Seren Ozmehmet Tasan

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.

Improving the genetic algorithms performance in simple assembly line balancing

In this paper, a hybrid GA approach combining genetic algorithm (GA) and tabu search (TS) is proposed to solve simple assembly line balancing problem. As this problem is combinatorial and NP hard in nature, the optimum seeking methods are impractical. Therefore, we proposed a hybrid approach, which unites the advantages and mitigates the disadvantages of the two algorithms. To increase the performance of the hybrid GA, we also optimized the control parameters such as the population size, rate of crossover and mutation. Moreover, to gain more insight on the performance of hybrid GA, we implemented it to various benchmark problems and observed that the hybridization of GA with TS improves the solution performance of the balancing problem.

An integrated selection and scheduling for disjunctive network problems

In network optimization problems, the application of conventional integrated selection and scheduling solution methods becomes complicated when the size of the problems, such as real life project management, assembly and transportation problems, get bigger. These kinds of problems often consist of disjunctive networks with alternative subgraphs. Traditionally, in order to handle alternative subgraphs in a disjunctive network, researchers consider first selection and then solution (scheduling) of the problem sequentially. However, the use of traditional approaches result in the loss of the problem structural integrity. When the approach losses its integrated structure, the network problem also losses its integrity. Therefore, these two issues, i.e. selection and scheduling, have to be considered together. To provide a new approach to maintain the problem integrity, we proposed an integrated genetic algorithm for solving this selection and scheduling problems together using a multi-stage decision approach. In this study, two newly defined problems with different disjunctive networks and different characteristics, i.e. resource constrained multiple project scheduling (rc-mPSP) models with alternative projects and variable activity times, and U-shaped assembly line balancing (uALB) models with alternative subassemblies, have been solved using the proposed solution approach to highlight the applicability and performance of the proposed solution approach.

A solution of human resource allocation problem in a case of hotel management

The purpose of this study is to optimally allocate the human resources to tasks while minimizing the total daily human resource costs and smoothing the human resource usage. This human resource allocation problem (hRAP) has two kinds of special constraints, i.e. operational precedence and skill constraints in addition to the ordinary constraints. To deal with the multiple objectives and the special constraints, first we designed this hRAP as a network problem and then proposed a Pareto multistage decision-based genetic algorithm (P-mdGA) to solve it. During the evolutionary process of P-mdGA, a Pareto evaluation procedure called generalized Pareto-based scale-independent fitness function approach was used to evaluate the solutions. Additionally, in order to improve the performance of P-mdGA, we used fuzzy logic controller for fine-tuning genetic parameters. Finally, in order to demonstrate the applicability and to evaluate the performance of the proposed approach, P-mdGA was applied to solve a case study in a hotel, where the managers usually need helpful automatic support for effectively allocating hotel staff to hotel tasks.

Performance Evaluation in Sustainability Conscious Manufacturing Companies by Using TOPSIS Method

In a manufacturing environment, managing limited resources has always been a main issue for engineers. Recently, the idea of managing limited resources without harming ecological environment adopted by manufacturing sector and sustainable manufacturing has become a key issue. While the concept of sustainability has been recognized, companies need to measure how sustainable they perform. Therefore, sustainability indicators are developed and used in order to assess companies’ production activities expediently to sustainable manufacturing. This paper presents a research indicating the application of TOPSIS method on sustainability indicators related to production for two different multi-criteria decision making problems in a sustainability conscious manufacturing company.

Multiobjective hybrid genetic algorithms for assembly line balancing models

The installation of an assembly line is a long-term decision and usually requires large capital investments. Therefore, it is important that an assembly line is designed and balanced so that it works as efficiently as possible. Most of the works related to the assembly lines concentrate on the assembly line balancing (ALB). The ALB model deals with the allocation of the tasks among stations so that the precedence relations are not violated and a given objective function is optimized. Besides balancing a newly designed assembly line, an existing assembly line has to be re-balanced periodically or after certain changes in the production process or the production plan. Because of the long-term effect of balancing decisions, the objective functions have to be carefully chosen while considering the strategic goals of the enterprise. The most of assembly line balancing models where even one objective must be minimized are often NP-hard. However in practical applications, it is often the case that the network to be built is required to multiobjective. In this presentatiın, we first investigate a broad spectrum of multiobjective assembly line balancing models, analyze the recent related researches, design and validate new effective multiobjective hybrid genetic algorithms for for three kinds of major multiobjective ALB models: multiobjective robotic assembly line balancing (mo-rALB), multiobjective u-shaped assembly line balancing (mo-uALB), multiobjective assembly line balancing with alternative subgraphs (mo-sgALB). Finally we discuss the future research issues in the area.

An integrated selection and scheduling for large-sized disjunctive network problems

In network optimization problems, the application of conventional integrated selection and scheduling solution methods becomes complicated when the size of the problems, such as real life project management and transportation problems, get bigger. Additionally, the problems often consist of disjunctive networks, which traditionally results in separating the steps of the integrated approach. When the approach losses its integrated structure, the network problem also losses its integrity. To provide a new approach to maintain the problem integrity, we proposed an integrated genetic algorithm for solving this selection and scheduling problems together using a multistage decision approach. In this study, two example problems with different disjunctive networks and different characteristics have been solved using the proposed solution approach to highlight the performance and applicability to several other network optimization problems.