Hierarchical Production Planning System Research Articles

Stabilized-cycle strategy for a multi-item, capacitated, hierarchical production planning problem in rolling schedules

Little research has been done on hierarchical production planning systems (HPPS) in the context of rolling schedules with service-level constraints. Here, we adapt the stabilized-cycle strategy, which has initially been created for the master planning level (Meistering and Stadtler in Prod Oper Manag 26:2247–2265, 2017), to a two-level, multi-item, capacitated (short-medium-term) HPPS with demand uncertainty. For each planning level, we present extensions for mixed-integer programming models from literature (CLSP-L, PLSP) and introduce anticipation functions, as well as linking constraints. In a computational study, we analyze the performance of the HPPS with different rolling schedule strategies: the period-based, the order-based, and the stabilized-cycle strategy. It turns out that the stabilized-cycle strategy dominates the period-based strategy for all studied instances. For some instances, the stabilized-cycle strategy even dominates the order-based strategy; while in remaining instances, the stabilized-cycle strategy provides non-dominated solutions with a significant smaller downside deviation from service-level agreements and only a minor increase of costs.

A possibilistic environment based particle swarm optimization for aggregate production planning

Development of an Aggregate Production Plan (APP), the top most level in a hierarchical production planning system, is a difficult task, especially when input and other production planning parameters are uncertain because of their inherent impreciseness. This therefore makes generation of a master production schedule highly complex. Regarding this point, in this paper, we present a scheme of a multi-period and multi-product APP which is formulated as an integer linear programming model. The proposed approach uses a triangular possibility distribution for handling all the imprecise operating costs, demands, and also for the capacity data. The proposed approach uses the strategy of simultaneously minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining lower total costs and minimizing the risk of obtaining higher total costs. A modified variant of a possibilistic environment based particle swarm optimization (PE-PSO) approach is used to solve the APP model. A numerical model for demonstrating the feasibility of the proposed model is also carried out. In the computational study, the considered case study data were experimented with and analyzed to evaluate the performance of the PE-PSO over both a standard genetic algorithm (GA) and a fuzzy based genetic algorithm (FBGA). The experimental results demonstrate that the PE-PSO variant provides better qualities in the aspects of its accuracy when compared to the other two algorithms.

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.

Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach

: Received 5 September 2012 Received in revised format 25 September 2012 Accepted September 27 2012 Available online 27 September 2012 In hierarchical production planning system, Aggregate Production Planning (APP) falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA) approach for solving the multi-product, multi-period aggregate production planning (APP) with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem) and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems.

P.6.a.008 Increased plasma brain-derived neurotrophic factor and nerve growth factor in the patients with alcohol dependence

A prototype system has been developed by integrating two higher-level hierarchical production planning application programs (aggregate production plan (APP), master production schedule (MPS)) using a common data model integration approach into an existing planning system for short-term scheduling and supervisory batch management which was originally proposed and developed by Richard, J. G., Macchietto, S. & Shah, N. (1999). Integrated decision support in flexible multipurpose plants. Computers & Chemical Engineering, 23, S539–S542. The hierarchical production planning system has been modelled and validated around an industrial scenario concerning multi-site, multipurpose batch process operations making household chemicals from start to transportation to regional warehouses. This preliminary work suggests that the idea of integrating software applications through a common data model, which was originally described by Stanley, G. M. (1994). The emerging trend towards knowledge-based frameworks for computer-integrated manufacturing. Advances in Instrumentation & Control, 49, 1121–1133 and further developed by Rickard, J. G., Macchietto, S. & Shah, N. (1999). Integrated decision support in flexible multipurpose plants. Computers & Chemical Engineering, 23, S539–S542 is feasible. However further research work, improvements and validations are required using varieties of industrial batch process operations and distribution problems to prove its viability.

An investigation on integration of aggregate production planning, master production scheduling and short-term production scheudling of batch process operations through a common data model

A prototype system has been developed by integrating two higher-level hierarchical production planning application programs (aggregate production plan (APP), master production schedule (MPS)) using a common data model integration approach into an existing planning system for short-term scheduling and supervisory batch management which was originally proposed and developed by Richard, J. G., Macchietto, S. & Shah, N. (1999). Integrated decision support in flexible multipurpose plants. Computers & Chemical Engineering, 23, S539–S542. The hierarchical production planning system has been modelled and validated around an industrial scenario concerning multi-site, multipurpose batch process operations making household chemicals from start to transportation to regional warehouses. This preliminary work suggests that the idea of integrating software applications through a common data model, which was originally described by Stanley, G. M. (1994). The emerging trend towards knowledge-based frameworks for computer-integrated manufacturing. Advances in Instrumentation & Control, 49, 1121–1133 and further developed by Rickard, J. G., Macchietto, S. & Shah, N. (1999). Integrated decision support in flexible multipurpose plants. Computers & Chemical Engineering, 23, S539–S542 is feasible. However further research work, improvements and validations are required using varieties of industrial batch process operations and distribution problems to prove its viability.

