Hierarchical Production Planning Research Articles

Material flow control in make-to-stock production systems: an assessment of order generation, order release and production authorization by simulation

AbstractMaterial flow control (MFC) is a key element of production planning and control. The literature typically categorizes different MFC methods according to how MFC is realized. This distinction overlooks that MFC decisions can be subdivided into three independent tasks that are executed as orders progress through the system: (i) order generation, (ii) order release, and (iii) production authorization. MFC methods are typically designed for only one of these three tasks, which leaves a large part of the order flow uncontrolled. This study therefore not only provides a new categorization of MFC methods, but also argues for the simultaneous application (or the combining) of three different MFC methods for order generation, order release, and production authorization. To support this argument, the performance effects of an integrated MFC approach are evaluated. Findings show that each individual MFC method impacts different performance metrics, which can be explained by the presence of a hierarchy of workloads, where each workload level constrains the succeeding hierarchical level. Each MFC method has a main impact on a different workload. This has important implications for the design of MFC methods and extends recent literature on hierarchical production planning and control systems.

A BOM model transformation method for hierarchical production planning management process of complex products

The Bill of Materials (BOM) is the core data in the hierarchical production planning management of complex products. In the current competitive market environment and increasing demand for individual customization, complex product manufacturing enterprises are exploring methods that fully use common data and efficiently organize the BOM according to order requirements. However, due to the lack of a unified data modeling method for hierarchical production planning, BOM data at different levels cannot be interpenetrated. Moreover, the single-tree BOM lacks flexibility in describing the dynamic change process of data in different planning management stages (i.e., order receiving, variant design, and manufacturing execution).This paper proposes a BOM model transformation method for hierarchical production planning management to efficiently and accurately organize the data in different planning management stages of complex products. First, the Hierarchical Bill of Materials (HBOMs) model is constructed to integrate BOM data of different levels of planning. Subsequently, to describe the data requirements in HBOMs for different plan management stages, a composite HBOMs containing three types was designed. The Generic-HBOM (G-HBOM) aims to manage the common data of the same product type. The Instance-HBOM before-manufacturing (I-HBOM (BM)) and Instance-HBOM after-manufacturing (I-HBOM (AM)) manage the personality data of single product variant design and manufacturing execution stages, respectively. The transformation process models of HBOMs are constructed by analyzing the transformation logic between three types of HBOMs. To support the transformation process of HBOMs, five transformation operators are summarized, and the node traversal algorithm logic is proposed. Finally, a steam turbine generator product is taken as an example to verify the feasibility and effectiveness of this method. This method can effectively improve the efficiency of BOM construction and reduce data redundancy while helping to ensure the integrity, consistency, and accuracy of BOM data.

Master Production Scheduling with Consideration of Utilization-Dependent Exhaustion and Capacity Load

A large number of researchers have addressed social aspects in hierarchical production planning. This article responds to research gaps identified in our previous literature review. Accordingly, consideration of social aspects and the economic implications of social improvements are required in a longer term planning approach. For this, we integrate work intensity as employee utilization in a general mixed-integer programming model for master production scheduling. Following existing fatigue functions, we represent the relationship between work intensity and exhaustion through an employee-utilization-dependent exhaustion function. We account for the economic implications through exhaustion-dependent capacity load factors. We solve our model with a CPLEX standard solver and analyze a case study based on a realistic production system and numerical data. We demonstrate that the consideration of economic implications is necessary to evaluate social improvements. Otherwise, monetary disadvantages are overestimated, and social improvements are, thus, negatively affected. Moreover, from a certain level of work-intensity reduction, demand peaks are smoothed more by pre-production, which requires more core employees, while temporary employment is reduced. Further potential may arise from considering and quantifying other interdependencies, such as employee exhaustion and employee days off. In addition, the relationship between social working conditions and employee turnover can be integrated.

