Job Shop Scheduling Model Research Articles

Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

Research on Flexible Job Shop Scheduling Problem

The adjustment time and processing time of the job on the machine are studied as an independent factor considering the influence of the adjustment time, such as installation and positioning, on the efficiency of production scheduling. A mathematical optimization model of flexible job shop scheduling with adjustment time is established based on minimizing the makespan. The improved genetic algorithm is used to solve the proposed model and achieve rapid optimization of the solution process. The Matlab software platform is used to solve the case. The experimental results show that the model and the improved algorithm are feasible and effective, and could be more effective in guiding the actual production.

A study on Multi-objective Flexible Job-shop Dynamic Scheduling Based on Control Window

It is of great practical significance to study the dynamic scheduling of production in flexible job-shop. In the flexible job-shop scheduling, process, cost, machine tool and processing environment constitute the constraint set of scheduling. In this paper, starting from the multi constraints of scheduling system, the multi-objective linear synthesis of cost, time, quality and resource utilization is carried out. The scheduling intelligent algorithm is improved by using the polychromatic sets contour matrix to improve the operational efficiency. On the basis of the multi-objective flexible job-shop scheduling model under this multi constraints condition, from the actual situation of production scheduling, this paper constructs a scheduling control window according to the dynamic influence domain formed by the dynamic factors in the scheduling process. Dynamic scheduling control is carried out through control window to realize flexible scheduling and intelligent production. The effectiveness and feasibility of the model are verified by dynamic scheduling analysis of a simulation example.

Rugalmas többutas gyártórendszer ütemezési feladatainak megoldása többcélú reaktív módszerrel

A cikk bemutatja a többutas diszkrét gyártórendszerek egy továbbfejlesztett ütemezési modelljét és annak többcélú reaktív megoldási módszerét. A rugalmas gyártórendszerekre jellemző alternatív technológiai útvonalak, korlátozottan rendelkezésre álló gépek, eltérő műveletvégzési és átállítási idők, valamint határidős és eltérő prioritású munkák kezelésére alkalmas ütemezési koncepció kerül bemutatásra. Az erőforrások allokálásának, az operációk időbeli végrehajtásának részletes ütemezésére egy kiterjesztett rugalmas job shop modellt definiáltunk. A cikk ismerteti a vizsgált feladattípus legfontosabb jellemzőit és a kifejlesztett többcélú reaktív megoldási módszer koncepcióját.

Job shop scheduling model for a sustainable manufacturing

Technological development was not always environmental-friendly but it led to problems that affect the entire population, such as climate change and the depletion of non-renewable resources. In recent years, we are trying to change the concept of industrialization, with a more prudent and focused view to environmental issues. In this perspective, the setting of the fourth industrial revolution, called Industry 4.0, seeks to make technological development sustainable with the crucial aim of resource saving firstly.Starting by this issue, this study aims at introducing a smart tool, based on the application of a mathematical model, already proposed in literature, for the job scheduling from an energy saving perspective into a real company IT system. After a preliminary introduction on the concepts of sustainability and industry 4.0, and how they are connected to each other, and a second part that will deal with Smart Factory, smart factories oriented to customization and flexibility, the mathematical model of activity scheduling will be discussed and than applied to a specific IT system. This model, in fact, properly implemented into a Manufacturing Execution System (MES) will represent a powerful tool oriented to the efficient use of energy resources.

Proactive Scheduling for Job-Shop Based on Abnormal Event Monitoring of Workpieces and Remaining Useful Life Prediction of Tools in Wisdom Manufacturing Workshop.

The job-shop scheduling is an important approach to manufacturing enterprises to improve response speed, reduce cost, and improve service. Proactive scheduling for job-shop based on abnormal event monitoring of workpieces and remaining useful life prediction of tools is proposed with radio frequency identification (RFID) and wireless accelerometer in this paper. Firstly, the perception environment of machining job is constructed, the mathematical model of job-shop scheduling is built, the framework of proactive scheduling is put forward, and the hybrid rescheduling strategy based on real-time events and predicted events is adopted. Then, the multi-objective, double-encoding, double-evolving, and double-decoding genetic algorithm (MD3GA) is used to reschedule. Finally, an actual prototype platform to machine job is built to verify the proposed scheduling method. It is shown that the proposed method solves the integration problem of dynamic scheduling and proactive scheduling of processing workpieces, reduces the waste of redundant time for the scheduling, and avoids the adverse impact on abnormal disturbances.

Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level

The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process, and scheduling could exert significant effects on the energy performance of manufacturing systems. However, only a few studies have specifically addressed energy-efficient scheduling for remanufacturing. Considering the uncertain processing time and routes and the operation characteristics of remanufacturing, we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing. An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines, batch machines, and uncertain processing routes and time. The algorithm demonstrated superior performance in terms of optimal value, run time, and convergent generation in comparison with other algorithms. Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW · h of energy saving for the investigated problem size. In addition, the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%. This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.

Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop

In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms.

A metaheuristic for energy adaptive production scheduling with multiple energy carriers and its implementation in a real production system

Due to climate change and the resulting introduction of sustainability goals by the UN and federal governments, there is growing pressure on manufacturers to increase the sustainability of production systems. In this paper a new, sustainable production scheduling model for job-shop scheduling is developed. The model is optimized using an adjusted genetic algorithm (GA) to minimize energy-related cost (ERC). The proposed model includes multiple energy sources and incorporates a time-of-use (TOU) demand response (DR) scheme for all energy sources. Furthermore, it considers five machine operating modes to reflect different energy states of machines. This means that underutilized machines can be powered down to use less energy, thus reducing ERC. The model and algorithm are evaluated within the Energy-Technology and Application (ETA) research factory environment using a Python application that interfaces with other components to get information about the production system.

An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment

Nowadays, manufacturing industry is under increasing pressure to save energy and reduce emissions, and thereby enhancing the energy efficiency of the machining system (MS) through operational methods on the system-level has attracted more attention. Energy-efficient scheduling (ES) has proved to be a typical measure suitable for all shop types, and an energy-efficient mechanism that a machine can be switched off and back on if it waits for a new job for a relatively long period is another proven effective energy-saving measure. Furthermore, their combination has been fully investigated in a single machine, flow shop and job shop, and the improvement in energy efficiency is significant compared with only applying ES for MS. However, whether such two energy-saving measures can be integrated in a flexible job shop environment is a gap in the existing study. To address this, a scheduling method applying an energy-efficient mechanism is proposed for a flexible job shop environment and the corresponding mathematical model, namely the energy-efficient flexible job shop scheduling (EFJSS) model, considering total production energy consumption (EC) and makespan is formulated. Besides, transportation as well as its impact on EC is taken into account in this model for practical application. Furthermore, a solution approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted, which can avoid the interference of subjective factors and help select a suitable machine for each operation and undertake rational operation sequencing simultaneously. Moreover, experimental results confirm the validity of the improved energy-efficient scheduling approach in a flexible job shop environment and the effectiveness of the solution.

Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation

Energy-aware production scheduling is a promising way to adapt the factories’ energy consumption behavior to the volatile electricity prices in the demand response initiative of smart grids. However, it may not be economical by simply scheduling production loads to the periods with lower electricity prices, as these periods often have higher labor wage, e.g., nights and weekends. Based on this gap, this paper proposes a many-objective integrated energy- and labor-aware flexible job shop scheduling model. Many objectives refer to the number of optimization objectives surpasses three (i.e., five objectives: makespan, total energy cost, total labor cost, maximal workload, and total workload), whereas the existing energy-aware production scheduling research is limited within three objectives. To enable energy awareness in the conventional production scheduling algorithms, a state-based shop floor wide energy model is proposed. To enable labor awareness, the number and type of human workers are matched to the scheduled production loads, with varying labor wage over shifts. As one of the most complex shop floor configurations, the partial flexible job shop further considers job recirculation and operation sequence-dependent machine setup times. The recently-proposed nondominated sorting genetic algorithm-III (NSGA-III) is tailored for this many-objective optimization problem (MaOP), including scheduling solution encoding and decoding, crossover, mutation, and solution evaluation using the energy- and labor-aware discrete-event simulation framework. Through numerical experiments under real-time pricing (RTP) and time-of-use pricing (ToUP), insights are statistically obtained on the relation among these five production objectives; the effectiveness and efficiency of NSGA-III in solving a MaOP are also demonstrated. This proposed scheduling method can be used to automated and enhance the decision making of factory managers in jointly allocating machine, human worker, and energy resources on the shop floor, such that the production cost is minimized even under time-varying electricity and labor prices.

