a heuristics optimization algorithm was developed and tested using a randomly generated data set for feasibility. The overall results and performance of each approach was compared between the two methods that would assist the decision maker in choosing a suitable solution for their manufacturing line.

2008

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2008.Thesis (Ph.D.) -- Bilkent University, 2008.Includes bibliographical references leaves 125-129.One of the key questions that engineers face in áow shop systems is the service time control, i.e., how long jobs should be processed at each machine. This is an important question because processing times can have great impacts on the cost e¢ ciency of the áow shop systems. In order to meet job completion deadlines and to decrease inventory costs, one may set the service times as small as possible; however, this usually comes at the expense of reduced tool life increasing service costs. In this thesis, we study the áow shop systems under such trade-o§s. We consider the service time optimization of deterministic áow shop systems processing identical jobs that arrive at the system at known times and are processed in the order they arrive within deadlines. The cost functi...