Flexible Flow Shop Research Articles

RETRACTED ARTICLE: Preventive maintenance for the flexible flowshop scheduling under uncertainty: a waste-to-energy system.

Nowadays, an efficient and robust plan for maintenance activities can reduce the total cost significantly in the equipment-driven industry. Maintenance activities are directly associated with the impact on the plant output, production quality, production cost, safety, and the environmental performance. To address this challenge more broadly, this paper presents an optimization model for the problem of flexible flowshop scheduling in a series-parallel waste-to-energy (WTE) system. To this end, a preventive maintenance (PM) policy is proposed to find an optimal sequence for processing tasks and minimize the delays. To deal with the uncertainty of the flexible flowshop scheduling of waste-to-energy in practice, the work processing time is modeled to be uncertain in this study. Therefore, a robust optimization model is applied to address the proposed problem. Due to the computational complexity of this model, a novel scenario-based genetic algorithm is proposed to solve it. The applicability of this research is shown by a real-life case study for a WTE system in Iran. The proposed algorithm is compared against an exact optimization method and a canonical genetic algorithm. The findings confirm a competitive performance of the proposed method in terms of time savings that will ultimately save the cost of the proposed PM policy.

A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing

Among the potential road maps to sustainable production, efficient manufacturing scheduling is a promising direction. This paper addresses the lack of knowledge in the scheduling theory by introducing a generalized flexible flow shop model with unrelated parallel machines in each stage. A mixed-integer programming formulation is proposed for such model, solved by a two-phase genetic algorithm (GA), tackling job sequencing and machine allocation in each phase. The algorithm is parallelized with a specialized island model, where the evaluated chromosomes of all generations are preserved to provide the final Pareto-Optimal solutions. The feasibility of our method is demonstrated with a small example from literature, followed with the investigation of the premature convergence issue. Afterwards, the algorithm is applied to a real-sized instance from a Belgium pasta manufacturer. We illustrate how the algorithm converges over iterations to trade-off near-optimal solutions (with 8.50% shorter makespan, 5.24% cheaper energy cost and 6.02% lower labor cost), and how the evaluated candidates distribute in the objective space. A comparison with a NSGA-II implementation is further performed using hypothesis testing, having 5.43%, 0.95% and 2.07% improvement in three sub-objectives mentioned above. Although this paper focuses on scheduling issues, the proposed GA can serve as an efficient method for other multi-objective optimization problems.

Effective constructive heuristics for distributed no-wait flexible flow shop scheduling problem

The distributed shop scheduling has gained a great deal of attention due to its high-efficient collaboration and production. This paper studies a distributed no-wait flexible flow shop scheduling problem (DNWFFSP) with the makespan criterion, where each factory includes an identical flexible flow shop with no-wait constraint that requires each job to be processed without any waiting time between consecutive stages. A mixed-integer linear programming model for the DNWFFSP is presented. A machine selection method based on the first earliest available machine rule is presented. Three greedy factory assignment rules incorporating NEH (Nawaz-Enscore-Ham) heuristic are developed to allocate jobs into factories. Fourteen dispatch rules involving simple sorting and decomposition method are developed to construct a priority sequence of jobs. Thus, several constructive heuristics are obtained by combining dispatch rules and factory assignment rules. Furthermore, a constructive heuristic version of variable neighborhood descend is designed to address the DNWFFSP. One of constructive heuristics is selected to generate an initial solution, and five neighborhood search methods are used as the local search procedure. A comprehensive numerical comparison and statistical analysis are carried out to investigate the performance of the proposed approaches. The distributed NEH2 with smallest largest-medium rule obtains better results than other constructive heuristics for large-size instances.

A Study of Flexible Flow Shop Scheduling Problem with Various Heterogeneous Labors

This study considers the necessity of hiring heterogeneous labors. So far, many studies focus more on manufacturing of equipment and control systems in the intelligent production planning. In fact, the regular labors’ processing time may be affected by the external factors and the disabled labors by the mentally handicapped. Therefore, this study sets the processing time of the two types of labors as fuzzy sets. The extra processing time from overtime generated by physical deterioration of old-aged labors is equal to the processing time of regular labors multiplied by the physical deterioration rate of old-aged labors on machine. The coefficient of the cost function is the stepwise function of cost structure. Besides, the dispatching rule based on floating time utilizes ant colony optimization to minimize the makespan. The data test results indicate that the proposed algorithm can efficiently dispatch and schedule the operations, with the average improvement ratio about 11.76%, and demonstrates high capability for the intelligent production planning

Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

In order to reduce the energy consumption of furnaces and save costs in the product delivery time, the focus of this paper is to discuss the uncertainty of demand in the rolling horizon and to globally optimize the sustainability of the production in the aluminum furnace hot rolling section in environmental and economic dimensions. First, the triples α/β/γ are used to describe the production scheduling in the aluminum furnace hot rolling section as the scheduling of flexible flow shop, satisfied to constraints of demand uncertainty, operation logic, operation time, capacity and demand, objectives of minimizing the residence time of the ingot in the furnace and minimizing the makespan. Second, on the basis of describing the uncertainty of demand in rolling horizon with the scenario tree, a multi-objective mixed integer linear programming (MILP) optimization model for sustainable production in the aluminum furnace hot rolling section is formulated. Finally, an aluminum alloy manufacturer is taken as an example to illustrate the proposed model. The computational results show that when the objective weight combination takes the value of α=0.7, β=0.3, the sustainability indicators of the environmental and economic dimensions can be optimized to the maximum extent possible at the same time. Increasingly, managerial suggestions associated with the trade-off between environmental and economic dimensions are presented. Scheduling in the rolling horizon can optimize the production process of the aluminum furnace hot rolling section globally, indicating that it is more conducive to the sustainable development of the environment and economic dimensions than scheduling in a single decision time period.

Scheduling on flexible flow shop with cost-related objective function considering outsourcing options

This study considers outsourcing decisions in a flexible flow shop scheduling problem, in which each job can be processed by either an in-house production line or outsourced. The selected objective function aims to minimize the weighted sum of tardiness costs, in-house production costs, and outsourcing costs with respect to the jobs due date. The purpose of the problem is to select the jobs that must be processed in-house, schedule processing of the jobs in-house, and finally select and assign other jobs to the subcontractors. We develop a mixed-integer linear programming (MILP) model for the research problem. Regarding the complexity of the research problem, the MILP model cannot be used for large-scale problems. Therefore, four metaheuristic algorithms, including SA, GA, PSO, hybrid PSO-SA, are proposed to solve the problem. Furthermore, some random test problems with different sizes are generated to evaluate the effectiveness of the proposed MILP model and solution approaches. The obtained results demonstrate that the GA can obtain better solutions in comparison to the other algorithms.

Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning

ABSTRACT Given the fact that finding the optimal sequence in a flexible flow shop is usually an NP-hard problem, priority-based sequencing rules are applied in many real-world scenarios. In this contribution, an innovative reinforcement learning approach is used as a hyper-heuristic to dynamically adjust the k-values of the ATCS sequencing rule in a complex manufacturing scenario. For different product mixes as well as different utilisation levels, the reinforcement learning approach is trained and compared to the k-values found with an extensive simulation study. This contribution presents a human comprehensible hyper-heuristic, which is able to adjust the k-values to internal and external stimuli and can reduce the mean tardiness up to 5%.

Dynamic scheduling of manufacturing systems: a product-driven approach using hyper-heuristics

ABSTRACT Dynamic scheduling of manufacturing systems is encountered in many real-world industries such as the food and pharmaceutical industries. The scheduling of these systems must not only be efficient but also reactive to cope with dynamic job arrivals and machine breakdowns. Over the last decade, products within Product-Driven Control Systems (PDCS) have become smart entities capable of actively handling the manufacturing process. In this paper, a PDCS based on the design of Smart Products is proposed. A generic model of the decisional strategy allows Smart Products to characterize different decisional contexts and thus switch efficiently from one scheduling rule to another using a novel Hyper-Heuristics (HH) based approach. The implementation and testing of the proposed PDCS on hybrid flexible flow-shops with multiple constraints inspired by the pharmaceutical industry are presented. The comparative study with 168 combinations of scheduling rules from the literature highlighted the superiority of the HH to minimize the Mean Completion Time. Furthermore, the proposed approach enhanced both the global performance and the reactivity of the manufacturing control system.

