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.     

A robust optimization model for stochastic aggregate production planning

The aggregate production planning (APP) problem considers the medium-term production loading plans subject to certain restrictions such as production capacity and workforce level. It is not uncommon for management to often encounter uncertainty and noisy data, in which the variables or parameters are stochastic. In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized. By adjusting penalty parameters, decision-makers can determine an optimal medium-term production strategy including production loading plan and workforce level while considering different economic growth scenarios. Numerical results demonstrate the robustness and effectiveness of the proposed model. The proposed model is realistic for dealing with uncertain economic conditions. The analysis of the tradeoff between solution robustness and model robustness is also presented.     

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.     

Computational complexity of algorithms for MRP and JIT production planning problems in enterprise resource planning systems

An enterprise resource planning system (ERP) is the information backbone of many manufacturing companies. At the core of ERP is a conventional material requirements planning (MRP) production planning system or a variation of MRP when just-in-time (JIT) principles are used in manufacturing. MRP and JIT both organize production planning into a hierarchy of long-, medium- and short-range problems. In all there are eight different problems. Some are common to MRP and JIT, others are specialized for a particular system. This paper analyses the computational requirements of these problems. This is important for ERP because it plans for large numbers of products (e.g. 50 000 products at 3M Company and 44 000 products at States Industries in Oregon) at large numbers of locations (e.g. 82 locations in 21 countries for Visteon Automotive Systems of Michigan and 19 locations at Boeing). We show that adequate algorithms exist for some problems, but better algorithms are needed for other problems if ERP is to provide useful production plans.     

Integration of process planning and production planning and control in cellular manufacturing

Organisations willing to succeed in global competition have to integrate their internal and external processes. This especially includes planning and production control (PPC) processes. Optimised allocation of the production resources and quick response to demand changes result in lower cost and improvement of production performance. Practitioners and researchers have been trying to achieve these goals using production planning techniques. Although the results are significant, it seems necessary to integrate production operations in order to improve the production performance. The goals, information and decisions taken in production planning and control and process planning are often very different and difficult to integrate in Cellular Manufacturing (CM) environments. Designing an efficient PPC system and integrating it with process planning in a cellular environment is of the same importance. The following paper proposes first a comprehensive framework of integrated process planning and production planning and control in CM. Then, with respect to this framework and utilising the domain knowledge in the area of CM systems, an integrated model based on Integrated Definition Modeling Language is developed. The application of the models has been considered as a case study for a production system in electronics and telecommunication sector in a plant in Iran. The validity and completeness of the proposed model is tested by a panel of experts in the areas of production planning and control in CM environments.     

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.     

Aggregate production planning using spreadsheet software

Firms can benefit by developing and using aggregate production plans. However, reports of ongoing production planning applications are rare. The complexity of production planning models has been cited as one of the reasons that firms do not develop formal production plans. This study was conducted to determine if a simple model, trial-and-error on a spreadsheet, could be used to produce cost-effective production planning solutions. Results indicate that good solutions can be obtained using this method. Furthermore, solutions tend to improve as the knowledge of the production planner increases and as time spent developing solutions increases. In addition, experience at producing spreadsheet-based solutions can translate into lower cost solutions for more complex problems while using less time for analysis. These findings indicate that spreadsheets can be effective decision aids for developing production planning solutions. By coupling the power and simplicity of a spreadsheet modelling package with the trial-and-error approach to production planning, model complexity should no longer be cited as a reason for not developing and using formal aggregate production 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.     

An aggregate production planning framework for the evaluation of volume flexibility

Flexibility in manufacturing has been identified as one of the key factors to success in the marketplace. Many types of flexibility have been identified in the literature among which volume flexibility is one of the most important. Volume flexibility of a manufacturing system is defined as its ability to be operated profitably at different overall output levels. Volume flexibility permits a manufacturing system to adjust production upwards or downwards within wide limits. In this paper, we develop an aggregate production planning model for volumeflexible production systems. The model can be used with a Monte Carlo simulation to evaluate the optimal level of investment in volume flexibility for a firm operating under a given set of market conditions. In addition, the model can be used to develop some conclusions about the relationship between the value of volume flexibility and the cost of holding inventory, the cost of shortage, forecast accuracy, and the length of the planning horizon.     

