Study on an ideal start-up model for a modified Scheibel column

The start-up process existed in every solvent extraction process. An optimal start-up strategy in the solvent extraction process can shorten the time to get to steady state greatly. In order to offer some useful methods for the study on the start-up in the extraction process, an ideal start-up model was developed for a modified Scheibel extraction column. The numerical solution for the multi-stage extraction system was obtained from solving the mathematical equations. From the calculated values, the relationships between variables and time were established. According to the relationships obtained above, suitable start-up strategy could be taken to drive the extraction system to the desired steady state.     

Measuring the baseline sales and the promotion effect for incense products: a Bayesian state-space modeling approach

One of the most important research fields in marketing science is the analysis of time series data. This article develops a new method for modeling multivariate time series. The proposed method enables us to measure simultaneously the effectiveness of marketing activities, the baseline sales, and the effects of controllable/uncontrollable business factors. The critical issue in the model construction process is the method for evaluating the usefulness of the predictive models. This problem is investigated from a statistical point of view, and use of the Bayesian predictive information criterion is considered. The proposed method is applied to sales data regarding incense products. The method successfully extracted useful information that may enable managers to plan their marketing strategies more effectively.     

Service provider portfolio selection for project management using a BP neural network

Service provider portfolio selection (SPPS) can be a major challenge for organizations to achieve project success. Hence, organizations need to decide on which service provider portfolio (SPP) is appropriate for project management (PM). However, there has been limited research on how to select a SPP in PM. To address this research gap, we establish a novel model for SPPS based on a BP neural network integrated with entropy-AHP from the perspective of the comprehensive economic benefit. This model employs a BP neural network due to its robustness and memory and nonlinear mapping abilities. Furthermore, we implement the proposed model for a construction project to verify the effectiveness. Our results indicate that the model performs well with a prediction accuracy of 97%. Moreover, the model is confirmed to be robust as it still achieves high prediction accuracy when the input data are disturbed randomly.

     

A hybrid model for selection of an outsourcing provider

Outsourcing is a good strategy for firms that need to reduce operating costs and improve competitiveness and it is important that firms scientifically select appropriate outsourcing providers. Some efforts have been made to find systematic ways to deal with outsourcing problems, but these efforts incorrectly assumed that the criteria used in the decision process are independent, which is not true in the real world. In this study, we propose a new hybrid multiple criteria decision-making (MCDM) model, which addresses the dependent relationships between the various criteria. The relations-structure among the criteria is built with the aid of the decision-making trial and evaluation laboratory (DEMATEL) method. Decision-makers tend to hold diverse opinions about their preferences due to incomplete information and knowledge, or inherent conflict between various departments. We further used the fuzzy preference programming and the analytic network process (ANP) to form a model for the selection of partners for outsourcing providers. The proposed model can help practitioners improve their decision making process, especially when criteria are numerous and inter-related. The method is demonstrated using data from a Taiwanese airline.     

Scheduling preparation of doses for a chemotherapy service

A fast realization of drugs is an important part in the quality of service of a hospital. In this paper we propose a scheduling method for the preparation of chemotherapy doses in order to reduce the patient waiting time. Two approaches have been defined: an off-line approach and a real time approach. The off-line approach is using a linear programming model for minimizing the maximum tardiness of jobs in a production day. This method is re-used during the real-time resolution combined with a greedy algorithm. The solution obtained respects constraints on the production center and the hospital organization. Our model is currently used in software which helps the decision maker of the service and allows increasing the patient satisfaction and the productivity of the service.     

The effect of information on queue-scalping service systems

We consider an M/M/1 queueing system with a single queue scalper who makes profit by selling his position in the queue. The equilibrium purchasing strategies of customers under observable case (with double-threshold strategy) and unobservable case (with mixed strategy) are derived. The profits of the queue scalper are obtained under two information levels. Numerical experiments suggest that the queue scalper is better off with unobservable case when the system is too congested or the operating cost is too low.     

Experimental study of a Bayesian method for daily electricity load forecasting

The method of Bayesian model discrimination is investigated for the possible contributions it may provide in the area of automatically forecasting the daily electricity demand cycle. A set of differing demand models have probabilities attached to them in such a way that these would be continuously updated with the available data and the actual forecasts obtained as expectations across all the models. Simulation experiments indicate significantly improved forecasting performance over a commonly used rescaling type of approach. Some practical issues in implementation are discussed.     

A supply chain under limited-time promotion: The effect of customer sensitivity

We consider a two-echelon supply chain with a supplier and a retailer facing stochastic customer demands. The supplier is a leader who determines a wholesale price. In response, the retailer orders products and sets a price which affects customer demands. The goal of both players is to maximize their profits. We find the Stackelberg equilibrium and show that it is unique, not only when the supply chain is in a steady-state but also when it is in a transient state induced by a supplier’s promotion. There is a maximum length to the promotion, however, beyond which the equilibrium ceases to exist. Moreover, if customer sensitivity increases, then the wholesale equilibrium price decreases, product orders increase and product prices drop. This effect, well-observed in real life, does not, however, necessarily imply that the promotion is always beneficial. Conditions for the profitability of a limited-time promotion are shown and analyzed numerically. We discuss both open-loop and feedback policies and derive the conditions necessary for them to remain optimal under stochastic demand fluctuations.     

Retailer’s inventory policies for a one time only manufacturer trade deal of uncertain duration

This paper models a retailer’s response to temporary manufacturer’s trade deals characterized by a time interval of random length, but with the ending date known before its occurrence. Uncertainty is handled through a reordering point, which serves as a trigger mechanism for a new special order and is activated at the discount termination date. The model generates ordering policies, applicable to any probability distribution and is shown to yield well-known deterministic optimal policies as a limiting case.     

