Scheduling Of Manufacturing Systems Research Articles

Petri nets-based approach for scheduling and rescheduling in multi-site manufacturing system

In multi-site manufacturing systems, operations are subject to a great number of unexpected interruptions that may withdraw the original manufacture schedules. In these cases, rescheduling is prevalent to preserve the performance of the whole system. There are many types of disturbances that can upset the schedule, including; machine failures, processing time delays, clients rush orders, quality problems, transportation failures, etc. In this work, we consider only the machine breakdown as disruption that require schedule updating (rescheduling). For that, we develop a Petri nets (PN) scheduling and rescheduling approach to model and simulate the multi-site manufacturing system while minimising the makespan. Finally, we give a case study to analyse the efficiency of the proposed model.

Petri nets-based approach for scheduling and rescheduling in multi-site manufacturing system

In multi-site manufacturing systems, operations are subject to a great number of unexpected interruptions that may withdraw the original manufacture schedules. In these cases, rescheduling is prevalent to preserve the performance of the whole system. There are many types of disturbances that can upset the schedule, including; machine failures, processing time delays, clients rush orders, quality problems, transportation failures, etc. In this work, we consider only the machine breakdown as disruption that require schedule updating (rescheduling). For that, we develop a Petri nets (PN) scheduling and rescheduling approach to model and simulate the multi-site manufacturing system while minimising the makespan. Finally, we give a case study to analyse the efficiency of the proposed model.

Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company)

PurposeThis study aims to propose a new genetic algorithm for solving supply chain scheduling and routing problem in a multi-site manufacturing system. The main research question is: How is the production and transportation scheduled in a multi-site manufacturer? Also the sub-questions are: How is the order assigned to the suppliers? What is the production sequence of the assigned orders to a supplier? How is the order assignment to the vehicles? What are the vehicles routes to convey the orders from the suppliers to the manufacturing centers? The authors’ contributions in this paper are: integration of production scheduling and vehicle routing in multi-site manufacturing supply chain and proposing a new genetic algorithm inspired from the role model concept in sociology.Design/methodology/approachConsidering shared transportation system in production scheduling of a multi-site manufacturer is investigated in this paper. Initially, a mathematical model for the problem is presented. Afterwards, a new genetic algorithm based on the reference group concept in sociology, named Reference Group Genetic Algorithm (RGGA) is introduced for solving the problem. The comparison between RGGA and a developed algorithm of literature closest problem, demonstrates a better performance of RGGA. This comparison is drawn based on many test problems. Moreover, the superiority of RGGA is certificated by comparing it to the optimum solution in the small size problems. Finally, the authors use real data collected from a drug manufacturer in Iran to test the performance of the algorithm. The results show the better performance of RGGA in comparison with obtained outputs from the real case.FindingsThe authors presented the mathematical model of the problem and introduced a new genetic algorithm based on the “reference group” concept in sociology. Robert K. Merton is a sociologist who presented the concept of reference groups in society. He believed that some people in each society such as heroes or entertainment artists affect other people. The proposed algorithm uses the reference group concept to the genetic algorithm, namely, RGGA. The comparison of the proposed algorithm with DGA and the optimum solution shows the superiority of RGGA. Finally, the authors implement the algorithm in a real case of drug manufacturing and the results show that the authors’ algorithm gives better outputs than obtained outputs from the real case.Originality/valueOne of the major objectives of supply chains is to create a competitive advantage for the final product. This intension is only achieved when each and every element of the supply chain considers customers’ needs in every function of theirs. This paper studies scheduling in the supply chain of a multi-site manufacturing system. It is assumed that some suppliers produce raw material or initial parts and convey them by a fleet of vehicles to a multi-site manufacturer.

Optimal scheduling of manufacturing and onsite generation systems in over-generation mitigation oriented electricity demand response program

Abstract Manufacturing system is considered a valuable source that can provide electricity load adjustment in electricity demand response program to balance the supply and demand of the electricity throughout the grid. In this paper, we propose a mathematical model to identify the optimal participation strategy for manufacturing end use customers with onsite energy generation system in the demand response program designed for mitigating electricity over-generation due to high penetration of renewable sources in electricity grid. The background of over-generation mitigation oriented demand response program is described first. Then, the manufacturer’s decision making procedure for identifying the optimal participation strategy is modeled as a mixed nonlinear integer programming. In particular, the manufacturers’ participation strategies including the decision of participating or not, and corresponding production schedule of manufacturing system as well as utilization schedule of onsite generation system, are modeled as decision variables in the objective function to minimize the overall cost considering the benefits due to the participation, energy billing cost, onsite generation cost, and production loss penalty cost. Particle swarm optimization is used to find a near optimal solution for the formulated problem. A numerical case study with sensitivity analysis is then conducted to demonstrate the effectiveness and robustness of the proposed model.