Integrated Dynamic Cellular Manufacturing Systems and Hierarchical Production Planning with Worker Assignment and Stochastic Demand

This study deals with the interaction of dynamic cellular manufacturing (DCM) and hierarchical production planning (HPP) problems with stochastic demands for the first time. Each of these alone does not consider the system factors such as stochastic demands and dynamic cellular formation separately. Accordingly, to fill this gap, this paper presents an integrated optimized model incorporating the most comprehensive design of DCM systems and HPP problems with stochastic demands. This model helps administrators get the optimal size and number of cells to decrease costs. Also, the model applies the principles of HPP to reduce the complexity of the integrated model. Since demands are uncertain, they need to be accurately predicted. Therefore, this study aims to combine the most precise decision variables with the most realistic conditions. A case study from an agriculture mechanization and industrial development company shows that an integrated model can provide managers with a feasible solution to meet demand, reconfigure cells in each period, provide new machinery to increase the required production capacity, and adjust production capacity to help them cope with demand fluctuations. A sensitivity analysis was performed and the results show that increase in forecast error and inter-cell move cost cause less significant changes in total cost but the total cost is sensitive to intra-cell move cost, available time capacities and cell quantity. It is also shown that the total cost was very sensitive to available regular time and available over time and the system should try to increase the time capacity.

Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme

Scarcity of resources, structural change during the further development of renewable energy sources, and their corresponding costs, such as increasing resource costs or penalties due to dirty production, lead industrial firms to adapt ecological actions. In this regard, research on energy utilization in production planning has received increased attention in the last years, resulting in a large number of research articles so far. With the paper at hand, we review the literature on energy-oriented production planning. The aim of this study is to derive similar core issues and related properties along energy-oriented models within hierarchical production planning. For this, we carry out a systematic literature review and analyze and synthesize 375 research articles. We classify the underlying literature with a novel two-dimensional classification scheme and identify three key topics and five frequently found characteristics, which are presented in detail throughout this article. Based on these results, we state several potentials for further research.

Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning

In hierarchical production planning, the consideration of interdependencies between superior top-level decisions and subordinate base-level decisions is essential. In this respect, the anticipation of base-level reactions is highly recommended. In this paper, we consider an example from the metal-processing industry: a serial-batch scheduling problem constitutes the top-level problem and a complex nesting problem constitutes the base-level problem. The top-level scheduling decision includes a batching decision, i.e., the determination of a set of small items to be cut out of a large slide. Thus, to evaluate the feasibility of a batch, the base-level nesting problem must be solved. Because solving nesting problems is time consuming even when applying heuristics, it is troublesome to solve it multiple times during solving the top-level scheduling problem. Instead, we propose an approximative anticipation of base-level reactions by machine learning to approximate batch feasibility. To that, we present a prediction framework to identify the most promising machine learning method for the prediction (regression) task. For applying these methods, we propose new feature vectors describing the characteristics of complex nesting problem instances. For training, validation, and testing, we present a new instance generation procedure that uses a set of 6,000 convex, concave, and complex shapes to generate 88,200 nesting instances. The testing results show that an artificial neural network achieves the lowest expected loss (root mean squared error). Depending on further assumptions, we can report that the approximate anticipation based on machine learning predictions leads to an appropriate batch feasibility decision for 98.8% of the nesting instances.

Developing a Master Production Schedule Using Fuzzy Mixed Integer Linear Programming

This paper considers the hierarchical production planning (HPP) concept to solve a production planning problem in the process industry in a fuzzy environment. The adopted fuzzy HPP consists of two levels in which a fuzzy aggregate production planning (FAPP) model is developed in the first level, and then a fuzzy disaggregate production planning (FDPP) model is developed at the second level. The FAPP was reported by Omar et al. [1] and therefore, this research paper discusses the FDPP model. We formulated the disaggregate model as a fuzzy mixed-integer linear programming model that aims to develop a master production schedule in which numbers of optimal batches are developed in presence of setup time. In addition, we evaluate the performance of the FMILP by comparing its results with a previously reported approach. The findings indicate that significant cost savings were achieved by adopting the fuzzy mathematical programming approach.