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.     

Decision support for multi-family aggregate production planning

Aggregate production planning decisions are inter mediate range decisions that can have a significant impact on both productivity and profitability. In this paper, we examine an interactive computer-based method that provides decision support for the aggregate planner. The proposed approach combines the judgement of the planner with the optimization of subproblems to arrive at an effective solution for multi-family aggregate production planning problems. In the interactive approach, the planner exercises direct control over sensitive workforce levels and production capacities. A network flow sub-problem solver is used to generate optimal production plans and inventory levels given the user-specified production capacities. Decision aids are provided to help the planner achieve a cost-effective solution that is consistent with judgement concerning workforce levels. Computational testing on five test problems indicates that very cost-effective solutions can be obtained. The results of applying the interactive method to a real-world problem are also reported.     

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.     

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.     

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.     

A finite capacity material requirements planning system

In a discrete parts manufacturing environment, material requirement planning (MRP) is carried out without considering the manufacturing resource capacity. As a result, during implementation, adjustments in planned orders may become necessary. This paper presents a finite capacity material requirements planning algorithm (FCMRP) to obtain capacity-based production plans. These plans need no costly adjustments to satisfy the capacity constraints. Performance of the FCMRP system, when studied through a set of test examples, has been found to be superior to the existing MRP system.     

考虑故障停机的生产控制与维修计划联合决策模型

通过生产控制与维修计划协同决策,降低生产成本。首先描述生产过程,分析各项费用。其次,建立了考虑生产过程失控、故障率和故障停时间的生产过程控制、生产计划优化和维修管理联合优化决策的模型。通过模型求解,联合制定出生产过程检查策略、生产计划（经济生产批量、生产批次）以及维修计划（PM间隔期）,实现单位时间内总费用最低的目标。再次,案例研究,分析生产过程失控、故障率和故障停机时间对于经济生产批量、生产过程检查策略和生产系统维修计划的影响。该模型从理论上解决了生产过程控制、生产计划优化和维修管理联合优化决策难题,对于指导企业制定生产计划和生产系统的检修计划,进而提高产品质量、降低生产成本、确保准时交货都具有指导意义和实用价值。     

Rough-cut capacity planning for remanufacturing firms

Capacity planning is a critical element of any successful production planning and control system. A method of rough-cut capacity planning is developed, based on the bill-of-resources approach, that can be used to plan for capacity required for firms in a remanufacturing including overhaul repair operations environment. The modified bill-of-resources approach developed takes into account two major stochastic elements inherent in this environment; probabilistic material replacement factors and probabilistic routing files. A detailed example from an actual repair overhaul operation is presented to illustrate the technique.     

Business planning in successful small firms

Strategic planning has been widely publicized during the last decade, but what has been accomplished by this management activity in smaller, high-growth firms? Business plans have been widely recommended for start-up funding, but we have noted additional uses for business plans in pratice. This article reports methods and results of strategic planning and written business plans in high-growth ‘INC. 500’ firms. In spite of a variety of obstacles, over half of the firms conduct strategic planning on a regular basis. Results of this study show that fast-growth firms develop written business plans as a product of strategic planning, mainly by five key management personnel. Written business plans are used more for internal management purposes than for start-up funding. Finally, the completion of strategic planning is positively associated with a firm's profitability. Based on survey and interview data, this article describes a four-phase model to help ensure effective integration of strategic planning processes and the resulting written business plans.     

DEA-based production planning

Production in large organizations with a centralized decision-making environment like supermarket chains or factories with many workshops, usually involves the participation of more than one individual unit, each contributing a part of the total production. This study is motivated by a production-planning problem regularly faced by the central decision-making unit to arrange new input and output plans for all individual units in the next production season when demand changes can be forecasted. Two planning ideas have been proposed in this paper. One is optimizing the average or overall production performance of the entire organization, measured by the CCR efficiency of the average input and output levels of all units. The other is simultaneously maximizing total outputs produced and minimizing total inputs consumed by all units. According to these two ideas, we develop two DEA-based production planning approaches to find the most preferred production plans. All these individual units, considered as decision-making units (DMUs), are supposed to be able to modify their input usages and output productions. A simple numerical example and a real world data set are used to illustrate these approaches.     

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.