Non-dominated sorting biogeography-based optimization for bi-objective reentrant flexible manufacturing system scheduling

Scheduling in flexible manufacturing systems (FMS) is described as an NP-Hard problem. Its complexity has increased significantly in line with the development of FMS over the past years. This paper presents a non-dominated sorting biogeography-based optimization (NSBBO) for scheduling problem of FMS having multi loading-unloading and shortcuts infused in the reentrant characteristics. This model is formulated to identify the near optimal trade-off solutions capable of addressing the bi-objectives of minimization of makespan and total earliness. The goal is to simultaneously determine the best machine assignment and job sequencing to satisfy both objectives. We propose the development of NSBBO by substituting the standard linear function of emigration-immigration rate with three approaches based on sinusoidal, quadratic and trapezoidal models. A selection of test problems was examined to analyze the effectiveness, efficiency and diversity levels of the proposed approaches as compared to standard NSBBO and NSGA-II. The results have shown that the NSBBO-trapezoidal model performed favorably and is comparable to current existing models. We conclude that the developed NSBBO and its variants are suitable alternative methods to achieve the bi-objective satisfaction of reentrant FMS scheduling problem.

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.

Opportunistic preventive maintenance scheduling for serial-parallel multistage manufacturing systems with multiple streams of deterioration

This paper proposes a novel opportunistic preventive maintenance (PM) scheduling methodology for the serial-parallel multistage manufacturing systems (SP-MMSs), where PM is performed to improve both the reliability and product quality. A SP-MMS has two essential characteristics: (i) the products can go through different process routes; (ii) the deterioration of a machine will not only affect the key product characteristics (KPCs) formed at the current stage but also propagate to downstream stages and then affect the KPCs formed there. Consequently, the propagation of machine deterioration along different routes will generate multiple streams of deterioration. This phenomenon is mathematically modeled to evaluate the quality of final products. The PM scheduling is based on the modeling of total maintenance cost including minimal repair cost, quality loss and PM-related costs. Firstly, a tentative PM schedule is made for each individual machine by minimizing the corresponding total maintenance cost. Secondly, the tentative PM schedules of all machines are further adjusted to obtain the optimal PM schedule for the system by jointly considering the economic dependence and control of final product quality. A case study shows that the methodology can ensure reasonably high reliability and final product quality for the system with a low total maintenance cost.

MULTI-OBJECTIVE ROUTING AND SCHEDULING IN FLEXIBLE MANUFACTURING SYSTEMS UNDER UNCERTAINTY

The efficiency of transportation system management plays an important role in the planning and operation efficiency of flexible manufacturing systems. Automated Guided Vehicles (AGV) are part of diversified and advanced techniques in the field of material transportation which have many applications today and act as an intermediary between operating and storage equipment and are routed and controlled by an intelligent computer system. In this study, a two-objective mathematical programming model is presented to integrate flow shop scheduling and routing AVGs in a flexible manufacturing system. In real-life problems parameters like demand, due dates and processing times are always uncertain. Therefore, in order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed model. Subsequently, to solve fuzzy mathematical programming model, one of the most effective technique in the literature is used. To solve the problem studied, two meta-heuristic algorithms of Non-dominated Sorting Genetic Algorithm-II (NSGAII) and multi-objective particle swarm optimization (MOPSO) are offered that the accuracy of mathematical models and efficiency of algorithms provided are assessed through numerical examples.

Job-shop like manufacturing system with variable power threshold and operations with power requirements

This paper addresses an important issue in manufacturing by considering the scheduling of a Job-shop like manufacturing system involving a power threshold that must not be exceeded over time. A power profile is attached to operations that must be scheduled. This power profile presents a consumption peak at the start of process in order to model most of real-world machining operations. These operations must be scheduled according to the instantly available power threshold. A mathematical formulation of the problem is proposed; its main goal is to minimise the total completion time of all operations. A set of instances is built based on classical format of instances for the Job-shop problem. As it is time-consuming to obtain exact solutions on these instances with the CPLEX solver, a Greedy Randomised Adaptive Search Procedure hybridised with an Evolutionary Local Search (GRASP × ELS) metaheuristic is designed. The GRASP × ELS is compared with two other metaheuristics: a Variable Neighbourhood Search and a Memetic Algorithm. The GRASP × ELS is also compared with several algorithms developed in the literature for the classical job-shop problem. Results show the relevancy of the metaheuristic approaches both in terms of computational time and quality of solutions.