Integrated optimization of dynamic cell formation and hierarchical production planning problems

The paper presents a non-linear mixed integer programming (MIP) model that integrates dynamic cell formation (DCF) and hierarchical production planning (HPP). In the model, the DCF problem optimizes reconfiguration of machine cells, with varying production quantities in different periods determined by the HPP model. The HPP problem, formulated as an integrated model, determines the optimal production plans that meet forecast demands in the planning horizon, with capacity limitations of the machine cells formed through the DCF model. Compared with prior studies that integrate DCF and production planning (PP) problems, this paper provides the most comprehensive options needed to meet demands in dynamic cellular manufacturing systems (DCMS). With the introduction of HPP, the model could incorporate more decision variables such as inventory, internal production, subcontracting and backlogging costs, inter- and intra-cell material handling costs, yet with less solution complexity. The model is solved with branch-and-bound method, and its complexity and solution results are analyzed using empirical data from a mold manufacturing plant, with the results of the model compared with those in the literature. Our analyses show that the proposed model is simpler and easier to solve, and that the total cost is insensitive to demand volatility and decreases inversely with the number of cells.

Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment

This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity.

The role of hierarchical production planning in food manufacturing SMEs

Purpose – In recent years there has been an increasing interest in make-to-stock and make-to-order combined strategies in food manufacturing operations. However, most scholarly work to-date has neglected the role of hierarchical production planning (HPP) in guiding small- and medium-sized enterprise (SME) implementation of such strategies. The purpose of this paper is to address food SME manufacturers’ readiness to adopt such strategies, in terms of internal integration and their capability to adopt formalised planning approaches. Design/methodology/approach – This study adopted an action research methodology to explore the potential impact of HPP in SME food manufacturers. Selected companies had identified product variety management as a challenge and also had recognised the need to enhance internal integration. Given this, the research team, from a theoretical perspective, proposed the use of HPP set within a broader decision-making conceptual framework to improve internal integration and planning. Findings – This paper adopts the fundamental position that HPP provides a useful framework in the establishment of strategic and tactical level constraints and priorities which then act as specific guides at the operational level, and presents empirical evidence in a food SME manufacturing context. In the cases the authors studies, the cascading effect of this decision-making framework focused attention on key metrics, encouraged greater internal integration and delivered tangible, significant improvements in performance. This was greatly facilitated by the provision of new key data on the cost of certain managerial trade-offs which these firms faced. Originality/value – SMEs are of a scale that requires a formalised planning approach; however production planning systems are typically designed for large scale enterprises. This paper addresses the need of SMEs in this regard. Well-established supply chain metrics were used to establish the benefits of both HPP and resulting improvement in internal integration and beyond, in terms of improvement in the quality of planning decisions.

Aggregate planning and forecasting in make-to-order production systems

In hierarchical production planning (HPP) systems, aggregate production planning (APP) is meant to balance capacity requirements and production quantities for medium term planning horizons. Aggregate plans provide the basic input for further planning steps. In recent years researchers came up with comprehensive models and sophisticated solution methods for this kind of high level planning. However, some practitioners claim that the aggregate planning concept is rarely applied in industry.We present a comprehensive HPP framework, which we use to investigate the impact of aggregate planning in a make-to-order (MTO) environment. The basic inputs for aggregate plans are market forecasts. Thus, we conduct experiments assuming different forecasting techniques. The planning problem is formulated as a linear mathematical model and solved to optimality by a standard optimization engine. A discrete-event simulation model is used to perform lower level planning steps and to mimic the shop floor where stochastic and nonlinear dependencies are considered. The performance of the system is evaluated based on service- and inventory levels. Real world data coming from the automotive supplier industry is used to define four demand scenarios. For each of them we compare the performance of the system with and without the inclusion of aggregate plans. If aggregate plans are used we assess the impact of different forecasting techniques. We analyze the results and give managerial recommendations. Our experiments show, for example, that in a setting of low capacity utilization, aggregate plans are rather unprofitable. Moreover, we observe that APP using time series forecasting techniques seems to be a good strategy if demand is highly volatile or resources are scarce.

