We consider the multitasking scheduling problem on unrelated parallel machines to minimize the total weighted completion time. In this problem, each machine processes a set of jobs, while the processing of a selected job on a machine may be interrupted by other available jobs scheduled on the same machine but unfinished. To solve this problem, we propose an exact branch‐and‐price algorithm, where the master problem at each search node is solved by a novel column generation scheme, called in‐out column generation, to maintain the stability of the dual variables. We use a greedy heuristic to obtain a set of initial columns to start the in‐out column generation, and a hybrid strategy combining a genetic algorithm and an exact dynamic programming algorithm to solve the pricing subproblems approximately and exactly, respectively. Using randomly generated data, we conduct numerical studies to evaluate the performance of the proposed solution approach. We also examine the effects of multitasking on the scheduling outcomes, with which the decision maker can justify making investments to adopt or avoid multitasking.

A complete coverage path planning algorithm, which combines local space coverage with global planning, is proposed. At first, environmental model of mobile robot in a space with obstacles is built by Boustrophedon unit decomposition method, and mobile robot realizes coverage in a reciprocating way in local space. Secondly, it takes local space dividing, sub-space connecting sequence and sub-space walking route into account, then a completely connected distance matrix that represents the connecting relationship of the coverage space are defined. Thirdly, dynamic programming algorithm is used to optimize this matrix and a shortest global coverage sequence is acquired. Simulation example proves the effectiveness of the proposed algorithm.

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This paper presents a complete coverage path planning method, which combines local space coverage with global motion planning. It is realized by modeling mobile robot environment based on Boustrophedon cell decomposition method; and according to the characteristics of regional environment model, the connectivity of the traversing space is represented by a complete weighted connected matrix. Then Genetic algorithm (GA) is used to optimize the subspace traversal distance to obtain the shortest global traversal sequence of mobile robot.