A new Hybrid Filtered Beam Search algorithm for deadlock-free scheduling of flexible manufacturing systems using Petri Nets

This paper presents a new Hybrid Filtered Beam Search algorithm for deadlock-free scheduling of flexible manufacturing systems. The proposed algorithm uses a diversification/intensification strategy that aims to selectively explore the state space of a Timed Place Petri Net that represents the dynamics of the system. The exploration strategy is based on a double filtering mechanism that limits the number of markings that can be expanded at each level of the search tree. The algorithm, which was tested on several benchmark instances from the literature, not only produces good quality schedules but it is also efficient in terms of memory requirements and computational time. In addition, the algorithm requires little tune-up and produces consistent results. Computational experiments show the effectiveness of the algorithm in comparison with other recent and state of the art methods.

FMS Scheduling under Availability Constraint with Supervisor Based on Timed Petri Nets

This paper proposes an optimal solution to large-scale Flexible Manufacturing System (FMS) scheduling problems under availability constraints based on Timed Petri Nets (TPNs). First a decomposition method of TPNs is proposed, then a mathematical model is derived based on their properties. The mathematical model is built to determine the optimal firing sequence of TPN transitions to minimize the total manufacturing time. The resulting firing sequence of TPN transitions is used to generate the manufacturing system supervisor operated by TPN and digital controllers. Several numerical examples and comparative studies are provided in this paper in order to prove the new approach’s efficiency.

Hybrid heuristic search approach for deadlock-free scheduling of flexible manufacturing systems using Petri nets

Based on the Petri net models of flexible manufacturing systems (FMSs), this paper focuses on deadlock-free scheduling problem with the objective of minimizing the makespan. Two hybrid heuristic search algorithms for solving such scheduling problems of FMSs are proposed. To avoid deadlocks, the deadlock control policy is embedded into heuristic search strategies. The proposed algorithms combine the heuristic best-first strategy with the controlled backtracking strategy based on the execution of the Petri nets. The scheduling problem is transformed into a heuristic search problem in the reachability graph of the Petri net, and a schedule is a transition sequence from the initial marking to the final marking in the reachability graph. By using the one-step look-ahead method in the deadlock control policy, the safety of a state in the reachability graph is checked, and hence, deadlock is avoided. Experimental results are provided and indicate the effectiveness of the proposed hybrid heuristic search algorithms in solving deadlock-free scheduling problems of FMSs. Especially, the comparison against previous work shows that both new algorithms are promising in terms of solution quality and computing times.

Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information

Since discrete manufacturing system (DMS) is a complicated dynamic network that comprises of processes, machines, and work in process, a coherent methodology for performance tracking and sustainable improvement at the system/network level is of great significance for manufacturers to respond rapidly in a mass customization paradigm. Fortunately, the radio frequency identification (RFID) technologies provide us the real-time tracking ability of the production process that suffers unpredictable and recessive disturbances. This paper proposes a dynamic scheduling approach based on multi-layer network metrics of RFID-driven DMS. Firstly, considering the elements of DMS (e.g., parts, manufacturing activities, and equipment) and relationships among them, a DMS model named complex manufacturing network (CMN) is proposed. Then, several multi-layer network metrics of the CMN are defined and analysed. The implications of these metrics lead to a better understanding of the current status and performance of DMS. Thirdly, a dynamic scheduling algorithm using these metrics as heuristic information is proposed to solve multi-resources and independent-task DMS. Finally, a Printing Machine manufacturing system is chosen as an example to illustrate the feasibility of the proposed approach.

A Firefly Algorithm for the Mono-Processors Hybrid Flow Shop Problem

Nature-inspired swarm metaheuristics become one of the most powerful methods for optimization. In discrete optimization, the efficiency of an algorithm depends on how it is adapted to the problem. This paper aims to provide a discretization of the Firefly Algorithm (FF) for the scheduling of a specific manufacturing system, which is the mono processors two-stage hybrid flow shop (HFS). This kind of manufacturing system appears in several fields as the operating theatre scheduling problem. Results of proposed discrete firefly algorithm are compared to results of other methods found in the literature. Computational results with different numbers of fireflies and on a standard HFS benchmark of about 55 cases, generating about 1900 instances demonstrates that the proposed discretized metaheuristic reaches the best makespan.

A Data-Driven Simulation-Based Optimisation Approach for Adaptive Scheduling and Control of Dynamic Manufacturing Systems

The increasing customisation of products, which leads to higher numbers of product variants with smaller lot sizes, requires a high flexibility of manufacturing systems. These systems are subject to dynamic influences and need increasing effort for the generation of the production schedules and for the control of the processes. This paper presents an approach that addresses these challenges. First, scheduling is done by coupling an optimisation heuristic with a simulation model to handle complex and stochastic manufacturing systems. Second, the simulation model is continuously adapted by real-time data from the shop floor. If, e.g., a machine breakdown or a rush order appears, the simulation model and consequently the scheduling model is updated and the optimisation heuristic adjusts an existing schedule or generates a new one. This approach uses real-time data provided by future cyber-physical systems to integrate scheduling and control and to manage the dynamics of highly flexible manufacturing systems.