Towards a new planning and scheduling approach for multistage production systems

Some specific industries (electronic equipment, armament, aeronautics) suffer from long and uncertain production lead times. Classical planning and scheduling tools are implemented in these industries but there is a lack of theory about how to coordinate them. In this paper, we study the interaction between planning and scheduling stages in a hierarchical production planning system. We show that significant lead time performances improvements result from a specific combination between MRP, PERT and some dynamic priority rule. These results are based on a simulation model integrating planning strategies as well as multi-level shop floor control.

A modified hierarchical production planning system integrated with MRP: A case study

In this paper, a hierarchical planning system is proposed which integrates aggregate capacity planning with MRP. This system is to be implemented in a metal box manufacturing company which multi-user MRP system covering manufacturing activities as well as procurement sales order processing and accounting systems. The hierarchical planning system includes a medium-range aggregate planning model adapted to the firm's requirements and strategies. The model consists of a mathematical formulation which covers labour capacity has already installed a constraints and includes certain cost estimations in the objective function. The planning horizon of the medium range planning is taken as twelve months in order to cover sales seasonality. The aggregate production quantities resulting from the optimized medium-range planning model are disaggregated according to procedures already found in the literature. Furthermore, the theoretical infeasibilities pertaining to the disaggregation procedures are also resolved in an heuristic manner. Using the latter modified disaggregation procedure, a feasible disaggregated plan is generated for the whole planning horizon. The proposed plan is compared with the current production policy of the firm and it is observed that the proposed plan leads to backorder reduction.

Heuristic family disaggregation techniques for hierarchical production planning systems

Hierarchical production planning (HPP) systems are developed to establish a structured decision-making environment in large production systems which would otherwise become intractable. Essentially, in HPP systems the production policy for the aggregate level is determined and then disaggregated to the next level of product families. However, infeasibility issues always exist when imposing constraints from higher to lower levels. These issues are sometimes the offspring of the aggregate information used at higher levels. On the other hand, in feasibilities pertaining to the disaggregation algorithms also exist. Here, we resolve the algorithmic infeasibilities that exist in the family disaggregation procedure given by Bitranand Hax (1981) (BH). A heuristic modification (MBH) to procedure BH and an alternative filling procedure (FP) which eliminate backorders completely are introduced. The procedures reduce total set up costs. Empirical results demonstrating the performance of the heuristics are reported.

On a two-stage hierarchical production planning system for process industries

The objective of this paper is to propose a relevant production planning procedure for multi-item continuous production by the specified petrochemical plant, which is one of the typical processes of petrochemical companies. The tough problem of such production system management is to determine the product-mix to be produced in the current planning period based on termwise forecasts of demand quantities, constraints of plant capability, etc. Most of the current procedures operated in actual factories are empirical heuristic approach and were relevant under stable demand structure. However, due to their rough structure and recent versatile market needs, inefficient utilisation of plant and growing indirect resources consumption for production activity such as man-power of management become urgent problems. From this point of view, a systematic procedure to eliminate such difficulties is proposed in terms of a two-stage automated hierarchical planning procedure, consisting of both a coordination function between production periods and an efficient job lot sequencing function. Then, the feasibility and relevance of the system are investigated through quantitative simulation experiments for further consideration of actual implementation.

Heuristic procedures for a stochastic lot-sizing problem in make-to-order manufacturing

We consider a single item, uncapacitated stochastic lot-sizing problem motivated by a Dutch make-to-order company producing steel pipes. Since no finished goods inventory is kept, a delivery date is fixed upon arrival of each order. The objective is to determine the optimal size of production lots so that delivery dates are met as closely as possible with a limited number of set-ups. Orders that are not satisfied on time are backordered and a penalty cost is incurred in those cases. We formulate the problem as a Markov Decision Process and determine the optimal production policy by dynamic programming. Since this approach can only be applied to very small examples, attention is given to the development of three simple lot-sizing rules. The first strategy consists of producing the orders for a fixed numberT of periods whenever the demand for the current period reaches a pre-specified limitx. A simple set of tests is proposed leading to cost improvements in situations where the best combination for the decision variablesx andT deviates from the optimal policy. The second lot-sizing rule is based on the well-known Silver-Meal heuristic for the case of deterministic time-varying demand. A fixed cycle production strategy is also derived. Numerical examples taking into account different demand patterns are provided. The analysis of the results suggests that the first heuristic is particularly suitable for the problem under consideration. Finally, the model is incorporated in the operations control level of the hierarchical production planning system of the Dutch company and assists the management in the evaluation of the quality of the aggregate decisions. A consequence of this feedback mechanism is the modification of the aggregate plans.