Flexible job shop scheduling problem with interval grey processing time

Job processing time is not always given accurately as a time quota in a production system, particularly in the complex products manufacturing process. It is meaningful to study novel model and algorithm based on uncertainty processing time so as to solve uncertainty job shop scheduling problems. This paper defines inaccurate time quota as interval grey processing time and subsequently proposes a novel uncertainty job shop scheduling model with the objective of minimizing the interval grey makespan. By defining the arithmetic operations and Gantt chart of interval grey processing time, the elitism genetic algorithm coupling elitism strategy in external memory is designed. Finally, the proposed algorithm is tested with different size cases of composite components job-shop scheduling. Results show the proposed algorithm is suitable for solving the uncertainty job shop scheduling problem with interval grey processing time.

A chaotic simulated annealing and particle swarm improved artificial immune algorithm for flexible job shop scheduling problem

Reasonable scheduling of flexible job shop is key to improve production efficiency and economic benefits; in order to solve the problem in flexible job shop scheduling problem, a novel flexible job shop scheduling method based on improved artificial immune algorithm is proposed. Firstly, a mathematical model of the flexible job shop scheduling is established, and the total shortest processing time is taken as the objective function. Secondly, artificial immune algorithm is used to solve the problem, and particle swarm optimization algorithm is taken as the operator to embed into manual immune algorithm for maintaining the diversity of population and prevent obtaining local optimal solution. Finally, the performance of the algorithm is tested by simulation experiments on standard set. The results show that the proposed algorithm can obtain better flexible job shop scheduling scheme and especially has more significant advantages in solving large-scale problems in comparison with other algorithms.

An integrated approach for scheduling health care activities in a hospital

To effectively utilise hospital beds, operating rooms (OR) and other treatment spaces, it is necessary to precisely plan patient admissions and treatments in advance. As patient treatment and recovery times are unequal and uncertain, this is not easy. In response, a sophisticated flexible job-shop scheduling (FJSS) model is introduced, whereby patients, beds, hospital wards and health care activities are respectively treated as jobs, single machines, parallel machines and operations. Our approach is novel because an entire hospital is describable and schedulable in one integrated approach. The scheduling model can be used to recompute timings after deviations, delays, postponements and cancellations. It also includes advanced conditions such as activity and machine setup times, transfer times between activities, blocking limitations and no wait conditions, timing and occupancy restrictions, buffering for robustness, fixed activities and sequences, release times and strict deadlines. To solve the FJSS problem, constructive algorithms and hybrid meta-heuristics have been developed. Our numerical testing shows that the proposed solution techniques are capable of solving problems of real world size. This outcome further highlights the value of the scheduling model and its potential for integration into actual hospital information systems.

A Game-theoretical Approach for Job Shop Scheduling Considering Energy Cost in Service Oriented Manufacturing

In service-oriented manufacturing mode, a large amount of enterprises open their workshops to provide manufacturing service, and production outsourcing and customer satisfaction are two crucial manners to make profits. In service oriented manufacturing workshops, jobs come from different customers. Competitive relationship among customers should be inevitably taken into account owing to the openness characteristic of workshops and interests’ maximization pursuit of each customer. Moreover, because of increasing energy price, carbon tax policy, carbon label policy, most of customers and manufacturing companies are urgently eager to reduce energy consumption to save cost. Based on above-mentioned requirements, in this paper, a non-cooperative game model for job shop scheduling based on individual profits maximization in service oriented manufacturing environment is given. It takes customer’s cost as optimal objectives in which energy cost of manufacturing process and transportation process are considered. Finally, one case is carried out to validate the rationality of the proposed model.

