Multi-site aggregate production planning with multiple objectives: A goal programming approach

This paper addresses the problem of aggregate production planning (APP) for a multinational lingerie company in Hong Kong. The multi-site production planning problem considers the production loading plans among manufacturing factories subject to certain restrictions, such as production import/export quotas imposed by regulatory requirements of different nations, the use of manufacturing factories/locations with regard to customers' preferences, as well as production capacity, workforce level, storage space and resource conditions of the factories. In this paper, a multi-objective model is developed to solve the production planning problems, in which the profit is maximized but production penalties resulting from going over/under quotas and the change in workforce level are minimized. To enhance the practical implications of the proposed model, different managerial production loading plans are evaluated according to changes in future policy and situation. Numerical results demonstrate the robustness and effectiveness of the developed model.     

The relative importance of planning and control systems in achieving good delivery performance

Amongst factors such as quick changeovers and workforce flexibility, managers in 533 UK manufacturing plants ranked a responsive planning and control system as the most important facilitator of good delivery performance on products made-to-order or assembled-to-order. The rankings indicate greater importance to companies in the Household and Engineering sectors than in Process and Electronics, where other factors are dominant. These results are combined with data such as customer lead times and item variety, to characterize and explain differences between the plants in these four sectors. Collectively the results indicate considerable differences in the production planning and control tasks. This implies that general statements on the importance of planning and control systems are inadequate. Practitioners need contextual information in order to ascertain when research is applicable to their circumstances.     

Selection and implementation of a PPC system

There are many computer-aided production planning and control (PPC) systems available on the market which can provide a solution to the complex task of production planning and control. However, the question remains, how can a company find an optimal system from the vast amount of available systems? This article proposes that a company, having decided to buy one of the available systems, starts a project for selection and implementation of the PPC system.     

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.     

Simplest strategies method for manufacturing workcell restoration

We consider the production process of a manufacturing workcell. Production items obtained from an outside supplier are not processed adequately as far as their quality is concerned. Production items meeting the required quality depend on the workcell state, which degrades according to the number of produced items. The workcell is completely restored by some restoring operations leading to its as-new condition. The method of deriving the restoration period, which leads to the maximum probability that produced items meet the required quality, is introduced. It is based on the nontraditional approach, i.e. on the simplest strategies method for the formulation of the problem presented here. The implementation of this optimization approach is illustrated with an example.     

Production planning and control tools

There are numerous tools available to be used for production planning and control purposes. The number of tools is ever increasing, and so are the levels of sophistication as well as complexity. For the specific manufacturing firm, the task of selecting the most appropriate set of tools is not trivial. However, in recent years, the understanding of the relationship between tools and manufacturing environments for which they are suitable has increased. The purpose of this paper is to provide an overview of production planning and control tools available today, as well as new trends, issues and ideas.     

Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives

Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are commonly imprecise because information is incomplete or unavailable, and the decision maker (DM) must simultaneously consider conflicting objectives. This study develops an interactive possibilistic linear programming (i-PLP) approach to solve multi-product and multi-time period APP problems with multiple imprecise objectives and cost coefficients by triangular possibility distributions in uncertain environments. The imprecise multi-objective APP model designed here seeks to minimise total production costs and changes in work-force level with reference to imprecise demand, cost coefficients, available resources and capacity. Additionally, the proposed i-PLP approach provides a systematic framework that helps the decision-making process to solve fuzzy multi-objective APP problems, enabling a DM to interactively modify the imprecise data and parameters until a set of satisfactory solutions is derived. An industrial case demonstrates the feasibility of applying the proposed approach to a practical multi-objective APP problem.     

Period batch control: Classic,not outdated

Period Batch Control was developed and first applied during the Second World War. The historic roots are discussed and the principles of this classic production planning approach explained, PBC compared to other production control concepts, and it is demonstrated that it may still be fruitfully applied, especially in combination with cellular manufacturing. It is therefore considered as a classic, but not outdated production planning concept.     

Aggregate production planning with workforce transferring plan for multiple product types

This paper presents the aggregate production planning for multiple product types where the worker resource can be transferred among the production lines. A mathematical model was formulated in spreadsheet format. Then the spreadsheet-solver technique was used as a tool to solve the model. A real situation of a manufacturing company was selected as a case study. The actual data was used to test and validate the proposed model. The optimal aggregate production plan provides the information on managing the available production capacity together with the useful workforce transferring plan. The obtained solutions were compared to those of another approach where the workers cannot be transferred among the production lines. The total cost is significantly reduced when the workers are allowed to transfer among the production lines.     

A framework for production control in health care organizations

The paper presents a hierarchical framework for production control of hospitals which deals with the balance between service and efficiency, at all levels of planning and control. The framework is based on an analysis of the design requirements for hospital production control systems. These design requirements are translated into the control functions at different levels of planning required for hospital production control. The framework consists of five levels of planning and control: patient planning and control, patient group planning and control, resources planning and control, patient volumes planning and control and strategic planning, though this last level does not make part of production control as such. Each of the levels of the framework is further elaborated in terms of the decisions made regarding patient flows and resources, and the co-ordination of the different planning levels. Implications of the framework are discussed by describing some points where current practice deviates from assumptions made in our approach. Recommendations for future research and development of the planning framework are formulated.     

