Flexible Job Shop Systems Research Articles

Enhanced NSGA-II for multi-objective energy-saving flexible job shop scheduling

To achieve green targets, manufacturing enterprises need to propose an effective energy-saving strategy for production scheduling. In this paper, a multi-objective energy-saving flexible job shop-scheduling problem (MO_EFJSP) is formulated with three criteria of optimizing the makespan, the total delay time and the total power consumption. To efficiently solve the MO_EFJSP, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is developed. The proposed ENSGA-II has two main innovative aspects: i) the diversity of children population in a local search is achieved by performing different neighborhood search procedures on the sparse solution space so that the accuracy of the current solution is improved; ii) the weighted method is applied to select the desirable compromised solution from the Pareto solution set. By conducting extensive computational experiments based on benchmark instances and real-world case studies, it is verified that the proposed ENSGA-II is applicable for saving power consumption in a flexible job shop system. Consequently, this study makes a significant contribution to the field of green (energy-saving or energy-efficient) production scheduling.

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

One of the most difficult challenges for modern manufacturing is reducing carbon emissions. This paper focuses on the green scheduling problem in a flexible job shop system, taking into account energy consumption and worker learning effects. With the objective of simultaneously minimizing the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multiobjective optimization model. Then, the improved multiobjective sparrow search algorithm (IMOSSA) is developed to find the optimal solution. Finally, we conduct computational experiments, including a comparison between IMOSSA and the nondominated sorting genetic algorithm II (NSGA-II), Jaya and the mixed integer linear programming (MILP) solver of CPLEX. The results demonstrate that IMOSSA has high precision, good convergence and excellent performance in solving the GFJSP in low-carbon manufacturing systems.

Concurrent scheduling of jobs and AGVs in a flexible job shop system: a parallel hybrid PSO-GA meta-heuristic

Concurrent scheduling of jobs and AGVs in a flexible job shop system: a parallel hybrid PSO-GA meta-heuristic

MILP model for the planning of a Computerized Numerical Control Lathes Machining Plant

This work introduces the formulation and application of a MILP model to solve the problem of planning the weekly production of a machining plant using numerical control lathes to manufacture spare parts for agricultural machines. The machining plant works under a Flexible Job Shop system and it has reduced workforce with different skills to operate the various high-complexity lathes and to carry out setup operations in each machine. The developed model is based on a basic formulation for the classic problem and we introduce some flexible adjustment for the various situations that may arise from different scheduling problems. The model is applied to various scenarios; and we include a discussion of the improvements brought about by the analysis.

A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context

Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery.

Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules

The utilization of a specific priority rule in scheduling operations in flexible job shop systems strongly influences production goals. In a context of production control in real practice, production performance indicators are evaluated always en bloc. This paper addresses the multi-criteria evaluating five selected conflicting production objectives via scalar simulation-based optimization related to applied priority rule. It is connected to the discrete-event simulation model of a flexible job shop system with partially interchangeable workplaces, and it investigates the impact of three selected priority rules—FIFO (First In First Out), EDD (Earliest Due Date), and STR (Slack Time Remaining). In the definition of the multi-criteria objective function, two scalarization methods—Weighted Sum Method and Weighted Product Method—are employed in the optimization model. According to the observations, EDD and STR priority rules outperformed the FIFO rule regardless of the type of applied multi-criteria method for the investigated flexible job shop system. The results of the optimization experiments also indicate that the evaluation via applying multi-criteria optimization is relevant for identifying effective solutions in the design space when the specific priority rule is applied in the scheduling operations.

Flexible Job Shop Scheduling Problem with Sequence Dependent Setup Time and Job Splitting: Hospital Catering Case Study

This paper aims to study a real case of an optimization problem derived from a hospital supply chain. The present work focuses on developing operational decision support models and algorithms for production process scheduling in hospital catering. The addressed production system is considered as a flexible job shop system. The objective is to minimize the total flow time. A novel mathematical model and two metaheuristics for the production scheduling of multi-product and multi-stage food processes are developed. These methods have proven their effectiveness for the scheduling of operations of the food production processes and allowed significant improvements in the performance of the studied production system.

