Optimal Production Plan Research Articles

A metaheuristic-based efficient strategy for multi-unit production planning with unique process constraints

Production planning provides optimal strategic planning to attain economic benefits in the petrochemical industries and helps to maintain competitiveness in the global market. This work proposes an efficient modeling strategy for combinatorial production planning involving unique process constraints in the development stage of the petrochemical industry. The decisions involved are the choice of products to be produced, the product quantity, the suitable process for the selected product, and the number of processing units to be implemented at the operating capacity. The objective is to determine an optimal production plan by maximizing the profit while satisfying all the resource and bound constraints. This work analyzed the strategy in the literature to identify the factors affecting metaheuristic techniques in determining better solutions for production planning involving unique process constraints. The compact and precise strategy proposed in this work reduces the number of decision variables and constraints up to 90% without compromising the rigor of the model. The efficacy of the proposed strategy is demonstrated on four case studies and is solved using eight different metaheuristic techniques, namely (i) Multi-Population based Ensemble of Mutation Strategies Differential Evolution, (ii) Sanitized Teaching Learning Based Optimization, (iii) Artificial Bee Colony, (iv) Dynamic Neighborhood Learning Particle Swarm Optimizer, (v) Sine Cosine Algorithm, (vi) Symbiotic Organisms Search, (vii) Single Phase Multi-Group Teaching Learning Based Optimization with Lévy Flight, and (viii) Harris Hawk Optimization. The results determined by these techniques in solving the case studies using the proposed strategy are better than those provided in the literature. It indicates the suitability of the proposed strategy with metaheuristic techniques and emphasizes the importance of efficient modeling of a problem. The proposed strategy provided up to 5% improvement in profit.

Research on uncertain integrated production planning and scheduling with risk management based on improved collaborative optimization

The optimization of production planning and scheduling is important for modern process industry. Due to different time dimensions of them, it is easy to create conflicts between the optimization results if they are solved separately, and the final results are not feasible. When facing a complex problem like production model, Collaborative optimization could be applied, but it still has shortcomings. Therefore, the improvement of collaborative optimization is proposed and improved collaborative optimization is applied to solve the uncertain production model. At last, the simulation results show that improved collaborative optimization can achieve better global optimization capability and practicability. This paper not only shows that the application scope of collaborative optimization has been expanded, but also provides a new idea for solving the uncertain production model.

Designing of Integrated Tactical Production-Quality Planning in a Rolling Horizon Under Demand Uncertainty: Case Study in the Automotive Wire Harness Industry

To minimize waste and scrap, reworking becomes an integral part of many manufacturing companies. In this study, the integrated production-quality planning in the tactical horizon considering the customer demand uncertainty is presented. A mixed-integer programming model is developed to find optimal dynamic planning production which minimizes costs and reduces the scrap. After each production cycle, the quality indicators and relative costs for good and poor quality are performed. These indicators are introduced into the developed model to determine the optimal production orders and stock levels considering the reworked and scrapped products. A real case study is presented to illustrate the applicability of the proposed model. By comparing the obtained results with real results from studied company, we proved that this new model gives an optimal production plan.

Research on Intelligent Decision Method of Optimal Production Planning and AGV In-time Delivery in Mixed-Model Assembly Line

An automated guided vehicle (AGV) system is an indispensable part of a mixed-model assembly line (MMAL) for flexible and efficient production, which is utilized for the material delivery. With the urgent needs of industry digitalization and market customization, along with the trend of corporate smart decision management, MMAL has been widely adopted in the manufacturing process of modularized products. To address the parameter matching and energy consumption optimization of the AGV system in MMAL, the overall architecture of an intelligent MMAL (IMMAL) is firstly proposed. And the model of this problem is constructed. Then, the research methodology is proposed, which comprehensively adopts the dispatching rule, genetic algorithm, design of experiment, BP neural network, and simulated annealing. Eventually, a real-life case is used to validate the practicality of the proposed solutions. The results show that the optimal solutions of MMAL production and AGV system delivery at the minimum energy consumption are obtained successfully. Furthermore, three factors: the quantity, capacity, and velocity of AGVs greatly impact the energy consumption of the AGV system, where the quantity of AGVs has the most negligible significance, while the capacity of AGVs has the most significance.

