Application of an MRP matrix-based hierarchical planning model to a furniture company

This paper is concerned with the definition of a feasible master schedule for operations management, obtained through an integrated planning system, using a hierarchical methodology (by means of different disaggregation stages) with an appropriate time horizon for connection with the manufacturing requirements planning unit (MRP II). The hierarchical model obtained considers not only product aggregation into types, families and items but also resources aggregation, structure of products aggregation or bill-of-materials aggregation, as well as temporary aggregation. It allows the creation of a master schedule for an adequate time horizon, that can be used as an     

Intelligent scheduling model and algorithm for manufacturing

This paper puts forward an intelligent scheduling model based on Hopfield neural network and a unified algorithm for manufacturing. The energy computation function and its dynamic state equation are derived and discussed in detail about their coefficients (parameters) and steps (Delta t) in iteration towards convergence. The unified model is focused on the structure of the above function and equation, in which the goal and penalty items must be involved and meet different schedule models. The applications to different schedule mode including jobshop static scheduling, scheduling with due-date constraint or priority constraint, dynamic scheduling, and JIT (just in time) scheduling are discussed, and a series of examples with Gantt charts are illustrated.     

Planning and Scheduling in Supply Chains: An Overview of Issues in Practice

This paper gives an overview of the theory and practice of planning and scheduling in supply chains. It first gives an overview of the various planning and scheduling models that have been studied in the literature, including lot sizing models and machine scheduling models. It subsequently categorizes the various industrial sectors in which planning and scheduling in the supply chains are important; these industries include continuous manufacturing as well as discrete manufacturing. We then describe how planning and scheduling models can be used in the design and the development of decision support systems for planning and scheduling in supply chains and discuss in detail the implementation of such a system at the Carlsberg A/S beerbrewer in Denmark. We conclude with a discussion on the current trends in the design and the implementation of planning and scheduling systems in practice.     

Linking long-term capacity management for manufacturing and service operations

For firms that combine manufacturing and service operations in one system, the task of managing capacity is not straightforward. New goods and services may not have the same set of competitive priorities, and the models and concepts available in the literature for service operations differ from those for manufacturing operations. We address this problem and review the concepts and models for capacity management in the long term in both streams of literature, i.e. manufacturing and services, to develop a unified framework for manufacturing and service operations. The framework creates transparency between new goods manufacturing and service operations, since the same long-term capacity management structure is used for both product types, as well as between capacity strategy and planning strategy, since new goods and services are treated simultaneously. In the framework, the concepts of chase and level strategies are redefined for service operations to allow for integration with manufacturing operations. A case study demonstrates the usefulness of the integrated approach for long-term capacity management.     

Multistage Stochastic Optimization for Production‐Inventory Planning with Intermittent Renewable Energy

A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.     

Development and analysis of mathematical and simulation models of decision-making tools for remanufacturing

This article presents decision-making tools for remanufacturing. The first decision-making tool was used to address inventory lot-sizing problems in a hybrid remanufacturing–manufacturing system with varying remanufacturing fraction. In this article, the new inventory lot-sizing model with variable remanufacturing lot sizes has been shown to exhibit better performance than the benchmark model with fixed remanufacturing lot sizes. The new inventory lot-sizing model is anticipated to become a valuable decision-making tool in companies that are planning to adopt remanufacturing. The second decision-making tool was applied to address a production and inventory planning problem in a remanufacturing system considering different remanufacturing policies for a given remanufacturing strategy. For a remanufacture-to-stock system with two quality remanufacturables groups four alternative policies were examined, a policy which specifies simultaneous processing utilising dedicated resources was shown to be the best policy to achieve a shorter remanufacturing cycle time. For a remanufacture-to-order system with two quality remanufacturables groups, the three relevant policies of the four alternative policies were examined, a policy which specifies sequential processing and switching between various quality remanufacturables groups was shown to be the best policy to achieve a shorter remanufacturing cycle time. The production and inventory planning simulation models in a remanufacturing system are expected to become significant decision-making tools in remanufacturing operations.     

