Search results

For prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.

This paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.

This paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the proposed model and the improved BBO.

With respect to limitations, only linear weighted transformations are used for objective optimization. In regards to research implications, this paper considers the production of prefabricated components in an environment where all parties in the supply chain of prefabricated components participate to solve the production scheduling problem. In addition, this paper creatively applies the improved BBO to the production scheduling problem of prefabricated components. Compared to other algorithms, the results show that the improved BBO show optimized result.

The proposed approach helps prefabricated component manufacturers consider complex requirements which could be used to formulate a more scientific and reasonable production plan. The proposed plan could ensure the construction project schedule and balance the reasonable requirements of all parties. In addition, improving the ability of prefabricated component production enterprises to deal with uncertain events. According to actual production conditions (such as the occupation of mold resources and storage resources of completed components), prefabricated component manufacturers could adjust production plans to reduce the cost and improve the efficiency of the whole prefabricated construction project.

The value of this article is to provide details of the procedures and resource constraints from the perspective of the precast components supply chain, which is closer to the actual production process of prefabricated components. In addition, developing the production scheduling for lean production will be in line with the concept of sustainable development. The proposed lean production scheduling could establish relationships between prefabricated component factory manufacturers, transportation companies, on-site contractors and production workers to reduce the adverse effects of emergencies on the prefabricated component production process, and promote the smooth and efficient operation of construction projects.

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

The purpose of this paper is to optimise the job shop scheduling problem using simulation and genetic algorithm.

The paper presents a simulation‐based genetic algorithm approach for the job shop scheduling problem. In total, three cases have been considered to access the performance of the job shop, with an objective to minimise mean tardiness and makespan. A restart scheme is embedded into regular genetic algorithm in order to avoid premature convergence.

Simulation‐based genetic algorithm can be used for job shop scheduling problems. Moreover, a restart scheme embedded into a regular genetic algorithm results in improvement in the fitness value. Single process plans selected on the basis of minimum production time criterion results in improved shop performance, as compared to single process plans selected randomly. Moreover, availability of multiple process plans during scheduling improves system performance measures.

The paper presents a simulation‐based genetic algorithm approach for job shop scheduling problem, with and without restart scheme. In this paper the effect of multiple process plans over single process plans, as well as criterion for selection of single process plans, are studied. The findings should be taken into account while designing scheduling systems for job shop environments.

It is common knowledge that during the last decade markets have become extremely competitive with product variety increasing continuously and product life cycles shortening. Many manufacturing companies, which hitherto satisfied their customers while operating specific production systems, were recently obliged to reconsider because of the potential superiority of other “manufacturing philosophies”. In the literature, we meet a great variety of production systems and manufacturing philosophies, while, on the other side, in industry we usually find different combinations of “primary” productions systems. In this paper, we present the existing “state‐of‐the‐art” theoretical and experiential knowledge about productions systems, as well as describe their basic characteristics in a useful, exact and comprehensive way for practitioners and software houses who want to have a knowledge base for further research and practical implementation in the wider field of production management, planning and scheduling.

The purpose of this paper is to investigate into the improvement strategies for lead time reduction of military aircraft major overhaul (enhance the overhaul task from present seven to nine-plus aircrafts as agreed to customer) for ABC Company.

This research has been conducted based on the available knowledge and extensive experience gained while working in the overhaul unit of the case organization for a long period of time. The research employs an integrated approach of multi-criteria decision method (in specific analytic hierarchy process (AHP)), theory of constraint (TOC) and simulation for M/M/1/K queuing environment for investigating the improvement strategies for lead time reduction in major overhaul of military aircraft. This has mainly included: Step 1: analysis of existing activity network using actual overhaul data; Step 2: ranking of constraint activities that most affects the lead time using AHP; Step 3: revised flow activity network using time buffers (TBs) concept of TOC; Step 4: mathematical model formulation for effective utilization of TB using single server finite queuing approach (M/M/1/K queuing environment); Step 5: discrete event simulation representing present overhaul conditions and revised overhaul conditions with different scenarios; and Step 6: sensitivity analysis and interpretation of results. The proposed methodology has been tested on an industrial case study dealing with an aircraft overhaul in India (namely ABC Company).

The proposed overhaul lead-time reduction plan points out the augmentation of resources in order to achieve more than 50 per cent reduction in lead time and waiting time under two scenarios – with overtime and without overtime.

An integrated approach proposed has so far been applicable to the specific aircrafts (Jaguar/Mirage/Kiran) of the ABC Company overhaul division. However, the particular methodology can be proved potentially useful and highly practical when applying to other aircraft types.