A genetic algorithm-based schedule optimisation of a job shop with parallel resources

This paper presents a genetic algorithm for a job shop scheduling problem with parallel machines. The objective is to minimise the makespan. After solving an example, the performance of the proposed algorithm was examined on a set of test problems. The computational test was performed with moderate benchmark instances given in the literature. Now, many industrial work centres are shifting their focus towards implementation of job shop scheduling model. Large throughput and just-in-time restrictions have augmented the requirement of additional parallel machines at various production stages. This generates the scope for research on optimising job shop problem with parallel machines. However, research on this topic is very limited as compared to other job shop problems. A genetic algorithm has been proposed in this research work which effectively finds the near optimal makespan schedules for the job shop problem with parallel machines. The algorithm was implemented on the modified benchmarks from the literature and the results were compared with heuristics methods available in literature for solving this problem. It is shown that the proposed GA performs reasonably well when compared to the other techniques under consideration.

A genetic algorithm-based schedule optimisation of a job shop with parallel resources

This paper presents a genetic algorithm for a job shop scheduling problem with parallel machines. The objective is to minimise the makespan. After solving an example, the performance of the proposed algorithm was examined on a set of test problems. The computational test was performed with moderate benchmark instances given in the literature. Now, many industrial work centres are shifting their focus towards implementation of job shop scheduling model. Large throughput and just-in-time restrictions have augmented the requirement of additional parallel machines at various production stages. This generates the scope for research on optimising job shop problem with parallel machines. However, research on this topic is very limited as compared to other job shop problems. A genetic algorithm has been proposed in this research work which effectively finds the near optimal makespan schedules for the job shop problem with parallel machines. The algorithm was implemented on the modified benchmarks from the literature and the results were compared with heuristics methods available in literature for solving this problem. It is shown that the proposed GA performs reasonably well when compared to the other techniques under consideration.

Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing

With the rapid advancement and widespread application of information and sensor technologies in manufacturing shop floor, the typical challenges that cloud manufacturing is facing are the lack of real-time, accurate, and value-added manufacturing information, the efficient shop floor scheduling strategy, and the method based on the real-time data. To achieve the real-time data-driven optimization decision, a dynamic optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real-time scheduling strategy and method. Contrast to the traditional scheduling strategy, each machine is an active entity that will request the processing tasks. Then, the processing tasks will be assigned to the optimal machines according to their real-time status by using game theory. The key technologies such as game theory mathematical model construction, Nash equilibrium solution, and optimization strategy for process tasks are designed and developed to implement the dynamic optimization model. A case study is presented to demonstrate the efficiency of the proposed strategy and method, and real-time scheduling for four kinds of exceptions is also discussed.

Particle swarm optimization with variable neighborhood search for multiobjective flexible job shop scheduling problem

The simulation on benchmarks is a very simple and efficient method to evaluate the performance of the algorithm for solving flexible job shop scheduling model. Due to the assignment and scheduling decisions, flexible job shop scheduling problem (FJSP) becomes extremely hard to solve for production management. A discrete multi-objective particle swarm optimization (PSO) and simulated annealing (SA) algorithm with variable neighborhood search is developed for FJSP with three criteria: the makespan, the total workload and the critical machine workload. Firstly, a discrete PSO is designed and then SA algorithm performs variable neighborhood search integrating two neighborhoods on public critical block to enhance the search ability. Finally, the selection strategy of the personal-best individual and global-best individual from the external archive is developed in multi-objective optimization. Through the experimental simulation on matlab, the tests on Kacem instances, Brdata instances and BCdata instances show that the modified discrete multi-objective PSO algorithm is a promising and valid method for optimizing FJSP with three criteria.

Job shop scheduling model for non-identic machine with fixed delivery time to minimize tardiness

Scheduling non-identic machines problem with low utilization characteristic and fixed delivery time are frequent in manufacture industry. This paper propose a mathematical model to minimize total tardiness for non-identic machines in job shop environment. This model will be categorized as an integer linier programming model and using branch and bound algorithm as the solver method. We will use fixed delivery time as main constraint and different processing time to process a job. The result of this proposed model shows that the utilization of production machines can be increase with minimal tardiness using fixed delivery time as constraint.