Preemptive scheduling on uniform parallel machines with controllable job processing times

In this paper, we provide a unified approach to solving preemptive scheduling problems with uniform parallel machines and controllable processing times. We demonstrate that a single criterion problem of minimizing total compression cost subject to the constraint that all due dates should be met can be formulated in terms of maximizing a linear function over a generalized polymatroid. This justifies applicability of the greedy approach and allows us to develop fast algorithms for solving the problem with arbitrary release and due dates as well as its special case with zero release dates and a common due date. For the bicriteria counterpart of the latter problem we develop an efficient algorithm that constructs the trade-off curve for minimizing the compression cost and the makespan

A Bicriteria Simulated Annealing Algorithm for Scheduling Jobs on Parallel Machines with Sequence Dependent Setup Times

The study considers the scheduling problem of identical parallel machines subject to minimization of the maximum completion time and the maximum tardiness expressed in a linear convex objective function. The maximum completion time or makespan is the date when the last job to be completed leaves the system. The maximum tardiness is indicated by the job that is completed with the longest delay relative its due date. Minimizing both criteria can help assuring a high utilization of the production system as well as a high level of service towards the client. Due to the complexity of the problem, a Simulated Annealing (SA) heuristic has been implemented to be able to obtain an efficient solution in a reasonable running time. A set of n jobs is assigned, to one of the m identical parallel machines. Each job is processed in only one operation before its completion after which it leaves the system. Constraints, such as due dates for each job and setup times for the machines, are considered. The resolution procedure consists of two phases and begins with an initial solution generator. Then a SA heuristic is applied for further improvement of the solution. 4 generators are used to create an initial solution and 3 to generate neighbour solutions. To test and verify the performance of the proposed resolution procedure, a computational experimentation has been realized on a set of test problems generated ad-hoc

An analysis of pure robotic cycles

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Sciences of Bilkent University, 2008.Thesis (Master's) -- Bilkent University, 20068Includes bibliographical references leaves 84-87.This thesis is focused on scheduling problems in robotic cells consisting of a number of CNC machines producing identical parts. We consider two different cell layouts which are in-line robotic cells and robot centered cells. The problem is to find the robot move sequence and processing times on machines minimizing the total manufacturing cost and cycle time simultaneously. The automation in manufacturing industry increased the flexibility, however it is not widely studied in the literature. The flexibility of machines enables us to process all the required operations for a part on the same machine. Furthermore, the processing times on CNC machines can be increased or decreased by changing the feed rate and cutting speed. Hence, we assume that a part is processed on one of the machines and the processing times are assumed to be controllable. The flexibility of machines results in a new class of cycles named pure cycles. We determined efficient pure cycles and corresponding processing times dominating the rest of pure cycles in the specified cycle time regions. In addition, for in-line robotic cells, the optimum number of machines is determined for given parameters.Yıldız, SerdarM.S

Handling Scheduling Problems with Controllable Parameters by Methods of Submodular Optimization

In this paper, we demonstrate how scheduling problems with controllable processing times can be reformulated as maximization linear programming problems over a submodular polyhedron intersected with a box. We explain a decomposition algorithm for solving the latter problem and discuss its implications for the relevant problems of preemptive scheduling on a single machine and parallel machines

Multiobjective scheduling for semiconductor manufacturing plants

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment