Job Shop Manufacturing System Research Articles

Peak power policies in job shop manufacturing systems

Peak power policies in job shop manufacturing systems

A decentralized production–distribution scheduling problem: Solution and analysis

In modern production–distribution supply chains, decentralization has increased significantly, due to increasing production network efficiency. This study investigates a production scheduling and vehicle routing problem in a make-to-order context under a decentralized decision-making structure. Specifically, two different decision makers hierarchically decide the production and distribution schedules to minimize their incurred costs and we formulate the problem as a bi-level mixed-integer optimization model as a static Stackelberg game between manufacturer and distributor. At the upper level, the manufacturer decides its best scheduling under a flexible job-shop manufacturing system, and at the lower level, the distributor decides its distribution scheduling (routing) which influences the upper-level decisions. The model derives the best production–distribution scheduling scheme, with the objective of minimizing the cost of the manufacturer (leader) at the lowest possible cost for the distributor (follower). As the lower level represents a mixed-integer programming problem, it is challenging to solve the resulting bi-level model. Therefore, we extend an efficient decomposition algorithm based on Duplication Method and Column Generation. Finally, to discuss the decentralization value, the results of the presented bi-level model are compared with those of the centralized approach.

Identification of influence of digital twin technologies on production systems: a return on investment-based approach

The object of this study is the impact of different digital twin solutions on the performance of job-shop manufacturing systems, while economic aspects are also taken into consideration. This paper proposes an approach to analyze the impact of different identification systems on the efficiency and ROI of digital twin deployment in production systems. In order to achieve this aim, let’s analyze the investment and operation cost of different Internet of Things technologies. The next phase of the research work was the definition of performance parameters, which makes it possible to analyze the impact of different digital twin solutions on the productivity of the job-shop manufacturing system. It is possible to choose four financial indicators to analyze the economic impact of digital twin solution on job-shop manufacturing: return on investment, compound annual growth rate, internal rate of return and net present value. Our approach is based on a novel agent-based simulation model using AnyLogic simulation tool. From the results of this productivity analyses, the model computes the financial indicators, which describe the expected financial impact of the investment and operation cost. It is compared the impact of barcodes and radiofrequency identification technologies on the financial and technological impact of the job-shop manufacturing environment. The numerical analysis of a job-shop manufacturing system shows, that the radiofrequency identification-based digital twin solution has 9.2 % higher return on investment, 53 % higher net present value and 1.6 % higher compound annual growth rate. The model can be easily converted to analyze other types of manufacturing systems, which can lead to increased efficiency of digital twin solutions

A Job-Shop Scheduling Decision-Making Model for Sustainable Production Planning With Power Constraint

Improving environmental sustainability and reducing energy cost are becoming central topics of decision-making in manufacturing. Increasingly manufactures are looking for new solutions that allow them to become more energy efficient and sustainable. Regarding energy cost issue, it is well known that the electricity costs in manufacturing systems strongly depend on the reached peak power. On the other hand, the power required by a manufacturing system affects the adoption of renewable energy sources. The use of renewables, indeed, encompasses fluctuations in energy supply and requires adapting production processes to a power constraint. The article proposes a decision model for the scheduling in a job-shop manufacturing system that simultaneously deals with the power constraint and the variable speed of machine tools. Defining the maximum value of the usable power, decisions can be taken on production scheduling, by adapting the processing speed of several machines that work simultaneously, to the power availability. The model allows decision makers to plan production, taking into account the impossibility of exceeding a certain power and assuming the minimization of the makespan as an objective function. Managers can utilize the scheduling model in all the different contexts, where there is a power limit. The test cases show how the proposed model allows to obtain an efficient scheduling under a constant and variable power constraint. The findings highlight that the model can be employed to reduce the peak power and encourage the use of renewables in manufacturing systems, thus obtaining an efficient scheduling and a more sustainable production.

Energy cost efficient scheduling in flexible job-shop manufacturing systems

This paper studies the problem of determining energy efficient schedules in a flexible job shop. The goal is to minimize the total energy cost, given a time-of-use pricing scheme, while ensuring that the schedule does not violate a maximum makespan. The problem is first formalized as a mixed integer program. Because it is already difficult to solve, the simpler problem with a fixed sequence of operations is then extensively studied. Some properties are derived for the specific problem with a fixed sequence. These properties show that the complexity of the problem depends on the structure of the energy pricing scheme. They are also used to propose two heuristic approaches. Relying on these heuristics, we further develop an iterative tabu search for the general problem. Extensive computational experiments are carried out to evaluate the solution methods and the potential gains on the total energy cost, depending on the flexibility associated to the maximum allowed makespan and on the time-of-use structures.

