Sustainability in project operations such as financial, social and environmental sustainability is one of the most prominent issues of the present times to address. The increased focus on sustainable business operations has changed the viewpoint of researchers and corporate community towards the project management. Today sustainability in business operations along with sustainability of natural and environmental resources are of paramount significance which has further caused a huge impact on conception, planning, scheduling and execution of the project management activities. In this paper, a literature review between 1987 and 2018 on different issues affecting the sustainability in project management is carried out. The present study also identifies and discusses the future possibilities to apply computational procedures in order to estimate and optimize the sustainability issues in the management of projects, for example the computational evolutionary algorithms can be applied to formulate the multi-objective decisionmaking problem after considering critical factors of sustainability in the projects and then yielding optimized solutions for the formulated problem to achieve sustainability in the projects. A new integrated framework with the inclusion of feedback function for assessment of each decision and actions taken towards the sustainability of the projects is also identified and presented.

The automated guided vehicles (AGVs) are extensively applied for material handling operations in the flexible manufacturing system (FMS) facilities. The scheduling decisions for the multi-load AGVs serving in the FMS with minimum travel time, waiting time and time to serve jobs are highly significant from the sustainable profits point of view. The present study proposes a combination of particle swarm optimization (PSO) for global search and memetic algorithm (MA) for local search termed as the modified memetic particle swarm optimization algorithm (MMPSO) for scheduling of multi-load AGVs in FMS. The newly proposed algorithm is applied for the generation of initial feasible solutions for scheduling of multi-load AGVs with minimum travel and minimum waiting time in the FMS. From the computational experiments, it is observed that the proposed MMPSO algorithm performs an effective and efficient exploration and exploitation process and further yields promising results for the multi-load AGVs scheduling problem in the FMS facility.

Automatic guided vehicle system (AGVs) plays a vital role in material handling operations for a flexible manufacturing system (FMS).Optimum AGVs fleet size selection is one of the most significant decisions in effective design and control of automated material handling system. The fleet size estimation and optimization of AGVs requires an in-depth understanding of the various factors that AGVs in the FMS relies on. In this paper, an investigation for fleet size optimization of AGVs in different layouts of FMS by application of the analytical method and grey wolf optimization algorithm (GWO) is carried out. Layout design is one of the significant factors for optimization of AGV's fleet size in any FMS. Results yield from analytical and grey wolf optimization algorithm are compared and validated for the different sizes of FMS layouts by computational experiments.

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This paper compares and evaluates the performance of five different conventional job selection dispatching rules for scheduling of multi-load automatic guided vehicles (AGVs) serving for material handling operations in variable sized flexible manufacturing system (FMS) layout. Four sizes of FMS layout are considered for the performance evaluation of the five types of conventional job selection dispatching rules. The FMS layouts under consideration are served by the two multi-load AGVs. The multi-load AGVs cruises under machine initiated the nearest vehicle (NV) dispatching rule for the material handling activities at all work centers (WCs) for all four sizes of FMS layout. Four sizes of FMS layout produce five different types of parts and consist of three, six, nine and twelve work centers and loading-unloading centers, respectively. In the simulation test, it is found that the identical destination first (IDF) job selection rule having selection criterion based on the destination similarity of two picked up jobs outperforms all other job selection dispatching rules for an overall production rate of the FMS (parts/hr) in all four FMS layouts.