Safety-based availability assessment at design stage

The availability of a system or equipment is one of the crucial characteristics that measures the customer satisfaction and strongly influences his final choice decision between concurrent products. The aim of this work is to provide an approach to improve the products availability assessment by taking into account the safety criteria by considering the use situations at design stage. Our work focuses on the routine design of complex products. The availability is often simply estimated considering reliability and maintainability. Basically, the intrinsic availability is the probability that it is operating satisfactorily at any point in time when used under conditions stated by design specifications. The time considered includes operating time and active repair time. Thus, intrinsic availability excludes from consideration all other times in the product lifecycle such as: accident management time, storage time, administrative time or logistic time. But many studies show that the loss of availability performance is also due to accidents that occur in different unforeseeable utilization situations. This engenders stops of the system to ensure the users safety according to standards recommendations. In this purpose, we consider the structural product architecture and the different use cases that correspond to the operational states and downtimes due to stop events that may happen during the utilization like failures, maintenance tasks and accidents. Then, we propose a product behavioral analysis including the use cases to describe interactions between the product and users or maintenance operators. We use Markov chains to model the use cases corresponding to operating time (OT), maintenance time (MT) and preparing time after accidents (RT). Then these three parameters are considered to specify a generic approach to improve the availability assessment. Such an approach provides the traceability of the product behavior along its lifecycle. In this way, the main causes of stop can be identified and this may guide the designer for improving the availability of the product future versions. To validate our approach, an application is presented considering a printing line. The comparison of our simulation considering an industrial case study shows a good agreement about the influence of safety on the availability.     

User experience and personal innovativeness: An empirical study on the Enterprise Resource Planning systems

Although user experience and personal innovativeness are two important factors in new technology adoption, there has been no prior study to test these factors with the Enterprise Resource Planning (ERP) adoption. This paper investigates moderating roles of user experience on the relationship between the personal innovativeness and the ERP adoption motivations. This issue is important because if the user has more experience with the systems then the power of influence of personal innovativeness on ERP adoption motivation would be different. Thus, this paper tests these important insights of ERP systems adoption with the two different field samples with high (more than three years) and low (less than three years) user experience, based on the innovation diffusion theory, self determination theory, and different types of motivations such as intrinsic and extrinsic motivations. The findings, based on the PLS analysis of the model using 107 ERP end users, show that there are clear moderating effects of user experience—such as impacts of personal innovativeness on ERP systems adoption motivations are higher in case of low user experience samples, as expected. Academic and practical implications are discussed in the paper based on these empirical findings.     

Intuitionistic Fuzzy Hybrid Weighted Aggregation Operators

Intuitionistic fuzzy sets (IFSs) have attracted more and more scholars’ attention due to their powerfulness in expressing vagueness and uncertainty. In the course of decision making with IFSs, aggregation operators play a very important role since they can be used to synthesize multidimensional evaluation values represented as intuitionistic fuzzy values into collective values. This paper proposes a family of intuitionistic fuzzy hybrid weighted aggregation operators, such as the intuitionistic fuzzy hybrid weighted averaging operator, the intuitionistic fuzzy hybrid weighted geometric operator, the generalized intuitionistic fuzzy hybrid weighted averaging operator, and the generalized intuitionistic fuzzy hybrid weighted geometric operator. All these newly developed operators not only can weight both the arguments and their ordered positions simultaneously but also have some desirable properties, such as idempotency, boundedness, and monotonicity. To show the applications of our proposed intuitionistic fuzzy hybrid weighted aggregation operators, a simple schema for decision making with intuitionistic fuzzy information is developed. An example concerning the human resource management is given to illustrate the validity and applicability of the proposed method and also the hybrid weighted aggregation operators.     

Parametric Weighting Function for WOWA Operator and Its Application in Decision Making

We propose a parametric weighting function for weighted ordered weighted averaging (WOWA) operator in decision making under uncertainty and risk environment. The weighting function is not only related to the attitude of the decision makers, but also to the event probability. Under the proposed method, the decision makers underweight small probability gains and overweight large probability gains, which is right in consistence with the psychology of the decision makers in the prospect theory. Meanwhile, it generalizes two specific weighting functions and proposed by Torra (International Journal of Intelligent Systems 12 (1997) 153–166). Moreover, the expected utility becomes a special case of the parametric weighting function for WOWA operator. To illustrate its application, an example of introducing and pricing new product is given, which shows the optimal strategies corresponding to different parameter values r.     

Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals

This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies–Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy–are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms–an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm)–to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals.     

A framework for SaaS software packages evaluation and selection with virtual team and BOCR of analytic network process

Software packages evaluation and selection is one of the most important activities encountered by software as a service (SaaS) users in the high performance networked computing environment, especially for the small or medium-sized enterprises. In this paper, we propose a framework for SaaS software packages evaluation and selection by combining the virtual team (VT) and the BOCR (benefits, opportunities, costs, and risks) of the analytic network process (ANP). Different from the traditional application of the BOCR model of ANP, the proposed VT-BOCR model attempts to solve the complex ANP model and overloaded pairwise comparisons by decomposing the tasks to four parts, and performed by benefits virtual team (B-VT), opportunities virtual team (O-VT), costs virtual team (C-VT), and risks virtual team (R-VT) separately. The interactive networked media on distributed environments not only makes the proposed framework possible without the limitations of time, space, and human resources, but also can take full advantage of the talent experts who are geographically dispersed. The proposed framework also shows great potentials for aiding practitioners and researchers concerned with the cloud services.     

Smart pricing scheme: A multi-layered scoring rule application

Defining appropriate pricing strategy for smart environment is important and complex task at the same time. It holds the primal fraction in Demand Response (DR) program. In our work, we devise an incentive based smart dynamic pricing scheme for consumers facilitating a multi-layered scoring rule. The proposed strategy characterizes both incentive based DR and price based DR programs facilities. This mechanism is applied between consumer agents (CA) to electricity provider agent (EP) and EP to Generation Company (GENCO). Based on the Continuous Ranked Probability Score (CRPS), a hierarchical scoring system is formed among these entities, CA–EP–GENCO. As CA receives the dynamic day-ahead pricing signal from EP, it will schedule the household appliances to lower price period and report the prediction in a form of a probability distribution function to EP. EP, in similar way reports the aggregated demand prediction to GENCO. Finally, GENCO computes the base discount after running a cost-optimization problem. GENCO will reward EP with a fraction of discount based on their prediction accuracy. EP will do the same to CA based on how truthful they were reporting their intentions on device scheduling. The method is tested on real data provided by Ontario Power Company and we show that this scheme is capable to reduce energy consumption and consumers’ payment.