Robust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown

Resource scheduling, job sequencing, and assigning them to available resources are the most critical issues in manufacturing systems, such as flexible job-shop systems. In addition, scheduling uncertainties have attracted significant attention in this field. This study investigates the dual-resource constrained flexible job-shop scheduling (DRCFJSS) problem under machine breakdown and operational uncertainty. Stochastic scenario-based methods were utilized to study the uncertain nature of the problem. Because process times have inherent uncertainty, they are considered fuzzy numbers and are controlled by a credibility-based measure. Robust scheduling must be developed to address unexpected disruptions, such as machine breakdowns and operational risks, such as uncertain process times. Accordingly, a novel robust fuzzy stochastic programming (RFSP) model is presented to solve this problem. In the proposed RFSP model, the objective function is formulated using a hybrid measure (i.e., a combined average-case and worst-case performance of the manufacturing system) under probable machine breakdown scenarios. Because the DRCFJSS problem is NP-hard, two types of meta-heuristic algorithms, evolutionary population-based, genetic algorithm (GA), and vibration damping optimization (VDO) algorithm, are used for large-size problems. Then, the proposed RFSP model was applied to a case study, and numerical experiments with randomly generated test problems were used. In small-sized problems, the proposed model is solved using the CPLEX solver, GA, and VDO algorithms. Computational studies indicate a higher performance of the GA compared with the other algorithms in terms of the objective function.

Minimizing total energy cost and tardiness penalty for a scheduling-layout problem in a flexible job shop system: A comparison of four metaheuristic algorithms

Job scheduling and machine layout are interrelated in improving energy consumption (EC) and productivity measures such as tardiness and represent two important decisions that must be made by manufacturers. This interdependency can be explained by transportation time, which connects scheduling and layout. Scheduling and layout, however, have not been thoroughly studied in conjunction using an integrated model in the context of sustainable manufacturing. Hence, we propose an energy-aware optimization model in which scheduling is integrated with layout in a single-level framework. More specifically, a single objective function is defined to minimize the facility energy cost and the job tardiness penalty, which control EC and tardiness respectively in a flexible job shop system. In order to model machine EC more accurately, we also consider three different machine states: a processing state and two idle states. Our case studies show that the integrated model exhibits better performance in controlling manufacturing EC and job tardiness than a non-integrated model in which machine locations are uncontrollable and transportation times between machines are unchangeable. To deal with large-sized problems, we also introduce four new metaheuristics. The performances of these new metaheuristics are compared in terms of objective function values and CPU times using various case studies. The results indicate that a hybrid ant colony optimization and simulated annealing (ACO-SA) algorithm provides better performance than the other algorithms. Specifically, our case studies show that the integrated model using ACO-SA can improve the objective function value by around 5% when compared to the non-integrated model.

A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

PurposeThe purpose of this paper is to present a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations. In this problem, each product is produced by assembling a set of several different parts. At first, the parts are processed in a flexible job shop system, and then at the second stage, the parts are assembled and products are produced.Design/methodology/approachAs the problem is non-deterministic polynomial-time-hard, a new hybrid particle swarm optimization and parallel variable neighborhood search (HPSOPVNS) algorithm is proposed. In this hybrid algorithm, particle swarm optimization (PSO) algorithm is used for global exploration of search space and parallel variable neighborhood search (PVNS) algorithm for local search at vicinity of solutions obtained in each iteration. For parameter tuning of the metaheuristic algorithms, Taguchi approach is used. Also, a statistical test is proposed to compare the ability of metaheuristics at finding the best solution in the medium and large sizes.FindingsNumerical experiments are used to evaluate and validate the performance and effectiveness of HPSOPVNS algorithm with hybrid particle swarm optimization with a variable neighborhood search (HPSOVNS) algorithm, PSO algorithm and hybrid genetic algorithm and Tabu search (HGATS). The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms.Practical implicationsScheduling of manufacturing parts and planning of assembly operations are two steps in production systems that have been studied independently. However, with regard to many manufacturing industries having assembly lines after manufacturing stage, it is necessary to deal with a combination of these problems that is considered in this paper.Originality/valueThis paper proposed a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations.

Designing a Multi-Objective Mathematical Model for Flexible Job Shop Scheduling Problem With the Earliness/Tardiness Penalty

The scheduling of flexible job shop systems is one of the most important issues in the various fields of production and is currently being addressed by many researchers in the field of optimization issues. The present research includes flexible job shop scheduling problem (FJSSP) with multi-objective, minimizing maximum completion time (makespan), maximum machine workload, total machines workload and also, earliness/tardiness penalty with different constraints. In this research, the researcher is looking to design a mathematical model that can cover all the constraints and assumptions related to the problem. Therefore, the mathematical model was designed with multi-objective and different constraints with exact details and different assumptions that are consistent with the actual situation of the problem and implemented at Comex Company. What that distinguishes this research from other similar researches is the approach of multi-objective with different constraints, which, at the same time, it raises the complexity of the problem but the problem gets closer to the actual situation, with less research done. Finally, the results of the study showed that this mathematical model designed, as well as in the real environment which has the flexible job shop system, can be implemented within a reasonable time with the highest efficiency before the implementation of the model.

