Robust optimization approach to production system with failure in rework and breakdown under uncertainty: evolutionary methods

2015 ◽  
Vol 35 (1) ◽  
pp. 81-93 ◽  
Author(s):  
Masoud Rabbani ◽  
Neda Manavizadeh ◽  
Niloofar Sadat Hosseini Aghozi
Keyword(s):  
Mathematical Model ◽  
Robust Optimization ◽  
Production Planning ◽  
Production System ◽  
Demand Uncertainty ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Optimization Approach ◽  
Content Type ◽  
Evolutionary Methods

Purpose – This paper aims to consider a multi-site production planning problem with failure in rework and breakdown subject to demand uncertainty. Design/methodology/approach – In this new mathematical model, at first, a feasible range for production time is found, and then the model is rewritten considering the demand uncertainty and robust optimization techniques. Here, three evolutionary methods are presented: robust particle swarm optimization, robust genetic algorithm (RGA) and robust simulated annealing with the ability of handling uncertainties. Firstly, the proposed mathematical model is validated by solving a problem in the LINGO environment. Afterwards, to compare and find the efficiency of the proposed evolutionary methods, some large-size test problems are solved. Findings – The results show that the proposed models can prepare a promising approach to fulfill an efficient production planning in multi-site production planning. Results obtained by comparing the three proposed algorithms demonstrate that the presented RGA has better and more efficient solutions. Originality/value – Considering the robust optimization approach to production system with failure in rework and breakdown under uncertainty.

scholarly journals Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 507
Author(s):  
Lima ◽  
Relvas ◽  
Barbosa-Póvoa ◽  
Morales
Keyword(s):  
Robust Optimization ◽  
Oil Industry ◽  
Computational Effort ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Optimization Approach ◽  
Solution Quality ◽  
Inaccurate Information ◽  
Computational Performance ◽  
Adjustable Robust Optimization

The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.

System dynamics modelling of fixed and dynamic Kanban controlled production systems: a supply chain perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jagan Mohan Reddy K. ◽  
Neelakanteswara Rao A. ◽  
Krishnanand Lanka ◽  
PRC Gopal
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Cycle Time ◽  
Production System ◽  
Demand Uncertainty ◽  
Production Systems ◽  
Decision Variable ◽  
Content Type ◽  
Pull Production ◽  
Kanban System

Purpose Pull production systems have received much attention in the supply chain management environment. The number of Kanbans is a key decision variable in the pull production system as it affects the finished goods inventory (FGI) and backorders of the system. The purpose of this study is to compare the performance of the fixed and dynamic Kanban systems in terms of operational metrics (FGI and backorders) under the demand uncertainty. Design/methodology/approach In this paper, the system dynamics (SD) approach was used to model the performance of fixed and dynamic Kanban based production systems. SD approach has enabled the feedback mechanism and is an appropriate tool to incorporate the dynamic control during the simulation. Initially, a simple Kanban based production system was developed and then compared the performance of production systems with fixed and dynamic controlled Kanbans at the various demand scenarios. Findings From the present study, it is observed that the dynamic Kanban system has advantages over the fixed Kanban system and also observed that the variation in the backorders with respect to the demand uncertainty under the dynamic Kanban system is negligible. Research limitations/implications In a just-in-time production system, the number of Kanbans is a key decision variable. The number of Kanbans is mainly depended on the demand, cycle time, safety stock factor (SSF) and container size. However, this study considered only demand uncertainty to compare the fixed and dynamic Kanban systems. This paper further recommends researchers to consider other control variables which may influence the number of Kanbans such as cycle time, SSF and container size. Originality/value This study will be useful to decision-makers and production managers in the selection of the Kanban systems in uncertain demand applications.

scholarly journals A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xixing Li ◽  
Shunsheng Guo ◽  
Yi Liu ◽  
Baigang Du ◽  
Lei Wang
Keyword(s):  
Mathematical Model ◽  
Production Planning ◽  
Flow Shop ◽  
Fitness Function ◽  
Capacity Constraint ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Make To Order ◽  
Delay Cost

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

When to integrate strategic and tactical decisions? Introduction of an asset/inventory ratio guiding fit for purpose production planning