A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty

Cell-load variation is considered as a major shortcoming in cellular manufacturing systems. It can cause long queues in front of machines and impose extra costs to the cellular layouts. In this paper the impact of inflation on cell-load variation in cellular manufacturing systems is examined. For this purpose, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim is finding the trade-off values between in-house manufacturing and using outsource services while system costs are not deterministic and may be varied from period to period by inflation. To solve the model, a hybrid genetic and simulated annealing algorithms is developed because of the high potential of outcomes to be trapped in the local optima. The results are then compared in LINGO® 12.0 software. In continue a Taguchi method (an L9 orthogonal optimization) is used to estimate parameters of the proposed method in order to solve experiments derived from literature. Our findings show that the condition of dynamic costs affects the routing of materials in process and may induce machine-load variation that yield to cell-load diversity. An increase in changing costs causes the loading level of each cell to vary, which in turn results in the development of “complex dummy sub-cells.” To measure the level of cell-load variation a new mathematical index is developed. Then, a new method is proposed for minimizing cell-load variation in the mentioned condition by using control lines. The results indicate that the proposed method can significantly reduce the level of cell-load variation in CMS.

Material supply scheduling in a ubiquitous manufacturing system

Recently, ubiquitous manufacturing has attracted wide attention in both academia and industry. To create a successful ubiquitous manufacturing system, an efficient material handling system is essential. In accordance with this reason, mobile robots have been used for transporting materials. This paper aims at developing a methodology for scheduling the material supply for a single mobile robot in a ubiquitous manufacturing environment. In this type of environment, the processing rate of the materials along with supply quantity corresponds to the cycle of material supply. The carrying capacity of the robots are limited and thus the problem of determining the material supply quantity and material supply schedule without lack of materials for production or service processes becomes complicated. In this work, a nonlinear program is formulated to schedule the supply of material and determine the required material quantity. A heuristic algorithm based on genetic algorithm is developed to solve the problem. From the numerical experiments conducted in this study, it is observed that the proposed algorithm shows good performance and can also be implemented to solve large scale problems.

A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS

In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in a dual resource constrained environment. The aim of this method is to find best production strategy of in-house manufacturing using worker assignment (both temporary and skilled workers) and outsourcing, while part demands are uncertain and can be varied periodically. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to use in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; a hybrid Tabu Search and Simulated Annealing algorithms and a hybrid Ant Colony Optimization and Simulated Annealing algorithms. A Taguchi method (L27 orthogonal optimisation) is used to estimate parameters of the proposed method in order to solve experiments derived from the literature. For evaluating the system imbalance in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain market demands affects the part allocating which may induce workstation-load variations that yield to cell-load variation accordingly. To solve this problem, two methods are offered. The results show that promoting staff and using freezing technique are promising ways to reduce system imbalance while confronting with the mentioned condition. The outcomes also show the superiority of the proposed hybrid method in providing solutions with better quality.

A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost

In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to find best production strategy of in-house manufacturing and outsourcing in small and medium scale cellular manufacturing companies. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to be used in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; Simulated Annealing algorithms; Fuzzy Art Control; Ant Colony Optimization and a hybrid Multi-layer Perceptron and Simulated Annealing algorithms. Our findings indicate that the uncertain condition of system costs affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed method can significantly reduce cell load variation while finding the best trading off values between in-house manufacturing and outsourcing.

Solving a mathematical model integrating unequal-area facilities layout and part scheduling in a cellular manufacturing system by a genetic algorithm.

In this article, a novel integrated mixed-integer nonlinear programming model is presented for designing a cellular manufacturing system (CMS) considering machine layout and part scheduling problems simultaneously as interrelated decisions. The integrated CMS model is formulated to incorporate several design features including part due date, material handling time, operation sequence, processing time, an intra-cell layout of unequal-area facilities, and part scheduling. The objective function is to minimize makespan, tardiness penalties, and material handling costs of inter-cell and intra-cell movements. Two numerical examples are solved by the Lingo software to illustrate the results obtained by the incorporated features. In order to assess the effects and importance of integration of machine layout and part scheduling in designing a CMS, two approaches, sequentially and concurrent are investigated and the improvement resulted from a concurrent approach is revealed. Also, due to the NP-hardness of the integrated model, an efficient genetic algorithm is designed. As a consequence, computational results of this study indicate that the best solutions found by GA are better than the solutions found by B&B in much less time for both sequential and concurrent approaches. Moreover, the comparisons between the objective function values (OFVs) obtained by sequential and concurrent approaches demonstrate that the OFV improvement is averagely around 17 % by GA and 14 % by B&B.