Amortized efficiency of list update and paging rules
Communications of the ACM
Computers and Operations Research
Parallel savings based heuristics for the delivery problem
Operations Research
On-line load balancing with applications to machine scheduling and virtual circuit routing
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
A better lower bound for on-line scheduling
Information Processing Letters
New algorithms for an ancient scheduling problem
Journal of Computer and System Sciences - Special issue on selected papers presented at the 24th annual ACM symposium on the theory of computing (STOC '92)
A better algorithm for an ancient scheduling problem
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Better Bounds for Online Scheduling
SIAM Journal on Computing
Discrete Online and Real-Time Optimization
IFIP World Computer Congress on Fundamentals - Foundations of Computer Science
Developments from a June 1996 seminar on Online algorithms: the state of the art
Developments from a June 1996 seminar on Online algorithms: the state of the art
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
A reactive method for real time dynamic vehicle routing problem
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Better bounds for online load balancing on unrelated machines
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Dynamic vehicle routing with stochastic requests
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Modeling warehouse logistics using agent organizations
CARE@AI'09/CARE@IAT'10 Proceedings of the CARE@AI 2009 and CARE@IAT 2010 international conference on Collaborative agents - research and development
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In this paper, we present a solution for a dynamic rescheduling problem involving new orders arriving randomly while static orders have been given in advance in warehouse environments. We propose two variations of an incremental static scheduling scheme: one based on the steepest descent insertion, called OR1, and the other, on multistage rescheduling, called OR2. Both techniques are enhanced by a local search procedure specifically designed for the problem at hand. We also implemented several existing online algorithms to our problem for evaluative purposes. Extensive statistical experiments based on real picking data indicate that the proposed methodologies are competitive with existing online schedulers and show that load-balancing algorithms, such as OR1, yield the best results on the average and that OR2 is effective in reducing the picking time when dynamism is low to moderate.