Abstract
Service firms not only need to develop differentiated services to meet the requirements of customers with various preferences, but also have to improve service flexibility and the efficiency of the service system. A service family is a strategy by which different modules are configured, based on the service platform, to create a variety of differentiated services. This research considered both the effect of multi-server queues and the heterogeneous service processes in service family design problems to establish a framework of service modularization from three different perspectives-process, activity, and component. To optimize the service family design, a nonlinear integer-programming model was established to determine the optimal configurations of modules and prices for the service family and the optimal number of servers. The model is transformed into a linear form, and thus, can be solved using a commercial optimization software for small-scale problems. An improved genetic algorithm integrated with a neighborhood search was further developed to solve large-scale problems. The correctness of the linearized model and the effectiveness of the meta-heuristic algorithm were demonstrated through case studies and numerical experiments.
Highlights
In recent years, the contribution of service industries to the world economy has gradually increased; the service industry in the USA accounts for over 80% of the country’s gross domestic product [1]
By combining the model linearization approach, we transformed the integer non-linear program model into a linear model so that the model can be solved by commercial optimization software packages (e.g., LINGO and ILOG CPLEX), and we proved that Model II has the same optimization result as that of Model I, as shown in Theorem 1: Model II has the same optimization result as that of Model I
We proposed using a commercial optimization software package, supporting the branch and bound algorithm (e.g., ILOG CPLEX), to solve Model II for small-sized problems, and we developed an improved genetic algorithm combined with a neighborhood search to obtain the near-optimal solutions for large-sized problems
Summary
The contribution of service industries to the world economy has gradually increased; the service industry in the USA accounts for over 80% of the country’s gross domestic product [1]. Scholars have proposed some theories and methods relating to service family design [7]–[9]; they did not consider the effect of the average waiting time in multi-server queues on customer purchase decisions. None of the existing research studies related to service family design considered multi-server queues and heterogeneous service processes, which are important characteristics of service products and systems. DESCRIPTION OF THE OPTIMIZATION PROBLEM Take for instance, a service firm planning to develop a service family for N market segments, based on an established service platform that contains I functional service modules, including common and variant functional modules. The optimization problem in this study involves achieving the maximum profit by reasonably configuring the services in a service family, while considering the average waiting time in the multi-server queue. The service company can respond quickly to some practical situations to avoid losing customers
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