Evaluation of safety stock methods in multilevel material requirements planning (MRP) systems

This paper evaluates alternative methods of establishing the safety stock level taking into consideration of historical measures of forecasting accuracy and the needs for master production scheduling and material requirement planning under a rolling time horizon. A computer model is used to simulate the forecasting, master production scheduling and material planning activities in a company that produces to stock and the production activities are managed by multilevel MRP systems. The simulation output is analysed to evaluate the impact of safety stock methods on MRP system performance. The result of the study shows that using safety stock can help to reduce total cost, schedule instability and improve service level in the MRP systems. Guidelines are developed to help managers select methods to determine safety stock in MRP system operations.     

Experimental earliness/tardiness production planning with the due window system

In order to use the philosophy of JIT to improve the production planning method of MRP-II, we propose the experimental software system of the earliness/tardiness produc tion planning problem with due window. By means of the approaches and model reported in this paper, the optimal production planning can be achieved. The recommended model extends the problem of due window from the shop scheduling level into the aggregated planning level of mass manufacturing systems. Simulation results have demonstrated that the experimental software is a useful tool for the production management of repetitive manufacturing enterprises.     

A production planning system based on linear programming

This paper describes the production planning system developed for the chemicals division of the Boliden Company Ltd., Sweden. The major elements of the production planning system are a linear programming model, a cost model, a scheduling program and a program to forecast the economic outcome. A special organization was established to implement the plans.     

A multistage approach for production and workforce planning in long-cycle product environments

Aggregate production planning (APP) has been studied extensively for the past two decades. The APP problem, also called production and workforce scheduling, is to determine the optimal workforce and production level in each period of the planning horizon in order to satisfy demand forecasts for these periods. The advantages of the APP are low cost of data collection and computational cost of the running model; the accuracy of data; and, effective managerial understanding of the results. If the product of concern takes longer than one period, it is called a long-cycle product. Examples of long-cycle products are aircraft, ships, buildings and special machines. A detailed model incorporating dynamic productivity and long-cycle products considerations is presented to solve the problem of production and workforce planning. Using a multistage production system approach, a search technique is developed to solve this class of problems where the objective function is linear and some of the constraint coefficients are dynamically nonlinear. The model provides a better solution than an aggregate production planning model, often used to solve these problems.     

Implementation of holonic scheduling and control in flow-line manufacturing systems: die-casting case study

The success of a flow-line manufacturing system depends on effective production scheduling and control. However, it has been found that current flow-line manufacturing scheduling and control algorithms lack the flexibility to handle interruptions or resource breakdowns; hence, system performance drops automatically and rapidly when interruptions occur. The objective of this research is to investigate if the performance of a flow-line manufacturing system can be improved by integrating agent-based, holonic scheduling and production control. A holonic manufacturing scheduling model has been developed and implemented into a die-casting manufacturing flow line throughout a simulation model. The analysis takes into account the comparisons of overall performances of the system models with the holonic scheduling and conventional scheduling approaches. Simulation results indicate that the holonic manufacturing scheduling and control can significantly increase the uptime efficiency and the production rate of the flow-line manufacturing system.     

PRODUCTION SCHEDULING OF CONTINUOUS FLOW LINES: MULTIPLE PRODUCTS WITH SETUP TIMES AND COSTS

The problem of production planning and setup scheduling of multiple products on a single facility is studied in this paper. The facility can only produce one product at a time. A setup is required when the production switches from one type of product to another. Both setup times and setup costs are considered. The objective is to determine the setup schedule and production rate for each product that minimize the average total costs, which include the inventory, backlog, and setup costs. Under the assumption of a constant production rate, we obtain the optimal cyclic rotation schedule for the multiple products system. Besides the decision variables studied in the classical economic lot scheduling problem (ELSP), the production rate is also a decision variable in our model. We prove that our solutions improve the results of the classical ELSP.     

Experimental push/pull production planning and control system

We introduce an experimental push/ pull production planning and control software system which is designed as an alternative to a MRP-II system for mass manufacturing enterprises in China. It has the following distinguishing features: (1) putting the philosophy of JIT into the master production scheduling of MRP-II via the earliness/ tardiness production planning method; (2) controlling material input by push and processing/ assembly by pull; and (3) adjusting the parameters of the production line by the‘ suggestion for improvement of production line’ module. Simulation results have shown that the proposed system can achieve better planning and control performance than existing systems.     

The Integration of Cost and Capacity Considerations in Material Requirements Planning Systems

Within the sequential framework of material requirements planning (MRP), a master production schedule (MPS) of end-item production is prepared and a bill-of-material processor is used to convert the MPS into a plan for needed subassemblies, parts, and materials. This study examines the impact of different procedures for considering inventory-related costs and capacity limitations in the two phases of planning: master production scheduling and bill-of-material (BOM) processing. A total of nine procedures are considered for integrating the two phases of planning. The results indicate that the integrated procedures have a significant effect on the trade-offs among inventory-related costs, work load variations, over/under time costs, and excess work loads. Further, the results suggest that the method used to develop the MPS has the primary influence on these trade-offs, but the method used by the BOM processor can sometimes have a moderating effect.     

