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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]

Due to successful applications of revenue management in the airline industry, in recent years, there has been a growing interest to adopt revenue management in make-to-order (MTO) manufacturing systems. Several interrelated decision problems such as order acceptance/rejection, short-term capacity planning, due date assignment, and order scheduling need to be studied simultaneously in order to manage revenues effectively in MTO manufacturing systems. Both the producer's and customer's requirements need to be taken into account through some negotiation mechanisms that are sensitive to the service-level reputation of the manufacturing companies. In this article, we propose a new dynamic bid price–based revenue management model that considers all of the aforementioned decision problems simultaneously. A simulation optimization approach is utilized in order to determine the best possible values of control parameters for bid price, due date assignment, and price increment/reduction mechanisms. The performance of the proposed integrated revenue management model is tested on both a hypothetical example and a real problem of a bridal gown company. The computational results show that the proposed model provides significant improvements in total revenue compared to other static and dynamic bid price policies. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy-stochastic optimization model is developed for an intermodal fleet management system of a large international transportation company. The proposed model integrates various strategic, tactical and operational level decisions simultaneously. Since real-life fleet planning problems may involve different types of uncertainty jointly such as randomness and fuzziness, a hybrid chance-constrained programming and fuzzy interactive resolution-based approach is employed. Therefore, stochastic import/export freight demand and fuzzy transit times, truck/trailer availabilities, the transport capacity of Ro-Ro vessels, bounds on block train services, etc. can also be taken into account concurrently. In addition to minimize overall transportation costs, optimization of total transit times and CO2 emission values are also incorporated in order to provide sustainable fleet plans by maximizing customer satisfaction and environmental considerations. Computational results show that effective and efficient fleet plans can be produced by making use of the proposed optimization model. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing attention to model and solve resource-constrained project scheduling problem (RCPSP) under uncertain environments. In most of the real-life cases, project managers may face with many uncertainties in activity durations, resource availabilities, resource requirements of the activities, the earliest and latest finishing times of the activities etc. In addition to these input parameters, project schedule which represents the starting and/or completion times of the activities should also be considered as uncertain variables in such a fully uncertain environments where all of the project data are imprecise. Based on this motivation, this paper presents an interval programming based transformation approach to overcome fully uncertain nature of the problem. In detail, classical discrete-time binary integer programming model of the deterministic problem was extended by incorporating interval-valued parameters and decision variables. Then, fully uncertain RCPSP was transformed into the crisp equivalent form by making use of interval programming, interval ranking and interval arithmetic operations. In the proposed approach, interval arithmetic operations are performed by using supplementary information obtained from the project manager. Thus, the proposed approach is also able to take into account the project managers’ attitude toward risk and produces more acceptable and risk-free solutions. Finally, a real-life liquefied natural gas (LNG) storage tank construction project in a petroleum refinery is presented for testing its validity and practicality. The computational results have shown that more applicable and information efficient project schedules can be derived via the proposed approach according to the project manager’s attitude toward risk. [ABSTRACT FROM AUTHOR]

Abstract: A considerable part of educational systems tends to be online and computer oriented. However, online examination may create some difficulties during the evaluation of student performance. Process mining which arises as a new concept presents various powerful techniques for processing and analyzing different types of data by making use of some advanced information technologies. This paper proposes a novel approach based on process mining for evaluating the performance of students that should follow certain tasks on the computer. The proposed approach is composed of two main phases which are process mining and similarity analysis. Automatic assessment is performed totally in six steps in order to obtain students’ final grades. In addition, cheat control is possible in the last step thanks to the similarity analysis. A real‐life application in an Enterprise Resource Planning (ERP) course is performed in order to present usefulness, validity and practicality of the proposed approach. Furthermore, to evaluate the performance of the assessment system, we compared the assessment mechanism against instructor. A total of 15 students’ answers belonging to computer‐aided exam are assessed by instructor and the results showed a very good agreement between the automatic assessment system and instructor. [ABSTRACT FROM AUTHOR]

Making accurate accept/reject decisions on dynamically arriving customer requests for different combinations of resources is a challenging task under uncertainty of competitors' pricing strategies. Because customer demand may be affected by a competitor's pricing action, changes in customer interarrival times should also be considered in capacity control procedures. In this article, a simulation model is developed for a bid price–based capacity control problem of an airline network revenue management system by considering the uncertain nature of booking cancellations and competitors' pricing strategy. An improved bid price function is proposed by considering competitors' different pricing scenarios that occur with different probabilities and their effects on the customers' demands. The classical deterministic linear program (DLP) is reformulated to determine the initial base bid prices that are utilized as control parameters in the proposed self-adjusting bid price function. Furthermore, a simulation optimization approach is applied in order to determine the appropriate values of the coefficients in the bid price function. Different evolutionary computation techniques such as differential evolution (DE), particle swarm optimization (PSO), and seeker optimization algorithm (SOA), are utilized to determine these coefficients along with comparisons. The computational experiments show that promising results can be obtained by making use of the proposed metaheuristic-based simulation optimization approach. [ABSTRACT FROM PUBLISHER]

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]

Recently, Reverse Logistics (RL) and product recovery options such as recycling, remanufacturing and reusing have become important issues due to the environmental, economical issues and legal regulations. Due to this fact, companies should take into account the utilized recovery option while preparing their strategic planning activities (like network design) instead of using traditional production planning models. However, since RL network design problems are in the class of NP-hard, solving large scaled problems by exact algorithms is very difficult. Therefore, many meta-heuristics optimization algorithms have been proposed to provide near optimal solutions for supply chain, RL and closed-loop supply chain network design problems in the literature. In this paper, available decoding algorithms for solving generic RL design problems are revised so as to balance the problem without introducing any dummy node on the chromosome. Moreover, the proposed decoding procedure takes into account 'equal transportation cost' situation. Then, a priority-based seeker optimization algorithm (SOA) which utilizes fuzzy reasoning procedure is developed for solution of the problem. In order to test performance of the algorithm, a numerical examined is provided and obtained results are compared with particle swarm optimization (PSO) algorithm which is another swarm intelligence technique. Computational results show that SOA is superior to PSO in terms of both solution quality and computational time for the example RL network design problem. [ABSTRACT FROM AUTHOR]

Nowadays, there has been a growing interest in reverse logistics, recycling, remanufacturing and reusing due to the environmental, economical issues and legal obligations. Due to this fact, companies should take into account the remanufacturing option while preparing the medium term planning (MTP) activities instead of using traditional production planning models. There are lots of studies in the literature related to the reverse logistics (RL) and closed-loop supply chain (CLSC) network design problem which takes place in strategic planning level but a few of them handles the medium-term planning activities. Thus, a fuzzy mixed integer programming model for medium-term planning in a CLSC related to a conceptual product with remanufacturing option is developed in this paper. In the proposed model, both forward and reverse flows are included and two production alternatives are considered: either 'production of new products directly in manufacturing plants' or 'bringing the returned products back to 'as new condition' in the remanufacturing facilities'. However, real world closed-loop supply chains are surrounded with uncertainty. Thus, storage capacities, retailers' and wholesalers' demands, return rates, acceptance ratios, weekly available production/remanufacturing times, transportation upper bounds and objective function value are considered as fuzzy in the proposed model. The proposed fuzzy mathematical programming model is converted into a crisp equivalent model by utilizing several fuzzy aggregation procedures from the literature. The proposed model is applied to an illustrative case and scenario analysis is also carried out for evaluating the effects of some parameters related to the concerned collection-recovery system. Solution of the proposed model is achieved by using ILOG OPL Studio 6.3. [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]