An approach to learning from both good and poor factory performance in a Kanban-based just-in-time production system

In a JIT manufacturing environment it may be desirable to learn from an archived history of data that contains information that reflects less than optimal factory performance. The purpose of this paper is to use rule induction to predict JIT factory performance from past data that reflects both poor (saturated or starved) and good (efficient) factory performance. Inductive learning techniques have previously been applied to JIT production systems (Markham et al. , Computers and Industrial Engineering, 34 , 717-726, 1998; Markham et al. , International Journal of Manufacturing Technology Management, 11 (4), 239-246, 2000), but these techniques were only applied to data sets that reflected a well-performing factory. This paper presents an approach based on inductive learning in a JIT manufacturing environment that (1) accurately classifies and predicts factory performance based on shop factors, and (2) identifies the important relationships between the shop factors that determine factory performance. An example application is presented in which the classification and regression tree (CART) technique is used to predict saturated, starved or efficient factory performance based on dynamic shop floor data. This means that the relationship between the variables that cause poor factory performance can be discovered and measures to assure efficient performance can then be taken.     

Resource-constrained materials requirements planning- MRP rc

Computerized MRP systems used in industrial practice have been criticized by researchers as well as industrial users with respect to their limited ability to generate feasible production schedules. In this paper the lot size planning stage of a typical MRP system is considered. Several modelling alternatives for multi-level lot sizing are discussed with the help of a small example. A resourceconstrained approach to lot sizing- called MRP rc- is presented that cooperates with standard production planning and control systems. In this approach the item-by-item lot size planning is substituted by the heuristic solution of a multi-level multi-item dynamic capacitated lot sizing problem with setup times for general product structures.     

A general approach to disassembly planning and control

Closing product and material cycles have emerged as a paradigm for industry in the 21st century, and are directed towards an economical and responsible use of our limited resources. Disassembly plays a key role in recycling. It enables the recovery of functioning components for re-use of pure materials for re-utilization and the separation of harmful substances. Major challenges are presented by the enormous variety of products to be disassembled, by uncertainty as to quantity and by their unknown properties. An appropriate planning and control of disassembly processes is essential for an economic disassembly. Based on an investigation of the suitability of common production planning and control (PPC) methods for disassembly, a specific disassembly planning and control concept (DPC) is outlined.     

A neural network clustering model for miscellaneous components production planning

The growing importance of providing service to customers, e.g. post-sale assistance, supplying of spare parts, upgrading and integration of new elements in installed systems, enhances the importance of planning and management of upgrading parts in most manufacturing industries. These parts are generally characterized by high technical heterogeneity and have a highly variable and difficult to forecast demand. In some areas (especially the most dynamic, e.g. high-tech products), these kinds of components are quite common, and represent a very strong relation between the manufacturing firm and the market. These parts are generally too many to be efectively supported on a planning database system with individual records and too heterogeneous ( and sometimes with a too high value) to be supported all together in a single record. In this paper, we want to study the application of adaptive techniques for the clustering of these components in classes based on the similarities in their market behaviour in order to build an optimal database for planning production and supplying of these components.     

A multiobjective genetic algorithm for job shop scheduling

In this paper, a Multi Objective Genetic Algorithm (MOGA) is proposed to derive the optimal machine-wise priority dispatching rules ( pdrs ) to resolve the conflict among the contending jobs in the Giffler and Thompson (GT) procedure applied for job shop problems. The performance criterion considered is the weighed sum of the multiple objectives minimization of makespan, minimization of total idle time of machines and minimization of total tardiness. The weights assigned for combining the objectives into a scalar fitness function are not constant. They are specified randomly for each evaluation. This in turn leads to the multidirectional search in the proposed MOGA, which in turn mitigates the solution being entrapped in local minima. The applicability and usefulness of the proposed methodology for the scheduling of job shops is illustrated with 28 benchmark problems available in the open literature.     

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.     

An icon-based data image construction method for production data visualization

Visualization takes advantage of human's rapid and flexible pattern recognition capacity to provide a powerful information-processing environment. It is especially suitable for identifying patterns that are difficult, or impossible, to be defined by a mathematical or computer model. This paper presents an icon-based method for constructing images to visualize the abstract, multidimensional production data stored in tables. The method employs a simple, topology-preserving mapping to convert numbers in a table to icons. This mapping strategy not only makes the meaning of the resulted data image easy to understand, but also makes the maximum number of dimensions and data items that could be displayed be virtually unlimited. Visual patterns in the data image are formed by adjacent icons that have similar grey scales. The area sampling techniques could be applied to reduce the size of the data image while still retaining its visual patterns. The proposed visualization method has been applied to several production planning and control activities, including analysing WIP patterns, production indices, and the machine idle cost performance of production plans. These industrial case studies demonstrate that the ability to quickly and easily examine large amount of manufacturing data through visualization is critical for manufacturing companies to understand their performance, discover possible problems and take necessary actions so that they could remain competitive in the market.     

A new model for aggregate output planning

Aggregate production planning has been aided in the recent past by the advent of mathematical programming models and the improvement in computer solution techniques. Such models have only considered single objectives; this paper formulates a multi-objective production planning model as a goal program and discusses the problems involved in its implementation.     

Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.