Production Planning Approach Research Articles

Towards the concept of gas-to-power demand response

Due to the war in Ukraine, the European Commission has released its “Save Gas for a Safe Winter” plan, communicating the goal of reducing gas consumption in the electricity sector, among others. In this paper, the gas consumption in the electricity sector is picked up and the well-established concept of demand response is brought into alignment with the consumption of gas in the electricity sector, leading to the concept of gas-to-power demand response. Two proposed programs based on this concept are then applied in a production planning approach that shows how companies could proactively contribute to easing the tense situation in Europe, particularly in Germany, especially using methods such as scheduling and/or lot-sizing. This article is intended to serve as a basis for further discussions in the political and economic sectors.

Optimizing the workload of production units of a make-to-order manufacturing system

This paper proposes a production planning approach for a job shop type manufacturing company in electronics sector that operates with make-to-order (MTO) convention and has a high-mix production range. A novel Mixed Integer Linear Programming (MILP) model is proposed that finds workload-dependent planning horizon by making order acceptance decisions. Partial acceptance of orders and delayed internal target deliveries contribute to the novelty of the proposed model. We establish the complexity of the problem and solve real-life problem instances of a medium-size manufacturing company in the south of The Netherlands. Our computational results show that the proposed MILP model can allocate desired workload to work centers by dynamically adjusting the time horizon for production plans. Our approach can practically be used within reasonable times for daily updates of internal production targets. We perform a sensitivity analysis to show how the time horizon is determined with varying system conditions. The obtained workload amounts satisfy minimum requirements of production units, as this cannot be achieved by the current practice.

Conceptual Model for Integrated Production and Maintenance Planning with Automated Prognostic Method Selection

Abstract With increasing digitalization, manufacturing systems provide data to describe their current condition, allowing for a digital representation of the physical system. This enables a transition to more efficient maintenance and production planning approaches. In this context, selecting suitable prognostic methods is crucial and requires a high level of expertise and domain knowledge. This paper proposes a conceptional model for an integrated approach to solve these problems that are usually treated separately. Following an introduction of enablers for advanced maintenance strategies and prognostic methods, the conceptional model is presented. Concurrently, the state of the research of the modules is outlined. To conclude, open research questions are summarized.

Multi-objective optimisation approach as decision-making support for product mixing and raw material allocation planning

This article is addressed to discuss a newly developed decision making support by using multi-objective optimisation approach for production planning and supplier selection problem of raw material. The problem faced is finding an optimal value for the planned production volume of each product at each time period and the raw material volume that should be purchased from each supplier for each type at each time period such that the total occurred cost is minimal and all occurred constraints are satisfied. We have solved the problem by finding the Pareto set to be the optimal solution by initially converting it into single objective optimisation by means of weighting method. From the computational simulation, the optimal decisions were obtained for all time periods which are the optimal volume of each raw material type that should be purchased from each available supplier, and the planned optimal volume of each product to be produced.

Multi-objective Optimization Approach as Decision Making Support for Product Mixing and Raw Material Allocation Planning

This article is addressed to discuss a newly developed decision making support by using multi-objective optimisation approach for production planning and supplier selection problem of raw material. The problem faced is finding an optimal value for the planned production volume of each product at each time period and the raw material volume that should be purchased from each supplier for each type at each time period such that the total occurred cost is minimal and all occurred constraints are satisfied. We have solved the problem by finding the Pareto set to be the optimal solution by initially converting it into single objective optimisation by means of weighting method. From the computational simulation, the optimal decisions were obtained for all time periods which are the optimal volume of each raw material type that should be purchased from each available supplier, and the planned optimal volume of each product to be produced.

The application of production planning approach on a large number of residential building construction project post natural disaster

After a large-scale natural disaster struck an urban area a large number of residential buildings construction is often needed. Such residential buildings are often built with more or less similar size and model therefore the construction is repetitive. The project management approach is normally employed in managing a large-scale residential building project. This study argues that due to its repetitive nature the construction of a large number of similar houses can also be viewed as a production system in addition to a project view. This study investigates the utilization of the production planning approach with the aid of a discrete event simulation method in managing the construction of a large number of residential buildings. This study simulates the construction of 500 similar houses. The study results show that the production management approach has the potential in managing a large-scale residential building project in addition to the project management approach.

Capacity-driven production planning

Traditional material requirements planning systems (MRP) schedule production orders for on-demand items without considering limited capacities of production resources. This article introduces a production planning approach which also incorporates consumption-based items and addresses capacity planning when planned orders are generated. The framework consists of three steps: drafted job list generation, capacity-driven forward scheduling and purchase order generation. Fewer predefined parameters for internally produced items than in MRP are necessary. No lot-sizing policy is applied. Lot-sizes are arranged in a forward scheduling procedure. No predefined constant lead times are used because lead times are also calculated dynamically in the planning procedure. The approach allows a (semi-) automatic finite-capacity production planning and capacity adjustment. The functionality of the approach is illustrated by a simple case study throughout the article.

