A new mathematical model for single machine batch scheduling problem for minimizing maximum lateness with deteriorating jobs

This paper presents a mathematical model for the problem of minimizing the maximum lateness on a single machine when the deteriorated jobs are delivered to each customer in various size batches. In reality, this issue may happen within a supply chain in which delivering goods to customers entails cost. Under such situation, keeping completed jobs to deliver in batches may result in reducing delivery costs. In literature review of batch scheduling, minimizing the maximum lateness is known as NP-Hard problem; therefore the present issue aiming at minimizing the costs of delivering, in addition to the aforementioned objective function, remains an NP-Hard problem. In order to solve the proposed model, a Simulation annealing meta-heuristic is used, where the parameters are calibrated by Taguchi approach and the results are compared to the global optimal values generated by Lingo 10 software. Furthermore, in order to check the efficiency of proposed method to solve larger scales of problem, a lower bound is generated. The results are also analyzed based on the effective factors of the problem. Computational study validates the efficiency and the accuracy of the presented model

Heuristics for batching jobs under weighted average completion time

Batching problems are machine scheduling problems, where a set of jobs with given processing requirements has to be scheduled on a single machine. The set of jobs has to be partitioned into subsets to form a sequence of batches. A batch combines jobs to run jointly, and each job\u27s completion time is defined to be the completion time of the entire batch. For a batching problem, it is also assumed that when each batch is scheduled, it requires a setup time. One seeks to find a schedule that minimizes the total weighted completion time; This problem is NP-complete, but the problem can be solved efficiently in O(n log (n)) time if the order of the jobs is given. This is accomplished through a non-trivial reduction to on-line matrix searching in a totally monotone array. An implementation of this algorithm is part of the thesis work; To remove the requirement of a fixed order and thus to solve the original NP-complete batching problem, the space of permutations is searched using a genetic algorithm technique. The implementation uses a library of object-oriented functions, GAlib, to implement genetic algorithms. This highly versatile library was written by Mathew Wall of MIT; The thesis also seeks to find techniques to obtain an upper bound, which can be used to measure the quality of the solutions found by the heuristic

Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe