Semra Tunali

A review of the current applications of genetic algorithms in assembly line balancing

Most of the problems involving the design and plan of manufacturing systems are combinatorial and NP-hard. A well-known manufacturing optimization problem is the assembly line balancing problem (ALBP). Due to the complexity of the problem, in recent years, a growing number of researchers have employed genetic algorithms. In this article, a survey has been conducted from the recent published literature on assembly line balancing including genetic algorithms. In particular, we have summarized the main specifications of the problems studied, the genetic algorithms suggested and the objective functions used in evaluating the performance of the genetic algorithms. Moreover, future research directions have been identified and are suggested.

A review of applications of genetic algorithms in lot sizing

Lot sizing problems are production planning problems with the objective of determining the periods where production should take place and the quantities to be produced in order to satisfy demand while minimizing production, setup and inventory costs. Most lot sizing problems are combinatorial and hard to solve. In recent years, to deal with the complexity and find optimal or near-optimal results in reasonable computational time, a growing number of researchers have employed meta-heuristic approaches to lot sizing problems. One of the most popular meta-heuristics is genetic algorithms which have been applied to different optimization problems with good results. The focus of this paper is on the recent published literature employing genetic algorithms to solve lot sizing problems. The aim of the review is twofold. First it provides an overview of recent advances in the field in order to highlight the many ways GAs can be applied to various lot sizing models. Second, it presents ideas for future research by identifying gaps in the current literature. In reviewing the relevant literature the focus has been on the main features of the lot sizing problems and the specifications of genetic algorithms suggested in solving these problems.

The state of the art on buffer allocation problem: a comprehensive survey

The buffer allocation problem is an NP-hard combinatorial optimization problem and it is an important research issue in designing manufacturing systems. The problem deals with finding optimal buffer sizes to be allocated into buffer areas in a production system to achieve a specific objective. This paper presents a comprehensive survey on buffer allocation problem in production systems. To provide a systematic review of current relevant research, first studies are grouped in two categories: 1. Reliable production lines, 2. Unreliable production lines. Next, the studies in each group are reviewed based on topology of the production line, the solution methodologies suggested and the objective function employed. The aim of this review is twofold. First, it provides an overview of recent advances in the field in order to highlight the new trends in solution methodology. Second, it presents ideas for future research by identifying gaps in the current literature.

An adaptive tabu search approach for buffer allocation problem in unreliable non-homogenous production lines

The buffer allocation problem, i.e. how much buffer storage to allow and where to place it within the line, is an important research issue in designing production lines. In this study, a novel adaptive tabu search approach is proposed for solving buffer allocation problem in unreliable and non-homogeneous production lines. The objective is to maximize the throughput of the line, which is constrained by the capacity of each buffer space and also the total buffer capacity to allocate to these spaces. Besides proposing a new strategy to tune the parameters of tabu search adaptively during the search, an experimental study is carried out to select an intelligent initial solution scheme among three alternatives so as to decrease the search effort to obtain the best solutions. The performance of the proposed approach is evaluated by computational tests and very promising results are obtained.

Small response surface designs for metamodel estimation

Abstract The primary objective of this study is to provide the novice researchers in simulation metamodeling with guidance on how to use small designs for metamodel estimation especially when cost effectiveness is a concern. This study was carried out in three phases: First, a group of second-order small designs were evaluated with respect to various criteria. Next, the metamodel of a time-shared computer system was estimated using these designs. Finally, the predictive capabilities of these small designs in giving the best metamodel fit were investigated, and also, the performance of small designs was compared with two large size standard designs. Results indicate that the metamodel estimated using the hybrid design has the best predictive capability among the small designs, and its performance competes with that of the standard designs studied.

A tabu search approach for buffer allocation in production lines with unreliable machines

The optimal allocation of buffers is an important research issue in designing production lines. In this study, a tabu search (TS) algorithm is proposed to find near-optimal buffer allocation plans for a serial production line with unreliable machines. The main objective is to maximize the production rate, i.e. throughput, of the line. The efficiency of the proposed method is also tested to solve buffer allocation problems with the objective of total buffer size minimization. To estimate the throughput of the line with a given specific buffer allocation, an analytical decomposition approximation method is used. The performance of the tabu search algorithm is demonstrated on existing benchmark problems. The results obtained by the TS algorithm are clearly encouraging, as the TS algorithm is much better than the other algorithms for all considered benchmark problems.

Evaluation of alternate routing policies in scheduling a job-shop type FMS

Abstract This paper presents a microcomputer-based simulation model of a job-shop type flexible manufacturing system. The model is written in SIMAN and interfaced with C routines in order to create a combined process/discrete-event modeling environment. One of the objectives of this study is to discuss how this environment can be used to schedule orders. The other is to present the results of a comparative simulation study on flexible and prefixed routing of parts in an FMS which is subject to unexpected machine breakdowns.

A review of the current applications of genetic algorithms in mixed-model assembly line sequencing

A mixed-model assembly line (MMAL) is a type of production line which is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such assembly lines involves several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from the researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). An important issue that complicates the sequencing problem is its combinatorial nature. Typically, an enormous number of possible production sequences exist, even for relatively small problems, so that finding the optimal solution is usually impractical. Due to the complexity of the problem, in recent years, a growing number of researchers have employed genetic algorithms (GAs). This paper reviews the genetic algorithm based MMAL sequencing approaches presented in the literature and provides two hierarchical classification schemes to classify academic efforts according to both specifications of MMALSP and specifications of GA-based approaches. Moreover, future research directions have been identified and are suggested.

Simultaneous Balancing and Scheduling of Flexible Mixed Model Assembly Lines with Sequence-Dependent Setup Times

We consider Simultaneous Balancing and Scheduling of Flexible Mixed Model Assembly Lines with Sequence-Dependent Setup Times (SBSFMMAL-SDST). We propose alternate Mixed Integer Programming (MIP) and Constraint Programming (CP) formulations. Our experiments show that while the MIP models could not solve relatively small instances, the CP approach seems more promising.

Dealing with the least squares regression assumptions in simulation metamodeling

The aim of this study is twofold. The first is to estimate a metamodel for a time-shared computer system using a sequential design procedure. The second is to deal extensively with the least squares regression assumptions during the metamodel development. In the first stage of the experimentation, a first-order metamodel is estimated using the two-level factorial design. Later, the design is augmented with replicated center points for curvature check. Upon the detection of the significance of the curvature, a central composite design is used for fitting a second-order metamodel, which explains the relation between the levels of the input factors, and the response of interest. In both stages, various diagnostic statistical tests such as normality test, variance homogeneity test, lack-of-fit test and etc. are carried out to make sure that the method of least squares is properly and efficiently applied.