Flexible job-shop scheduling problem (FJSP) is a generalization of the classical job-shop scheduling problem (JSP). It takes shape when alternative production routing is allowed in the classical job-shop. However, production scheduling becomes very complex as the number of jobs, operations, parts and machines increases. Until recently, scheduling problems were studied assuming that all of the problem parameters are known beforehand. However, such assumption does not reflect the reality as accidents and unforeseen incidents happen in real manufacturing systems. Thus, an optimal schedule that is produced based on deterministic measures may result in a degraded system performance when released to the job-shop. For this reason more emphasis is put towards producing schedules that can handle uncertainties caused by random disruptions. The current research work addresses solving the deterministic FJSP using evolutionary algorithm and then modifying that method so that robust and/or stable schedules for the FJSP with the presence of disruptions are obtained. Evolutionary computation is used to develop a hybridized genetic algorithm (hGA) specifically designed for the deterministic FJSP. Its performance is evaluated by comparison to performances of previous approaches with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. After that, the previously developed hGA is modified to find schedules that are quality robust and/or stable in face of random machine breakdowns. Consequently, a two-stage hGA is proposed to generate the predictive schedule. Furthermore, the effectiveness of CHAPTER 2
Owing to economic and environmental benefits, new generations of materials/commodities follow “from waste to wealth” strategy. Recently, there has been a huge upsurge in research on the development of eco‐composites using recycled plastic polymers and agro‐residues because the eco‐composites satisfy the stringent environment regulations and are cost‐effective. Herein, we present a detailed review on the potential use of several types of natural fillers as an efficient reinforcement for recycled plastic polymers. In particular, the characterization of different categories of eco‐composites according to their morphological, physical, thermal, and mechanical properties is extensively reviewed and their results are analyzed, compared, and highlighted. Furthermore, a framework to produce functional eco‐composites, which includes functionalization of ingredients, critical issues on microstructural parameters, processing, and fabrication methods, is outlined and supported with sufficient data from the literature. Finally, the review outlines the emerging challenges and future prospects of eco‐composites to be addressed by interested researchers to bridge the gap between research and commercialization of such a class of material. Overall, the acquired knowledge will guide researchers, scientists, and manufacturers to plan, select, and develop various forms of eco‐composites with enhanced properties and optimized production processes.
Purpose Modular product development is a turning table concept in terms of benefits and impact in manufacturing companies. It offers added benefits to the companies with respect to reduce lead-time, improve assemble ability and agility in supply chain management, promote product family and enhance customer satisfaction. This paper aims to identify the patterns of modular product development strategy by using measurement techniques. Design/methodology/approach Both quantitative and qualitative approaches are considered. In the qualitative section, relevant data on product design are collected from six case companies. On the other hand, in the quantitative section, collected data are analyzed to measure the product modularity level with the case companies. Findings This research identifies potential metrics which can be used successfully to measure product modularity level or index in manufacturing industries. Selection of such metrics also depends on individual company’s objectives to measure modularity index. Originality/value This research contributes to the development of modular product design that supports product family design with lean and agile (leagile) supply chain process. It also provided a parsimonious framework to mapping modules within a product, which is ultimately used to measure modularity index.
The work presented in this paper proposes hybridized genetic algorithm architecture for the Flexible Job Shop Scheduling Problem (FJSP). The efficiency of the genetic algorithm is enhanced by integrating it with an initial population generation algorithm and a local search method. The usefulness of the proposed methodology is illustrated with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. Results highlight the ability of the proposed algorithm to first obtain optimal or near-optimal solutions, and second to outperform or produce comparable results with these obtained by other best-known approaches in literature.
Purpose -This research attempted to identify the most critical factors and their inter-relationships to ensure designing agile supply chain, especially in oil and gas industry. This factors identification process is performed through developing a conceptual framework and the use of Interpretive Structural Modelling (ISM) tool.Design/methodology/approach -This study is conducted through an extensive literature review and questionnaires survey to identify and refine the critical factors that ensure the agile supply chain in oil and gas industry. In addition, several brainstorming sessions with the experts in the field of oil and gas industries were organized with the objective to interpret the contextual interrelationships between the identified factors. The outcomes from the literature reviews, interview questions and experts' opinions were used to develop a diagraph and MICMAC analysis to know the drivers of agility in supply chain.Findings -From this study, 34 enablers and 12 factors were identified, which are responsible to ensure agile supply chain in oil and gas industry. Out of these identified factors, top management commitment, strategic alignment, competency of management and integration of information and systems technology are found to be the critical drivers of supply chain agility. On the other hand, government regulations, transportation and logistics flexibility and production planning and control falls under the category of dependent factors.Originality/value -The identified factors and their interrelationships can be a valuable aid to ensure and measure the agility in supply chain, especially in oil and gas industry. These identified factors and their defined consequences will help managers and concerned authorities in oil and gas industry to take better decision to improve the agility level of their supply chain.
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