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Nowadays, there has been an increasing attention to enterprise resource planning (ERP) software implementation projects due to the rapid improvements in information technologies. In this context, project managers may face with several uncertainties during the project planning and scheduling phases. In this paper, scheduling of a multi-objective ERP implementation project is targeted under human resource constraints and uncertainty. Through the proposed interval programming based solution approach, uncertainties of the project parameters/inputs as well as the project schedules/outputs which represent the starting or completion times of the activities can be easily handled. Therefore, several alternative project schedules can be generated by making use of the proposed approach according to the risk appetite of the project managers. Additionally, efficiency and reliability factors of the human resources are also taken into account. Finally, computational results of a real-life application have shown that more applicable and reliable compromise solutions can be generated by the proposed approach considering both project makespan and effective utilization of the human resource capacities. [ABSTRACT FROM AUTHOR]

Recently, there has been a growing attention to load planning problems in intermodal logistics networks by global logistics sector. However, most of the studies in the literature were concentrated on single mode problems from the perspectives of logistics companies/production firms. Since increasing importance of intermodal transportation, the problem need to be solved by handling various complex decisions simultaneously (i.e., transportation mode selection, load allocation, outsourcing and sizing of maritime and railway cruises), which makes the problem much more complex. In this study, a mixed-integer programming model is developed for a multi-objective, multi-mode and multi-period load planning problem from the perspective of freight forwarders. In the proposed model, both import/export transport demands of the customers are considered. In addition to minimize overall transport costs, it is also aimed to minimize total transit time and environmental impact resulted from CO2 emissions. In order to handle these conflicting objectives and to provide compromise solutions under uncertainty, a fuzzy goal programming approach is used. According to the computational results, effective/efficient load plans can be generated in terms of economic and environmental issues. [ABSTRACT FROM AUTHOR]

Although many meta-heuristic algorithms were developed for solving combinatorial optimization problems, very few of them were realized in an agent based environment. Especially the algorithms which model dynamics of Artificial Immune Systems (AIS) are population based approaches with adaptability characteristics, therefore AIS can be better realized in an agent based modeling environment. For this purpose first time in the literature a clonal selection algorithm which is an AIS based algorithm is modeled in a multi-agent environment for solving the travelling salesmen problem which is a combinatorial optimization problem. In order to observe the behavior of the algorithm, simulation experiments are carried out on several test problems. Netlogo software is utilized for developing agent based models and simulation tests. Moreover, receptor change process and crossover mechanisms are integrated into the proposed model in order to improve the performance of the classical clonal selection algorithm. It is shown that there is a high potential to obtain good solution by making use of agent oriented approaches which more realistically model the natural phenomenon. [ABSTRACT FROM AUTHOR]