Flow Shop Scheduling Model Research Articles

Optimizing Road–Rail Multimodal Transport Schedule for Emergency Response with Congestion and Transshipment Sequence Selection

The current literature mainly uses hub capacity or transport route selection to manage the congestion of emergency multimodal transport and pays less attention to transshipment scheduling. This paper proposes an integrated optimization problem of transport routes and transshipment sequences (ITRTSP) and constructs a hybrid flow shop scheduling model to describe it. Based on this model, a recursive method is proposed to calculate the minimum waiting times that cargoes consume in queues at hubs, given the transport routes and transshipment sequences. Furthermore, a memetic algorithm is designed with route selection as the outer layer and transshipment sequence selection as the inner layer for solving ITRTSP. Compared with existing achievements, the model and algorithms can quantify the dependency between transshipment sequence selection and emergency transport time in multimodal transport network settings. The model and algorithms are applied to solve some real-scale examples and compared with the first-come-first-served (FCFS) rule commonly used in the current literature. The results indicate that the makespan is reduced by up to approximately 4.2%, saving 33.68 h. These findings demonstrate that even with given hub capacities and transport routes, congestion can still be managed and the schedule optimized through transshipment scheduling, further improving emergency transport efficiency.

No idle flow shop scheduling models with separated set-up times and concept of job weightage to optimize rental cost of machines

The current paper investigates a two-stage flow shop scheduling model with no idle restriction, in which the time taken by machines to set-up is separately considered from the processing time. Owing to inherent usefulness as well as relevance in real-world situations, jobs' weight has additionally included. To eliminate machine idle time and cutting machine cost of rental, the reason for the conduct of the study is to provide a heuristic algorithm which, once put into practice, processes jobs in an optimal way, guarantees in smallest conceivable make span. Multiple computational examples generated in MATLAB 2019a serve as testament to the efficacy of the proposed strategy. The outcomes are contrasted with the current methods that Johnson, Palmer and NEH have demonstrated.

Heuristic Approach For Weighted Two Stage Flow Shop Model In A String Of Disjoint Job Block Under No Idle Constraint

The current paper investigates a two-stage flow shop scheduling model with no idle restriction, in which jobs are scheduled to run in a string of two job blocks that are disjoint in nature, and the jobs processing time is correlated with probabilities. Owing to inherent usefulness as well as relevance in real-world situations, jobs' weight has additionally included. In order to eliminate machine idle time and cutting machine cost of rental, the reason for the conduct of the study is to provide a heuristic algorithm which, once put into practice, processes jobs in optimal way, guarantees in smallest conceivable make span. Multiple computational examples generated in MATLAB 2019a serve as testament to efficacy of the proposed strategy operates. The outcomes are contrasted with the current methods that Johnson and Palmer have demonstrated.

Hybrid Flow-Shop Scheduling Problems with Missing and Re-Entrant Operations Considering Process Scheduling and Production of Energy Consumption

A hybrid flow shop scheduling model with missing and re-entrant operations was designed to minimize the maximum completion time and the reduction in energy consumption. The proposed dual-population genetic algorithm was enhanced with a range of improvements, which include the design of a three-layer gene coding method, hierarchical crossover and mutation techniques, and the development of an adaptive operator that considered gene similarity and chromosome fitness values. The optimal and worst individuals were exchanged between the two subpopulations to improve the exploration ability of the algorithm. An orthogonal experiment was performed to obtain the optimal horizontal parameter set of the algorithm. Furthermore, an experiment was conducted to compare the proposed algorithm with a basic genetic algorithm, particle swarm optimization algorithm, and ant colony optimization, which were all performed on the same scale. The experimental results show that the fitness value of the proposed algorithm is above 15% stronger than the other 4 algorithms on a small scale, and was more than 10% stronger than the other 4 algorithms on a medium and large scale. Under the condition close to the actual scale, the results of ten repeated calculations showed that the proposed algorithm had higher robustness.

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

Makespan dominates the manufacturing expenses in bakery production. The high energy consumption of ovens also has a substantial impact, which bakers may overlook. Bakers leave ovens running until the final product is baked, allowing them to consume energy even when not in use. It results in energy waste, increased manufacturing costs, and CO2 emissions. This paper investigates three manufacturing lines from small and medium-sized bakeries to find optimum makespan and ovens’ idle time (OIDT). A hybrid no-wait flow shop scheduling model considering the constraints that are most common in bakeries is proposed. To find optimal solutions, non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA2), generalized differential evolution (GDE3), improved multi-objective particle swarm optimization (OMOPSO), and speed-constrained multi-objective particle swarm optimization (SMPSO) were used. The experimental results show that the shortest makespan does not always imply the lowest OIDT. Even the optimized solutions have up to 231 min of excess OIDT, while the makespan is the shortest. Pareto solutions provide promising trade-offs between makespan and OIDT, with the best-case scenario reducing OIDT by 1348 min while increasing makespan only by 61 min from the minimum possible makespan. NSGA-II outperforms all other algorithms in obtaining a high number of good-quality solutions and a small number of poor-quality solutions, followed by SPEA2 and GDE3. In contrast, OMOPSO and SMPSO deliver the worst solutions, which become pronounced as the problem complexity grows.

