Supporting Operations Management Decisions with LP Parametric Analyses Using AIMMS

Organizations all over the world use Business Analytics (BA) to gain insight in order to drive business strategy and planning. With the increasing amount of available data larger models are created to support decision making, but managers also must deal with the uncertainty of the input parameters. In this perspective Linear Programming (LP) models have two valuable properties: the required computation time allows large models to be solved and further valuable insight can be gained about the problem using sensitivity analysis. There is a wide range of tools available to solve LP problems. Many of these tools use an implementation of the simplex method and provides an optimal solution related sensitivity information. The sensitivity information generated by such solvers are often used by managers in the decision making process. There are situations when managers may have a hard time taking decision based on the information provided by most of the commercially available LP solvers. If the optimal solution of the primal problem (dual degeneracy) or the dual problem (primal degeneracy) is not unique, the resulting sensitivity information can be misleading for managers. In other cases, the resulted ranges may be too tight for decision support, thus information about a wider range is required. In this paper parametric analysis information is recommended to complete the traditional LP results in order to increase the insight of operations managers when using LP models for operation improvement

An analysis of task assignment and cycle times when robots are added to human-operated assembly lines, using mathematical programming models

Abstract Adding robots to a human-operated assembly line influences both the short- and long-term operation of the line. However, the effects of robots on assembly line capacity and on cycle time can only be studied if appropriate task assignment models are available. This paper shows how traditional assembly line balancing models can be changed in order to determine the optimal number of workstations and cycle time when robots with different technological capabilities are able to perform a predetermined set of tasks. The mathematical programming models for the following three cases are presented and analysed: i) only workers are assigned to the workstations; ii) either a worker or a robot is assigned to a workstation; iii) a robot and a worker are also assigned to specific workstations. The data of an assembly line producing power inverters is used to illustrate the proposed calculations. Both the assignment of tasks and the changes of cycle time are analysed within the AIMMS modelling environment. The computational characteristics of the proposed mathematical programming models are also examined and tested using benchmark problems. The models presented in this paper can assist operations management in making decisions relating to assembly line configuration