Search results

The purpose of this paper is to solve a flexible job shop scheduling problem where alternate machines are available to process the same job. The study considers the Flexible Job Shop Problem (FJSP) having n jobs and more than three machines for scheduling.

FJSP for n jobs and more than three machines is non polynomial (NP) hard in nature and hence a multi‐objective genetic algorithm (GA) based approach is presented for solving the scheduling problem. The two objective functions formulated are minimizations of the make‐span time and total machining time. The algorithm uses a unique method of generating initial populations and application of genetic operators.

The application of GA to the multi‐objective scheduling problem has given optimum solutions for allocation of jobs to the machines to achieve nearly equal utilisation of machine resources. Further, the make span as well as total machining time is also minimized.

The model can be extended to include more machines and constraints such as machine breakdown, inspection etc., to make it more realistic.

The paper presents a successful implementation of a meta‐heuristic approach to solve a NP‐hard problem of FJSP scheduling and can be useful to researchers and practitioners in the domain of production planning.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

Addresses the problem of sequencing a set of PCBs on a single assembling machine. Considers two objectives, namely minimizing tardiness and minimizing the set‐ups. Reduces component change‐overs by exploiting the similarity between PCBs. Proposes a new measure to sequence the PCBs, taking care of both the objectives. Develops a heuristic for solving the bi‐criteria problem. The proposed method performs better than the existing heuristics for the comparable situation of sequencing in a single machine job‐shop.

This study addresses the production optimization of a cellular manufacturing system (CMS) in magnetic production enterprises. Magnetic products and raw materials are more critical to transport than general products because the attraction or repulsion between magnetic poles can easily cause traffic jams. This study needs to address a method to promote the scheduling efficiency of the problem.

To address this problem, this study formulated a mixed-integer linear programming (MILP) model to describe the problem and proposed an auction and negotiation-based approach with a local search to solve it. Auction- and negotiation-based approaches can obtain feasible and high-quality solutions. A local search operator was proposed to optimize the feasible solutions using an improved conjunctive graph model.

Verification tests were performed on a series of numerical examples. The results demonstrated that the proposed auction and negotiation-based approach with a local search operator is better than existing solution methods for the problem identified. Statistical analysis of the experiment results using the Statistical Package for the Social Sciences (SPSS) software demonstrated that the proposed approach is efficient, stable and suitable for solving large-scale numerical instances.

An improved auction and negotiation-based approach was proposed; The conjunctive graph model was also improved to describe the problem of CMS with traffic jam constraint and build the local search operator; The authors’ proposed approach can get better solution than the existing algorithms by testing benchmark instances and real-world instances from enterprises.

This paper aims to propose a framework for the evaluation of artefacts in Design Science and test it using an exemplar case of a knowledge management system (KMS) developed for an Australian refrigeration manufacturing company.

The research uses Design Science research methodology in a specific case study context. The artefact studied was developed using an ontology based on an engineering design conceptualisation and created using an ontology generator, Protégé. Research data for the evaluation of the framework were collected using a combination of document analysis, interviews, shadowing and observations.

The evaluation framework developed for the research and applied to the KMS specifically built for the company was shown to be useful in determining the efficacy and effectiveness of the research outcomes in terms of usefulness to the company engineers in the technical analysis of their work, and for the CEO and COO as part of their strategic planning for the company. The evaluation framework helped the researcher and the engineers as collaborators to demonstrate the extent of improvement in the design and build processes in the company.

Prior research in both Information System and Design Science has not provided a specific, generalizable, evaluation framework for system developers to use as a guide during the systems development process. This research proposes an evaluation framework which covers all broad aspects of evaluation and efficacy, accepting that evaluation frameworks must be flexible in enabling changes to accommodate variations in the types and purposes of artefacts developed.

The purpose of this research is to study the Multi-Skill Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where (1) durations of activities depend on the familiarity levels of assigned workers, (2) more efficient workers demand higher per-day salaries, (3) projects have different due dates and (4) the budget of each period varies over time. The proposed model is bi-objective, and its objectives are minimization of completion times and costs of all projects, simultaneously.