2018 ◽  
Vol 48 (5) ◽  
pp. 545-568 ◽  
Author(s):  
Jesper Normann Asmussen ◽  
Jesper Kristensen ◽  
Kenn Steger-Jensen ◽  
Brian Vejrum Wæhrens
Keyword(s):  
Production Planning ◽  
Production System ◽  
Real Life ◽  
Capital Cost ◽  
Future Research ◽  
Capital Investments ◽  
Content Type ◽  
Planning Decisions ◽  
Theory Practice Gap ◽  
Environment Influence

Purpose Significant transitions in firms (e.g. outsourcing) may impact the relative importance of production and inventory assets, affecting the hierarchical separation of planning decisions. The purpose of this paper is to contribute to planning literature by investigating how the production system and the planning environment influence the performance difference between hierarchical and monolithic planning. Further, it seeks to reduce the prevailing theory-practice gap in tactical planning. Design/methodology/approach Through an action research study, a monolithic model integrating tactical production planning decisions, subject to upstream supply chain constraints, with strategic investments decisions was developed, tested and implemented in a global OEM. Using the developed model and a measure of the capital cost of production assets relative to the cost of holding inventory, it is numerically examined how the production system and planning environment influence the performance of hierarchical and monolithic planning. Findings The research demonstrates the potential of integrating decisions and reveals significant performance differences between hierarchical and monolithic planning for firms with low capital cost relative to inventory holding cost. Research limitations/implications The findings suggest a fit between planning processes, the production system and planning environment. Future research should empirically validate the findings and propositions. Originality/value The paper combine capital investments and production planning decisions, which usually transpire at different hierarchical levels and on different time-horizons, and investigates the consequences of hierarchical separation through a real-life validated case and numerical analysis.

An active control for hydrostatic journal bearing using optimization algorithms

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Waheed Ur Rehman ◽  
Xinhua Wang ◽  
Yingchun Chen ◽  
Xiaogao Yang ◽  
Zia Ullah ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Peer Review ◽  
Fractional Order ◽  
Journal Bearing ◽  
Optimization Techniques ◽  
Performance Criteria ◽  
Content Type ◽  
Control Techniques ◽  
Fractional Order Controller ◽  
Fractional Order Pid

Purpose The purpose of this paper is to improve static/dynamic characteristics of active-controlled hydrostatic journal bearing by using fractional order control techniques and optimizing algorithms. Design/methodology/approach Active lubrication has ability to overcome the unpredictable harsh environmental conditions which often lead to failure of capillary controlled traditional hydrostatic journal bearing. The research develops a mathematical model for a servo feedback-controlled hydrostatic journal bearing and dynamics of model is analyzed with different control techniques. The fractional-order PID control system is tuned by using particle swarm optimization and Nelder mead optimization techniques with the help of using multi-objective performance criteria. Findings The results of the current research are compared with previously published theoretical and experimental results. The proposed servo-controlled active bearing system is studied under a number of different dynamic situations and constraints of variable spindle speed, external load, temperature changes (viscosity) and variable bearing clearance (oil film thickness). The simulation results show that the proposed system has better performance in terms of controllability, faster response, stability, high stiffness and strong resistance. Originality/value This paper develops an accurate mathematical model for servo-controlled hydrostatic bearing with fractional order controller. The results are in excellent agreement with previously published literature. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2020-0272

Joint optimal production planning and proactive maintenance policy for a system subject to degradation

2019 ◽  
Vol 25 (2) ◽  
pp. 236-252 ◽  
Author(s):  
Lin Wang ◽  
Zhiqiang Lu ◽  
Xiaole Han
Keyword(s):  
Production Planning ◽  
Production System ◽  
Planning Horizon ◽  
Joint Model ◽  
Joint Production ◽  
Periodic Review ◽  
Maintenance Policy ◽  
Batch Production ◽  
Content Type ◽  
Proactive Maintenance

