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Thus far in the available literature, capability-based distributed layout (CBDL) design approaches were only developed under certain environments. Indeed, uncertainties embedded in the machine unavailability (or random machine breakdowns), product demands, and process flow data were not considered by the previous studies to achieve a robust CBDL design. However, many real-life facility layout design applications may involve different types of uncertainties simultaneously, like fuzziness and stochasticity. Based on this motivation, for the first time in the literature, this paper introduces a novel robust capability-based distributed layout (R-CBDL) design problem under a mixed fuzzy-stochastic environment. First, a new fuzzy-stochastic optimization model of the R-CBDL design problem is developed by considering the random machine breakdowns and fuzzy demand/process flow data. Then, a hybrid solution approach based on a chance-constrained stochastic programming technique with a well-known interactive fuzzy resolution method is proposed. Thus, the random machine breakdowns and fuzzy part flow rates among different machining capabilities could be easily handled via the proposed approach. Fortunately, the proposed approach can also generate various risky and risk-free robust layout design alternatives under different probabilistic scenarios and uncertainty levels (α-cuts) according to the facility designer’s risk attitude. To show the validity and applicability of the proposed R-CBDL problem and hybrid solution approach, an extensive computational study with comparative analysis is first presented based on an illustrative numerical example under different machine capability overlap cases and probability distributions. Then, the performance of the proposed approach is also tested on a real-life cellular manufacturing system of a company. The computational experiments have shown that the proposed approach can accomplish more efficient robust layout design alternatives with on average 24.5% better total expected layout score when compared to the existing cellular layout of the manufacturing company.

The educational content of the undergraduate departments in all of the universities all overthe world is mainly based on the knowledge, skills, and capabilities that the graduates willrequire in their business life. Many graduates will use these skills or capabilities, which areusually gained from the compulsory and elective courses in the universities to meet therequirements and specifications in their business life. Therefore, the university coursetimetabling which is NP-hard and a class of scheduling problems is of great importance totraining well-equipped individuals in both business and daily life. However, none of theavailable studies in the literature have considered the optimal distribution of thecapabilities/skills over the curriculum that are essentially gained from compulsory andopened elective courses. Based on this motivation, this research first introduces a novelcapability-based course timetabling approach, which provides a wide variety of courses witha maximal capability set to the students during their university education. To do this, severallearning outcomes and course contents are first clustered to obtain basic capabilities that thestudents should acquire. Then, a mixed-integer non-linear programming model with bothhard and soft constraints is developed to provide appropriate distribution of these capabilitiesover the whole curriculum and also to gain the maximal capability for the students in differentclasses. By making use of the proposed capability-based course timetabling approach, inaddition to gaining the maximum number and variety of capabilities, it is also intended toassign the opened courses to the proper time slots by minimizing the distance between timedifferences of the given courses by each lecturer. Moreover, another objective is alsoformulated as an aggregated penalty function to minimize the weighted deviations in all thesoft constraints. To handle these conflicting objectives simultaneously and to producecompromise course timetables for the university department managers, a fuzzy goalprogramming approach with different importance and priorities is applied. Finally, thevalidity and applicability of the proposed capability-based course timetabling approach arealso demonstrated by a real-life case study at DEU Industrial Engineering department.&nbsp

Most knowledge-intensive industries, especially companies developing software engineering projects such as Enterprise Resource Planning (ERP) implementation projects, generally necessitate finding the optimal trade-off between the project duration and total usage cost of the renewable resource costs (e.g., human resource expertise costs). Therefore, the MRC-DTCTP, which integrates classical multi-mode resource-constrained project scheduling (MRCPSP) and discrete time–cost trade-off problems (DTCTP), can be seen as a more applicable problem since it better reflects the objectives and requirements of today’s real-life software project applications. The MRC-DTCTP is a much more complex variant of the MRCPSP since it aims to minimize total direct/indirect costs of the resources simultaneously under a pre-specified project deadline. Based on this motivation, a new explicit integer-linear programming (ILP) model of the MRC-DTCTP was first developed based on the implicit non-linear programming model of Wuliang and Chengen (2009). Due to its NP-hard nature, we also proposed a constraint programming (CP) model that includes several search strategies to solve large-sized problem instances within reasonable computation time. In addition, a genetic algorithm (GA) approach in combination with a Modified Serial Schedule Generation scheme (SSGS) is implemented to make further comparisons on several benchmark instances, which are generated based on the existing MRCPSP data sets taken from the project scheduling problem library (PSPLIB) by considering additional problem characteristics. A comprehensive experimental study has shown that the proposed CP model and GA approach can provide superior results in shorter run times for large-sized benchmark instances. Finally, an international Enterprise Resource Planning (ERP) Software Company’s real-life application is presented. The ERP projects generally necessitate finding the optimal trade-off between project makespan and human resource costs, making the MRC-DTCTP much more difficult than classical MRCPSPs & DTCTPs. For further analysis, time–cost trade-off curves under different human resource availabilities and project deadlines are drawn to provide managerial insights to ERP project managers.