An Integrated Multi-project Scheduling, Materials Ordering and Suppliers Selection Problem

Abstract

Conventionally, project managers schedule the activities first; later prepare for materials management, procurement, and supplier selection in project planning. This disjointed process leads in a loss of expected profit for the company owing to a lack of planning coordination. In this paper, a mixed integer programming model is developed for resource constrained multi-project scheduling, materials ordering (MO), and supplier selection (SS) problems to maximize the net profit of the organization. Since the resource constraint multi-project scheduling with MO and SS belongs to the class of problems that are NP-hard. Thus, a genetic algorithm-based memetic algorithm (MA) is proposed to solve the proposed model. The proposed MA utilizes the basic components of genetic algorithm (GA) like selection, crossover, and mutation with a local search. The proposed algorithm is tested on self-generated 16 instances with a range of 2 to 5 projects in a multi-project set with 30 to 90 activities in a project with 6 to 8 suppliers. The results revealed that the suggested MA generates better solutions than the GA by achieving projects higher net present value (NPV). The findings of this paper provide managerial insights for the project managers to secure organizations’ profitability.