Resource‐constrained project scheduling through the goal programming model: integration of the manager's preferences

Abstract

The aim of this paper is to present a multi‐objective project scheduling approach to help project managers when deciding on a baseline schedule. The concepts of satisfaction functions and goal programming are incorporated to generate this baseline schedule that represents the best compromise among a set of conflicting project objectives. An efficient computerized procedure based on the tabu search algorithm is proposed and enables the handling of large planning and scheduling projects.

Value analysis planning with goal programming

“…Studies that combine goal programming with the critical path method for project planning include Pollack-Johnson and Liberatore (2006) where authors combined project time/cost information in an engineering application on quality management tradeoffs and Kull and Talluri (2008) which examined risk assessment in supply chain supplier selection projects. More recently Gagnon et al (2012) demonstrated how GP could be combined with a critical path method to include manager's preferential input into the decision process. Supporting goal programming's use, Filho and Uzsoy (2014) found in researching value analysis methodologies cite the importance of GP in scheduling production and other engineering applications.…”

Measuring the social responsibility of European companies: a goal programming approach

“…where f i (X ) is usually a linear function of the ith goal and g i is its aspiration level. Some recent practical applications of GP can be found in Gagnon et al (2012), Mezghani et al (2012), García et al (2013), and Munoz et al (2018). Linares and Romero (2002) propose a GP-based methodology that allows the aggregation of individual preferences provided by several social groups toward different criteria in a cardinal manner.…”

A lexicographic approach to the robust resource‐constrained project scheduling problem

“…These authors tested and compared the algorithm performances on instances with up to 30 activities and five resources. Gagnon et al (2012) introduced goal programming and satisfaction functions for MORCPSP in order to help PMs to design a baseline schedule considering project duration, resource allocation, costs as well as PMs' preferences. Recently, Dridi et al (2014) proposed an ant-colony optimization approach combined with a local search to solve the multiobjective assignment and scheduling problem (MMASP).…”

“…Gagnon et al. () introduced goal programming and satisfaction functions for MORCPSP in order to help PMs to design a baseline schedule considering project duration, resource allocation, costs as well as PMs' preferences. Recently, Dridi et al.…”