Purpose This paper integrates condition-based maintenance (CBM) with production planning in a single-stage production system that deteriorates with usage during a specified finite planning horizon. The purpose of this paper is to develop an integrated production and maintenance model to minimize the expected total cost over the horizon. Design/methodology/approach A joint production planning and CBM model is proposed. In the model, a set of products must be produced in lots. The system degradation is a stationary gamma process and the degradation level is detected by inspection between production lots. Maintenance actions including imperfect preventive maintenance (PM) should be taken when the failure risk exceeds the maintenance threshold. A fix-iterative heuristic algorithm is proposed to address the joint model. Findings The proactive policy expressed as a prognosis maintenance threshold is introduced to integrate CBM with batch production perfectly. Experiments are carried out to conduct sensitivity analysis, which provides some insights to facilitate industrial manufacturing. The superiority of the proposed joint model compared with a separate decision method is demonstrated. The results show an advantage in cost saving. Originality/value Few studies have been made to integrate production planning and CBM decisions, especially for a multi-product system. Their maintenance decisions are usually based on a periodic review policy, which is not appropriate for batch production system. A prognosis maintenance threshold based on system condition and production quantity is suitable for the integrated decisions. Moreover, the imperfect PM is taken into consideration in this paper. A fix-iterative algorithm is developed to solve the joint model. This work forms a proactive maintenance for batch production.

scholarly journals A robust multi response surface approach for optimization of multistage processes

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Moslemi ◽  
Mahmood Shafiee
Keyword(s):  
Robust Optimization ◽  
Response Surface ◽  
Numerical Study ◽  
Ordinary Least Squares ◽  
Response Surfaces ◽  
Estimation Methods ◽  
Design Parameters ◽  
Optimization Approach ◽  
Content Type

PurposeIn a multistage process, the final quality in the last stage not only depends on the quality of the task performed in that stage but is also dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. One of the most efficient statistical approaches used to model the multistage problems is the response surface method (RSM). However, it is necessary to optimize each response in all stages so to achieve the best solution for the whole problem. Robust optimization can produce very accurate solutions in this case.Design/methodology/approachIn order to model a multistage problem, the RSM is often used by the researchers. A classical approach to estimate response surfaces is the ordinary least squares (OLS) method. However, this method is very sensitive to outliers. To overcome this drawback, some robust estimation methods have been presented in the literature. In optimization phase, the global criterion (GC) method is used to optimize the response surfaces estimated by the robust approach in a multistage problem.FindingsThe results of a numerical study show that our proposed robust optimization approach, considering both the sum of square error (SSE) index in model estimation and also GC index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method.Originality/valueTo the best of the authors’ knowledge, there are few papers focusing on quality-oriented designs in the multistage problem by means of RSM. Development of robust approaches for the response surface estimation and also optimization of the estimated response surfaces are the main novelties in this study. The proposed approach will produce more robust and accurate solutions for multistage problems rather than classical approaches.

Feasibility Robust Optimization via Scenario Generation and Reduction

2018 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Solution

Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains without any known probability distributions. The proposed approach integrates a new sampling-based scenario generation scheme with a new scenario reduction approach in order to solve feasibility robust optimization problems. An analysis of the computational cost of the proposed approach was performed to provide worst case bounds on its computational cost. The new proposed approach was applied to three test problems and compared against other scenario-based robust optimization approaches. A test was conducted on one of the test problems to demonstrate that the computational cost of the proposed approach does not significantly increase as additional uncertain parameters are introduced. The results show that the proposed approach converges to a robust solution faster than conventional robust optimization approaches that discretize the uncertain parameters.

Robust Optimization of an Accelerometer Considering Fabrication Errors

2009 ◽  
Vol 628-629 ◽  
pp. 353-356 ◽  
Author(s):  
Guang Jun Liu ◽  
Tao Jiang ◽  
An Lin Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Sensitivity Analysis ◽  
Robust Optimization ◽  
Optimization Problem ◽  
Design Space ◽  
Optimization Approach ◽  
Objective Functions ◽  
Micro Fabrication ◽  
Fabrication Errors

A robust optimization approach of an accelerometer is presented to minimize the effect of variations from micro fabrication. The sensitivity analysis technology is employed to reduce design space and to find the key parameters that have greatest influence on the accelerometer. And then, the constraint conditions and objective functions for robust optimization and the corresponding mathematical model are presented. The optimization problem is solved by the Multiple-island Genetic Algorithm and the results show that an accelerometer with better performance is obtained.

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