An Improved Simulated Annealing-Based Decision Model for the Hybrid Flow Shop Scheduling of Aviation Ordnance Handling.

Aviation ordnance handling is critical to the firepower projection of the time-critical cyclic flight operation on aircraft carriers. The complexity of the problem depends on the supply and demand features of ordnance. This paper examines the scheduling of aviation ordnance handling of an operational aircraft carrier under the framework of hybrid flow shop scheduling (HFS) and derives a method based on the simulated annealing (SA) algorithm to get the HFS problem's solution. The proposed method achieves the minimum possible flow time by optimizing the ordnance assignment through different stages. The traditional SA algorithm depends heavily on the heuristic scheme and consumes too much time to compute the optimal solution. To solve the problem, this paper improves the SA by embedding a task-based encoding method and a matrix perturbation method. The improved SA remains independent of the heuristic scheme and effectively propagates the local search process. Since the performance of SA is also influenced by its embedded parameters, orthogonal tests were carried out to carefully compare and select these parameters. Finally, different ordnance loading plans were simulated to reveal the advantage of the improved SA. The simulation results show that the improved SA (ISA) can generate better and faster solution than the traditional SA. This research provides a practical solution to stochastic HFS problems.

Branch and bound technique for two stage flow shop scheduling model with equipotential machines at every stage

Among the different jobs in manufacturing industries job scheduling is one of the most significant at the performance judgment level to facilitate organisation to attain competitiveness. Scheduling consist in allotment of limited resources to jobs over time in order to achieve one or more optimisation objectives. Scheduling has been mostly investigated in case of single machine at each stage or several machine at one stage and sole machine at rest of the stages. This study proposes an exact branch and bound algorithm to schedule n-jobs on two machines with parallel equipotential machines at every phase. The purpose of this work is to find best possible schedule of jobs with the intention to diminish the entire elapsed time and allocate in an optimal mode the lead time of the jobs to parallel equipotential machines of both the type so that total cost of doing all the jobs is minimised.

Optimization of No-Wait Flowshop Scheduling Problem in Bakery Production with Modified PSO, NEH and SA

In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance of PSO is significantly influenced by the weight distribution of exploration and exploitation in a run time. Due to the modification to the acceleration parameter, MPSO outperforms PSO, SA, and NEH in respect to effectively finding an optimized schedule. The best solution to the real case problem obtained by MPSO shows a reduction of the total idle time (TIDT) of the machines by 12% and makespan by 30%. The result of the optimized schedule indicates that for small- and medium-sized bakery industries, the application of the hybrid NWFSSM along with nature-inspired optimization algorithms can be a powerful tool to make the production system efficient.

Sustainable distributed permutation flow-shop scheduling model based on a triple bottom line concept

Based on a triple bottom line concept, sustainable development is characterized by the simultaneous pursuit of economic, environmental and social goals. The implementation of this concept in production scheduling can result in the resolution of a sustainable Distributed Permutation Flow Shop Scheduling Problem (DPFSP). The present study conceptually shifts an energy-efficient DPFSP to a sustainable DPFSP, simultaneously contributing to economic, environmental and social improvements. The study aims not only to minimize the total energy consumption related to production, but also, to maximize, for the first time, the social factors linked to job opportunities and lost working days. Different production centers and technologies are considered as new suppositions to establish a sustainable DPFSP. In this regard, a novel multi-objective mixed integer linear model is developed. To manage the high complexity of the proposed model, a novel multi-objective learning-based heuristic is established, as an extension of the Social Engineering Optimizer (SEO). The applicability of the proposed model is determined in the context of the wood industry in Canada. Several simulated tests are considered to verify the model. The proposed heuristic is compared with one of the other well-known, recent and state-of-the art methods. In order to guarantee a fair comparison, the Taguchi method is used to tune the parameters of the algorithms. Finally, sensitivity analyses are done to assess the efficiency of the proposed model.