Интеллектуальный программный комплекс моделирования процесса планирования многоассортиментных промышленных производств

The article discusses issues related to the development of a flexible intelligent software package for solving the problem of optimal planning of multi-assortment production. These industries are characterized by a large range of products, many types and configurations of equipment, with an increase in the dimension of the problem, the number of options for production schedules grows exponentially, therefore, it is extremely important to develop a specialized complex for effective optimal planning and scheduling, insisting on the characteristics of various multi-assortment industries. The purpose of this work is to increase the productivity of multi-assortment enterprises and reduce the time of production of products by developing methods and algorithms for optimizing scheduling in the form of a problem-oriented software package. The article presents a mathematical formulation of the optimization problem and a set of mathematical models and algorithms for the formation of objective functions for optimal scheduling of reconfigurable productions. Conducting this study is based on the use of methods of scheduling theory, optimization and evolutionary calculations, tools for object-oriented development of complex software systems and databases. The proposed software package has various intelligent user interfaces, supplemented by databases of products, equipment and technological regulations, a library of objective functions and mathematical optimization methods, an expert system tuning module, as well as an interactive system for visualizing the resulting production plans in the form of a Gantt chart and decision tree of the optimization problem. Testing of the software package was carried out on the data of polymer and metallurgical enterprises in Russia and Germany and confirmed the effectiveness of solving planning problems. Implementation of the proposed software package makes it possible to ensure efficient loading of enterprise equipment, reduce production costs and simplify the process of making managerial decisions in the course of production planning.

The profitability index and its impact on sustainable development decisions

The profitability index plays an important role in helping decision-makers, whether they are short-term decisions or long-term decisions, the profitability index is used in various ways to assist short-term decision-makers related to: - By using scarce economic resources and developing the optimum production plan. - Determining production lines or products that continue to work and those lines or products that are discontinued based on their profitability. - Pricing of products. - Granting incentives to salesmen. These decisions are part of sustainability activities for any company, Also, the profitability index is used in the comparison between long-term investment projects when the net present value criterion is positive for these projects, meaning that they are economically feasible, and it is necessary to choose some of them without the other.

Integrated modeling and optimization of production planning and scheduling in hybrid flow shop for order production mode

This paper studies the integration of production planning and scheduling in the forging process of small batch orders. Firstly, the demand for orders and the processing constraints of forging equipment are comprehensively considered. Then by integrating periodic requirements for orders, the cycle of production is assigned based on production status and inventory, and each cycle of production is independently scheduled, a two-level decision-making model for production planning and adjustment of hybrid flow shop for forging is established. Secondly, in order to realize the closed-loop integration of high-level planning and low-level scheduling, a particle swarm-genetic hybrid algorithm is proposed to obtain the feasible integrated solution of production planning and scheduling. Thirdly, through a large number of numerical experiments, the genetic algorithm parameters of scheduling layer are optimized and its optimization ability is verified, and the optimal results also show that the parameters improve the overall algorithm performance. Lastly, a case of hybrid flow workshop for forging is presented to demonstrate the practicability of the proposed model and algorithm.

A formal skill model to enable reconfigurable assembly systems

ABSTRACT As assembly systems move into the era of mass customisation, the complexity of design processes, (re)configurations and operations rises. Well-structured data are key in keeping this complexity manageable. Here to, this paper presents a multidimensional formal skill model designed to deliver generic descriptions of needs and capacities with skills as the connector between products, processes and resources. The model formalises resource structures in relation to the processes they master and products they can produce. This paper discusses the case-based evaluation in a reconfigurable assembly system and highlights the added-value of a skill-based modelling approach. The presented formal model combines concepts coming from both offline and online modelling perspectives and allows for various applications and levels of detail. The resource structures embedded in the prerequisites of a skill enable matchmaking of resources for workspace design and reconfigurations. The mapping of the model to standardised ISA-95 models couples production needs to the resources allowing for more optimal production planning, control and a structured interface between enterprise and control systems. The possibility to couple states to the assembly environment allows for optimal runtime orchestration.

Collaborative production and predictive maintenance scheduling for flexible flow shop with stochastic interruptions and monitoring data

Flexible flow shop problem (FFSP) has been extensively researched in recent years. However, machine deterioration and stochastic production interruptions noticeably affect the regular job process in practice, incurring great cost. It is significant to incorporate the condition-based predictive maintenance scheduling and the production modification for interruptions with traditional FFSP. Nevertheless, this issue is rarely investigated in the existing FFSP studies. To solve the FFSP with maintenance and stochastic interruptions (FFSP-MSI), this paper proposes a collaborative optimization policy of production and maintenance (COPPM) by considering stochastic interruptions and monitoring data utilization for total cost reduction. For maintenance scheduling, a real-time prognosis updating method is leveraged based on monitoring data to capture the individual machine degradations. Assisted by the prognosis output, the optimal maintenance intervals are dynamically derived through a cost rate model. For production optimization, a variable neighborhood search (VNS) algorithm is presented to obtain the nearly- optimal initial production plan and the production modification for stochastic interruptions caused by machine failures and random jobs. This proposed COPPM comprehensively determines the cost-effective maintenance cycle, job order and machine selection for each job. Computational experiments of the wind turbine blade process are presented to certify the economic effectiveness of COPPM. Compared with the traditional production and maintenance scheduling method, the COPPM achieves 13.68 % total cost reduction in average under various problem scales.