Heuristic procedures for a stochastic batch service problem

This paper considers a multi-class batch service problem that involves a class-dependent waiting cost and a service cost in determining customer batch sizes. Unlike a fixed service cost used widely in standard models, the service cost considered in this work is incurred only if the total service time is over the capacity. We formulate this problem as an infinite horizon Markov decision process, and exploit its structural properties to establish theoretical results, including bounds on the optimal action space. We use the results to improve the value iteration procedure. Furthermore, we design heuristic algorithms for large problems. The numerical experiments demonstrate that the class-dependent waiting cost has a considerable influence on the optimal customer batch size. Finally, we evaluate the efficiency of the proposed value iteration procedure and the quality of the heuristic solutions.     

New models for locating a moving service facility

In this paper we analyze a new location problem which is a generalization of the well-known single facility location model. This extension consists of introducing a general objective function and replacing fixed locations by trajectories. We prove that the problem is well-stated and solvable. A Weiszfeld type algorithm is proposed to solve this generalized dynamic single facility location problem on L ^p spaces of functions, with p ∈(1,2]. We prove global convergence of our algorithm once we have assumed that the set of demand functions and the initial step function belong to a subspace of L ^p called Sobolev space. Finally, examples are included illustrating the application of the model to generalized regression analysis and the convergence of the proposed algorithm. The examples also show that the pointwise extension of the algorithm does not have to converge to an optimal solution of the considered problem while the proposed algorithm does.     

Least-cost network topology design for a new service

We describe an implementation of the tabu search metaheuristic that effectively finds a low-cost topology for a communications network to provide a centralized new service. Our results are compared to those of a greedy algorithm which applies corresponding decision rules, but without the guidance of the tabu search framework. These problems are difficult computationally, representing integer programs that can involve as many as 10,000 integer variables and 2000 constraints in practical applications. The tabu search results approach succeeded in obtaining significant improvements over the greedy approach, yielding optimal solutions to problems small enough to allow independent verification of optimality status and, more generally, yielding both absolute and percentage cost improvements that did not deteriorate with increasing problem size.This research was partially supported by the Air Force Office of Scientific Research and the Office of Naval Research Contract No. F49629-90-C-0033.     

A Signalling Problem for Near-Equilibrium Flows in the Fickett-Majda Model of Combustion

The signalling problem for chemico-acoustic interactions ina simplified model of combustion and detonation is considered.The problem is examined in different limits, depending uponthe sensitivity of the reaction rate with the degree of reactioncoordinate and with the thermodynamic state. In one limit, theproblem is singular, and singular perturbation techniques areused to obtain an approximate solution. In the general case,a combination Riemann function-Laplace transform method is usedto obtain exact solutions with arbitrary boundary data. Thiscombined method allows the Riemann method to be applied to certainwave problems with discontinuous data.     

Taming the bullwhip effect whilst watching customer service in a single supply chain echelon

We study a generalised order-up-to policy that has highly desirable properties in terms of order and inventory variance and customer service levels it generates. We quantify exactly the variance amplification in replenishment orders, i.e. the bullwhip effect, and the variance of inventory levels over time, for i.i.d. and the weakly stationary auto regressive (AR), moving average (MA) and auto regressive moving average (ARMA) demand processes. We demonstrate that high customer service as measured by fill-rate, and smooth replenishments need not increase inventory cost substantially. We observe that in some instances of the ARMA demand pattern this comes at the expense of a relatively small increase in safety stock, whilst in other instances inventory levels can actually be reduced.     

An exact solution of start-up flow for the fractional generalized Burgers’ fluid in a porous half-space

Modified Darcy’s law for fractional generalized Burgers’ fluid in a porous medium is introduced. The flow near a wall suddenly set in motion for a fractional generalized Burgers’ fluid in a porous half-space is investigated. The velocity of the flow is described by fractional partial differential equations. By using the Fourier sine transform and the fractional Laplace transform, an exact solution of the velocity distribution is obtained. Some previous and classical results can be recovered from our results, such as the velocity solutions of the Stokes’ first problem for viscous Newtonian, second grade, Maxwell, Oldroyd-B or Burgers’ fluids.     

Strong approximations for a Kumar-Seidman network under a priority service discipline

In this paper,we derive the strong approximations for a four-class two station multi-class queuing network（Kumar-Seidman network） under a priority service discipline.Within a group,jobs are served in the order of arrival,that is,a first-in-first-out disciple,and among groups,jobs are served under a pre-emptiveresume priority disciple.We show that if the input data（i.e.,the arrival and service processe） satisfy an approximation（such as the functional law-of-iterated logarithm approximation or the strong approximation）,the output data（the departure processes） and the performance measures（such as the queue length,the work load and the sojourn time process） satisfy a similar approximation.     

Planning for disaster: developing a multi-agency counselling service

Multi-agency planning is becoming increasingly important to organisations, especially those concerned with delivering services for the benefit of the community. This paper describes how a modified version of the methods from soft systems methodology (SSM), chosen through methodological reflections informed by critical systems thinking, was used to support the planning of a multi-agency counselling service that could be activated in the event of a disaster. Representatives of nineteen agencies were involved in this exercise, working together in six, one-day workshops. Feedback from participants, using four evaluation criteria (derived from the principles of SSM and the stated priorities of workshop participants), suggests that the methods of SSM, modified as described, show a great deal of promise as a support to multi-agency planning.