The results indicate several problems with the current overhaul strategies that need focused process improvements and a need for capacity augmentation for achieving the enhanced overhaul task of nine+ aircrafts per year with reduced overhaul lead time. The proposed approach also provides the maintenance planners with a platform for decision analysis and support often ignored the capacity augmentation.

Motivated by recent research indicating that the operational performance of an enterprise can be enhanced by building a supporting data-driven environment in which to operate, this paper presents a simulation framework that enables an examination of the effects of applying smart manufacturing principles to conventional production systems, intending to transition to digital platforms.

To investigate the extent to which conventional production systems can be transformed into novel data-driven environments, the well-known constant work-in-process (CONWIP) production systems and considered production sequencing assignments in flowshops were studied. As a result, a novel data-driven priority heuristic, Net-CONWIP was designed and studied, based on the ability to collect real-time information about customer demand and work-in-process inventory, which was applied as part of a distributed and decentralised production sequencing analysis. Application of heuristics like the Net-CONWIP is only possible through the ability to collect and use real-time data offered by a data-driven system. A four-stage application framework to assist practitioners in applying the proposed model was created.

To assess the robustness of the Net-CONWIP heuristic under the simultaneous effects of different levels of demand, its different levels of variability and the presence of bottlenecks, the performance of Net-CONWIP with conventional CONWIP systems that use first come, first served priority rule was compared. The results show that the Net-CONWIP priority rule significantly reduced customer wait time in all cases relative to FCFS.

Previous research suggests there is considerable value in creating data-driven environments. This study provides a simulation framework that guides the construction of a digital transformation environment. The suggested framework facilitates the inclusion and analysis of relevant smart manufacturing principles in production systems and enables the design and testing of new heuristics that employ real-time data to improve operational performance. An approach that can guide the structuring of data-driven environments in production systems is currently lacking. This paper bridges this gap by proposing a framework to facilitate the design of digital transformation activities, explore their impact on production systems and improve their operational performance.

Maintenance stands are the most valuable maintenance resources and provide the necessary maintenance space and maintenance facilities for aircraft maintenance. To expand the maintenance market, maintenance, repair and overhaul (MRO) urgently need to achieve a reasonable schedule between aircraft maintenance requirements and maintenance stand capability to improve aircraft maintenance continuity and reduce the risk of scratching due to aircraft movement. This study aims to design a maintenance stand scheduling (MSS) model based on spatiotemporal constraints to solve the problem of maintenance stand schedules.

To address the problem of maintenance stand schedules, this study introduces mixed-integer programming algorithm to design the MSS model on the basis of classical hybrid flow shop structure. When designing the optimization objective function of MSS modeling, the spatiotemporal constraints are mainly considered. Specifically, first, the spatial constraints between maintenance stands are fully considered so that more aircraft can be parked in the workshop. Second, the optimization objective is designed to minimize the number of aircraft movements by defining multiple maintenance capabilities of the stand. Finally, a solution based on spatiotemporal constraints is proposed in the solving process.

A set of MRO production data from Guangzhou is used as a test data set to demonstrate the effectiveness of the proposed MSS model.

The types of maintenance stands are defined and divided into four categories: fixed stand, temporary stand, half-body stand and engine ground test stand, which facilitates optimal modeling; a new scheduling model is designed considering both temporal constraints and spatial constraints, which can improve both the utilization of maintenance stand and safety (reduce the risk of scratching between aircraft).

Presents a case study of a production seat booking system for the photographic color film manufacturing industry. This manufacturing process is a hybrid flow shop, which consists of two manufacturing stages: first, processing of the film bulk‐rolls in batches, and second, packing of the final product specifications in a continuous‐process line. A production seat booking system is a new scheduling system, which reduces planning lead‐time significantly, by simplifying the time‐for‐delivery adjustment work. It was found possible to shorten planning lead‐time sharply based on this booking system. Additionally, it became possible simultaneously to reduce inventory levels and logistic‐related costs through such management innovation as shortening planning lead‐time and other entry delivery systems and such improvement activities as making small lot‐size.

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.

A hybrid flowshop (HFS) problem on the pre‐sewing o perations and a master production scheduling (MPS) problem of apparel manufacture are solved by a proposed two‐tier scheduling model. The first objective of this paper is to plan a MPS for the factory so that the costs are minimized when the production orders are completed before and after the delivery dates required by the customers. The second objective is to minimize the completion time of the pre‐sewing operations in the cutting department while the production quantities required by the sewing department at several predetermined times can be fulfilled by the cutting department. Experimentation is conducted and the results show the excellent performance of the proposed scheduling model for the apparel industry.