Development and Validation of an Energy Consumption Model for Animal Houses Achieving Precision Livestock Farming

Indoor environmental control is usually applied in poultry farming to ensure optimum growth conditions for birds. However, these control methods represent a considerable share of total energy consumption, and the trend of applying new equipment in the future for precision livestock farming would further increase energy demand, resulting in an increase in greenhouse gas emissions and management costs. Therefore, to ensure optimum efficiency of both energy use and livestock productivity, a customized hourly model was developed in the present study to interpret and analyze the electronically collected data. The modules for estimating indoor gas concentrations were incorporated into the present model, as this has not been properly considered in previous studies. A validation test was performed in a manure-belt layer house using sensors and meters to measure the indoor environmental parameters and energy consumption. The predicted results, including indoor temperature, relative humidity, carbon dioxide and ammonia concentrations, showed good agreement with the measured data, indicating a similar overall trend with acceptable discrepancies. Moreover, the corresponding differences between the measured and simulated energy consumption for heating, tunnel ventilation and base ventilation were 13.7, 7.5, and 0.1%, respectively. The total energy demand estimated by the model showed a limited discrepancy of approximately 10.6% compared with that measured in reality. Although human factors, including inspection, cleaning, vaccination, etc., were not included in the model, the validation results still suggested that the customized model was able to accurately predict the indoor environment and overall energy consumption during poultry farming. The validated model provides a tool for poultry producers to optimize production planning and management strategies, increase the production rate of unit energy consumption and achieve precision livestock farming from an energy consumption standpoint.

PENERAPAN FUZZY GOAL PROGRAMMING DALAM PENGOPTIMALAN PERENCANAAN PRODUKSI

Production planning is a very important activity for companies in considering production factors ranging from the number of products that must be produced, when the product must be completed, and other things needed. Improper production planning risks causing losses to the company because the use of production costs and profit estimates are not optimized. One method that can be used in optimizing production planning is the fuzzy goal programming method. Fuzzy goal programming is an extension of linear programming that can solve problems of more than one goal in the presence of target constraints. This study aims to examine and apply the fuzzy goal programming method which is solved by the simplex method in Ayune Kitchen production planning by maximizing revenue and minimizing raw material costs. The data used in this study include product data (comb bread, dulce, brulee, mentai, lasagna, and briyani rice), production prices, and raw materials for making products from Ayune Kitchen which will be analyzed using linear programming and fuzzy goal programming methods. simplex. The result of this research is the optimal income that will be obtained by Ayune Kitchen is Rp. 8.830.000 and the optimal expenditure incurred by Ayune Kitchen to purchase raw materials is Rp. 6.687.821

An Optimal Production Plan for Cashew Nuts Community Enterprise Using Metaheuristic Algorithms

The proper production plan plays an important role in the cashew nuts market enterprise in order to reduce cost. This study aims to find the optimal production plan for cashew nuts using ant lion optimization (ALO), symbiotic organisms search (SOS), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC). The novel objective function is introduced in this study. Three input data set, including production cost, holding cost and inventory quantity are investigated. The experiment cases consist of the frequency of production cycle time in January, February and March, respectively. As a results, four algorithms are available to estimate not only the proper production plan of cashew nuts but also an ability in reducing the inventory and the holding costs. In summary, the ALO algorithm provides better predictive skill than others for the cashew nuts production plan with the lowest RMSE value of 0.0913.

Development of Medium-Term Water Inflow Forecasting Models for Planning Electricity Generation in Isolated Power Systems

Reliable operation of power systems with a significant share of hydropower plants (HPPs) in the energy mix depends in many respects on how accurately the water inflow is forecasted. Taken together, the water inflow prediction and optimal planning of production define energy security, ensure the possibility of protection from floods, and eliminate idle discharges at hydroelectric power plants. The solution of such problems is complicated by lack of reliable information about the water inflow, its being stochastic in nature, a variable electricity consumption pattern, and inaccurate prediction and planning models. Improvement of prediction accuracy is focused on determining the water inventory for planning prospective electricity generation at HPPs taking into account regulation in the medium term. Such regulation is necessary to meet the power system load in the load curve peak and semi-peak parts. The paper considers the problem of constructing a medium-term water inflow prediction model for planning electricity generation for a week ahead taking into account climate changes in isolated operating power systems taking as an example the electric power systems of the Gorno-Badakhshan autonomous oblast in Tajikistan. For taking into account constant climate changes, it is proposed to use an approach based on machine learning methods, which features a self-adaptation capability. Based on the results of accomplished experimental and industry-grade numerical analyses, the expediency of using a model based on an ensemble of regression decision trees has been shown.

