Tom Page

A GRASP algorithm for flexible job-shop scheduling problem with limited resource constraints

A greedy randomised adaptive search procedure (GRASP) is an iterative multi-start metaheuristic for difficult combinatorial optimisation. The GRASP iteration consists of two phases: a construction phase, in which a feasible solution is found and a local search phase, in which a local optimum in the neighbourhood of the constructed solution is sought. In this paper, a GRASP algorithm is presented to solve the flexible job-shop scheduling problem (FJSSP) with limited resource constraints. The main constraint of this scheduling problem is that each operation of a job must follow an appointed process order and each operation must be processed on an appointed machine. These constraints are used to balance between the resource limitation and machine flexibility. The model objectives are the minimisation of makespan, maximum workload and total workload. Representative benchmark problems are solved in order to test the effectiveness and efficiency of the GRASP algorithm. The computational result shows that the proposed algorithm produced better results than other authors’ algorithms.

Time to market prediction using type-2 fuzzy sets

Purpose – Time to market is the essential aim of any new product introduction process. Performance measures are simple quantities that indicate the state of manufacturing organisations and are used as the basis of decision‐making at this crucial early stage of the process. Fuzzy set theory is a method for using qualitative data and subjective opinion. Fuzzy sets have been used extensively in manufacturing for applications including control, decision‐making, and estimation. Type‐2 fuzzy sets are a novel extension of type‐1 fuzzy sets. Aims to examine this subject.Design/methodology/approach – This research explores the increased use of type‐2 fuzzy sets in manufacturing. In particular, type‐2 fuzzy sets are used to model “the words that mean different things to different people”.Findings – A model that can leverage design process knowledge and predict time to market from performance measures is a potentially valuable tool for decision making and continuous improvement. A number of data sources, such as pro...

Usability of text input interfaces in smartphones

This paper reviews a number of different text input methods on smartphones and proposes how smartphone typing methods may inhibit user interaction rather than enhance it. It explores design and ergonomic considerations underpinning the use of six commonly used smartphone text input methods in order to determine and compare their usability. Furthermore, it discusses the results from testing smartphone users to assess which method is considered most efficient. It concludes that alternative input methods such as Swype and SwiftKey offer substantial benefits to users and are comparable with common typing speeds found on computer keyboards.

A GRASP algorithm for the Integration of Process Planning and Scheduling in a flexible job-shop

The Integration of Process Planning and Scheduling (IPPS) is an important research issue in achieving optimum manufacturing processes. In IPPS, vast search spaces and complex technical constraints prove to be significant barriers to the effectiveness of the processes. This paper proposes a Greedy Randomised Adaptive Search Procedures (GRASP) algorithm for the integration of process planning with production scheduling in a flexible job-shop environment. The GRASP algorithm is a metaheuristic characterised by multiple initialisations. Basically, it comprises two phases: construction phase and local search phase. For this work, the construction phase is considered through computational experiments. The performance of the presented algorithm is evaluated and compared with benchmark problem and the results demonstrate that the proposed algorithm is an effective and practical approach for the flexible job-shop.

Application of Adaptive Genetic Algorithm and Particle Swarm Optimisation in scheduling of jobs and AS/RS in FMS

Flexible Manufacturing Systems (FMS) are advanced production systems used in industries today. In this context, this paper deals with the problem of scheduling of jobs and Automated Storage and Retrieval Systems (AS/RS) assignments in a FMS environment because in some industries they have machines and this kind of AS/RS. Non-traditional optimisation techniques such as Adaptive Genetic Algorithm (AGA) and Particle Swarm Optimisation (PSO) are implemented to get optimal schedules and storage assignments. The objective function minimises the distance travelled by the Storage and Retrieval (S/R) machine. The results are presented and compared.

Solving flexible job-shop scheduling problem using hybrid particle swarm optimisation algorithm and data mining

Flexible job-shop scheduling problem (FJSSP) is an extension of the classical job-shop scheduling problem that allows an operation to be processed by any machine from a given set along different routes. It is very important in both fields of production management and combinatorial optimisation. This paper presents a new approach based on a hybridisation of the particle swarm optimisation (PSO) algorithm with data mining (DM) technique to solve the multi-objective flexible job-shop scheduling problem. Three minimisation objectives - the maximum completion time, the total workload of machines and the workload of the critical machines are considered simultaneously. In this study, PSO is used to assign operations and to determine the processing order of jobs on machines. The objectives are optimised by data mining technique which extracts the knowledge from the solution sets to find the near optimal solution of combinatorial optimisation problems. The computational results have shown that the proposed method is a feasible and effective approach for the multi-objective flexible job-shop scheduling problems.

Feature creep and usability in consumer electronic product design

This research considers the existing literature discussing the usability of microelectronic products and reports on the research undertaken by the author in identifying the consumer perception of contemporary and legacy electronic products. The effect of microelectronic advancement on product usability is investigated and analysed in combination with case-based investigation into consumer attitudes towards product purchase and the usability of digital cameras. Research was conducted through a structured questionnaire, focus groups and interviews. The findings suggest that users see benefits in microelectronics, such as useful functionalities and improvements in size, weight and general versatility, but also found the increased complexity and reduced reliability of electronic products as detrimental to usability. Non-electronic products were identified as largely simpler and more intuitive and, therefore, more usable. Ultimately, users demanded high functionality and usability, an amalgamation largely achieved by digital cameras through the use of well-designed and intuitive interfaces and menu systems.

Design optimisation of bevel gear pair

In this paper, an attempt has been made to optimise bevel gear pair design using a non-linear programming optimising software LINGO and meta-heuristics such as real coded genetic algorithm, ant colony optimisation and particle swarm optimisation algorithms. A combined objective function which maximises the power, efficiency and minimises the overall weight, centre distance has been considered in this model. The efficiency of the proposed algorithms is validated through gear design problems and the comparative results are studied.

The business enabling network - a tool for regional development

This paper presents the business enabling network model (BEN), which can be utilised in the regional development activities in order to evaluate the current state of a network, and to plan and select development actions to be taken. The BEN model illustrates the networking concept through five elements, which are: n nA group of regional actors that should be able to work intentionally must have a common goal that guides the operations and decisions. Working together efficiently insists mutual trust between actors. In order to make business at all in the network there must be competencies that can be utilised to fulfil customer needs. Appropriate infrastructure is prerequisite for the successful business. Finally, continuity, that is, the network must renew itself to sustain its competitiveness.

Concurrent tolerance allocation using an artificial neural network and continuous ant colony optimisation

The allocation of tolerances for the components of a mechanical assembly strongly influences manufacturing cost and functional performance. In order to get a reliable tolerances and costs, it is necessary to obtain manufacturing cost-tolerance models. Traditionally, these models are established by various curve-fitting techniques using empirical experimental data. Existing empirical models, however, have considerably large model fitting error, inconsistent modelling accuracy over the tolerance range of typical manufacturing processes. Using these mathematical models will introduce a considerably large error in optimal design of component tolerances. This work presented in this paper uses an artificial neural network (ANN), to overcome above limitations, for establishing manufacturing cost-tolerance models for various manufacturing processes. Having built the ANN cost-tolerance models, continuous ant colony optimisation (CACO) algorithm is used to obtain optimum combination of tolerances for minimum manufacturing cost. A typical tolerance design example is used to illustrate the effectiveness and reliability of the proposed approach.