This study focuses on a job-shop scheduling problem with the objective of minimizing total tardiness for the job orders that have different due dates and different process flows. We suggest the dispatching rule based scheduling algorithm to generate fast and efficient schedule. First, we show the delay schedule can be optimal for total tardiness measure in some cases. Based on this observation, we expand search space for selecting the job operation to explore the delay schedules. That means, not only all job operations waiting for process but also job operations not arrived at the machine yet are considered to be scheduled when a machine is available and it is need decision for the next operation to be processed. Assuming each job operation is assigned to the available machine, the expected total tardiness is estimated, and the job operation with the minimum expected total tardiness is selected to be processed in the machine. If this job is being processed in the other machine, then machine should wait until the job arrives at the machine. Simulation experiments are carried out to test the suggested algorithm and compare with the results of other well-known dispatching rules such as EDD, ATC and COVERT, etc. Results show that the proposed algorithm, MET, works better in terms of total tardiness of orders than existing rules without increasing the number of tardy jobs.

  In this research article, scheduling a casting sequence in a job-shop type foundry involving a variety of casts made of an identical alloy but with different shapes and weights, has been investigated. The objective is to produce the assigned mixed order

  This study presents the new dispatching rules for improving performance measures of job shop scheduling related to completion time and due dates. The proposed dispatching rule considers information, which includes the comparison value of job workload, w

This paper considers a job shop environment where machines are shared by several sub-production systems. The local objective of a sub-production system is the minimization of total completion time. In a centralized environment, a single decision maker has complete information of processing time, job routing and local objectives. In this case, the problem is a traditional job shop scheduling problem to minimize the total completion time which is well-known NP-hard problem. Meanwhile, it is assumed that no sub-production system has a complete view of the entire system in a distributed environment. This paper proposes a distributed scheduling methodology that maintains autonomy of each sub-production system while pursuing system-wide performance in job shop environment. The proposed method is compared to the performance of centralized solutions.

This study presents the new dispatching rules of job shop scheduling with auxiliary resource constraint to improve the schedule performance measures related to completion time and due dates. The proposed dispatching rules consider the information of total

This study presents the new dispatching rules of job shop scheduling with auxiliary resource constraint to improve the schedule performance measures related to completion time and duedates. The proposed dispatching rules consider the information of total work remaining and machine utilization to decrease mean flowtime and mean tardiness. The results of computer experiments show that those schedule performances are significantly improved by using the new dispatching rules. The results provide guidance for the researchers and practitioners of auxiliary resource constrained job shop scheduling to decrease mean flowtime and mean tardiness.

The objective of this paper is to develop the efficient heuristic method for solving the minimum makespan problem of the job shop scheduling. The proposed heuristic method is based on a constraint satisfaction problem technique and a improved randomizing search algorithm. In this paper, ILOG programming libraries are used to embody the job shop model, and a constraint satisfaction problem technique is developed for this model to generate the initial solution. Then, a improved randomizing search algorithm is employed to overcome the increased search time of constrained satisfaction problem technique on the increased problem size and to find a improved solution. Computational experiments on well known MT and LA problem instances show that this approach yields better results than the other procedures.