This paper presents a tabu-search heuristic for the capacitated lot-sizing problem (CLSP) with set-up carryover. This production-planning problems allows multiple items to be produced within a time period, and setups for items to be carried over from one period to the next. Two interrelated decisions, sequencing and lot sizing, are present in this problem. Our tabu-search heuristic consists of five basic move types---three for the sequencing decisions and two for the lot-sizing decisions. We allow infeasible solutions to be generated at a penalty during the course of the search. We use several search strategies, such as dynamic tabu list, adaptive memory, and self-adjusting penalties, to strengthen our heuristic. We also propose a lower-bounding procedure to estimate the quality of our heuristic solution. We have also modified our heuristic to produce good solutions for the CLSP without set-up carryover. The computational study, conducted on a set of 540 test problems, indicates that on average our heuristic solutions are within 12% of a bound on optimality. In addition, for the set of test problems our results indicate an 8% reduction in total cost through set-up carryover.Lot Sizing, Tabu Search, Set-up Carryover

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Mobile cloud computing (MCC) enables the mobile devices to offload their applications to the cloud and thus greatly enriches the types of applications on mobile devices and enhances the quality of service of the applications. Under various circumstances, researchers have put forward several MCC architectures. However, how to reduce the response latency while efficiently utilizing the idle service capacities of the mobile devices still remains a challenge. In this paper, we firstly give a definition of MCC and divide the recently proposed architectures into four categories. Secondly, we present a Hybrid Local Mobile Cloud Model (HLMCM) by extending the Cloudlet architecture. Then, after formulating the application scheduling problems in HLMCM and bringing forward the Hybrid Ant Colony algorithm based Application Scheduling (HACAS) algorithm, we finally validate the efficiency of the HACAS algorithm by simulation experiments.

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