PERENCANAAN PRODUKSI DENGAN METODE DE NOVO PROGRAMMING UNTUK MENGOPTIMALKAN KEUNTUNGAN DI PT. VARIA USAHA BETON WARU - SIDOARJO

ABSTRACT
 PT. Varia Usaha Beton is one of the companies engaged in the manufacturing industry that produces products in the form of various types of precast concrete and ready-to-use liquid concrete. One of the problems faced by PT. Varia Usaha Beton is that there is always a residual raw material from its production activities due to inaccurate production planning, especially in determining the numbers of precast concrete products that can produce optimal profits. To overcome these problems, the De Novo Programming approach is used as an alternative solution to obtain a more optimal production planning. The De Novo Programming method is an optimization method that uses a total system approach so that it can determine both the best combination of product types to be produced and provide suggestions regarding the use of available resources or budgets. Based on the results of the analysis using the De Novo Programming approach, the combination of products that can optimize company profits is Paving Block as many as 3,566,746 units, Paving TU as many as 142,238 units, Kanstein as many as 19,672 units, Tile tiles as much as 1,460,579 units, and Batako as many as 326,370 units. This product combination leads to an increase in profit of about 45,554,100 IDR compared to the existing condition

Adaptive Genetic Algorithm Based on Fuzzy Reasoning for the Multilevel Capacitated Lot-Sizing Problem with Energy Consumption in Synchronizer Production

The multilevel capacitated lot-sizing problem (MLCLSP) is a vital theoretical problem of production planning in discrete manufacturing. An improved algorithm based on the genetic algorithm (GA) is proposed to solve the MLCLSP. Based on the solution results, the distribution of energy consumption in a synchronous production case is analyzed. In the related literature, the GA has become a much-discussed topic in solving these kinds of problems. Although the standard GA can make up for the defects of the traditional algorithm, it will lead to the problems of unstable solution results and easy local convergence. For these reasons, this research presents an adaptive genetic algorithm based on fuzzy theory (fuzzy-GA) to solve the MLCLSP. Firstly, the solving process of the MLCLSP with the fuzzy-GA is described in detail, where algorithms for key technologies such as the capacity constraint algorithm and the algorithm of solving fitness value are developed. Secondly, the auto-encoding of decision variables for MLCLSPs is studied; within this, the decision variables of whether to produce or not are encoded into a hierarchical structure based on the bill of material; combined with external demand, the decision variables of lot-sizing are constructed. Thirdly, the adaptive optimization process of parameters of the GA for the MLCLSP based on fuzzy theory is expounded, in which membership function, fuzzy rule, and defuzzification of the MLCLSP is mainly presented. Experimental studies using the processed dataset collected from a synchronizer manufacturer have demonstrated the merits of the proposed approach, in which the energy consumption distribution of the optimized production plan is given. The optimal lot-sizing is closer to the average value of the optimal value compared with the standard GA, which indicates that the proposed fuzzy-GA approach has better convergence and stability.

A review of machine learning applications for underground mine planning and scheduling

Production planning and scheduling optimisation for underground mining operations has continued to attract significant attention over the last decades. This has been necessitated by the growing need for operations to meet their shareholder's expectations sustainably under increasingly challenging operational dynamics. Several studies have been undertaken to utilise mathematical programming models such as mixed-integer programming, heuristics and simulation algorithms including combinations of these techniques for production scheduling optimisation with some notable achievements noted in extant literature. However, the limited reach of standalone mathematical optimisation models under increasing volumes of input data spurred by the booming information technology (IT) platforms has become more apparent and pertinent for increased scholarly attention. The growing emergence of big data, driven by the industrial digitisation and automation has seen an increased appetite for data-driven optimisation planning and scheduling largely in manufacturing and operations management. However, the scarcity of discussion in this novel and fast-evolving area in the underground mining space presents a glaring blind spot that appeals for thoughtful conversations to narrow that gap. This paper seeks to discuss opportunities for application of data analytics and machine learning to improve production planning and scheduling efficacy in underground mining. Specific focus will then be narrowed to opportunities for incorporating predictive analytics and machine learning to improve the accuracy of mathematical optimisation models. The overarching intent is to support the attainment of mineral production targets through enabling schedule dynamic response to variability in key determinant variables such as ore grade and tonnages.