This paper proposes a two-phase approach based on the Statistical Process Control (SPC) to solve this problem. This approach aims to develop a control chart so as to monitor the performance of an optimizer during the optimization process. In the first phase, a multi-objective statistical model has been used to obtain control limits of this chart. To solve this model, a Multi-Objective Greedy Randomized Adaptive Search Procedure (MOGRASP) has been hired. In the second phase, the MSRCMPSP is solved via a New Version of the Multi-Objective Variable Neighborhood Search Algorithm (NV-MOVNS). In each iteration, the developed control chart monitors the performance of the NV-MOVNS to obtain proper solutions. When the control chart warns about an out-of control state, a new procedure based on the Conway’s Game of Life, which is a cellular automaton, is used to bring the algorithm back to the in-control state.

The proposed two-phase approach has been used in solving several standard test problems available in the literature. The results are compared with the outputs of some other methods to assess the efficiency of this approach. Comparisons imply the high efficiency of the proposed approach in solving test problems with different sizes.

The proposed model and approach have been used to schedule multiple projects of a construction company in Iran. The outputs show that both the model and the NV-MOVNS can be used in real-world multi-project scheduling problems.

Due to the numerous numbers of studies reviewed in this research, the authors discovered that there are few researches on the multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with the aforementioned characteristics. Moreover, none of the previous researches proposed an SPC-based solution approach for meta-heuristics in order to solve the MSRCMPSP.

In general, cloud computing is a model of on-demand business computing that grants a convenient access to shared configurable resources on the internet. With the increment of workload and difficulty of tasks that are submitted by cloud consumers; “how to complete these tasks effectively and rapidly with limited cloud resources?” is becoming a challenging question. The major point of a task scheduling approach is to identify a trade-off among user needs and resource utilization. However, tasks that are submitted by varied users might have diverse needs of computing time, memory space, data traffic, response time, etc. This paper aims to proposes a new way of task scheduling.

To make the workflow completion in an efficient way and to reduce the cost and flow time, this paper proposes a new way of task scheduling. Here, a self-adaptive fruit fly optimization algorithm (SA-FFOA) is used for scheduling the workflow. The proposed multiple workflow scheduling model compares its efficiency over conventional methods in terms of analysis such as performance analysis, convergence analysis and statistical analysis. From the outcome of the analysis, the betterment of the proposed approach is proven with effective workflow scheduling.

The proposed algorithm is more superior regarding flow time with the minimum value, and the proposed model is enhanced over FFOA by 0.23%, differential evolution by 2.48%, artificial bee colony (ABC) by 2.85%, particle swarm optimization (PSO) by 2.46%, genetic algorithm (GA) by 2.33% and expected time to compute (ETC) by 2.56%. While analyzing the make span case, the proposed algorithm is 0.28%, 0.15%, 0.38%, 0.20%, 0.21% and 0.29% better than the conventional methods such as FFOA, DE, ABC, PSO, GA and ETC, respectively. Moreover, the proposed model has attained less cost, which is 2.14% better than FFOA, 2.32% better than DE, 3.53% better than ABC, 2.43% better than PSO, 2.07% better than GA and 2.90% better than ETC, respectively.

This paper presents a new way of task scheduling for making the workflow completion in an efficient way and for reducing the cost and flow time. This is the first paper uses SA-FFOA for scheduling the workflow.

The purpose of this paper is to analyze the impact of learning in a project‐driven organization and demonstrate analytically how the learning, which takes place during the execution of successive projects, and the forgetting that takes place during the dormant time between the project executions, can impact performance and productivity in the future.