Flexible Flow Shop Scheduling and Energy Saving Optimization Strategy under Low Carbon Target

In order to reduce the carbon emissions of the flow shop which consists of unrelated parallel machine, a kind of scheduling mathematical mode with low carbon emission target is built by analyzing the carbon emissions of the machine tools under different running conditions. Not only the carbon emissions produced in the handling process which is rarely discussed in the previous studies, but also the carbon emissions produced during the start-up and shutdown process of the selected machine tools are considered in the model. Based on this model, a multi-state alternation energy saving optimization strategy is proposed to reduce carbon emissions during the idle waiting period of machine tools. An improved genetic algorithm is employed to solve this optimization problem. In the end, the t cases are studied to validate the strategy.

Scheduling problems on a new setting of flexible flowshops: ℓ-Machine proportionate flowshops

The classical flexible flowshop () environment consists of -stages in series with -machines in parallel at each stage, where each job has to undergo a particular series of actions. In this paper we consider a similar, yet different, and still a challenging real-life setting, that, to the best of our knowledge, has not been studied to date. We assume that there are -sets in parallel in which each of the sets is an -machine proportionate flowshop. In this new setting, each job can be processed on each one of the sets, but once a set is chosen, the job must be processed on all of its machines, in first-in-first-out method. We study several fundamental scheduling measures such as makespan, maximum tardiness with common due-date, total tardiness with common due-date, and total load. Moreover, we consider optional job-rejection and focus on minimising the total completion time. All problems are shown to be NP-hard, and pseudo-polynomial dynamic programming (DP) solution algorithms and Simulated Annealing (SA) metaheuristics are provided. The efficiency of all our proposed algorithms is validated through an extensive numerical study.

Minimizing the operational costs in a flexible flow shop scheduling problem with unrelated parallel machines

This paper investigates a flexible flow shop scheduling problem with the aim of minimizing the operational costs as a new objective function. In this production system, there are some unrelated parallel machines with different performances and different technology levels in the first stage and each other stage consists of a single machine. Setup times are assumed as sequence-dependent and are need when a machine starts to process a new job. Some of the parallel machines in the first stage are multifunctional and can do several processes on jobs. So, the jobs that are assigned to these machines do not need to be processed in some next stages. This problem is described with an example, and its parameters and decision variables are defined. Then a mathematical model based on mixed-integer linear programming (MIP) is developed to solve the problem in small-sized scales. As this problem is discussed in an Np-hard environment, the Genetic Algorithm (GA) is applied to solve the considered problem on practical-sized scales. Due to the result, the operational costs conflict with makespan as a common objective function in scheduling problems. Therefore, the supplementary analysis has been presented considering a restriction on the makespan.

Production Scheduling in a Flexible Hybrid Flow Shop in the Food Industry Based on the Theory of Constraints

This paper proposes a mathematical model for production scheduling, whose objective is to maximize the profits or Throughput of a company in the food sector through a Flexible Hybrid Flow, based on the theory of constraints. Considering the company's production configuration, which is a two-stage hybrid flow line, a mixed integer linear model programming (MILP) was formulated and programmed to adequately represent the real situation. The mathematical model developed in this study that is an easy and effective tool that helps to control the production process, by optimizing the quantities of each product to be produced, as well as establishing the sequence in which they must be carried out, which becomes an advantage against its competitors and also obtain a timely response to the needs of demand and compliance with the commitments made to its customers. The results obtained with the MILP, with reasonable computational times, allow for maximizing profits, considering the constraints of the problem.

An efficiency improvement of flexible flow shop scheduling in automotive part company

Flexible flow shop is becoming more interested and applied in industries due to its impact from higher workloads. Flexible flow shop scheduling problem is focused to minimize the makespan. A metaheuristic model based on Hybrid Tabu Search is developed to overcome this problem. Firstly, Hybrid Tabu Search is modelled based on the factory data. The Earliest Due Date rule is used as the scheduling method for the current status. FlexSim simulation software is used to evaluate the Hybrid Tabu Search model. The outcome is validated with two different basic heuristic solutions; Campbell, Dudek and Smith’s and Gupta’s heuristics. It is found that the proposed model can improve the job sequence based on makespan criteria.

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

Flow shop scheduling is a type of scheduling where sequence follows for each job on a set of machines for processing. In practice, jobs in flow shops can arrive at irregular times, and the no-wait constraint allows the changes in the job order to flexibly manage such irregularity. The flexible flow shop scheduling problems with no-wait have mainly addressed for flow optimization on the shop floor in manufacturing, processing, and allied industries. The scope of this paper is to identify the literature available on permutation and non-permutation flow shop scheduling with no-wait constraint. This paper organizes scheduling problems based on performance measures of variability and shop environments. The extended summary of two/three-machine and m-machine problems has been compiled, including their objectives, algorithms, parametric considerations, and their findings. A systematic appearance of both conceptual and analytical results summarizes various advances of the no-wait constraint. The paper includes independently investigated problems and suggestions for future research directions.