THE USE OF INDUSTRIAL SOFTWARE TO CREATE EXPERIENTIAL LEARNING ACTIVITIES IN OPERATIONS MANAGEMENT COURSES

Computer-based manufacturing planning and control (MPC) systems are widely used in industry to gain competitive advantage through integration and coordination of managerial activities. In collegiate business schools, important operations management activities are taught and studied, often by sequential examination of discrete topics such as aggregate production planning, master production scheduling, capacity planning, material planning, and production activity control. This paper explores the potential use of industrial MPC software in the classroom to create experiential learning activities that address the dynamic and integrative nature of operations management. Experiences with this pedagogical approach over the past decade are reported.     

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.     

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.     

Addressing the Challenge of Modeling for Decision‐Making in Socially Responsible Operations

Companies seek sustainability by combining the quest for profitability with the pursuit of social responsibility. Since socially responsible operations are characterized by the presence of multiple stakeholders with conflicting goals, applying classical optimization models would seem premature; we first need to capture the behavior of the entire system before attempting to optimize sub‐systems to ensure that we focus on the ones driving the behavior of interest. Alternative methodologies are required if we are to gain insight into the most important drivers of socially responsible operations in order to apply traditional operations research (OR)/management science (MS) models correctly. This study presents an umbrella approach which combines different methodologies to tackle the complexity, unfamiliar context, and counter‐intuitive behavior of socially responsible operations at the overall system level.     

A DISCRETE PRODUCTION SWITCHING RULE FOR AGGREGATE PLANNING

Aggregate planning (AP) is a necessary activity for manufacturing and services alike. A shift toward high-volume batch and continuous flow processes within American manufacturing has given rise to increasing numbers of crew-loaded facilities. A majority of AP approaches incorporate continuous decision variables and require frequent adjustments to both production and work-force settings. Despite the availability and diversity of these approaches, few significant applications have been reported. This paper presents the detailed development of a discrete AP switching rule that can be applied to a variety of cost environments. Inventory costs are estimated using an interval approach rather than traditional point estimates. The model allows incorporation of overtime options and is interactive in nature. Decision variables from the model can be disaggregated and linked directly to lower-level planning activities. Actual results of model implementation are reported. An overview of the model's incorporation into the larger context of hierarchical production planning is found in [21].     

Assessment of aircraft logistics planning models

An assessment of military logistics planning models offers a great deal of information about the modelling state of art. Such tools include both analytic and simulation model types. They can deal with both static and dynamic characteristics of the environment. They require highly detailed data for their operation and can compute over a large number of interacting variables. Unfortunately, these models do not satisfy adequately the requirements of the particular logistics issue for which they were assessed, which is whether such models can be used in early logistics planning for new weapon systems. One difficulty is that such planning must make extensive use of tradeoffs and sensitivity analysis to take account of the flexibility and uncertainties existing at the early stages. Another is that the existent models call for detailed data which is usually not available at that time. Therefore, although the models do fulfill a particular useful planning function, they must be replaced or augmented by a new class of models that will much more closely satisfy the planning need. This new capability requires a serious research effort that will benefit not only the military logistics planners, but also other planners dealing with large capital development programs.     

A multicriteria model for supporting setup reduction investment decisions

One critical manufacturing challenge of the 1990s is for firms to effectively apply new operations management techniques while embracing wider philosophies such as total quality management (TQM) and computer integrated manufacturing (CIM), etc. Setup cost and/or time reduction is one such technique capable of producing many benefits for manufacturing firms, including reduced inventory, better equipment utilization, and improved quality. It is thereby viewed as an important component of just-in-time (JIT) manufacturing practice. Existing problems with the setup reduction decision include the many factors that must be considered, as well of an absence of validated and usable models for estimating potential benefits from setup reduction investment made in different contexts. This paper discusses the attainment of gains from setup reduction mainly by improving existing equipment and work practices rather than purchasing new equipment or technology. The model proposed in this paper is based on the application of the analytic hierarchical process (AHP) on seven weighted factors to obtain a preliminary indication as to whether investment in setup reduction is desirable in a given manufacturing context, and the expected benefits of such investment. A flexible scaling system, thus obtained, allows the model to handle a wide range of managerial predispositions to setup reduction.     

