Integration Of Production Scheduling Research Articles

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

In recent years, integration of production scheduling and machine maintenance has gained increasing attention in order to improve the stability and efficiency of flowshop manufacturing systems. This paper proposes a Q-learning-based aquila optimizer (QL-AO) for solving the integrated optimization problem of blocking flowshop scheduling and preventive maintenance since blocking in the jobs processing requires to be considered in the practice manufacturing environments. In the proposed algorithmic framework, a Q-learning algorithm is designed to adaptively adjust the selection probabilities of four key population update strategies in the classic aquila optimizer. In addition, five local search methods are employed to refine the quality of the individuals according to their fitness level. A series of numerical experiments are carried out according to two groups of flowshop scheduling benchmark. Experimental results show that QL-AO significantly outperforms six peer algorithms and two state-of-the-art hybrid algorithms based on Q-Learning on the investigated integrated scheduling problem. Additionally, the proposed Q-learning and local search strategies are effective in improving its performance.

An Exact Method and Ant Colony Optimization for Single Machine Scheduling Problem With Time Window Periodic Maintenance

This paper considers a time window periodic maintenance strategy with different duration windows and job scheduling activities in a single machine environment. The aim is to minimize the number of tardy jobs through the integration of production scheduling and periodic maintenance intervals. A mixed-integer linear programming model (MILP) is proposed to optimize small-sized test instances. Furthermore, an ant colony optimization (ACO) algorithm is developed to solve larger sized test instances. Subsequently, to measure the efficiency of the solutions obtained by ACO, Moore's algorithm is also developed to benchmark with ACO. To test the efficiency and the effectiveness of the ACO algorithm, a set of data for small and large sized problems was generated in which several parameters were adopted and then ten replicates were solved for each combination. The small sized instances were solved by the MILP. Then, the results obtained showed that the proposed ACO was able to obtain the exact solutions within reasonable CPU times, thus, it outperformed the CPLEX solver with respect to CPU. The large sized instances were solved by the Moore's algorithm and compared to ACO. Then, the results obtained showed that the ACO outperforms Moore's algorithm for all the instances tested. It can be concluded that the developed ACOis very efficient and effective in solving the problem considered in this paper.

Integration of Production Scheduling and Group Maintenance Planning in Multi-unit System employing TLBO algorithm

This paper deals with joint optimization of production scheduling and group maintenance planning in single machine system with multi units arranged in series. The objective is to achieve optimum production sequence, preventive maintenance (PM) intervals and grouping of units which minimize the total integrated cost per unit time of the system. Teaching-Learning Based Optimization (TLBO) algorithm is applied to optimize the objective function. The peculiarity of TLBO as against other algorithms is that it is independent of algorithm specific parameters. The largest order value rule is utilized to retrieve the permutation vector while grouping of units is performed under PM intervals. Computational results reveal the effectiveness of the proposed approach.

Parameterizations of data-driven nonlinear dynamic process models for fast scheduling calculations

Global competition and increasingly complex product slates and supply chains motivate a continuous drive towards enterprise-wide optimization and integrated decision-making in the chemical process industries. Integration of production scheduling and process control poses particular challenges: the resulting optimization problems tend to be high-dimensional and nonlinear, calling for development of new computational methods. In this work, we propose a novel modeling framework for integrated scheduling and control. We build on existing methods which use data-driven Hammerstein–Wiener models to represent the dynamics of (scheduling-relevant) process variables. This model structure is leveraged to reduce the size of the scheduling optimization problem, by identifying parsimonious parametric representations of the underlying dynamics. The advantages of the approach are demonstrated on two case studies, in which the computational effort is shown to be significantly reduced compared to existing methods, while still capturing the relevant process dynamics.

Integration of production scheduling and energy-cost optimization using Mean Value Cross Decomposition

Integrated optimization of the procurement cost of electric energy with production planning is increasingly considered in various industries. The traditional approach in industry is production driven, i.e. the production is scheduled first, followed by the energy supply optimization to find the best available energy portfolio, which is usually sub-optimal. The combined scheduling and energy procurement optimization can be formulated as an integrated monolithic optimization model, resulting in intractable problems, even if solutions to the two isolated problems are available. We propose to use Mean Value Cross Decomposition for solving the combined problem by iterating between energy-aware production scheduling and energy-cost optimization, possibly building on existing solutions. We apply the approach to a pulping process and a steel production process. MILP-based models are employed for the two scheduling problems and for the energy cost optimization a Minimum-Cost Flow Network model is used, resulting in good quality solutions within reasonable computation times.

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.

A proactive approach to solve integrated production scheduling and maintenance planning problem in flow shops

Abstract This paper deals with the integration of production scheduling and maintenance planning in order to optimize the bi-objective of quality robustness and solution robustness for flow shops with failure uncertainty. First, a proactive model is proposed to formulate the problem mathematically. Then, Monte Carlo sampling method is adopted to obtain the objective value for feasible solutions and a surrogate measure is proposed to approximate the objective function efficiently. Based on the sampling method and surrogate measure, a two-loop algorithm is devised to optimize the sequence of jobs, positions of preventive maintenances and idle times simultaneously. Computational results indicate that solution robustness and stability of quality robustness can be significantly improved using our algorithm compared with the solutions obtained by the traditional way.

