Aggregate planning today

Work Study 04/1995; 44(3):4-7. DOI: 10.1108/00438029510085339

ABSTRACT A firm must plan its manufacturing activities at a variety of levels
and operate these as a system. Aggregate planning is medium-range
capacity planning which typically covers a time horizon of anywhere from
three to 18 months. The goal of aggregate planning is to achieve a
production plan which will effectively utilize the organization's
resources to satisfy expected demand. Planners must make decisions on
output rates, employment levels and changes, inventory levels and
changes, back orders, and subcontracting. Aggregate planning determines
not only the output levels planned but also the appropriate resource
input mix to be used.

43 Reads
  • Source
    • "In 1992 Nam and Ogendar [6] conducted a survey of APP techniques and categorized them into these classes: trial and error method, graphical technique, mathematical technique, linear decision rule, search decision rule, management coefficients method, parametric production planning, production switching heuristic, linear programming, goal programming, mixed integer programming, transportation method and simulation model. Three years later, Pan and Kleiner [7], categorized available models into seven classes including informal approach, mathematical model, linear programming model, linear decision rule [8], [9], heuristic technique, the management coefficients model and search procedure using computer simulation. However, another classification of recently used techniques was given in 2004 by Reay-Chen and Tien-Fu [10]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Aggregate Production Planning (APP) is a medium-term planning which is concerned with the lowest-cost method of production planning to meet customers' requirements and to satisfy fluctuating demand over a planning time horizon. APP problem has been studied widely since it was introduced and formulated in 1950s. However, in several conducted studies in the APP area, most of the researchers have concentrated on some common objectives such as minimization of cost, fluctuation in the number of workers, and inventory level. Specifically, maintaining quality at the desirable level as an objective while minimizing cost has not been considered in previous studies. In this study, an attempt has been made to develop a multi-objective mixed integer linear programming model that serves those companies aiming to incur the minimum level of operational cost while maintaining quality at an acceptable level. In order to obtain the solution to the multi-objective model, the Fuzzy Goal Programming approach and max-min operator of Bellman-Zadeh were applied to the model. At the final step, IBM ILOG CPLEX Optimization Studio software was used to obtain the experimental results based on the data collected from an automotive parts manufacturing company. The results show that incorporating quality in the model imposes some costs, however a trade-off should be done between the cost resulting from producing products with higher quality and the cost that the firm may incur due to customer dissatisfaction and sale losses.
    IOP Conference Series Materials Science and Engineering 06/2013; 46(1):2015-. DOI:10.1088/1757-899X/46/1/012015
  • Source
    • "Pega et al. [15] developed an integrated approach to address the aggregate planning problem and applied it to a firm, which yielded significant savings in the operational costs of the firm. In addition, excellent surveys of APP models can be found in Nam and Logendran [16], and Pan and Kleiner [17]. The approach presented in this study has to do with multiple criteria mathematical programming, which so far appears to have received less than sufficient research attention. "
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we present an aggregate production planning (APP) model applied to a Portuguese firm that produces construction materials. A multiple criteria mixed integer linear programming (MCMILP) model is developed with the following performance criteria: (1) maximize profit, (2) minimize late orders, and (3) minimize work force level changes. It includes certain operational features such as partial inflexibility of the work force, legal restrictions on workload, work force size (workers to be hired and downsized), workers in training, and production and inventory capacity. The purpose is to determine the number of workers for each worker type, the number of overtime hours, the inventory level for each product category, and the level of subcontracting in order to meet the forecasted demand for a planning period of 12 months. Additionally, a decision support system (DSS) based on the MCMILP model is proposed. It will help practitioners find the “best” solution for an APP problem without having to familiarize themselves with the mathematical complexities associated with the model. An example to illustrate the use of the DSS is also included.
    Omega 04/2006; 34(2-34):167-177. DOI:10.1016/ · 4.38 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper introduces an original planning model which integrates production, human resources and cash management decisions, taking into account the consequences that decisions in one area may have on other areas and allowing all these areas to be coordinated. The most relevant characteristics of the planning problem are: (1) production capacity is a non-linear function of the size of the staff; (2) firing costs may depend on the worker who is fired; (3) working time is managed under a working time account (WTA) scheme, so positive balances must be paid to workers who leave the company; (4) there is a learning period for hired workers; and (5) cash management is included. A mixed integer linear program is designed to solve the problem. Despite the size and complexity of the model, it can be solved in a reasonable time. A numerical example is included to illustrate its performance. Preprint
Show more


43 Reads
Available from