Scheduling plays an important role in many different service industries. In this paper we provide an overview of some of the more important scheduling problems that appear in the various service industries. We focus on the formulations of such problems as well as on the techniques used for solving those problems. We consider five areas of scheduling in service industries, namely (i) project scheduling, (ii) workforce scheduling, (iii) timetabling, reservations, and appointments, (iv) transportation scheduling, and (v) scheduling in entertainment. The first two areas are fairly general and have applications in many different service industries. The third, fourth and fifth areas are more related to some very specific service industries, namely the hospitality and health care industries, the transportation industries (of passengers as well as of cargo), and the entertainment industries. In our conclusion section we discuss the similarities and the differences between the problem formulations and solution techniques used in the various different industries and we also discuss the design of the decision support systems that have been developed for scheduling in the service industries.

For seasonal products, the retailers usually launch various marketing efforts, like advertising and promotion, to promote them in a selling season. While facing large demand from the customers, one should take the capacity constraint and outsourcing into consideration. Considering the shorten life cycles of most products, in this paper we adopt the traditional newsvendor model to investigate the optimal marketing effort along with optimal order quantity. We address the risk aversion issue and characterize the influence of the sellers’ risk propensity with CVaR criterion, and we develop an effective algorithm to obtain the optimal strategy. The impact of sellers’ risk propensity on the performance of the system is illustrated via numerical examples. The innovation of this paper is threefold. First, the optimal joint strategy of the marketing effort and order quantity is investigated and an efficient algorithm to find the optimal strategy is developed. Second, the capacity constraint option and an outsourcing strategy are studied jointly for excess products. Finally, the risk propensity of the seller and its influence are investigated by using the CVaR criterion, through which we obtain some new managerial insights.

The problem of nonparametric identification of a multivariate nonlinearity in a D-input Hammerstein system is examined. It is demonstrated that if the input measurements are structured, in the sense that there exists some hidden relation between them, i.e. if they are distributed on some (unknown) d-dimensional space M in R ^D, d < D, then the system nonlinearity can be recovered at points on M with the convergence rate O(n ^−1/(2+d)) dependent on d. This rate is thus faster than the generic rate O(n ^−1/(2+D)) achieved by typical nonparametric algorithms and controlled solely by the number of inputs D.

It has been widely recognized that the efficiency of a thermal power system can be improved by technological advancement of electricity generation and manipulation of electricity consumption. The smart meter enables two-way communication between the customers and the electricity generation system. The electricity generation system uses price incentive (i.e. a higher price in the peak period and a lower price in the off-peak period) to shift part of demands from peak to off-peak period under the smart grid environment. Given the fact that fuel consumption in each period is a strictly increasing convex function of power output, we propose two-period and multi-period pricing strategies, and study the effect of different pricing strategies on reducing fuel consumption.

In technology-intensive markets, it is a common strategy for companies to develop long-term multiple generation product lines instead of releasing consecutive single products. Even though this strategy is more profitable than sequentially introducing single product generations, it can also result in inter-product line cannibalization. Cannibalization of multiple-generation product lines is a complex problem that needs to be taken into account at the early product line planning stage in order to sustain long-term profitability. In this paper, we propose an agent-based model that can simulate the potential cannibalization scenarios within a multiple-generation product line. We view a multiple-generation product line (MGPL) as complex adaptive system where each product generation in the MGPL adjusts its sales price over time based on the shifts in the market demand. The proposed model provides insights into how various pricing strategies impact the overall lifecycle profitability of MGPL and can be used to assist companies in developing appropriate dynamic pricing strategies at the early product line planning stages.