Simple systems for computer-aided production management

Future production concepts where workshop employees responsibly carry through planning and scheduling tasks call for new principles ofcomputer-aided production management (CAPM). With the aid of a critical analysis ofdifferent planning and scheduling mechanisms it will first be laid down why the field of computer-aided production management is suited for a dialogue-oriented planning support based on simple models rather than for the objective of automation with the aid of refined optimization algorithms. Consequently, an alternative design is proposed. This is a prototypically realized concept aiming at computer-aided support of production management of production teams.     

A production management information model for discrete manufacturing

The Manufacturing Systems Integration (MSI) project at the National Institute of Standards and Technology is developing a system architecture that incorporates an integrated production planning and control environment. The development of this architecture includes the definition of information models describing the information which needs to be shared among production management systems (production planning, scheduling and control systems) in order to achieve the integration of manufacturing systems. This paper presents the production management information model within the MSI project. The main focus of the model is to identify and characterize the relationships between orders and workpieces, to identify the information necessary to achieve workpiece tracking and to identify the information necessary to achieve resource requirements specifications for process plans.     

An intelligent workstation controller for integrated planning and scheduling of FMS cell

This paper presents an experimental design developed to determine a combination of robust planning and scheduling rules for an intelligent workstation controller (IWC). The IWC is used as part of the control system for an automated flexible manufacturing system. A three-level hierarchical control structure (shop, workstation and equipment)is adopted in order effectively to control a shop-floor. At the top level is a shop controller which receives orders and their associated manufacturing information, and manages interactions among workstations. The IWC defines and resolves the production control activities necessary to coordinate a group of equipment controllers so as to ensure the completion of orders. Specifically, the IWC is responsible for selecting a specific process routeing for each part, allocating resources, scheduling and coordinating the activities across the equipment, monitoring the progress of activities, detecting and recovering from errors, and preparing reports. These activities are accomplished using planning, scheduling, and execution functions. In order to demonstrate the effectiveness and robustness of the IWC, all the controllable and uncontrollable factors need to be identified and detailed. Controllable factors are those which are set by the controller and cannot be directly affected by the production environment. Uncontrollable factors are those which vary with the production environment and cannot be directly changed by the controller developer. The objective of the paper is to illustrate how a good set of controllable factors that dictate the IWC's effectiveness and robustness over various uncontrollable factors can be determined. Due to the number of possible combinations of all the factors, a complete validation of the effectiveness and robustness of the IWC is extremely time consuming and far beyond the scope of this paper. Therefore, a few planning and scheduling strategies are selected and a formal experiment is conducted. The experiment illustrates how significant performance dependencies for various planning and scheduling strategies can be identified.     

REQUIREMENTS PLANNING FOR CONTROL OF INFORMATION RESOURCES

Producing reports assembled from files, records, and raw data is a major function of the computer in modern organizations. Transaction processing, records management, file organization, and data-base management are aspects of the information production function that have received a good deal of attention. But planning, scheduling, and controlling the production of information products have been neglected. For complex applications involving assembly of reports from multilevel information sources, the requirements planning model is suggested as an effective alternative to present methods. Thus, our suggestion is that the computer be used to plan, schedule, and control computer production of information products. Presently available material requirements planning software may be employed with minor modifications that depend on the given information system characteristics. In this paper an example of requirements planning as applied to production of information in a satellite control system is presented.     

Interfacing aggregate plans and master production schedules via a rolling horizon feedback procedure

Aggregate planning and master scheduling are two important levels of a hierarchical production planning process. They serve as the front end of production and operations planning and control systems. It is imperative to have the front end well planned and coordinated. In this study, we demonstrate that a rolling horizon feedback procedure can be an effective coordination mechanism for interfacing aggregate planning and master scheduling. Each time the schedule is rolled ahead, the feedback of performance results from master scheduling provides the aggregate planning subsystem useful information for improving its planning activities. Our experiments show that there are at least three factors that make a rolling horizon procedure attractive: (i) the use of a horizon with a fractional portion of a seasonal cycle, (ii) high smoothing costs, and (iii) high setup costs. Using rolling horizons reduces the total aggregate costs in these situations relative to not using rolling horizons. The potential impacts of the rolling-horizon strategy are greater under some circumstances than others.     

