Efficient heuristics for the parallel blocking flow shop scheduling problem

We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them –especially the IGA– perform considerably better than those algorithms adapted from the DPFSP literature.Peer ReviewedPostprint (author's final draft

Doubly Exponential Solution for Randomized Load Balancing Models with General Service Times

In this paper, we provide a novel and simple approach to study the supermarket model with general service times. This approach is based on the supplementary variable method used in analyzing stochastic models extensively. We organize an infinite-size system of integral-differential equations by means of the density dependent jump Markov process, and obtain a close-form solution: doubly exponential structure, for the fixed point satisfying the system of nonlinear equations, which is always a key in the study of supermarket models. The fixed point is decomposited into two groups of information under a product form: the arrival information and the service information. based on this, we indicate two important observations: the fixed point for the supermarket model is different from the tail of stationary queue length distribution for the ordinary M/G/1 queue, and the doubly exponential solution to the fixed point can extensively exist even if the service time distribution is heavy-tailed. Furthermore, we analyze the exponential convergence of the current location of the supermarket model to its fixed point, and study the Lipschitz condition in the Kurtz Theorem under general service times. Based on these analysis, one can gain a new understanding how workload probing can help in load balancing jobs with general service times such as heavy-tailed service.Comment: 40 pages, 4 figure