Fuzzy approach for production planning by using a three-dimensional printing-based ubiquitous manufacturing system

AbstractA ubiquitous manufacturing (UM) system is used in manufacturing for obtaining the Internet of things solutions and provides location-based manufacturing services. Human-induced uncertainty and early termination are two complications that hamper the effectiveness of an UM system based on three-dimensional (3D) printing. To resolve these complications, several solutions were considered in this study. First, fuzzy-valued parameters were defined to determine uncertainty. Subsequently, slack was derived to determine whether to restart an early terminated 3D printing process in the same 3D printing facility. Consequently, two optimization models – a fuzzy mixed-integer linear programming model and a fuzzy mixed-integer quadratic programming model – were developed in this study. Based on the two optimization models, a fuzzy 3D printing-based UM system that considers uncertainty and early termination was developed. The effectiveness of the proposed methodology was tested by conducting a regional experiment. The experimental results revealed that the proposed methodology could shorten the average cycle time by 9% and could enable 3D printing facilities to make real-time, online reprinting decisions.

A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

In reality, the forecast of uncertainties often becomes more accurate with the approaching of the forecasted period. This article proposes a rolling horizon approach to dynamically determine the production plan and the maintenance plan for a degradation system under uncertain environment. In each rolling horizon, demand forecasts are updated with new information from customers, and the degradation level of system is confirmed by inspection. By taking advantage of the updated uncertainties, at each decision point, the maintenance plan is determined by an advance-postpone balancing approach and the production plan is optimized by a heuristic algorithm in a two-stage stochastic model. Numerical results validate that the rolling horizon approach has great superiority over traditional stochastic programming approach in terms of real total cost and service level.

Methodology for integrative production planning in highly dynamic environments

Flexibility has become one of the most important characteristics in modern manufacturing. As a result, process chains must be highly adaptable to varying demands and to new products. Furthermore, many process chains contain external processes from suppliers to handle the fluctuating process utilisation. Moreover, novel manufacturing processes that allow an even higher flexibility, such as additive manufacturing, have been introduced. In order to identify optimal parameters for flexible process chains and considering interactions between processes, a new approach for production planning is necessary. This article presents a methodology for integrative production planning in highly dynamic environments thereof. The introduced methodology is applied on two industrial use cases with the aim of identifying optimal process elements and parameters taking production costs and time under consideration. The results show that the developed methodology allows for successful modelling and optimisation of the process chain.

Approach for a Production Planning and Control System in Value-Adding Networks

The increasing demand for individualised high-quality products at low cost is forcing companies to offer products based on their core competencies in value-adding networks. To reach overall objectives, such as shorter throughput times or less inventory throughout the network, an intense transfer of progress-relevant information among the network partners is required. This publication presents an approach for a network-wide production planning and control (PPC) system focusing on technical, organisational and monetary dimensions. The proposed approach comprises the modelling of the value-adding network, the development of both a system architecture and an incentive scheme, as well as a hierarchical optimisation.

PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories.

Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust complex production plans through a data-driven approach. However, developing a systematic analytics approach for production planning is challenging due to the large volume of production data, the complex dependency between products, and unexpected changes in the market and the plant. Previous studies only provide summarized results and fail to show details for comparative analysis of production plans. Besides, the rapid adjustment to the plan in the case of an unanticipated incident is also not supported. In this paper, we propose PlanningVis, a visual analytics system to support the exploration and comparison of production plans with three levels of details: a plan overview presenting the overall difference between plans, a product view visualizing various properties of individual products, and a production detail view displaying the product dependency and the daily production details in related factories. By integrating an automatic planning algorithm with interactive visual explorations, PlanningVis can facilitate the efficient optimization of daily production planning as well as support a quick response to unanticipated incidents in manufacturing. Two case studies with real-world data and carefully designed interviews with domain experts demonstrate the effectiveness and usability of PlanningVis.

Demand Forecasting in Hybrid MTS/MTO Production Systems

This article describes how simplifying production-planning approaches for demand responsiveness has been well recognized as an operative means of accomplishing production efficiency. To support an effective decision making in manufacturing environments, this study will focus on adopting time series analysis concepts. It will attempt to focus on bringing forward novel structures for classifications of available surveying materials, which helps companies using time series analysis within production strategies to make a logical prediction of demands in hybrid manufacturing systems. In this regard, the authors will present two different categorizing structures as efficient ways of helping practitioners and academicians to find new approaches for applying near possible future forecasts by means of time series analysis methods.

A Project Based Learning approach for Production Planning and Control: analysis of 45 projects developed by students

Abstract The project-based learning (PBL) approach has been a part of the University of Brasilia's programme from the inception of its Industrial Engineering syllabus. Production Systems Project 4 course is one among eight courses that drive the utilization of PBL approach at the curriculum, the subject of this study, in which we present a set of analyses of the projects developed between 2013 and 2016. The projects involved real-world problems, related to public and private sector enterprises in Brazil’s Federal District. The conducted analyses aimed to identify the organizations’ profiles and the PPC (Production Planning and Control) techniques which were used to achieve project objectives. Therefore, statistical analyses were performed, such as Correlation Analysis, Cluster Analysis, as well as qualitative documental analysis. The results of this study indicate the profile of external partners that have the highest probability of achieving satisfactory results, as well as the main planning elements which impact the final grades of the projects.