Competitive bi-agent flowshop scheduling to minimise the weighted combination of makespans

Customer satisfaction is a prevalent issue amongst manufacturing enterprises. Multi-agent scheduling models aim to optimise the given criteria for improving customer satisfaction by fulfilling the customisation requirements. An investigation is executed on a bi-agent flowshop scheduling model, where a mass of tasks maintained by two competitive agents share a group of successive processors over time. The objective is to determine a feasible schedule that minimises the weighted combination of makespans belonging to two different agents. Asymptotic and worst-case analyses are conducted on a class of dominant-agent-based heuristics proposed to find approximate solutions for large-scale instances. An effective branch and bound algorithm is presented to achieve optimal solutions for small-scale instances, where the release-date-based branching rules and the preemption-based lower bounds significantly speed up the convergence of the proposed algorithm. A discrete artificial bee colony algorithm is introduced to find high-quality solutions for medium-scale instances. Extensive computational experiments are conducted to reveal the effectiveness of the proposed algorithms.

Solving Constrained Flow-Shop Scheduling Problem through Multistage Fuzzy Binding Approach with Fuzzy Due Dates

This paper deals with constrained multistage machines flow-shop (FS) scheduling model in which processing times, job weights, and break-down machine time are characterized by fuzzy numbers that are piecewise as well as quadratic in nature. Avoiding to convert the model into its crisp, the closed interval approximation for the piecewise quadratic fuzzy numbers is incorporated. The suggested method leads a noncrossing optimal sequence to the considered problem and minimizes the total elapsed time under fuzziness. The proposed approach helps the decision maker to search for applicable solution related to real-world problems and minimizes the total fuzzy elapsed time. A numerical example is provided for the illustration of the suggested methodology.

Task Scheduling Problem of Double-Deep Multi-Tier Shuttle Warehousing Systems

Double-deep multi-tier shuttle warehousing systems (DMSWS) have been increasingly applied for store-and-retrieval stock-keeping unit tasks, with the advantage of a reduced number of aisles and improved space utilization. Scheduling different devices for retrieval tasks to increase system efficiency is an important concern. In this paper, a Pareto optimization model of task operations based on the cycle time and carbon emissions is presented. The impact of the rearrangement operation is considered in this model. The cycle time model is converted into a flow-shop scheduling model with parallel machines by analyzing the retrieval operation process. Moreover, the carbon emissions of the shuttle in the waiting process, the carbon emissions of the lift during the free process, and the carbon emissions of the retrieval operation are considered in the carbon emissions model, which can help us to evaluate the carbon emissions of the equipment more comprehensively during the entire retrieval task process. The elitist non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the non-linear multi-objective optimization function. Finally, a real case is adopted to illustrate the findings of this study. The results show that this method can reduce carbon emissions and improve system efficiency. In addition, it also help managers to reduce operational costs and improve the utilization of shuttles.

Flexible flow shop scheduling with interval grey processing time

Purposepurpose of this paper is providing a solution for flexible flow shop scheduling problem with uncertain processing time in aeronautical composite lay-up workshop.Design/methodology/approachA flexible flow scheduling model and algorithm with interval grey processing time is established. First, according to actual needs of composite laminate shop scheduling process, interval grey number is used to represent uncertain processing time, and interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart, and other methods are further applied. Then a flexible flow shop scheduling model with interval grey processing time (G-FFSP) is established, and an artificial bee colony algorithm based on an adaptive neighbourhood search strategy is designed to solve the model. Finally, six examples are generated for simulation scheduling, and the efficiency and performance of the model and algorithm are evaluated by comparing the results.FindingsResults show that flexible flow shop scheduling model and algorithm with interval grey processing time can provide an optimal solution for composite lay-up shop scheduling problems and other similar flow shop scheduling problems.Social implicationsUncertain processing time is common in flexible workshop manufacturing, and manual scheduling greatly restricts the production efficiency of workshop. In this paper, combined with grey system theory, an intelligent algorithm is used to solve flexible flow shop scheduling problem to promote intelligent and efficient production of enterprises.Originality/valueThis paper applies and perfects interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart and other methods. A flexible flow shop scheduling model with interval grey processing time is established, and an artificial bee colony algorithm with an adaptive domain search strategy is designed. It provides a comprehensive solution for flexible flow shop scheduling with uncertain processing time.

A Reinforcement Learning Method to Scheduling Problem of Steel Production Process

In this paper, a reinforcement learning method is utilized to solve the steel production scheduling problem. Based on characteristics of steel production processing, the model of hybrid flow-shop scheduling problem is constructed. Then the model is attributed to a Markov Decision Process, and corresponding states, actions, reward function are put forward. When trading off the exploration and exploitation, an improved ε -greedy policy is designed. Finally, this hybrid flow-shop scheduling model based on reinforcement learning is applied to the scheduling example of steel production processing. Compared to genetic algorithm, the reinforcement learning method gets the better result.

