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Multiproduct systems with both setup times and costs: Fluid bounds and schedules
 Operations Research
, 2004
"... This paper considers a multiproduct, singleserver production system where both setup times and costs are incurred whenever the server changes product. The system is maketoorder with a per unit backlogging cost. The objective is to minimize the longrun average cost per unit time. Using a fluid m ..."
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This paper considers a multiproduct, singleserver production system where both setup times and costs are incurred whenever the server changes product. The system is maketoorder with a per unit backlogging cost. The objective is to minimize the longrun average cost per unit time. Using a fluid model, we provide a closedform lower bound on system performance. This bound is also shown to provide a lower bound for stochastic systems when scheduling is static, but is only an approximation when scheduling is dynamic. Heavytraffic analysis yields a refined bound that includes secondmoment terms. The fluid bound suggests both dynamic and static scheduling In this paper we consider a production environment where a number of different products are produced on a single machine and setup activities are necessary when switches of product type are made. These setup activities require both time and cost that depend on the specific product type. Throughout the paper we assume that the setups do not depend on the previous product produced
Optimal dispatching control for elevator systems during uppeak traffic
 IEEE Transactions on Control Systems Technology
, 1997
"... Abstract — In this paper we develop optimal dispatching controllers for elevator systems during uppeak traffic. An uppeak traffic period arises when the bulk of the passenger traffic is moving from the first floor up into the building (e.g., the start of a business day in an office building). The c ..."
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Abstract — In this paper we develop optimal dispatching controllers for elevator systems during uppeak traffic. An uppeak traffic period arises when the bulk of the passenger traffic is moving from the first floor up into the building (e.g., the start of a business day in an office building). The cars deliver the passengers and then return empty to the first floor to pick up more passengers. We show that the structure of the optimal dispatching policy minimizing the discounted or average passenger waiting time is a thresholdbased policy. That is, the optimal policy is to dispatch an available car from the first floor when the number of passengers inside the car reaches or exceeds a threshold that depends on several factors including the passenger arrival rate, elevator performance capabilities, and the number of elevators available at the first floor. Since most elevator systems have sensors to determine the car locations and the number of passengers in each car, such a threshold policy is easily implemented. Our analysis is based on a Markov decision problem formulation with a batch service queueing model consisting of a single queue served by multiple finitecapacity bulk servers. We use dynamic programming techniques to obtain the structure of the optimal control policy and to derive some of its important properties. Several numerical examples are included to illustrate our results and to compare the optimal threshold policy to some known ad hoc approaches. Finally, since many transportation systems can be modeled as multiserver batch service queueing systems, we expect our results to be useful in controlling those systems as well. Index Terms—Bulkservice queueing networks, dynamic programming, Markov decision problems, optimal control, optimization problems, queueing theory, thresholds, transportation models. I.
Applications of polling systems M.A.A. Boon∗
, 2011
"... Since the first paper on polling systems, written by Mack in 1957, a huge number of papers on this topic has been written. A typical polling system consists of a number of queues, attended by a single server. In several surveys, the most notable ones written by Takagi, detailed and comprehensive des ..."
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Since the first paper on polling systems, written by Mack in 1957, a huge number of papers on this topic has been written. A typical polling system consists of a number of queues, attended by a single server. In several surveys, the most notable ones written by Takagi, detailed and comprehensive descriptions of the mathematical analysis of polling systems are provided. The goal of the present survey paper is to complement these papers by putting the emphasis on applications of polling models. We discuss not only the capabilities, but also the limitations of polling models in representing various applications. The present survey is directed at both academicians and practitioners.
MULTIOBJECTIVE OPTIMIZATION IN ELEVATOR GROUP CONTROL
, 2004
"... Abstract. Modern elevator systems in highrise buildings consist of groups of elevators with centralized control. The group control allocates hall calls to the most suitable elevators by optimizing a cost function. This problem can be viewed as a combination of online scheduling, resource allocation ..."
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Abstract. Modern elevator systems in highrise buildings consist of groups of elevators with centralized control. The group control allocates hall calls to the most suitable elevators by optimizing a cost function. This problem can be viewed as a combination of online scheduling, resource allocation, and stochastic control. The usual performance criterion to be optimized when scheduling passenger pickups is the average waiting time of all passengers in the system, i.e., the time period from the moment when the passenger arrives until the moment when this passenger boards some elevator. Alternative criteria are sometimes used as well, such as the ride time, defined from the time of boarding until a passenger arrives at the desired floor, the percentage of waiting times exceeding a certain limit or the number of elevator stops, which is related to energy consumption. Considering two or more of these criteria simultaneously results into a multiobjective optimization problem, where the group control algorithm should be tuned in order to find a good compromise between the different criteria. We study the relations between the criteria and some tuning parameters of to our Estimated Time of Arrival (ETA) algorithm by simulating different traffic patterns. Then we define a linear utility function form combining the criteria. Based on reasonable weights for the criteria we determine find optimal values for the tuning parameters. 1