Approach for Production Planning in Reconfigurable Manufacturing Systems

To stay competitive and to fulfill the changing market needs, manufacturing companies have to adapt their manufacturing systems in frequent and short intervals. Hence, changeable and reconfigurable manufacturing systems (RMS) are proposed and discussed in a multitude of research publications. While production planning becomes increasingly complex in this context, it has to be reliable and quick at the same time. Therefore, the performance and flexibility of manufacturing systems depends on actual and suitable planning data with high quality and wide range. In this context, a new approach for production planning in reconfigurable manufacturing systems is exposed in this paper. Data models, a configuration management and a sequential method for the resource planning help to integrate reconfigurable manufacturing systems’ key characteristics in production planning and control (PPC). Finally a prototypical application scenario, for the evaluation and demonstration of the feasibility of the planning approach, is outlined.

A hybrid heuristic approach for production planning in supply chain networks

Planning distributed manufacturing facilities is one of the most challenging tasks in the supply chain management. This paper proposes a production planning algorithm for the multi-level, multi-item capacitated lot-sizing problem (MLCLSP) in a supply chain network that takes back order into account. MLCLSP is a mixed integer linear programming (MIP) problem and is NP-hard. This paper presents an efficient, hybrid, heuristic algorithm named greedy rolling horizon search (GRHS) that combines a rolling horizon local search heuristic with an exact linear program (LP) solver. Computational experiments show that GRHS performs well in terms of total costs and computational time and is superior to existing meta-heuristics, such as tabu search, simulated annealing, and genetic algorithms.

Dynamic Drum-Buffer-Rope approach for production planning and control in capacitated flow-shop manufacturing systems

Drum-Buffer-Rope-based production planning and control (PPC) approaches provide production managers with effective tools to manage production disruptions and improve operational performance. The corner stone of these approaches is the proper selection of time-buffers which are considered as exogenously defined constant. However, the majority of real-world manufacturing systems are characterized by the dynamic change of demand and by stochastic production times. This fact calls for a dynamic approach in supporting the decision making on time-buffer policies. To this end, we study a capacitated, single-product, three-operation, flow-shop manufacturing system. We propose a dynamic time-buffer control mechanism for short/medium-term PPC with adaptive response to demand changes and robustness to sudden disturbances in both internal and external shop environment. By integrating the control mechanism into the flow-shop system, we develop a system dynamics model to support the decision-making on time-buffer policies. Using the model, we study the effect of policies on shop performance by means of analysis of variance. Extensive numerical investigation reveals the insensitivity of time-buffer policies to key factors related to demand, demand due date and operational characteristics such as protective capacity and production times.

A Finite Capacity of Production Planning Approach for Industrial Manufacturing

This paper discusses a production planning approach that takes the finite capacity of the manufacturing system into account. The approach is based on a reduced level of detail modeling of the process flows. The paper considers explicitly only bottleneck steps. A mixed integer programming (MIP) formulation is suggested to determine completion time targets for the bottleneck steps of the lots. A Lagrange relaxation technique is applied to solve the MIP approximately. Intermediate capacity management is related to rough-cut capacity planning, linked to the master schedule, and capacity requirements planning, and linked to the material requirements plan.

An Integrated Model for Production Planning and Cell Formation in Cellular Manufacturing Systems

Cellular manufacturing (CM) is a production approach directed towards reducing costs, as well as increasing system's flexibility in today's small-to-medium lot production environment. Many structural and operational issues should be considered for a successful CM design and implementation such as cell formation (CF), production planning, and facility layout. Most researchers have addressed these issues sequentially or independently, instead of jointly optimizing a combination of these issues. In order to attain better results to ensure that the system will be capable of remaining efficient in unknown future situations, these issues should be addressed simultaneously. In this paper, a mathematical model is developed using an integrated approach for production planning and cell formation problems in a CM. A set of numerical examples are provided from existing the literature in order to test and illustrate the proposed model. In order to evaluate and verify the performance of the proposed model, it is compared with a well-known cell formation methods (rank order clustering and direct clustering analysis), using group capability index (GCI) measure. The results and comparisons indicate that the proposed model has a significantly higher and satisfactory performance and it is reliable for the design and the analysis of CM systems.

Optimum Planning of Electricity Production

The problem of planning the production of a pool of power plants has been deeply investigated. Maintenance management and load allocation problems have been assumed as crucial aspects for achieving maximum plant profitability. A production-planning approach has been developed, and genetic algorithm techniques have been adopted to implement the developed approach. Life consumption of gas turbines’ hot-section components has been considered as a key element required in simulating plants’ behaviors. As a result, a deterioration model has been developed and included into the planning algorithm. The developed approach takes market scenarios, as well as actual statuses and performances of plant components into account. The plants’ physical models are developed on a modular approach basis and provide the operating parameters required by the planning algorithm. Neural network techniques have been applied to speed up the simulation. Economic implications related to maintenance strategies, including postponement or anticipation of maintenance interventions, are investigated and the results obtained by the numerical simulation are presented and widely discussed.