Peak power policies in job shop manufacturing systems

Peak power policies in job shop manufacturing systems

An efficient approach for solving a job shop scheduling problem with resources constraints: a case study iCIM 3000

In this work, we are interested in a job shop scheduling problem (JSSP) with resources availability constraints. The aim consists in scheduling a set of N jobs on M machines. To be processed in the system, each job needs an number of consumable resources that are available in a limited quantity. Solving such a problem means finding better jobs sequencing in order to minimise the maximum execution time. We suggest two different methods to solve the above-mentioned problem. We firstly propose a set of four heuristics based on priority rules. Then, we make call to genetic algorithm. Using a real job shop manufacturing system data, a large-scale experiment was performed in order to analyse the performance of the proposed methods. The studied system is called iCIM 3000. The simulation results reveal that the new proposed heuristics are better than genetic algorithms and achieve to good solutions in shorter time.

An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II

Improving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify effective factors, determine their weights on multi-objective functions, and, accordingly, schedule manufacturing systems in a fuzzy environment. The aim is to optimize product completion time and operational and product defect costs in a job-shop-based multi-objective fuzzy scheduling problem. In the first and second phases of the proposed framework, it was shown that the existing uncertainty of the internal factors for the studied cases causes the weights of factors to change up to 44.5%. Then, a fuzzy-weighted NSGA-II is proposed (FW-NSGA-II) to address the developed Non-linear Fuzzy Multi-objective Dual resource-constrained scheduling problem. Comparing the outcomes of the proposed method with other solving algorithms, such as the Sine Cosine Algorithm, Simulated Annealing, Tabu Search, and TLBO heuristic, using seven series of comprehensive computational experiments, indicates the superiority of the proposed framework in scheduling manufacturing systems. The outcomes indicated that using the proposed method for the studied cases saved up to 5% in the objective function for small-scale, 11.2% for medium-scale, and 3.8% for large-scale manufacturing systems. The outcomes of this study can help production planning managers to provide more realistic schedules by considering fuzzy factors in their manufacturing systems. Further investigating the proposed method for dynamic product conditions is another direction for future research.

Memetic Algorithm for Dynamic Joint Flexible Job Shop Scheduling with Machines and Transportation Robots

In this study, the dynamic joint scheduling problem for processing machines and transportation robots in a flexible job shop is investigated. The study aims to minimize the order completion time (makespan) of a job shop manufacturing system. Considering breakdowns, order insertion and battery charging maintenance of robots, an event-driven global rescheduling strategy is adopted. A novel memetic algorithm combining genetic algorithm and variable neighborhood search is designed to handle dynamic events and obtain a new scheduling plan. Finally, numerical experiments are conducted to test the effect of the improved operators. For successive multiple rescheduling, the effectiveness of the proposed algorithm is verified by comparing it with three other algorithms under dynamic events, and through statistical analysis, the results verify the effectiveness of the proposed algorithm.

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization

With the rapid development of Industrial 4.0, the modern manufacturing system has been experiencing profoundly digital transformation. The development of new technologies helps to improve the efficiency of production and the quality of products. However, for the increasingly complex production systems, operational decision making encounters more challenges in terms of having sustainable manufacturing to satisfy customers and markets’ rapidly changing demands. Nowadays, rule-based heuristic approaches are widely used for scheduling management in production systems, which, however, significantly depends on the expert domain knowledge. In this way, the efficiency of decision making could not be guaranteed nor meet the dynamic scheduling requirement in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system with unexpected machine failure. The proximal policy optimization (PPO) algorithm was used in the DRL framework to accelerate the learning process and improve performance. The proposed method was testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art methods.

Explainable reinforcement learning in production control of job shop manufacturing system

Manufacturing in the age of Industry 4.0 can be characterised by a high product variety and complex material flows. The increasing individualisation of products requires adaptive production planning and control systems. Research in the area of Machine Learning demonstrates the applicability and potential of Reinforcement Learning (RL) systems for the control of complex manufacturing. However, a major disadvantage of RL-methods is that they are usually considered as ‘black box’ models. For this reason, this paper investigates methods of explainable reinforcement learning in production control. Based on a comprehensive literature review an approach to increase the plausibility of RL-based control strategies is presented. The approach combines the advantages of high prediction accuracy (e.g. neural networks) and high explainability (e.g. decision trees). In doing so, understandable control strategies such as heuristics can be generated, and an advanced RL-system can be designed including specific domain expertise. The results are demonstrated based on a real-world system, taken from semiconductor manufacturing, which is investigated in a simulated approach.

