Ertan Güner

Parallel machine earliness/tardiness scheduling problem under the effects of position based learning and linear/nonlinear deterioration

This paper considers a parallel machine earliness/tardiness (ET) scheduling problem with different penalties under the effects of position based learning and linear and nonlinear deterioration. The problem has common due-date for all jobs, and effects of learning and deterioration are considered simultaneously. By the effects of learning we mean that the job processing time decreases along the sequence of partly similar jobs, and by the effects of deterioration we mean slowing performance or time increases along the sequence of jobs. This study shows that optimal solution for ET scheduling problem under effects of learning and deterioration is V-shape schedule under certain agreeable conditions. Furthermore, we design a mathematical model for the problem under study and algorithm and lower bound procedure to solve larger test problems. The algorithm can solve problems of 1000 jobs and four machines within 3s on average. The performance of the algorithm is evaluated using results of the mathematical model.

Analyzing the behaviors of virtual cells (VCs) and traditional manufacturing systems: Ant colony optimization (ACO)-based metamodels

The aim of this paper is two fold. First we investigate the three different types of systems, namely cellular layout (CL), process layout (PL) and virtual cells (VCs). VCs are addressed by using family-based scheduling rule, developed by a part allocation algorithm in a PL. Simulation is used to compare three types of systems under the performance metrics such as mean flow time and mean tardiness. Results indicate that VCs have better responsiveness in terms of the performance metrics. Second we develop a new ant colony optimization-based metamodels fed by existing simulation runs to represent the prospective simulation runs, which require a lot of time and effort. Regression metamodels, which allow us to obtain much faster results, are seen to be promising to estimate the systems behaviors.

A bicriteria scheduling with sequence-dependent setup times

In this paper a bicriteria scheduling problem with sequence-dependent setup times on a single machine is considered. The objective function of the problem is minimization of the weighted sum of total completion time and total tardiness. An integer programming model is developed for the problem which belongs to NP-hard class. Results of computational tests show that the proposed model is effective in solving problems with up to 12 jobs. For solving problems containing large number of jobs a special heuristic algorithm is proposed. Besides the proposed heuristic algorithm tabu search based heuristic is also used for large jobs problems. To improve the performance of tabu search method the result of the proposed heuristic algorithm is taken as an initial solution of tabu search method. According to computational results both heuristic algorithms are effective in finding problem solutions with up to 1000 jobs. According to the best of our knowledge, no works exists on the minimization of the weighted sum of total completion time and total tardiness with sequence-dependent setup times.

Assembly line balancing problem with deterioration tasks and learning effect

In this study, we introduce simultaneous effects of learning and linear deterioration into assembly line balancing problem. In many realistic settings, although the actual task time of a task is modeled as an increasing function of its starting time due to deterioration effects the produced worker(s) (or machine(s)) develops continuously by repeated the same or similar activities. The objective of problem is to minimize the station number. For this purpose, a mixed nonlinear integer programming model is developed. This paper also discusses the adaptation of the COMSOAL approach for large scale assembly line balancing problem with deterioration task and learning effect.

Single machine scheduling problems under the effects of nonlinear deterioration and time-dependent learning

Job deterioration and machine learning co-exist in various real life scheduling settings. This paper studies several single machine scheduling problems under the joint effect of nonlinear job deterioration and time-dependent learning. We assume that the processing time of a job increases when its processing is delayed. In addition, it is assumed that the machine undergoes a learning process, decreasing the time required to process a given job. The following objectives are considered: the makespan, the sum of completion times (square) and the maximum lateness. We derive polynomial-time optimal solutions for all the objectives.

The common due-date early/tardy scheduling problem on a parallelmachine under the effects of time-dependent learning and linear and nonlinear deterioration

This paper considers earliness/tardiness (ET) scheduling problem on a parallel machine environment with common due-date under the effects of time-dependent learning and linear and nonlinear deterioration. In this paper, the effects of learning and deterioration are considered simultaneously. By the effects of learning and deterioration, we mean that the processing time of a job is defined by increasing function of its execution start time and position in the sequence. This study shows that optimal solution for ET scheduling problem under effects of learning and deterioration is V-shape schedule under certain agreeable conditions. Furthermore, we present a mathematical model for the problem under study and an algorithm for solving large test problems. The algorithm can solve problems of 1000 jobs and four machines within 3s on average.

A Bicriteria Scheduling Problem with a Learning Effect: Total Completion Time and Total Tardiness

Abstract This paper considers a bicriteria scheduling problem with a learning effect on a single machine to minimize a weighted sum of total completion time and total tardiness. A mathematical programming model is developed for the problem, and optimal solutions are only obtained up to 25 jobs. Since the problem is NP-hard, heuristic methods based on tabu search and random search are proposed to solve large size problems, and their effectiveness is comparatively investigated considering 3600 problems.

Parallel machine scheduling problem to minimize the earliness/tardiness costs with learning effect and deteriorating jobs

The focus of this work is to analyze parallel machine earliness/tardiness (ET) scheduling problem with simultaneous effects of learning and linear deterioration, sequence-dependent setups, and a common due-date for all jobs. By the effects of learning and linear deterioration, we mean that the processing time of a job is defined by an increasing function of its starting time and a decreasing function of the position in the sequence. We develop a mixed integer programming formulation for the problem and show that the optimal sequence is V-shaped: all jobs scheduled before the shortest jobs and all jobs scheduled after the shortest job are in a non-increasing and non-decreasing order of processing times, respectively. The developed model allows sequence-dependent setups and sequence-dependent early/tardy penalties. The illustrative example with 11 jobs for 2 machines and 3 machines shows that the model can easily provide the optimal solution, which is V-shaped, for problem.

A bicriteria flowshop scheduling problem with setup times

Most of research in production scheduling is concerned with the minimization of a single criterion. However, scheduling problems often involve more than one aspect and therefore require bicriteria analysis. In this study, bicriteria two-machine flowshop scheduling problem with setup times is considered. The objective function of the problem is minimization of the weighted sum of total completion time and total tardiness. An integer programming model is developed for the problem which belongs to NP-hard class. Only small size problems with up to 20 jobs can be solved by the proposed integer programming model. Heuristic methods are also used to solve large size problems. These heuristics are four tabu search based heuristics and random search method. According to computational results the tabu search based methods are effective in finding problem solutions with up to 1000 jobs.

Three-finger SMA robot hand and its practical analysis

Gripping of different types of objects with a multi-finger robot hand is a vital task for robot arms. Grippers, which are end effector elements in robot applications, are employed in various industrial operations such as transferring, assembling, welding and painting. However, if a gripper is considered for handling different jobs or to carry different types of parts in an assembly line, a general-purpose robot hand is going to be required. There are various technological actuators of robot hands such as electrical, hydraulic and pneumatic motors, etc. Besides these conventional actuators, it is possible to include Shape Memory Alloys (SMA) in the category of technological actuators. The SMA can give materials motion by moving to a predetermined position, at a specific temperature. The conversion of this motion to a gripping action of the robot hand is the heart of the matter. In this study, a robot hand is developed using Ni-Ti SMA and a set of experiments were performed in order to check the compatibility of the system in an industrial environment.