Sagar U. Sapkal

An improved heuristic to minimize total flow time for scheduling in the m-machine no-wait flow shop

In this paper, we present a constructive heuristic to minimize total flow time criterion for the well-known NP-hard no-wait flow shop scheduling problem. It is based on the assumption that the priority of a job in the initial sequence is given by the sum of its processing times on the bottleneck machines. The initial sequence of jobs thus generated is further improved using a new job insertion technique. We show, through computational experimentation, that the proposed method significantly outperforms the best-known heuristics while retaining its time complexity of O(n^2). Statistical tests of significance are used to confirm the improvement in solution quality.

A penalty-shift-insertion-based algorithm to minimize total flow time in no-wait flow shops

This paper proposes a penalty-shift-insertion (PSI)-based algorithm for the no-wait flow shop scheduling problem to minimize total flow time. In the first phase, a penalty-based heuristic, derived from Vogel’s approximation method used for the classic transportation problem is used to generate an initial schedule. In the second phase, a known solution is improved using a forward shift heuristic. Then the third phase improves this solution using a job-pair and a single-job insertion heuristic. Results of the computational experiments with a large number of randomly generated problem instances show that the proposed PSI algorithm is relatively more effective and efficient in minimizing total flow time in a no-wait flow shop than the state-of-the-art procedures. Statistical significance of better results obtained by the proposed algorithm is also reported.

Application of VAM to Manufacturing Scheduling Problems

No-wait flow shop scheduling is a type of manufacturing scheduling which finds applications in advanced manufacturing systems and falls under the category of NP-hard combinatorial optimization problem. Heuristic methods are found to be the most suitable ones for obtaining solutions to these problems. We propose a heuristic method based on Vogel’s Approximation Method for developing the solution for the objective of minimizing total flow time in no-wait flow shop. The computational results are compared with the results of the well known existing heuristics in no-wait flow shop for minimizing the total flow time criterion. The results reveal that the proposed method significantly outperforms the existing heuristics, with comparable computational time. Statistical tests are used to validate the performance of the proposed method.

An improved scheduling heuristic algorithm for no-wait flow shops on total flow time criterion

Since the no-wait flow shop scheduling problems have been proved to be NP-hard, heuristic procedures are considered as the most suitable ones for their solution, especially for large — sized problems. We present a constructive heuristic for minimizing total flow time criterion in no-wait flow shop scheduling problems. The proposed heuristic is based on the assumption that the priority of a job in the sequence is given by the sum of its processing times on the bottleneck machine(s) for selecting the initial sequence of jobs. The final sequence is based on the principle of job insertion for minimizing the total flow time. The computational results show that the proposed heuristic significantly outperforms the existing heuristics, while not affecting its computational CPU time.

Comparison of Initial Solutions of Heuristics for No-wait Flow Shop Scheduling

No-wait flow shop scheduling problems have been proved to be NP-hard. Therefore, heuristics are considered as the most suitable ones for obtaining near optimal solutions and are generally developed in two phases namely, initial solution phase and improvement solution phase. We propose a method for obtaining the initial solution with a view to minimize total flow time. The exhaustive computational results reveal that the proposed method performs better than the existing heuristics with respect to both quality of solution and computational time.

Optimization Techniques for No-Wait Manufacturing Scheduling: A Review

This paper deals with a general continuous or no-wait manufacturing scheduling problem. Due to its applications in advanced manufacturing systems, no-wait scheduling has gained much attention in both practical and academic fields. Due to its NP-hard nature, most of the contributions focus on development of approximation based optimization methods or heuristics for the problem. Several heuristic procedures have been developed to solve this problem. This paper presents a survey of various methodologies developed to solve no-wait flow shop scheduling problem with the objective of minimizing single performance measure

Application of Particle Swarm Optimization for Production Scheduling

Production scheduling is an interdisciplinary challenge of addressing optimality criteria such as minimizing makespan, mean flow time, idle machine time, total tardiness, number of tardy jobs, in-process inventory cost, cost of being late. Research till date used various AI techniques, heuristics and metaheuristics to optimize scheduling criteria. If problem size goes on increasing heuristics is not able to give optimal results. The enumerations for finding the probabilities for improving the utilization of resources turn this problem towards NP-Hard. This paper presents comprehensive coverage of PSO application in solving optimization problems in the area of production scheduling. The paper discusses about use of PSO for improvement in the results of optimality criteria.

An Improved Immune Algorithm for Manufacturing Scheduling

This paper addresses an n-job, m-machine permutation flow shop scheduling problem to minimize makespan criterion. We present a modification of the best-known immune algorithm of Engin and Döyen (2004) [A new approach to solve hybrid flowshop scheduling problems by artificial immune system. Future Generations Computer Systems 2004;20:1083-1095] for this problem. To evaluate the solution quality and efficiency of the proposed method, the experiments are carried out in two phases on a set of benchmark problems and analyzed. We show, through computational experimentation, that this modification considerably improves its performance without affecting its time complexity. The proposed method also has been compared with the currently best simulated annealing from the literature.