Reactive Scheduling Based on Adaptive Manipulator Operations in a Job Shop Configuration with Two Machines

Manufacturing systems are affected by uncertainties, such as machine failure or tool breakage, which result in system downtime and productivity deterioration. In machining processes, system downtime must be reduces. This study aims to establish an automated scheduling technique that flexibly responds to unforeseen events, such as machine failure, based on adaptive operations of the handling manipulator instead of an operation schedule for the machine tools. We propose an “adaptive manipulation” procedure for establishing a reactive revision policy. The reactive revision policy modifies a portion of the manipulator operation sequence, followed by the machine operation sequence. We conduct a physical scheduling simulation on a material-handling manipulator system imitating a job-shop manufacturing system. Through simulations involving machine breakdown scenarios, the applicability of the reactive revision policy based on adaptive manipulation is demonstrated.

Switch off policies in job-shop manufacturing systems including workload evaluation

ABSTRACT Energy efficiency is more relevant in the last years and drives enterprises to increase the efficiency of the manufacturing systems. This paper proposes switch-off policies to reduce energy consumption in job shop systems when the machines are in idle state. The model proposed uses the workload approach studied in the literature to take into account the relationships among the machines where the routing of the parts is random. The model proposed use the combination of direct and indirect workload of the machines to support the decisions on switch off/on. A simulation model is developed to test the proposed policies compared to another one presented in the literature. The main results highlighted how the model found in the literature for production lines can be efficiently adapted in the job-shop context. The proposed methods lead to a better compromise between energy-saving and lead time reducing the number of switch-off. This study has proposed a novel model to reduce energy consumption in manufacturing systems and the findings derived from the numerical analysis have provided an extended knowledge sustainable job-shop systems literature.

Production and transport scheduling in flexible job shop manufacturing systems

This paper addresses an extension of the flexible job shop scheduling problem by considering that jobs need to be moved around the shop-floor by a set of vehicles. Thus, this problem involves assigning each production operation to one of the alternative machines, finding the sequence of operations for each machine, assigning each transport task to one of the vehicles, and finding the sequence of transport tasks for each vehicle, simultaneously. Transportation is usually neglected in the literature and when considered, an unlimited number of vehicles is, typically, assumed. Here, we propose the first mixed integer linear programming model for this problem and show its efficiency at solving small-sized instances to optimality. In addition, and due to the NP-hard nature of the problem, we propose a local search based heuristic that the computational experiments show to be effective, efficient, and robust.

SimEvents-based discrete-event simulation modelling and performance analysis for dynamic job-shop manufacturing system

The present paper deals with the application of discrete-event simulation (DES) to develop simulation model for dynamic job-shop manufacturing system (DJMS). In the present time, DES software tool ...

SimEvents-based discrete-event simulation modelling and performance analysis for dynamic job-shop manufacturing system

The present paper deals with the application of discrete-event simulation (DES) to develop simulation model for dynamic job-shop manufacturing system (DJMS). In the present time, DES software tool is very known to engineers in designing manufacturing system layout and production lines, it facilitates to get a good solution for the analysis and validation of any problem. In this research work, DES software tool 'SimEvents' has been used for developing simulation model. Dynamic processing plus waiting (DPPW) time due date assignment method and four dispatching rules are investigated in this article. The performance of the DJMS is evaluated through different measures such as mean flow time, mean tardiness, standard deviation of flow time, standard deviation of tardiness, production output and work-in-process. A combination of dispatching rules and different job types is taken for simulation experiments. This paper analyses the simulation results statistically and graphically.

Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints

In job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the proposed approach.

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

The reliability of machinery and automated guided vehicle has been one of the most important challenges to enhance production efficiency in several manufacturing systems. Reliability improvement would result in a simultaneous reduction of both production times and transportation costs of the materials, especially in automated guided vehicles. This article aims to conduct a practical multi-objective reliability optimization model for both automated guided vehicles and the machinery involved in a job-shop manufacturing system, where different machines and the storage area through some parallel automated guided vehicles handle materials, parts, and other production needs. While similar machines in each shop are limited to failures based on either an Exponential or a Weibull distribution via a constant rate, the machines in different shops fail based on different failure rates. Meanwhile, as the model does not contain any closed-form equation to measure the machine reliability in the case of Weibull failure, a simulation approach is employed to estimate the shop reliability to be further maximized using the proposed model. Besides, the automated guided vehicles are restricted to failures according to an Exponential distribution. Furthermore, choosing the best locations of the shops is proposed among some potential places. The proposed NP-Hard problem is then solved by designing a novel non-dominated sorting cuckoo search algorithm. Furthermore, a multi-objective teaching-learning-based optimization, as well as a multi-objective invasive weed optimization are designed to validate the results obtained. Ultimately, a novel AHP-TOPSIS method is carried out to rank the algorithms in terms of six performance metrics.

A simulated multi-objective model for flexible job shop transportation scheduling

This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The objectives considered in scheduling jobs and transportation tasks in a flexible job shop manufacturing system include makespan, robot travel distance, time difference with due date and critical waiting time. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.