Dynamic scheduling model for the construction industry

Aiming at the problem of limited resources and operational time combat air defense combat, naval combat formation analysis dynamic resources scheduling needs, on the basis of the establishment of ship dynamic scheduling model of warship formation combat resources dynamic combat formation resource scheduling, the operational resources and operational objectives, relationship between reasonable allocation of resources to play the warship formation combat operations the maximum efficiency. The design of three kinds of air defense command models: centralized platform free attack mode, command mode, request attack mode of air defense strategy, put forward the dynamic scheduling hybrid algorithm for solving the model, according to the naval fleet air defense combat scenario example for simulation. The simulation results show that the air defense strategy with the request attack mode can have effects on multi-target attacks under the shared combat situation, and the hybrid algorithm based on combat resource dynamic scheduling can meet the real-time operational requirements.

PurposeThe purpose of this paper is to provide insights into the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in New Zealand (NZ).Design/methodology/approachThis study adopted narrative inquiry methodology that involved semi-structured interviews (SSI)/expert opinion and systematic literature review (SLR) data to determine the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in NZ. The data were analysed by using descriptive analysis, Likert scale and thematic analysis techniques to understand the relationship of these factors to propose a system dynamic model.FindingsThis study concludes that weather, pandemic, material, geotechnical and disaster factors highly influence while other factors such as equipment shortage, breakdown, design error, labour and event had mixed impact on the dynamic scheduling (DS) that aids on-time project delivery. The proposed system dynamic model can enhance the understanding of factors affecting DS.Research limitations/implicationsSLR is limited to English literature. The limitations of an SSI and a small sample size are acknowledged.Practical implicationsThe proposed model can reduce the uncertainty and scheduling errors during the planning phase and aid in the lesser scheduling modification during the execution phase. In practice, this study will be helpful for road contractors to understand environment, surroundings and resource in-control and out-of-control factors, overcome road construction delays, reduce cost, aid in stakeholder management and sustainable development.Social implicationsThe inclusion of environment, resource and surroundings factors in force majeure clauses will bring an understanding between contracting parties and in turn reduce disputes and delays and help social causes such as on-time infrastructure delivery.Originality/valueFor the first time in a road construction, dynamic project scheduling model that collectively included and linked environment, resource, and surroundings factors to determine the in-control and out-of-control factors for an organisation is proposed. The novelty in the paper is provided by the inclusion of the events, disasters, and pandemics influence on DS in the NZ road construction industry for the first time.

Due to inventory redundancy problem caused by automakers mixed-model production mode, a practical scheduling modeling and multi-objective optimization strategy is presented to increase production and inventory efficiency in this paper. Numerous factors including the general assembly shop, the painting shop, and linear buffer between two workshops have been considered, and a novel dynamic production scheduling model is proposed to achieve three optimization goals: (i) equalize parts consumption rate in the general assembly shop so changes in parts inventory can be predicted; (ii) reduce color switching frequency in the painting shop’s production queue; (iii) reduce waiting time in car body’s buffer zone. Based on this model, an embedded heuristic algorithm with NSGA-2 (No-domination Sorting Genetic Algorithms-II) is employed to solve multi-objective optimization problem. Simulations are finally conducted, when compared with a traditional algorithm, the results are obviously better than traditional algorithm, which validate effectiveness of the proposed model and optimization algorithm.

When the current method is used to schedule railway transportation, the scheduling takes a long time, and the difference between the obtained scheduling result and the expected one is large, and there is a problem of low scheduling efficiency and poor validity. A dynamic scheduling method based on pre-classification is proposed, which pre-classifies railway transportation resources, improves the utilization rate of railway transportation resources, and shortens the time required for scheduling. The execution time is taken as the optimization target, and the railway transportation dynamic scheduling model is constructed based on the pre-classification results. The genetic algorithm is used to solve the dynamic scheduling model of railway transportation, and the dynamic scheduling of railway transportation is realized. The experimental results show that the proposed method takes less time for railway transportation scheduling, and requires less time to perform tasks after scheduling. The proposed method has high scheduling efficiency and good effectiveness.

In the steelmaking-continuous casting (SCC) production process, machine breakdown is one of the most common disturbances which may make the current schedule unrealisable. Existing rescheduling models for machine breakdown only employ one constraint that charges cannot be processed on this machine in its failure period. However, this method is effective for steelmaking furnace breakdown and refining furnace breakdown but invalid for continuous caster breakdown. Due to the production characteristics of continuous caster, reallocating a casting order and a continuous caster for each unfinished charge on the broken down continuous caster is necessary before making a new schedule. Different reallocation strategies have different impacts on charge’s processing time and processing stage route in the dynamic scheduling process. Therefore, SCC dynamic scheduling for the continuous caster breakdown is different from the other machines. In this paper, the impacts of these strategies are studied, and a dynamic scheduling model which can be used to generate a new schedule for each strategy is built. To obtain a high-quality solution in acceptable computational time for this model with NP-hard feature, a hybrid algorithm featuring a genetic algorithm combined with a general variable neighbourhood search is developed based on the problem-specific characteristics. Computational experiments on practical production data show that the proposed rescheduling method is effective for SCC dynamic scheduling with continuous caster breakdown.

