An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming

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.

Territorial Planning for Coastal Zones in Chile: The Need for Geographical-Environmental and Natural Risk Indicators for Spatial Decision Support Systems

“…For decades, scientists and researchers from a wide variety of disciplines, such as economics, psychology, computer science, etc., have focused their interest on understanding behaviour and the methodological challenges of implementing decision support models [45,46]. In general terms the most widely published models for DSS include hierarchic analytical processes, decision matrices and trees, multi-criterion and multi-objective models, prediction and simulation models, optimization, and many more [47][48][49][50][51]. However, it must always be borne in mind that DSS do not take decisions unaided: they are only a support tool, and the results must therefore never be considered literally, but rather as a reference [52].…”

On operations research and value conflicts

“…The next [16] is another selection problem, this time of a global supplier, and the criteria are: cost, quality, service performance, supplier's profile and risk factor. Paper [17] studies aggregate production planning, and the criteria are: profit, number of late orders, and work force level changes, which are all clear intrinsic stakeholder values. Locating facilities strategically is the topic of [18], and the criteria are: price, quality, delivery, flexibility.…”

“…All the papers mentioned above employ rather sophisticated quantitative methods, and it is fair to say that there is seldom moral conflict among the criteria. But there are examples of stakeholder conflicts that may amount to moral conflicts, such as between customer service and financial stability in [15], and profit and work force level changes in [17]. In all of them, the DM has to provide some kind of opinion or information regarding the importance of the criteria, as they contribute to the wellbeing of the firm, or perhaps as loci of intrinsic value in the case of stakeholders.…”

A Model-Driven Decision Support System for Reallocation of Supply to Orders Under Uncertainty in Ceramic Companies

“…Thus when human intervention is necessary, DSSs can be a solution. One main advantage of DSSs is that the decision maker (DM) does not need to understand the complexities of mathematical modelling (Gomes da Silva et al 2006). For this reason, the above two models were introduced into a DSS that provides the DM with different functionalities in order to deal with the complexity and uncertainty of the system.…”