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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.

Production and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.

In this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.

The optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.

This study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

Takes the development of a new optimization process for production scheduling and develops it into a systematic‐mathematical algorithm. Tests this algorithm against a simulated production environment and compares the generated schedules against those generated by EOQ, MRP, JIT, and OPT. The result is that bottleneck allocation methodology (BAM), with its critical resource based capacity scheduling out‐performs these other models in an intermittent demand discrete manufacturing environment.

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

The purpose of this paper is to present a method for generating a set of feasible, optimized production schedules for the erection process of compact shipyards building complex ship types.

A bi-objective mathematical model is developed based on the process constraints. A Pareto front of possible erection schedules is created using a the Non-dominated Sorting Genetic Algorithm II with a custom heuristic fitness function and constraint violation.

It was possible to consistently generate a wide variety of production schedules with superior performance to those manually created by shipyard planner in negligible computational time.

The set of optimized production schedules generated by the developed methodology can be used as a starting point by existing shipyard planners when drafting the initial erection planning for a new project. This allows the planners to consider wider variety of options in less time.

No other published approach for the automatic generation of optimized production schedules of the erection process is specifically tailored to the construction of complex ships.

Although the previous generations of the Industrial Revolution have brought many advantages to human life, scientists have been looking for a substantial breakthrough in creating technologies that can improve the quality of human life. Nowadays, we can make our factories smarter using new concepts and tools like real-time self-optimization. This study aims to take a step towards implementing key features of smart manufacturing including  preventive self-maintenance, self-scheduling and real-time decision-making.

A new bi-objective mathematical model based on Industry 4.0 to schedule received customer orders, which minimizes both the total earliness and tardiness of orders and the probability of machine failure in smart manufacturing, was presented. Moreover, four meta-heuristics, namely, the multi-objective Archimedes optimization algorithm (MOAOA), NSGA-III, multi-objective simulated annealing (MOSA) and hybrid multi-objective Archimedes optimization algorithm and non-dominated sorting genetic algorithm-III (HMOAOANSGA-III) were implemented to solve the problem. To compare the performance of meta-heuristics, some examples and metrics were presumed and solved by using the algorithms, and the performance and validation of meta-heuristics were analyzed.

The results of the procedure and a mathematical model based on Industry 4.0 policies showed that a machine performed the self-optimizing process of production scheduling and followed a preventive self-maintenance policy in real-time situations. The results of TOPSIS showed that the performances of the HMOAOANSGA-III were better in most problems. Moreover, the performance of the MOSA outweighed the performance of the MOAOA, NSGA-III and HMOAOANSGA-III if we only considered the computational times of algorithms. However, the convergence of solutions associated with the MOAOA and HMOAOANSGA-III was better than those of the NSGA-III and MOSA.

In this study, a scheduling model considering a kind of Industry 4.0 policy was defined, and a novel approach was presented, thereby performing the preventive self-maintenance and self-scheduling by every single machine. This new approach was introduced to integrate the order scheduling system using a real-time decision-making method. A new multi-objective meta-heuristic algorithm, namely, HMOAOANSGA-III, was proposed. Moreover, the crowding-distance-quality-based approach was presented to identify the best solution from the frontier, and in addition to improving the crowding-distance approach, the quality of the solutions was also considered.

Discusses a new manufacturing philosophy, Schedule‐based Manufacturing and compares it to the widely used Manufacturing Resource Planning [MRPII]. Looks at the history of the various manufacturing philosophies and how they have developed. Describes how SBM grew from interactive real‐time shop floor scheduling and outlines its benefits.

The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops.

An analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model.

Significant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s.

Considerable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints.

The disjunctive graph is a network representation of the job‐shop scheduling problem, while the longest path problem (LPP) is one of the most important subjects in this research field. This paper aims to study the special topological structure of the disjunctive graph, and proposes a suite of quick value‐setting algorithms for solving the LPPs commonly encountered in job‐shop scheduling.

The topological structure of the disjunctive graph is analyzed, and some properties and propositions regarding LPPs are presented. Subsequently, algorithms are proposed for solving LPPs encountered in job‐shop scheduling.

The proposed algorithms significantly improve the efficiency of the shifting‐bottleneck procedure, making it practicable to realise real‐time scheduling and hence effective operations of modern manufacturing systems.

The paper demonstrates that it is possible to develop very efficient algorithms by imposing a special topological structure on the network.

Building to order enables manufacturing to better respond to market conditions. The time lost between changes in customer preferences and product mix disappears and customer demand can both be anticipated and shaped by the sales system. An automotive build to order supply chain must be able to meet seasonality within markets, and understand the detailed demand volatility for certain elements of the complex product mix, from which much of the profitability is derived. Market responsive manufacturing entails adaptive and flexible production and supply capability in conjunction with real‐time market interaction through revenue management. The combination of late capacity setting and revenue management can enable the whole extended enterprise to operate as a single entity. This article outlines the demand volatility examined for automotive products, integrated revenue and demand management as a solution, and the subsequent order system investment requirements.