Application of Hybrid Simulation in production scheduling in job shop systems

This work seeks to study one of the most complex and important issues in production scheduling research: flexible job shop systems. These systems are extremely important for industry, which uses the make-to-order strategy and seeks mix and volume flexibility. The model system will use agents within discrete-event simulation models, generating a Hybrid Simulation model. The agent will sequence the production orders at the beginning of the process and re-sequence them, when necessary, in order to achieve a multi-objective optimization. For this, the agent will bring together two different logics that have opposing goals. This work consists of the comparison of the results of three scheduling methods: firstly, with the sequence of arrival; secondly, with the agent using one sequencing logic; and, finally, using the same logic, but with adjustments in the sequence during the batch production, seeking to improve the negative points generated by the logic. It also stresses that this schedule ensures that the Manager Agent reduces makespan and increases machine utilization while increasing its interference in the model. This is a quantitative study, using the modeling and simulation method and following an empirical model.

Optimizing Lot Size of Flexible Job Shop Problems by Considering Expiration Aspect

Production planning for manufacturing companies is an aspect to meet production needs for production process. The production need of fish canning companies is uncertainty aspect and this includes in the flexible job shop system, the issues of uncertainty. The process of fulfilling the needs with consideration of expiration in the fish canning company is to optimize lot size with assumption that customer demand can be met on time without reducing consumer trust. Mathematical formulations used to meet the needs of the FJSS problem use lot-sizing techniques in making decisions. Stochastic programs can be used to obtain the desired formulation. Solution for ordering quantities and the number of quantities ordered plus expiration considerations are formulated with mixed integer linear programing.

An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company

Recently, many modern industries have adopted joint scheduling of production and distribution decisions. Such coordination is necessary in make-to-order (MTO) businesses, where it is challenging to achieve timely delivery at minimum total cost and meet the requirements for high customization. To deal with these challenges, a practical production configuration and delivery method is required, in addition to a closer link between production and distribution schedules. Hence, in this study, we address an integrated production scheduling-vehicle routing problem with a time window, where it is assumed that production is performed in a flexible job-shop system. Our framework is modeled as a novel bi-objective mixed integer problem, in which the first objective function aims to minimize a sum of the production and distribution scheduling costs, and the second objective function tries to minimize a weighted sum of delivery earliness and tardiness. To practically validate the application of our framework, a case study from a furniture manufacturing company producing customized goods is considered, and experimental data are derived. Based on the real data, the model is first optimally solved by an ε−constraint method, and then a Hybrid Particle Swarm Optimization (HPSO) algorithm is developed to solve the model for medium- and large-sized problems in a reasonable time. We discuss the benefits of integration by comparing the results of the proposed model with that of the separate approach. The results show that the company can establish a proper rational balance between cost and customer concerns, and they can use the integration policy as a lever to improve customer satisfaction without the system experiencing a significant increase in total operational cost.

Sustainable evaluation of environmental and occupational risks scheduling flexible job shop manufacturing systems

Sustainability has become a challenge of relevant importance for organizational management. Companies should not only achieve appropriate levels of economic efficiency, but also of environmental and social efficiency. In this sense, the inclusion of sustainability principles in the scheduling of flexible job shop systems (FJS) has focused on the evaluation of energy consumption and key economic indicators (makespan, total weighted tardiness …). An industrial case (Medellín, Colombia) from the metal-mechanic sector is used to illustrate the simultaneous evaluation of the three sustainability dimensions: economic, environmental and social. The makespan is the indicator associated with the economic performance; CO2 equivalent emissions, water consumption, metal waste, and chrome waste are the indicators to evaluate the environmental performance; and weight manipulated at workstations, noise, ambient temperature, and vibrations are the indicators associated with the social performance. The measurement, normalization, and weighted sum of the previous indicators, allow for the estimation of the performance of each sustainability dimension. The NSGA-II and NSGA-III methods are used in order to carry out the multi-objective evaluation process, based on the values estimated for the three general objectives (i.e., environmental, social, and economic). Consequently, in the industrial case analyzed, the applied methods identify the corresponding structure of the Pareto optimal fronts. The post-optimal analysis of the solutions shows that the solutions can be directly associated with market demand tendencies, and their implementation can be detected in advance. Solutions that balance economic, environmental, and social efficiency, could be potentially selected according to the market trend, reaching a win-win scenario for all actors involved.