Using inter-enterprise architecture as an instrument for decision-making under the arrival of unexpected events in hierarchical production planning

The use of inter-enterprise architecture (IEA) facilitates the generation of collaborative networks (CNs) since its tools guide enterprises step by step in the implementation of collaborative processes, in both strategic and tactical levels. At the strategic level, companies that make up CN begin the process of defining the collaboration domain, teams, people, objectives, processes, among others, to achieve common goals. At the tactical level, in a specific context of hierarchical production planning (HPP), companies could find advantage in using of decision-support systems (DSSs) that allow the management of unexpected events that affect production planning. This paper describes the main elements of the IEA proposed: framework, methodology and modelling language. To validate the correct definition of the different elements of our proposal and their relation with one another, we proposed a relationship meta-model.

MECHANISMS FOR COORDINATION OF MASTER PLANNING AND LOT SIZING WITHIN A HIERARCHICAL PRODUCTION PLANNING MODEL

This paper presents a simulation based analysis of different coordination mechanisms in Hierarchical Production Planning. According to the results of the simulations the effects of Aggregation and Capacity Assignments on the overall costs are discussed.

Simulation-based optimization methods for setting production planning parameters

This paper refers to a hierarchical production planning system in a make-to-order environment. A challenging task in this context is to determine good production parameter settings in order to benefit from established planning methods. We present a framework for hierarchical production planning which we use to identify good settings for three planning parameters, namely planned leadtimes, safety stock, and lotsizes. Within a discrete-event simulation which mimics the production system we use a mathematical optimization model for replicating the decision problem. This mathematical model is solved to optimality using a standard optimization engine. We use data referring to four different demand market situations in order to derive general statements concerning the quality and sensitivity of the three analyzed planning parameters.For exploring the parameter space we follow the concept of simulation-based optimization. We compare the performance of six different optimization methods to a kind of systematic enumeration of parameter combinations. We show that among these a search procedure based on the idea of Variable Neighborhood Search (VNS) leads to the best results in this context.

Empirical validation of meta-models of work centres in order release planning

We consider the problem of planning future order releases in hierarchical production planning and control systems. An established research direction is the clearing function concept: The planned material flow through a production unit is modeled by inventory balance equations for WIP and final products, and the consequences of the stochastic properties of the material flow are modeled by clearing functions, which represent the functional relationship between the level of WIP and the maximum output of a work centre in a period.Theoretical insights suggest that modeling the output of a work centre in a period as a function of one independent variable is not sufficient for this type of models because of the time-varying transient states. This paper tests one- and two-dimensional clearing functions on simulation and empirical data obtained from a make-to-order production system. The fit of the clearing functions will be made by a regression through the origin and evaluated by the adjusted R2. The fits of the different clearing functions differ depending on the source of data. The possibilities for improvements by adding independent variables depend both on the period length and on the stationarity of the process. The findings lead to suggestions which additional independent variables should be added to a clearing function in order to improve the estimation of a work centre's future output and hence improve order release planning models.

Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system

Nowadays, manufacturing organisations face increasing pressures from the frequent change in product type, continuous demand fluctuation and unexpected change in customer requirements. In order to survive in the turbulent environment, manufacturing organisations must become flexible and responsive to these dynamic changes in the business environment. This paper presents a hierarchical agent bidding mechanism that is particularly designed for Make-to-Order manufacturing system and attempts to enhance the operational flexibility of manufacturing system in dealing with dynamic changes in the business environment. The novelty of this mechanism is that it enables manufacturing resources to be self-organised cost-efficiently within structural constraints of manufacturing system for fulfilling customer orders. However, when orders cannot be fulfilled within the structural constraints of manufacturing systems, the mechanism can enable manufacturing resources to be regrouped flexibly across system boundaries but with minimum disturbances to existing system structure. Based on an example application to a manufacturing company, this paper demonstrates that the operational flexibility provided by this mechanism is able to help manufacturing system to respond demand fluctuation through balancing the capacity across the entire system. Meanwhile, this mechanism potentially enables manufacturing systems to deal with unexpected changes in product type. As long as the manufacturing system has the technicality required by a new product, this mechanism enables resources across the manufacturing system to be cost-efficiently and flexibly self-organised to fulfil the new product.