Building Decision Support Systems in Excel for Production and Distribution Planning: A Case Study

We develop a decision support system in Microsoft Excel that integrates production and distribution for a manufacturer of natural fiber-based products in North America. The production and distribution of the company’s products were optimized using a linear programming model, implemented in Excel. The spreadsheet dynamically adjusts the formulation to reflect the user’s current requirements, solves the optimization model in the background, and generates detailed managerial reports. In addition, it allows users to conduct what-if analyses by varying the number of plants and warehouses. It demonstrates the ability of a Linear Programming Model run on an Excel platform to provide the firm with an optimized production plan resulting in significant, cost savings since implementation.

Aggregate production planning considering organizational learning with case based analysis

Responding rapidly to customer needs is one of the main targets of industrial organizations that want to survive in the current market competition. This objective can be attained through robust planning. Workforce productivity is considered one of the important entities in production planning. However, it has a dynamic nature, i.e. the productivity growths thanks to on-job training or learning phenomenon. Considering this fact in manufacturing planning enhances the robustness of the developed plans. The present paper presents a mathematical model for medium-range production planning that is used to find the optimal aggregate production plan. The model aims to optimize the total production costs while respecting most of the operational constraints and considering the process of organizational learning. The presented model is constructed relying on the real industrial practices; the outcome is a mixed-integer linear program. The model was validated and checked using real data collected from an Egyptian factory that produces electric motors for home appliances . The proposed mathematical model was optimally solved using “ILOG-CPLEX 12.6”. By comparing the results obtained versus that of the method adopted in the factory, a cost reduction of 6.3% is achieved for the presented data set. A set of managerial aspects are concluded after the model analysis. Moreover, the impact of using detailed learning rates on the production cost is discussed.

Solving a parallel-line capacitated lot-sizing and scheduling problem with sequence-dependent setup time/cost and preventive maintenance by a rolling horizon method

This paper presents a novel Mixed-Integer Nonlinear Programming (MINLP) model for a parallel-line Capacitated Lot-Sizing Problem (CLSP) with the sequence-dependent setup time/cost, due date, and preventive maintenance planning. The target of this integrated model is to specify optimal lot sizes, inventory, and shortage levels along with the optimal preventive maintenance plan and the best sequence, starting time, and completion time of the lots. The production and scheduling aspect of the problem is modeled as the CLSP with the sequence-dependent setup time/cost and due date, while maintenance sub-problem is modeled by using the time-based maintenance technique and age-reduction modeling concept with the actual run-time and Exponential lifetime distribution. For the complexity of the model, the MIP-based Rolling Horizon (RH) heuristic algorithms are developed to solve the linearized model in a reasonable truncated time. The computational results obtained from generated small- to large-sized instances show that the proposed RH1 and RH2 heuristic methods report the final solutions with better quality (lower relative gap and total cost) compared with the solutions reported by the truncated simple CPLEX method. Especially, the RH2 method provides a reasonable production plan, better-adjusted schedules with respect to the due dates, and a proper PM plan in comparison with the other solution methods. Also, the featured model develops an optimal production, scheduling, and maintenance plan with higher feasibility, more available production time, and lower total cost in comparison with the classic CLSP with the sequence-dependent setups that neglects the effects of sudden failures and corrective maintenance actions.

Modeling and mitigating supply chain disruptions as a bilevel network flow problem

Years of globalization, outsourcing and cost cutting have increased supply chain vulnerability calling for more effective risk mitigation strategies. In our research, we analyze supply chain disruptions in a production setting. Using a bilevel optimization framework, we minimize the total production cost for a manufacturer interested in finding optimal disruption mitigation strategies. The problem constitutes a convex network flow program under a chance constraint bounding the manufacturer’s regrets in disrupted scenarios. Thus, in contrast to standard bilevel optimization schemes with two decision-makers, a leader and a follower, our model searches for the optimal production plan of a manufacturer in view of a reduction in the sequence of his own scenario-specific regrets. Defined as the difference in costs of a reactive plan, which considers the disruption as unknown until it occurs, and a benchmark anticipative plan, which predicts the disruption in the beginning of the planning horizon, the regrets allow measurement of the impact of scenario-specific production strategies on the manufacturer’s total cost. For an efficient solution of the problem, we employ generalized Benders decomposition and develop customized feasibility cuts. In the managerial section, we discuss the implications for the risk-adjusted production and observe that the regrets of long disruptions are reduced in our mitigation strategy at the cost of shorter disruptions, whose regrets typically stay far below the risk threshold. This allows a decrease of the production cost under rare but high-impact disruption scenarios.