Alternate Routing Strategies in Batch Manufacturing: An Evaluation

Batch manufacturing firms are experiencing significant changes because of technological developments in work center design, such as flexible manufacturing systems (FMS) and planning/control tools like computer-aided process planning (CAPP). These new developments provide production managers with some solutions to a number of complex problems. For example, numerical-controlled (NC) machine center installations are effective in providing quality parts because of tight tolerance specifications built into the equipment. However, these highly efficient centers create bottlenecks that constrain shop throughput, since production planners tend to rely too much on them. To help improve manufacturing planning, we introduce an important element to the batch production scheduling component of CAPP's mission—evaluating possible alternate routes. Production scheduling encompasses job route selection as well as machine center assignment (loading), job releasing, and setting due dates. In this paper, three routing strategies requiring different levels of shop floor information are tested and evaluated using computer simulation. Shop performance is measured by total cost and traditional measures of job flow time, lateness, and tardiness.     

Solving the part families problem in discrete-parts manufacture by simulated annealing

In the production planning and control of discrete-parts manufacture, aggregation of parts into families, on the basis of similarity, is carried out to ease both long-horizon planning and short-horizon scheduling. Additional advantages are related to those of group technology (GT), such as simplifying the flow of parts and tools and reducing both set-up and production costs. The problem of formally forming part families is presented and discussed. Previous work is reviewed and assessed. Two solution approaches, one based on a location model, the other on simulated annealing, are presented and compared along with test results. The location formulation, which results in an integer programming model solved by Lagrangian relaxation, proved capable of producing solutions of excellent quality, but only for relatively small problem instances. In contrast, simulated annealing, which is a general heuristic approach to combinatorial optimization, produced solutions of comparable quality and could handle realistically large problem instances. However, careful design of the simulated annealing algorithm is crucially important. An effective design is presented.     

Integrating MRP and finite capacity planning

Traditional production control systems based on the manufacturing resource planning concept do not sufficiently support the planner in solving capacity problems, ignore capacity constraints and assume that lead times are fixed. This leads to problems on the shop floor, that cannot be resolved in the short term. This paper focuses on solving these capacity problems by improving capacity planning at the material requirements planning MRP level through integration of MRP and finite capacity planning. This results in a planning method for simultaneous capacity and material planning. The planning method is based on a new and more accurate primary process model, giving the planning algorithm more flexibility in solving capacity problems. The algorithm is based on advanced scheduling techniques and uses aggregated information, thus combining speed and accuracy. The algorithm is designed to use the available flexibility: alternative routeings, safety stock, and replanning of production orders and requirements. This paper also discusses such related issues as robustness, memory and the role of the human planner.     

Production planning of mixed-model assembly lines: a heuristic mixed integer programming based approach

Process technology capabilities are becoming increasingly important as flexible manufacturing continues to be more prevalent, and as competition compels companies to provide expanded variety, at ever lower cost, so introducing plant and processes technological constraints. Model flexibility can also benefit from an appropriate production planning process, especially concerning mixed-model assembly lines, since it can facilitate master scheduling and line balancing activities, which are essential aspects of flexibility. Robust and practical planning approaches have to take into account two different aspects: the first consists in ensuring that the elaborated aggregate plan can be disaggregated into at least one detailed feasible plan for the realised demand, whereas the second in ensuring that this detailed plan is feasible at the operational level. This article faces the model flexibility challenge, reviewing and discussing the planning problem of a real world assembly manufacturing system, producing high volume and a variety of agricultural tractors and machines, analysing and resolving some important issues related to technological, organisational and managerial constraints. This article illustrates the implementation of an Advanced Planning System integrated with a mixed integer-programming model, which is solved by a new iterative heuristic approach capable of achieving interesting planning improvements for model-flexibility management.     

Scheduling with flexible process plans

When a process planning module is embedded into the scheduling paradigm, the solution space of the scheduling problem is expanded due to possible generation and use of alternative process plans, i.e. the combined system of process planning and scheduling is equivalent to scheduling with flexible process plans. This flexibility provides a potential for obtaining improved schedules, but it increases the complexity of the structure of the scheduling problem. This paper describes a solution approach for the integrated system and analyses the performance of a new algorithm under various simulated scenarios.     

Genetic algorithm to production planning and scheduling problems for manufacturing systems

Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.     

Tactical production and distribution planning with dependency issues on the production process

Tactical production-distribution planning models have attracted a great deal of attention in the past decades. In these models, production and distribution decisions are considered simultaneously such that the combined plans are more advantageous than the plans resolved in a hierarchical planning process. We consider a two-stage production process, where in the first stage raw materials are transformed into continuous resources that feed the discrete production of end products in the second stage. Moreover, the setup times and costs of resources depend on the sequence in which they are processed in the first stage. The minimum scheduling unit is the product family which consists of products sharing common resources and manufacturing processes. Based on different mathematical modelling approaches to the production in the first stage, we develop a sequence-oriented formulation and a product-oriented formulation, and propose decomposition-based heuristics to solve this problem efficiently. By considering these dependencies arising in practical production processes, our model can be applied to various industrial cases, such as the beverage industry or the steel industry. Computation tests on instances from an industrial application are provided at the end of the paper.