Integration of scheduling and discrete event simulation systems to improve production flow planning

The increased availability of data and computer-aided technologies such as MRPI/II, ERP and MES system, allowing producers to be more adaptive to market dynamics and to improve production scheduling. Integration of production scheduling and computer modelling, simulation and visualization systems can be useful in the analysis of production system constraints related to the efficiency of manufacturing systems. A integration methodology based on semi-automatic model generation method for eliminating problems associated with complexity of the model and labour-intensive and time-consuming process of simulation model creation is proposed. Data mapping and data transformation techniques for the proposed method have been applied. This approach has been illustrated through examples of practical implementation of the proposed method using KbRS scheduling system and Enterprise Dynamics simulation system.

Integration of production scheduling and delivery in two echelon supply chain

ABSTRACTFor a production scheduling and delivery problem, there are multi-products processed by a distribution centre and then delivered to retailers. The resolution of the problem is to determine the production sequence, retailers' needs to heterogeneous fleet of vehicles and the visiting sequence of each vehicle within time windows. In this article, the problem is considered with minimising the total cost which includes delivery cost, fixed vehicle cost, and penalty cost, subjected to satisfy all demands of each retailer. The adaptive genetic algorithms (AGAs) are designed and tested in variety of production and delivery scenarios. The computational experiments show that backward-AGA (B-AGA) has distinguished performance between two AGAs. However, F-AGA has better solving efficiency when N ⩾ 50 and the solving performance is fairly similar subjected to the geographical group R and N ⩾ 50. In addition, the experiments indicate that combined with more vehicle types in delivery stage is not only reduced the total cost but also fixed vehicle cost and variable routing cost. Some management recommendations for different map features are provided as well.

Integration of production scheduling and dynamic optimization for multi-product CSTRs: Generalized Benders decomposition coupled with global mixed-integer fractional programming

Integration of production scheduling and dynamic optimization can improve the overall performance of multi-product CSTRs. However, the integration leads to a mixed-integer dynamic optimization problem, which could be challenging to solve. We propose two efficient methods based on the generalized Bender decomposition framework that take advantage of the special structures of the integrated problem. The first method is applied to a time-slot formulation. The decomposed primal problem is a set of separable dynamic optimization problems and the master problem is a mixed-integer nonlinear fractional program. The master problem is then solved to global optimality by a fractional programming algorithm, ensuring valid Benders cuts. The second decomposition method is applied to a production sequence formulation. Similar to the first method, the second method uses a fractional programming algorithm to solve the master problem. Compared with the simultaneous method, the proposed decomposition methods can reduce the computational time by over two orders of magnitudes for a polymer production process in a CSTR.

Joint job scheduling and preventive maintenance on a single machine

This paper considers the integration of production scheduling and preventive maintenance (PM) scheduling on a single machine that is subject to random failures. PM that restores the machine to an ‘as good as new’ condition is to be performed before the start of job processing, if needed. The objective is to determine the job schedule as well as PM schedule that minimise the total weighted expected jobs completion times. The problem is formulated to jointly model production scheduling and maintenance decisions as a mixed integer programme. Solving the model results in the optimum schedule of maintenance as well as production, which is usually determined independently.

Integration of scheduling and control—Theory or practice?

Today's industries are more than ever forced to lower their operational costs, especially during the ongoing economic recession where the whole existence of many businesses is endangered. This has created a need of increased information sharing between various decision-support systems. The target is to have full transparency and ensure that the overall result of individual optimization tasks is “globally optimal”, i.e., that the decisions taken on different levels do not negate each other. This leads inevitably to more complex optimization systems as the models tend to cover larger problem entities in order to merge previously separated optimization targets or include earlier not considered aspects in order to avoid getting stuck into unfavorable local optima. Another complexity arises from the need of communication between the tasks—earlier isolated problems become networked, which means that the definition of optimization goals cannot be derived from local facts alone. This paper discusses some issues emerging from the integration of production scheduling and control. Some existing standards to support the integration challenge are presented, followed by an overview of recent research results. An interesting question is to what extent such an integration activity is the task of a scientist or a practitioner? The paper concludes by highlighting important open questions and raises some remaining challenges for the future.

Empowering financial tradeoff with joint financial and supply chain planning models

The concept behind improved enterprise systems is the overall integration of its whole functionality through optimised financial links. This paper addresses the implementation of financial cross functional links through the integration of production scheduling and planning with financial management in a modelling framework aimed at chemical process industries. Altogether models must play a more active role, recommending besides schedules and plans, the optimal budgetary decisions for each new scenario. The platform built combines a deterministic cash flow management model with a MILP formulation of process operation. The advantages of this approach are highlighted through case studies.