Dispatching algorithm for production programming of flexible job-shop systems in the smart factory industry

In today highly competitive and globalized markets, an efficient use of production resources is necessary for manufacturing enterprises. In this research, the problem of scheduling and sequencing of manufacturing system is presented. A flexible job shop problem sequencing problem is analyzed in detail. After formulating this problem mathematically, a new model is proposed. This problem is not only theoretically interesting, but also practically relevant. An illustrative example is also conducted to demonstrate the applicability of the proposed model.

Mixed integer lineal programming model to schedule flexible job-shop systems in make to order environments

Introduccion: Los sistemas de produccion Job Shop (JS) se caracterizan porque los trabajos a ser procesados tienen diferentes rutas de produccion. Una generalizacion de este tipo de sistemas es el Job Shop Flexible (FJS), en el cual se cuenta con mas de una maquina por estacion para realizar alguna de las operaciones.Objetivo: El objetivo de este proyecto fue proponer un modelo de programacion lineal entera mixta para programar sistemas FJS con el objetivo de minimizar el numero de trabajos tardios.Metodologia: El modelo se desarrollo utilizando un enfoque basado en variables de secuencia-posicion. Este enfoque utiliza variables binarias para decidir si una operacion determinada es asignada a una posicion en la secuencia de procesamiento de la maquina asignada. Para validar el desempeno del modelo se utilizaron datos de una pequena empresa con un sistema productivo tipo FJS que desarrolla sus operaciones en un entorno bajo pedido (Make to Order, MTO). Por esta razon los indicadores de desempeno mas importantes para la empresa son los asociados al nivel de servicio.Resultados: Los resultados muestran un desempeno razonable en terminos del objetivo que se persigue. Se encontro el programa optimo de produccion en menos de 3600 segundos en instancias de menos de 14 ordenes de produccion. En instancias mas grandes su obtuvieron soluciones factibles en el tiempo limite definido.Conclusiones: El modelo permite definir programas de produccion en sistemas en los cuales el cumplimiento de fechas de entrega es de vital importancia. Los resultados han permitido que la empresa mejore su desempeno y disminuya los costos asociados a los incumplimientos. Futuras investigaciones se pueden desarrollar para encontrar metodos de solucion mas eficientes con relacion a los tiempos computacionales para obtener soluciones de instancias de mayor tamano.

Pollux: a dynamic hybrid control architecture for flexible job shop systems

Nowadays, manufacturing control systems can respond more effectively to exigent market requirements and real-time demands. Indeed, they take advantage of changing their structural and behavioural arrangements to tailor the control solution from a diverse set of feasible configurations. However, the challenge of this approach is to determine efficient mechanisms that dynamically optimise the configuration between different architectures. This paper presents a dynamic hybrid control architecture that integrates a switching mechanism to control changes at both structural and behavioural level. The switching mechanism is based on a genetic algorithm and strives to find the most suitable operating mode of the architecture with regard to optimality and reactivity. The proposed approach was tested in a real flexible job shop to demonstrate the applicability and efficiency of including an optimisation algorithm in the switching process of a dynamic hybrid control architecture.

Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate

Due to environmental circumstances encountered in manufacturing processes, operating machines need to be maintained preventively, so as to ensure satisfactory operating condition. This paper investigates a scheduling problem in a flexible job-shop system with maintenance considerations where each operation can be processed by a machine out of a set of capable machines, and so, jobs may have alternative routes. Machine failure rates are assumed to be time-varying. This is a real assumption comes from a fact in realistic environments, where failure rate of a machine is variable when environmental situations like shop temperature, shop light, shop humidity or even worker skill change significantly. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing times and due dates are considered to be stochastic parameters. A mixed integer linear programming (MILP) model is constructed for addressed problem with the objective of number of tardy jobs and a minimum total availability constraint. Then a simulation-optimization framework based on a simulated annealing (SA) optimizer and Monte Carlo (MC) simulator is presented to solve the problem.

Integrating Genetic Algorithm with Time Control for Just-In-Time Scheduling Problems

To reduce inventory costs and increase customer satisfaction in a real production environment, Just-in-Time (JIT) manufacturing should be combined with more flexibility. Flexibility in manufacturing system ensures improved product quality and mass customization. In this paper, we consider flexible job-shop problem (FJSP) to include several types of flexibility. Prior research on JIT scheduling problems paid little attention to flexible job shop systems. In this paper, an optimization method for FJSP for JIT manufacturing was proposed, genetic algorithm and time control are integrated to improve the JIT performance. Computational simulations and comparisons demonstrate that the proposed method shows more competitive performance than other evolutionary algorithms.