Lee H. S. Luong

A genetic algorithm for the optimisation of assembly sequences

This paper describes a Genetic Algorithm (GA) designed to optimise the Assembly Sequence Planning Problem (ASPP), an extremely diverse, large scale and highly constrained combinatorial problem. The modelling of the ASPP problem, Which has to be able to encode any industrial-size product with realistic constraints, and the GA have been designed to accommodate any type of assembly plan and component. A number of specific modelling issues necessary for understanding the manner in which the algorithm works and how it relates to real-life problems, are succinctly presented, as they have to be taken into account/adapted/solved prior to Solving and Optimising (S/O) the problem. The GA has a classical structure but modified genetic operators, to avoid the combinatorial explosion. It works only with feasible assembly sequences and has the ability to search the entire solution space of full-scale, unabridged problems of industrial size. A case study illustrates the application of the proposed GA for a 25-components product.

Assembly sequence planning and optimisation using genetic algorithms: Part I. Automatic generation of feasible assembly sequences

Abstract This paper attempts to formalise, solve and optimise (S/O) the Assembly Sequence Planning Problem (ASPP), a large scale, highly constrained combinatorial problem. Due to the complexity of the subject and the number of related matters to be considered/adapted/solved prior to S/O the ASPP, the paper is split in two, self-contained, parts: Part I—Automatic Generation of Feasible Assembly Sequences and Part II—optimisation of assembly sequences using Genetic Algorithms. The first part deals with formalising the ASPP—modelling and representation issues—and generating feasible assembly sequences (solving the ASPP). The second part is concerned with the optimisation of the ASPP and will present in detail the Genetic Algorithm designed to optimise it, the genetic operators that compose the algorithm and the definition of the fitness function (optimisation function). The ASPP is considered here as a full-scale, unabridged problem.

A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.

Optimization of a Two-Echelon Supply Network Using Multi-objective Genetic Algorithms

The overall performance of a supply-chain (SC) is influenced significantly by the decisions taken in its production-distribution (P-D) plan. A P-D plan integrates decisions in production, transport and warehousing as well as inventory management. One key issue in the performance evaluation of a Supply Network (SN) is the modeling and optimization of P-D planning problem considering its actual complexity. Based on the integration of Aggregate Production Planning and Distribution Planning, this paper firstly develops a mixed integer formulation for a two-echelon supply network considering the real-world variables and constraints. A multi-objective genetic algorithm (MOGA) is then designed for the optimization of the developed mathematical model. Finally, a real-world case study incorporating multiple products, multiple plants, multiple warehouses, multiple end-users, and multiple time periods will be considered for investigating the performance evaluation of the MOGA method against the traditional approaches of SC planning.

Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part II: Optimization

This paper addresses optimization of the flexible job-shop problem (FJSP) by using real-coded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGAs parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple genetic operators and random initial population, the proposed RCGA can produce promising results comparable to those achieved by other best-known approaches in the literatures. These results demonstrate the robustness of the RCGA.

Optimisation of distribution networks using Genetic Algorithms. Part 2 ? the Genetic Algorithm and Genetic Operators

This paper presents a methodology developed for the optimisation of distribution networks based on Genetic Algorithms (GA), specifically capacitated location-allocation problems. Due to the complexity and extent of the paper, it was split into two parts. Modelling issues and automatic generation of initial population of chromosomes were treated in the first part. This second part details the rest of the GA. Due to the intricacy of the problem, the GA was designed to work only with feasible chromosomes and modified operators were chosen to handle its highly constrained character. They are presented in detail. An example of applying the algorithm for 25 Production Facilities (PF), 10 Distribution Centres (DCs) and 25 Retailers (R) – including 520 variables, tightly interconnected – is presented, demonstrating the robustness of GA and its capacity to tackle problems of considerable size.

Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms - part 1: Modelling and representation

Two NP-hard and strongly related problems in flexible manufacturing system (FMS), part type selection problem and loading problem, are addressed in this paper. Various flexibilities including alternative production plans are considered. This effort will further exploit the flexibility of the FMS and improve system productivity. Real coded genetic algorithms (RCGA) which uses an array of real numbers as chromosome representation is proposed to solve these problems. Hybridizing the RCGA with variable neighborhood search (VNS) is performed to obtain better results. A strategy to maintain population diversity and avoid a premature convergence is also implemented. This first part of the paper addresses a modeling of the problems and discusses how the chromosome representation of the RCGA can handle various flexibilities of operations in the FMS. The second part of the paper will discuss the effectiveness of this hybrid approach to solve several test bed problems.

Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms - part 2: Genetic operators and results

This paper as the continuation of the first part addresses two NP-hard and strongly related problems in flexible manufacturing system (FMS), part type selection problem and loading problem. This first part of the paper detailed a modeling of the problems and discussed how the chromosome representation of the real coded genetic algorithms (RCGA) can handle various flexibilities of operations in the FMS. Hybridizing the RCGA with variable neighborhood search (VNS) and a strategy to maintain population diversity were implemented. This second part of the paper discusses the effectiveness of this hybrid approach to solve several test bed problems. This approach improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could produces promising results and the hybridization can improve the performance of the RCGA.

Optimisation of distribution networks using Genetic Algorithms. Part 1 ? problem modelling and automatic generation of solutions

This paper presents a generalised methodology developed for the optimisation of the distribution networks based on Genetic Algorithms (GA). Specifically, it focuses on capacitated Location–Allocation problems. The approach is general, permitting, at this stage, the use of any combination of transportation and warehousing costs for a deterministic demand. Moreover, the methodology has been designed to have the flexibility to be adapted, in the future, for other realistic conditions and constraints: stochastic conditions, multi-echelon Supply Chain, direct and reverse logistics, single or multi-commodities, seasonal production, etc. Due to the complexity and extent of the problem, the paper was split into two parts. The first part deals with modelling of the problem and the automatic generation of the initial population of chromosomes – a set of solutions to the problem. The second part of the paper details the full GA and the genetic operators. An example of applying the algorithm for 25 Production Facilities (PFs), 10 warehouses and 25 retailers (520 variables interrelated with complex constraints) is presented, demonstrating the robustness of the algorithm and its capacity to tackle problems of practical size.

Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part I: Modelling

This paper and its companion (Part 2) deal with modelling and optimization of the flexible job-shop problem (FJSP). The FJSP is a generalised form of the classical job-shop problem (JSP) which allows an operation to be processed on several alternatives machines. To solve this NP-hard combinatorial problem, this paper proposes a customised Genetic Algorithm (GA) which uses an array of real numbers as chromosome representation so the proposed GA is called a real-coded GA (RCGA). The novel chromosome representation is designed to produces only feasible solutions which can be used to effectively explore the feasible search space. This first part of the papers focuses on the modelling of the problems and discusses how the novel chromosome representation can be decoded into a feasible solution. The second part will discuss genetic operators and the effectiveness of the RCGA to solve various test bed problems from literature.