Multi-Agent Customer Allocation in a Stochastic Service System

Article Abstract:

In many service systems, customers interact with an agent who directs customers to specific service facilities. Each agent, as a decision maker, seeks to allocate his-her customers to the service centers so as to optimize a measure of performance based on the customers' expected waiting time and the expected number of customers in service. In this paper, the problem of multiple agents, each optimizing his-her customer allocation decision in a stochastic service system, is analyzed as a noncooperative game. It is shown that an equilibrum point to such a game exists and sufficient conditions for which this equilibrium point is unique are also given. Finally, the relative efficiency of the multi-agent system is examined by comparing the customers' average waiting time in the multi-agent system to the one-agent case. It is shown that, in general, the multi-agent system is not as efficient as the one-agent nn terms of customer welfare. (Reprinted by Permission of Publisher.)

Customer service, Efficiency, Analysis, Decision Making, Queues, Equilibrium

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Simple Approaches to Shift, Days-Off and Tour Scheduling Problems

Article Abstract:

Integer programming approaches to workforce scheduling are often used to analyze shift and days-off scheduling problems. A linear programming model was used to produce an optimal solution. It is easy to implement. A simple heuristic of rounding down the linear programming solution produces near optimal solutions for a wide variety of workforce problems. Tables present numerical results.

Linear programming, Integer programming, Planning Applications, Operations Research, Manpower Planning

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Methods for the Evaluation of Permutations as Strategies in Stochastic Scheduling Problems

Article Abstract:

A job-shop has a single machine that can process jobs. A processing strategy is needed which minimizes total cost. The problem, in general, is complex but algorithms exist that can yield optional results among those strategies that are simply permutations of the job set. How well these permutations perform as optional strategies is studied.

Statistics (Data), Dynamic programming, Modeling, Data modeling software, Permutations, Production, Mathematical Programming, Statistics, Markov Process, Probability, Job Scheduling

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