Customer Enquiry Management: Part of a Hierarchical System to Control Lead Times in Make-to-Order Companies

The authors are currently developing a hierarchical production planning system specifically designed for the ‘make-to-order’ sector of industry. Its aim is to control the delivery and manufacturing lead times of all orders processed by a firm. Two major decision levels are identified - the customer enquiry stage and the order release stage. Input/output control is exercised at both stages. This paper is concerned with the control mechanisms used at the customer enquiry stage. Two important backlogs of work, along with their associated backlog lengths, are identified. The system aims to maintain these backlogs between predetermined minimum and maximum lengths in order to process all orders within an acceptable length of time. It is shown that the two backlogs are linked in a manner that enables them to be controlled simultaneously. Hence, a procedure for dealing with incoming customer orders is presented.

Job Release: Part of a Hierarchical System to Manage Manufacturing Lead Times in Make-to-order Companies

A methodology to manage manufacturing lead times is currently being developed by the authors. The system is specifically designed to address the needs of small- to medium-sized make-to-order companies. It involves a hierarchical production planning system in which integration between the production and marketing functions is facilitated. Considerations of capacity are included at both of the decision levels addressed—the customer enquiry stage and the job release stage. This paper describes the job release stage, showing how it is linked with the higher-level stage by controlling a hierarchy of backlog lengths. At the job release stage the released backlog length for each work centre is maintained between predetermined minimum and maximum levels. It is shown that shop floor throughput times—an important part of manufacturing lead times—can be controlled by controlling released backlog lengths. The releasing mechanism is described and it is argued that there can be many benefits of job release—including reduced shop congestion, lower work-in-progress and lower costs.

Job Release: Part of a Hierarchical System to Manage Manufacturing Lead Times in Make-to-order Companies

A methodology to manage manufacturing lead times is currently being developed by the authors. The system is specifically designed to address the needs of small- to medium-sized make-to-order companies. It involves a hierarchical production planning system in which integration between the production and marketing functions is facilitated. Considerations of capacity are included at both of the decision levels addressed—the customer enquiry stage and the job release stage. This paper describes the job release stage, showing how it is linked with the higher-level stage by controlling a hierarchy of backlog lengths. At the job release stage the released backlog length for each work centre is maintained between predetermined minimum and maximum levels. It is shown that shop floor throughput times—an important part of manufacturing lead times—can be controlled by controlling released backlog lengths. The releasing mechanism is described and it is argued that there can be many benefits of job release—including reduced shop congestion, lower work-in-progress and lower costs.

Hierarchical production planning system for a two-stage process

This paper deals with the problem of designing a hierarchical production planning system, where higher level decisions impose constraints on lower level decisions. This paper supports the choice of a production planning system for a two-stage process. First, analytical models clarify the relationship between production planning rules and the role of buffer inventory level in terms of manufacturing performance. Second, from the results of the analysis, a numerical example is presented on a production planning system designed under various management policies.

A hierarchical production planning system in the motor industry

This paper presents a production planning system with a hierarchical framework, which includes feedback based on human judgement between any two production planning levels. This system was applied to a motor industry with some favorable results, compared with the traditional system.

Consistency analysis in a hierarchical production planning system

This paper deals with the planning process of a manufacturing system. The aggregation of products sharing similar characteristics leads to a hierarchical structure. Special attention is given to the decision consistency in a two level structure. Necessary and sufficient consistency conditions of a disaggregation procedure are presented in the general case. A reduction of these conditions is then carried out for some particular cases. Finally a disaggregation with strong consistency properties is presented.

On the design of the aggregate model in a hierarchical production planning system

When applying an aggregate approach to production planning the system has a natural hierarchical structure. The solution of the aggregate problem is disaggregated on the lower level by changing the objective functions and the constraints in the more detailed short range problems. Often the aggregate model is formulated as a linear program since non-linear costs like set-up costs instead can be recognized on the lower level. The traditional formulation of the aggregate model has several disadvantages, especially in case production and inventory on the lower level are controlled by separate subsystems. Therefore an alternative model with the following properties is formulated: • The production in different machine groups as well as the flow of orders for different product groups are obtained from the model. These results constitute relevant inputs into the lower level production planning on the one hand and inventory control on the other. • The work-in-progress is represented explicitly. • The material requirements planning is part of the model. • If only a small number of seasonal patterns need to be considered the size of our model will be very small compared to the traditional model.