An efficient heuristic method for joint optimization of train scheduling and stop planning on double-track railway systems

In this study, a new mathematical programming approach for solving the joint timetabling and train stop planning problem in a railway line with double-track segments is proposed. This research aims to design an optimized train timetable subject to the station-capacity and time-dependent dwell time constraints. The objective function is to maximize the railway infrastructure capacity by minimizing the schedule makespan. The problem is formulated as a particular case of blocking permutation flexible flow shop scheduling model with dynamic time window constraints. Due to the combinatorial nature of the problem, heuristic algorithm and bound tightening methods are proposed that generate approximate solutions, which are computationally efficient for large-size instances of the problem. Computational experiments based on the Iranian railway data set are conducted to examine the performance of the heuristic method. The results of the numerical experiments verified the efficiency of the heuristic method in solving moderately large instances of the problem. The outcomes show that the heuristic method works efficiently in finding high-quality solutions with optimality gaps of 2.1% on average.

Minimization of Rental Cost for Specially Structured Scheduling Model with Setup Time and Transportation Time

In the paper under consideration we have developed a new heuristic technique, to get the optimal sequence in which the jobs are to be processed so as to minimize the utilization time for which the machines are required and hence optimize the total expenditure incurred in hiring the machines on rent in case of a two stage specially structured flow shop scheduling model under pre-defined rental policy in which the processing times and setup times are linked with their relative probabilities, together with time taken for transporting the jobs from one processing unit to the next unit. The study taken up is quite broader and relates to the state of affairs existing in industrial units. The method given in this paper is reasonably simple and easy to apply and also offer an important means for taking decision regarding achieving an optimal sequence of jobs. Algorithm given in this paper is also explained by means of a numerical example.

Dynamic Production Scheduling for Prefabricated Components Considering the Demand Fluctuation

A dynamic optimized production scheduling which takes into account demand fluctuation and uncertainty is very critical for the efficient performance of Prefabricated Component Supply Chain. Previous studies consider only the conditions in the production factory and develop corresponding models, ignoring the dynamic demand fluctuation that often occurs at the construction site and its impact on the entire lifecycle of prefabricated construction project. This paper proposes a dynamic flow shop scheduling model for prefabricated components production, which incorporates demand fluctuation such as the advance of due date, insertion of urgent component and order cancellation. An actual prefabrication construction project has been used to validate the proposed multi-objective genetic algorithm model. The experimental results show that the proposed model can achieve a cost saving of up to 43.2%, which shows that the proposed model can cope well with the occurrence of demand fluctuation. This research contributes to the dynamic decision support system for managing prefabricated components.

Two Stage Flow Shop Scheduling Model Including Transportation Time with Equipotential Machines at Every Stage

This study presents a solution algorithm for the problem of minimizing the makespan on equipotential parallel machines at every stage in two stage flow shop scheduling model. The processing time of all the jobs on all the two machines is given and the time for which parallel equipotential machines are available is also given. Transportation time for moving the jobs from first machine to second machine is also taken into consideration. A mathematical illustration is also given in support of the algorithm proposed.

Minimization of Utilization Time for Specially Structured n × 3 Scheduling Model with Jobs in a String of Disjoint Job Blocks

The present paper investigates n×3 specially structured flow shop scheduling model with processing of jobs on given machines in a string of disjoint job blocks and with probabilities associated to the processing times of jobs. The objective is to minimize utilization time of second and third machine and also minimize the total elapsed time for processing the jobs for n×3 specially structured flow shop scheduling problem. The algorithm developed in this paper is quite straightforward and easy to understand and also present an essential way out to the decision maker for attaining an optimal sequence of jobs. The algorithm developed in this paper is validated by a numerical illustration.

A modified branch and bound algorithm for a vague flow-shop scheduling problem

Uncertainty plays a significant role in modeling and optimization of real world systems. Among uncertain approaches, fuzziness describes impreciseness while for ambiguity another definition is required. Vagueness is a probabilistic model of uncertainty being helpful to include ambiguity into modeling different processes especially in industrial systems. In this paper, a vague set based on distance is used to model a flow-shop scheduling problem being an important problem in assembly production systems. The vagueness being used as octagon numbers are employed to represent vague processes for the manufacturing system. As a modeling effort, first a flow-shop scheduling problem is handled with vagueness. Then, for solving and analyzing the proposed vague flow-shop scheduling model, a modified Branch and Bound algorithm is proposed. As an implementation, an example is used to explain the performance and to analyze the sensitivity of the proposed vague approach. The validity of the proposed model and modified algorithm is demonstrated through a robust ranking technique. The outputs help the decision makers to counteract the vagueness and handle operational decisions in flow-shop scheduling problems within dynamic environments.