A learn‐forget model was developed using the learning curve concept prevalent in many manufacturing processes. The model assumes that learning occurs while project tasks are being performed and forgetting takes place during dormant times between the successive implementations. The log‐linear model was adapted, with both learning and forgetting rates being a function of the doubling or tripling of output. Forgetting is inhibited through the use of knowledge transfer tools such as use of close‐out documents or content management platforms. The model is applied to a simulated project environment where a number of projects are executed sequentially, and the results are evaluated using the reduction in total duration and return on investment.

Computational results demonstrate that the learning and forgetting rates and level of project close‐out effort impact project performance, in the form of reduction in duration, much more significantly compared to the impact of the length of dormant times between the project initiations. Furthermore, even in a slow learning environment, using close‐out reports as a knowledge transfer tool, managers can achieve more than a 40 percent reduction in duration after several successive implementations.

Although the theoretical development is applicable to a general organizational setting, the empirical testing of the model is done in project‐driven organizations where projects are implemented on an ongoing basis.

Managers can significantly benefit from the findings of this study. It is shown that the accumulated learning which represents knowledge generated during the implementation of a project, if transferred successfully, improves productivity and enables faster implementation. In a project‐driven organization an almost 80 percent reduction in total duration is achievable with the use of close‐out documents. This result promotes the importance of the learning process and managers should enable their team members to learn as much as they can while implementing a task and to document it methodically.

This study constitutes an initial effort to illustrate quantitatively how the level of learning and forgetting impact performance in a project‐driven organization. This study is also original in that it methodically demonstrates the importance of spending time during the phase‐out, documenting the project artifacts, that enables knowledge transfer, and thus improves performance.

In a complex semiconductor manufacturing system (SMS) environment, the implementation of dynamic production scheduling and dispatching strategies is critical for SMS distributed collaborative manufacturing events to make quick and correct decisions. The purpose of this paper is to assist manufacturers in achieving the real time dispatching and obtaining integrated optimization for shop floor production scheduling.

In this paper, an integrated model is designed under assemble to order environment and a framework of a real time dispatching (IRTD) system for production scheduling control is presented accordingly. Both of the scheduling and ordering performances are integrated into the days of inventory based dispatching algorithm, which can deal with the multiple indicators of dynamic scheduling and ordering in this system to generate the “optimal” dispatching policies. Subsequently, the platform of IRTD system is realized with four modules function embedded.

The proposed IRTD system is designed to compare the previous constant work in process method in the experiment, which shows the better performance achievement of the IRTD system for shop floor production dynamic scheduling and order control. The presented framework and algorithm can facilitate real time dispatching information integration to obtain performance metrics in terms of reliability, availability, and maintainability.

The presented system can be further developed to generic factory manufacturing with the presented logic and architecture proliferation.

The IRTD system can integrate the real time customer demand and work in process information, based on which manufacturers can make correct and timely decisions in solving dispatching strategies and ordering selection within an integrated information system.

The purpose of this paper is to apply group assembly (GA) considerations to the construction industry and to provide evidence of construction sector industrialization with quantitative results. Moreover, a flexible assembly system is proposed, especially designed to cope with variability: this can be easily extendable to other industrial sectors, especially when dealing with extremely variable environments.

The paper presents a case study conducted at an Italian company leader in the design, manufacture and installation of architectural claddings and lightweight continuous facades.

The research empirically demonstrates how the application of GA and the creation of project families lead to consistent enhancement also within the construction industry. The case study reveals great improvement in terms of both operating and ergonomic performances, agile assembly system reconfiguration design and make span reduction. The possibility of correlating a new project to an identified family gives the opportunity to understand the best assembly line layout configuration which should be assigned to the project, to improve the throughput time and the controllability of the assembly process and to guarantee efficient floor space utilization, lead‐time control, accuracy and reliability.

The novelty of the study lies in the way the assembly layout is designed to cope with variability: the assembly line, which is dedicated to more stable processes, is coupled with pre‐assembly stations, easily reconfigurable, meant to be “variability absorbers”. As far as the authors know, this is also the first time GA is applied to the construction industry. Moreover, a timely topic such as construction sector industrialization is confirmed by quantitative results.