Intelligent manufacturing plays a significant role in Industry 4.0. Dynamic shop scheduling is a key problem and hot research topic in the intelligent manufacturing systems, which is NP-hard. However, traditional shop scheduling mode, dynamic event prediction approach, scheduling model and scheduling algorithm, cannot cope with increasingly complicated problems under kinds of scales production disruptions in the real-world production. To deal with these problems, this paper proposes a new joint data-model driven dynamic scheduling architecture for intelligent workshop. The architecture includes four new and key characteristics in the aspects of scheduling mode, dynamic event prediction, scheduling model and algorithm. More specifically, the new scheduling mode introduces data analytics methods to quickly and accurately deal with the dynamic events encountered in the production process. The new prediction model improves the deep learning method, and further applies it predict the dynamic events accurately to provide reliable input to the dynamic scheduling. The new scheduling model proposes a new hybrid rescheduling and inverse scheduling model, which can cope with almost scales of abnormal production problems. The new scheduling algorithm hybridizes dynamic programming and intelligent optimization algorithm, which can overcome the disadvantages of the two methods based on the analysis of solution space. The dynamic programming is employed to provide high-quality initial solutions for the intelligent optimization algorithm by reducing the computation time greatly. To sum up, the presented architecture is a new attempt to understand the problem domain knowledge and broaden the solving idea, which can also provide new theories and technologies to manufacturing system optimization and promote the applications of the theoretical results.

This paper addresses the parameter modelling and optimisation issues in the application of a bio-inspired model for the scheduling of dynamic flexible job shop with sequence-dependent setups. The study sets the model parameters using two steps, termed mapping and tuning. Mapping establishes a set of coefficients to link the model parameters with the scheduling problem characteristics. Tuning determines good values of these coefficients and is then used to compute the model parameters. Such a tuning procedure is accomplished by extensive computational experiments and statistical analyses. A data set from semiconductor manufacturing was used to show the effectiveness of the parameter setting approach. The performance of the proposed multiagent model was compared with that of another scheduling method which is based on dispatching rules. It is concluded that the proposed parameter setting method is effective and worth considering when applying bio-inspired division of labour to dynamic manufacturing scheduling. [Received 5 October 2006; Revised 2 January 2007; Accepted 1 March 2007]

In the formulation of single process fault free intelligent RGV dynamic scheduling strategy, a double objective optimization model is established to improve the efficiency of RGV and shorten the production time of single material. For the solution of the model, firstly, the shortest moving path of RGV is determined according to Greedy algorithm and Tabu search algorithm, and the loading and unloading sequence of RGV is determined according to the short job priority scheduling algorithm. For intelligent RGV dynamic scheduling problem with single process failure, The failure time is determined by the cumulative failure probability model of the system, According to the period of the fault point, after determining the fault CNC, a new dynamic scheduling model is obtained according to the no fault scheduling strategy. Finally, the model and the algorithm are verified by the data.

As a kind of sustainable technology, prefabricated construction has increasingly gained momentum internationally due to its numerous benefits that include, but are not limited to, safe construction, waste minimization, quality improvement, and productivity enhancement. However, productivity in this domain is reliant on the efficiency of both on-site and off-site operations. On this basis, focusing on collaborative scheduling mechanisms, the current paper develops a static scheduling model and a dynamic scheduling model in prefabricated construction, and uses a simulated annealing algorithm (SA) to settle the optimization of operation planning considering delays by risks. The developed models are validated using data from a construction project with multiple suppliers of prefabricated elements. This study contributes to the body of knowledge in prefabricated construction management by streamlining collaborative scheduling in prefabrication. The established models provide construction managers with decision support systems with the aims of minimizing delays and related cost overruns.