A goal programming approach to production planning for flexible manufacturing systems

The increased use of flexible manufacturing systems (FMS) reduces production time variability as the fixed processing time of automated systems replaces the variable processing time of human resources in production systems. This reduction in the variability permits the application of deterministic, constrained optimization models that recognize the conflicting multi-objective nature of resource utilization in an FMS environment. One such multi-objective optimization model is linear goal programming. Unlike most linear programming models, linear goal programming is designed to handle conflicting multi-objective problems. A goal programming model is developed a and presented in this paper as an aid in FMS planning. An application is also presented to illustrate the informational benefits of using the proposed model.     

大量定制环境下基于延迟制造的多级供应控制模型研究

针对大量定制环境下延迟制造活动的特点,提出将客户订单分离点(CODP)前移到供应商流程内部实现供应延迟,给出了基于延迟制造的多级供应控制生产计划模型,研究了面向多级供应控制的物料清单(BOM)和产品设计问题。     

An enhanced loading model for the probabilistic workload control under workload imbalance

Load-oriented manufacturing control (LOMC), a well known probabilistic approach to workload control, is based on limiting and smoothing workload using one static parameter for each workcentre, called load limit (LL). The value of this parameter is set by the shop managers based on the planned lead time at each workcentre. In this paper the use of LL is shown to be inappropriate for the smoothing of workloads when the workload is not sufficiently balanced. We propose to enhance the LOMC model by introducing two sets of parameters:

(i) limiting parameters (LPs), that are statical parameters of the workcentres, set by the shop managers. LPs are used to limit the workload released to the shop;

(ii) smoothing parameters (SPs), that are dynamical parameters of the workcentres, computed as a function of their real workload. SPs are used to smooth the jobs workload over downstream workcentres.

A simulation model was used to compare the enhanced model, based on two parameters sets, with the traditional LOMC model, based on a single parameter set. The simulation runs were earned out with different conditions of due-date assignments, dispatching rules and production mix. The statistical analysis performed on experimental results confirmed that the enhanced model achieves significantly better due dates under unbalanced workload conditions.     

Dynamic Benchmarks for Operations Evaluation Using Soft Computing Methods*

New dynamic benchmarks are developed as performance metrics for operational activities in the ingot mill of an aluminium smelter. This new type of high-level performance measure enables both a systematic and a holistic appraisal of operations performance that is not possible at present. The benchmarks also allow changes in performance of each distinct resource group to be clearly identified. The method for computing such measures is based on a wide range of shop-floor data, and possibility theory is used to model the soft characteristics of much of the input. Fuzzy performance estimates are dynamically updated with special attention to combining the different forms of uncertainty that exist in the input information. Although these procedures are site specific, the methodology is applicable to other types of manufacturing systems.     

Achieving manufacturing excellence through the integration of enterprise systems and simulation

This paper discusses the significance of the enterprise systems and simulation integration in improving shop floor’s short-term production planning capability. The ultimate objectives are to identify the integration protocols, optimisation parameters and critical design artefacts, thereby identifying key ‘ingredients’ that help in setting out a future research agenda in pursuit of optimum decision-making at the shop floor level. While the integration of enterprise systems and simulation gains a widespread agreement within the existing work, the optimality, scalability and flexibility of the schedules remained unanswered. Furthermore, there seems to be no commonality or pattern as to how many core modules are required to enable such a flexible and scalable integration. Nevertheless, the objective of such integration remains clear, i.e. to achieve an optimum total production time, lead time, cycle time, production release rates and cost. The issues presently faced by existing enterprise systems (ES), if properly addressed, can contribute to the achievement of manufacturing excellence and can help identify the building blocks for the software architectural platform enabling the integration.     

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.