Novel bi-level hierarchical production planning in hybrid MTS/MTO production contexts

A hybrid make-to-stock (MTS)/make-to-order (MTO) production strategy is one of the most appealing production strategies that has recently been investigated by academics and practitioners. In this paper, a hierarchical production planning (HPP) structure is developed in hybrid MTS/MTO production contexts for the first time. The proposed structure includes mid-term and short-term production planning levels by proposing a systematic and integrated approach towards tactical and operational issues. To cope with the problem, diverse novel modules are developed at each level and then they are interrelated from a hierarchical point of view. Moreover, a hybrid meta-heuristic algorithm is developed to tackle the computational complexity of a scheduling task. Finally, numerical experiments validate the proposed solution methodology.

A hierarchical production planning model for kanban systems

During the past few decades, a substantial amount of research has been undertaken in the area of hierarchical production planning. Hax and Meal [19] first proposed a hierarchical approach to the production planning process, with higher-level decisions imposing constraints on the lower-level decision process. Besides computational advantages, a hierarchical procedure reflected the actual hierarchy of decisions prevalent in organizations. Subsequently, Bitran et al., developed a hierarchical planning procedure for single-stage production systems [10] and for two-stage production systems [11]. A hierarchical planning model for kanban systems is developed in this paper. Solution techniques for the aggregate problem and a disaggregation procedure are described. Application of the model to CON- WIP and base-stock systems is also discussed.

Capacitated production planning problem considering the detailed scheduling constraints in a flow shop environment

In this paper, a multi-product multi-period production system organized as a flow shop is considered. To overcome the shortcomings of hierarchical production planning, detailed production planning scheduling and sequencing constraints are integrated. A new integrated mixed-integer programming (MIP) model, capable of simultaneously performing production planning and nonpermutation scheduling, is proposed for the considered problem. For precise machine workload calculation, detailed scheduling constraints are used to determine a feasible production plan. The objective function includes production, inventory, backlog, set-up costs, and the costs associated with machine idle time and overtime. A hybrid simulated annealing (HSA) algorithm with dispatching rules and an exact solution method is proposed to solve the integrated model. The proposed algorithm searches the solution space for both problems and finds a combination of production planning and scheduling that is feasible and close to optimum. To evaluate the performance of the proposed model and solution method, problems of different scales are studied. The total costs of the proposed solution method are smaller than the results of hierarchical production planning.

An integrated hierarchical production and maintenance‐planning model

PurposeThe purpose of this study is to propose and formulate an integrated hierarchical production, and maintenance‐planning model.Design/methodology/approachThe proposed model is formulated mathematically and tested for some hypothetical cases. A two‐level planning is proposed to address the hierarchical planning problem, i.e. aggregate planning and detailed planning. A preventive maintenance planning is integrated into the aggregate planning, while machine breakdowns, which require corrective maintenance actions, are investigated in the detailed planning. The proposed general preventive maintenance model is tested against cyclical preventive maintenance models for some cases, and for evaluating the performance of the models, in terms of costs, and service levels.FindingsThe proposed general preventive maintenance model gives a better solution in terms of cost than the cyclical maintenance model (i.e. 6 per cent less costly), if the maintenance planning is executed separately from the production planning. In terms of service level, however, both models perform equally well with average service levels equal to 97.6 per cent. The effect of tight capacity, long maintenance duration, and small machine parameters similarly tightens the capacity. In these cases, it is shown that a stable level of capacity is more beneficial to achieve a better service level, which is gained if the preventive maintenance actions are carried out monthly.Practical implicationsAt the aggregate level, the proposed preventive maintenance model considers a non‐cyclical planning, which means that the preventive maintenance periods do not necessarily fall at equally distant times. The inventory movement constraints at the aggregate level decouple machines to operate independently; hence the detailed level problem can be solved separately for each machine. In a rolling horizon approach, only the first period of the aggregate plan is implemented and disaggregated into the production of items at the detailed level.Originality/valueThe paper proposes an integrated model of hierarchical production and maintenance planning. A general preventive maintenance is integrated into the aggregate planning, while machine breakdowns are investigated in the detailed planning. To the best of one's knowledge, such a hierarchical view of production planning and maintenance has not been addressed adequately.