Resource-allocation capabilities of commercial project management software. An experimental analysis

When project managers determine schedules for resource-constrained projects, they commonly use commercial project management software packages. Which resource-allocation methods are implemented in these packages is proprietary information. The resource-allocation problem is in general computationally difficult to solve to optimality. Hence, the question arises if and how various project management software packages differ in quality with respect to their resource-allocation capabilities. None of the few existing papers on this subject uses a sizeable data set and recent versions of common software packages. We experimentally analyze the resource-allocation capabilities of Acos Plus.1, AdeptTracker Professional, CS Project Professional, Microsoft Office Project 2007, Primavera P6, Sciforma PS8, and Turbo Project Professional. Our analysis is based on 1560 instances of the precedence- and resource-constrained project scheduling problem RCPSP. The experiment shows that using the resource-allocation feature of these packages may lead to a project duration increase of almost 115% above the best known feasible schedule. The increase gets larger with increasing resource scarcity and with increasing number of activities. We investigate the impact of different complexity scenarios and priority rules on the project duration obtained by the software packages. We provide a decision table to support managers in selecting a software package and a priority rule.

Adapting to climate change through sustainable land management: experiences of a pilot project in Tajikistan

Tajikistan is particularly exposed to the risks of climate change. Its widely degraded landscapes are badly prepared to cope with changes in precipitation patterns, increased temperatures, droughts, and the spread of pests and disease. Sustainable land management (SLM) provides a “basket of opportunities” to address these challenges, particularly for increasing land productivity, improving livelihoods, and protecting ecosystems. Within the Pilot Program for Climate Resilience (PPCR) in Tajikistan 70 SLM technologies and approaches on how to implement SLM were documented with the World Overview of Conservation Approaches and Technologies (WOCAT ) tools in 2011. For this purpose a climate change adaptation module was developed and tested in order to enhance the understanding about climate change resilience of SLM practices and community workshops conducted to on adaptation mechanisms by rural communities in Tajikistan. The analysis came up with four guiding principles for applying SLM for adapting to climate change: 1. Diversification of land use technologies and farm incomes; 2. Intensification of use of natural resources; 3. Expansion of highly productive land use technologies; 4. Protection of land and livelihoods from extreme weather events. Furthermore, SLM must be up-scaled from isolated plots to entire zones or landscapes and the project developed the concept of three concentric villages zones, the in-, near- and off-village zones. Land users, advisors, and decision- and policy makers face the task of finding management practices that best suit site-specific conditions. This task is most efficiently addressed in collaborative effort, and building up and managing a respective knowledge platform.

Resource allocation with project management software

We present results of a benchmark test evaluating the resource allocation capabilities of the project management software packages Acos Plus.1 8.2, CA SuperProject 5.0a, CS Project Professional 3.0, MS Project 2000, and Scitor Project Scheduler 8.0.1. The tests are based on 1560 instances of precedence– and resource–constrained project scheduling problems. For different complexity scenarios, we analyze the deviation of the makespan obtained by the software packages from the best feasible makespan known. Among the tested software packages, Acos Plus.1 and Project Scheduler show the best resource allocation performance. Moreover, our numerical analysis reveals a considerable performance gap between the implemented methods and state–of–the–art project scheduling algorithms, especially for large–sized problems. Thus, there is still a significant potential for improving solutions to resource allocation problems in practice.