Business Reputation and Labor Efficiency, Productivity and Cost

Finance for Managers

Most of the earlier research in dynamic scheduling (DS) of diffusion furnace (DF), considers only future arrival of jobs. However, in reality along with the future arrival of jobs, various unexpected real time events (RTE) related to jobs, and/or resources will occur. Hence, this study addresses the important real life characteristics of both future arrival jobs and the occurrence of a RTE while scheduling DF, called as dynamic real time scheduling (DRTS), with the scheduling objective of minimising total weighted tardiness (TWT). This study first explains the mathematical model for DS of single DF to minimise TWT. Then, this study proposes 12 variants of meta-heuristics (six variants of simulated annealing and six variants of tabu search) by considering six different initial solutions obtained from six variants of greedy heuristic algorithm for DRTS of DF. From empirical and statistical analyses on 270 problem instances, this study observed that one of the variants of simulated annealing consistently performing better. Copyright © 2020 Inderscience Enterprises Ltd.

The cloud radio access network (C-RAN) is a promising architecture for future radio access networks (RANs) due to its advantages in cost efficiency, flexibility, and utilization efficiency. To fully reap these benefits, this paper focuses on joint optimization of user grouping, virtual base station (VBS) clustering, and transmit beamforming in C-RAN downlink networks for maximizing the system utility, subject to the diverse quality-of-service (QoS) requirements of users and the power constraints of distributed remote radio heads (RRHs). To tackle the high computational complexity in solving the nonconvex combinatorial optimization problem, a two-stage solution is proposed. Specifically, a dynamic user-centric scheduling algorithm is developed to form user groups and cluster RRHs into VBSs by exploiting the nonuniform distribution of users. Then, an iterative transmit beamformer optimization algorithm is devised to coordinate the transmit beamforming among the VBSs to mitigate the intracell and intercell interference, hence further enhancing the overall system utility. Evaluation results demonstrate that the proposed algorithm achieves significant performance gain over various reference algorithms in terms of system utility, system throughput, and energy efficiency.

The semiconductor manufacturing industry, inherently capital and technology-intensive, faces growing complexities in production scheduling due to increasing process sophistication and stringent energy requirements. Diffusion Furnaces (DFs), which are essential in semiconductor doping processes, are critical bottlenecks due to their long processing times, high energy consumption, and the need to accommodate incompatible job families with specific time constraints. This study proposes a dynamic scheduling model for DFs aimed at minimizing total costs, including penalties for tardiness, time window violations, and electricity usage under a Time-of-Use (TOU) tariff structure. A mathematical model is formulated for non-identical parallel DFs, incorporating machine eligibility restriction (MER). The proposed mathematical model is empirically validated using randomly generated small size numerical problem and the LINGO solver. The results obtained from LINGO Solver for the numerical problem confirms the correctness of the model proposed for the research problem considered in this study. While the proposed model achieves optimal total cost, they exhibit computational intractability for large-scale problems. This research contributes to the advancement of efficient scheduling strategies for DFs in semiconductor manufacturing, with potential for significant cost reductions and improvements in energy efficiency.

PurposeDairy farms, along with livestock and specialty crop farms, face a tight labor supply and increasing labor costs. To overcome the challenging labor market, farm managers can increase labor-use efficiency through both human resource and capital investments. However, little is known about the relationship between such investments and farm profitability. The purpose of this paper is to examine the relationship between dairy farm financial performance and labor-use efficiency, as measured by labor productivity (milk sold per worker equivalent); labor costs (hired labor cost per unit of milk sold and hired labor cost per worker); and investment in labor-saving equipment.Design/methodology/approachCluster analysis is applied to partition dairy farms into three performance categories (high/middle/low), based on farms’ rate of return on equity, asset turnover ratios and net dairy income per hundredweight of milk. Next, the annual financial rank is fitted into both random- and farm-level fixed-effects ordered logit and linear models to estimate the relationship between dairy farms’ financial performance and labor-use efficiency. This study also investigates the implications of using a single financial indicator as a measure of financial performance, which is the dominant approach in literature.FindingsThe study finds that greater labor productivity and cost efficiency (as measured by hired labor cost per unit of milk sold) are associated with better farm financial performance. No statistically significant relationship is found between farm financial performance and both hired labor cost per worker and advance milking systems (a proxy of capital investment in labor-saving technology). Future studies would benefit from better measurements of labor-saving technology. This study also demonstrates inconsistency in regression results when individual financial variables are used as a measure of financial performance. The greater labor-use efficiency on high-performing farms may be a combination of hiring more-skilled workers and managerial strategies of reducing unnecessary labor activities. The results emphasize the importance of managerial strategies that improve overall labor-use efficiency, instead of simply minimizing total labor expenses or labor cost per worker.Originality/valueThis study examines the importance of labor productivity and labor cost efficiency for dairy farm management. It also develops a novel approach which brings a more comprehensive financial performance evaluation into regression models. Furthermore, this study explicitly demonstrates the potential for inconsistent results when using individual financial variable as a measure of financial performance, which is the dominant measurement of financial performance in farm management studies.