A linear programming approach for hybrid flexible flow shop with sequence-dependent setup times to minimise total tardiness.

A linear programming approach for hybrid flexible flow shop with sequence-dependent setup times to minimise total tardiness.

Heuristic approaches for scheduling jobs and vehicles in a cyclic flexible manufacturing system

Abstract This paper addresses the scheduling of automated guided vehicles (AGVs) in a cyclic flexible flow shop environment. The vehicles travel along a single loop. All production machines are located alongside the track in the required order, with a possibility for multiple machines per stage. All AGVs are to be scheduled for a specific starting time and will then continuously circle the track. Pickup and delivery times are included in the travel time of a vehicle, stops are forbidden. Jobs may start upon arrival if their predecessor has been started for processing. Therefore, job completion times are dynamic. The considered objectives are the minimization of the number of AGVs and of the total makespan. For the regarded problem, different local search variants are proposed. Optimal results are produced using a brute force enumeration algorithm. Finally, fixed permutation schedules are compared to processing jobs according to a first-come-first-serve rule.

Research on Conflict Resolution Method of Production Evaluation Index in Flexible Manufacturing Field Based on Multiplayer Cooperative Game

Multiple local assignment rules are common in production lines with complex production elements to direct the production process. Different local assignment rules have different degrees of impact on each production evaluation index, which aggravates the conflict between each production evaluation index. In order to solve the problem of increasing conflict among multiple production evaluation indexes in complex production scheduling process, based on the mathematical model of flexible flow shop with limited buffer and public buffer (FFSP-PB), game elements such as game player, game strategy and game order are established according to the idea of multiplayer cooperative game, and design the corresponding game strategy selection method to predict and control the game behavior of each player in the problem. Then a conflict resolution method of production evaluation index based on multiplayer cooperative game is proposed to solve the conflict between multiple production evaluation indexes, and then improve each production evaluation index. Finally, according to the actual production data of a bus manufacturing enterprise based on flexible manufacturing mode, several simulation schemes are designed to analyze and verify the effectiveness and practicability of the proposed method in resolving the conflicts between production evaluation indexes, and provides a reliable new solution for the conflict resolution of production evaluation index in the field of flexible manufacturing.

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.

Energy-efficiency scheduling of multi-cell manufacturing system considering total handling distance and eligibility constraints

This study investigates an energy-efficiency scheduling problem of multi-cell manufacturing system with eligibility constraints, in which each manufacturing cell is configured as a flexible flow shop. The purpose of the energy-efficiency scheduling is to optimize energy consumption, total handling distance and makespan. Firstly, a new mathematical model is established for the investigated problem. Secondly, in order to effectively reduce energy consumption and balance the load of the machines, a novel decoding scheme is proposed. Thirdly, an improved multi-objective evolution algorithm based on decomposition which uses a local search strategy to improve the neighborhoods solutions of the sub-problem with the least non-dominated solutions is proposed to solve the established model. Finally, a large number of experiments are carried out to verify the effectiveness of the proposed approach. The results demonstrate that the proposed model and approach have feasibility in energy efficiency improvement, and the proposed approach has better performance than the existing methods.

Hyper-heuristic for integrated due-window scheduling and vehicle routing problem for perishable products considering production quality

ABSTRACT In today’s competitive environment, industrial units are seeking to reduce costs and increase customer numbers; if their business involves perishable products, achieving these goals is even more important. By integrating scheduling and vehicle routing problems for perishable products, this study tries to minimize costs and maximize customers’ purchase probability. In the scheduling stage, a flexible flowshop scheduling problem considering production quality is studied. After completion of the last job, the distribution stage begins and each product must be delivered in its time-window. This problem is formulated as mixed-integer linear programming and solved by the GAMS solver. Owing to the NP hardness of this problem, a hyper-heuristic is proposed to solve large-size instances. In the proposed algorithm, the acceptance of the solution is based on the Monte Carlo criterion. Finally, the numerical results and analysis demonstrate the proposed algorithm’s superiority on some criteria compared to the non-dominated sorting genetic algorithm-II (NSGA-II).