ABSTRACT A dynamic vehicle routing problem that models the relief distribution operations in a po... more ABSTRACT A dynamic vehicle routing problem that models the relief distribution operations in a post-disaster environment is addressed. As an approximate solution method, a multi-agent system with two hierarchical levels is proposed. Within the proposed framework, the vehicles have the ability to dynamically re-route, bid for new tasks and de-commit to previously undertaken tasks to take advantage of the continuous flow of incoming information. In order to evaluate the proposed architecture, a discrete-event simulator was built in an object-oriented language. A series of simulation cases were identified and the behavior of the proposed approach was compared to that of a centralized, on-line heuristic solution approach.
Journal of Intelligent Manufacturing, May 21, 2015
ABSTRACT Most sequencing problems deal with deterministic environments where all information is k... more ABSTRACT Most sequencing problems deal with deterministic environments where all information is known in advance. However, in real-world problems multiple sources of uncertainty need to be taken into consideration. To model such a situation, in this article, a dynamic sequencing problem with random arrivals, processing times and due-dates is considered. The examined system is a manufacturing line with multiple job classes and sequence-dependent setups. The performance of the system is measured under the metrics of mean WIP, mean cycle time, mean earliness, mean tardiness, mean absolute lateness, and mean percentage of tardy jobs. Twelve job dispatching rules for solving this problem are considered and evaluated via simulation experiments. A statistically rigorous analysis of the solution approaches is carried out with the use of unsupervised and supervised learning methods. The cluster analysis of the experimental results identified classes of priority rules based on their observed performance. The characteristics of each priority rule class are documented and areas in objective space not covered by existing rules are identified. The functional relationship between sequencing priority rules and performance metrics of the production system was approximated by artificial neural networks. Apart from gaining insight into the mechanics of the sequencing approaches the results of this article can be used (1) as a component for prediction systems of dispatching rule output, (2) as a guideline for building new dispatching heuristic with entirely different characteristics than existing ones, (3) to significantly decrease the length of what-if simulation studies.
The International Journal of Advanced Manufacturing Technology, Jul 3, 2014
ABSTRACT Single-model multi-stage serial production/inventory systems with stochastic order arriv... more ABSTRACT Single-model multi-stage serial production/inventory systems with stochastic order arrival and service times are examined. The manufacturing systems are controlled by Kanban, Base Stock, CONWIP, and CONWIP/Kanban Hybrid mechanisms. Discrete-event simulation models of the manufacturing systems are developed. Four simulation cases are examined where optimal or near-optimal parameters for the control policies are obtained by integrating the simulation models with multi-objective evolutionary algorithm in order to minimize mean WIP and mean number of backordered demands simultaneously. The non-dominated sets are compared in terms of several metrics for comparing Pareto fronts.
ABSTRACT A dynamic vehicle routing problem that models the relief distribution operations in a po... more ABSTRACT A dynamic vehicle routing problem that models the relief distribution operations in a post-disaster environment is addressed. As an approximate solution method, a multi-agent system with two hierarchical levels is proposed. Within the proposed framework, the vehicles have the ability to dynamically re-route, bid for new tasks and de-commit to previously undertaken tasks to take advantage of the continuous flow of incoming information. In order to evaluate the proposed architecture, a discrete-event simulator was built in an object-oriented language. A series of simulation cases were identified and the behavior of the proposed approach was compared to that of a centralized, on-line heuristic solution approach.
Journal of Intelligent Manufacturing, May 21, 2015
ABSTRACT Most sequencing problems deal with deterministic environments where all information is k... more ABSTRACT Most sequencing problems deal with deterministic environments where all information is known in advance. However, in real-world problems multiple sources of uncertainty need to be taken into consideration. To model such a situation, in this article, a dynamic sequencing problem with random arrivals, processing times and due-dates is considered. The examined system is a manufacturing line with multiple job classes and sequence-dependent setups. The performance of the system is measured under the metrics of mean WIP, mean cycle time, mean earliness, mean tardiness, mean absolute lateness, and mean percentage of tardy jobs. Twelve job dispatching rules for solving this problem are considered and evaluated via simulation experiments. A statistically rigorous analysis of the solution approaches is carried out with the use of unsupervised and supervised learning methods. The cluster analysis of the experimental results identified classes of priority rules based on their observed performance. The characteristics of each priority rule class are documented and areas in objective space not covered by existing rules are identified. The functional relationship between sequencing priority rules and performance metrics of the production system was approximated by artificial neural networks. Apart from gaining insight into the mechanics of the sequencing approaches the results of this article can be used (1) as a component for prediction systems of dispatching rule output, (2) as a guideline for building new dispatching heuristic with entirely different characteristics than existing ones, (3) to significantly decrease the length of what-if simulation studies.
The International Journal of Advanced Manufacturing Technology, Jul 3, 2014
ABSTRACT Single-model multi-stage serial production/inventory systems with stochastic order arriv... more ABSTRACT Single-model multi-stage serial production/inventory systems with stochastic order arrival and service times are examined. The manufacturing systems are controlled by Kanban, Base Stock, CONWIP, and CONWIP/Kanban Hybrid mechanisms. Discrete-event simulation models of the manufacturing systems are developed. Four simulation cases are examined where optimal or near-optimal parameters for the control policies are obtained by integrating the simulation models with multi-objective evolutionary algorithm in order to minimize mean WIP and mean number of backordered demands simultaneously. The non-dominated sets are compared in terms of several metrics for comparing Pareto fronts.
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