Tabu search for no-wait flowshop scheduling problem to minimize maximum lateness

In order to solve the no-wait flowshop scheduling problem to minimize the maximum lateness,three job-block-based neighborhoods are proposed,among which the block exchange neighborhood have a size of O（n4）while the block swap and the simplified block exchange neighborhoods have a size of O（n3）.With larger sizes than the existing neighborhoods,the proposed neighborhoods can enhance the solution quality of local search algorithms.Speedup properties for the neighborhoods are developed,which can evaluate a neighbor in constant time and explore the neighborhoods in time proportional to their proposed sizes. Unlike the dominance-rule-based speedup method,the proposed speedups are applicable to any machine number.Three neighborhoods and the union of block swap and the simplified block exchange neighborhoods are compared in the tabu search.Computational results on benchmark instances show that three tabu search algorithms with O（n3）neighborhoods outperform the existing algorithms and the tabu search algorithm with the union has the best performance among all the tested algorithms.

Minimizing Maximum Lateness on Unbounded Single Batching Machine with Family Jobs

The scheduling problem on a single batching machine with family jobs was proposed.The single batching machine can process a group of jobs simultaneously as a batch.Jobs in the same batch complete at the same time.The batch size is assumed to be unbounded.Jobs that belong to different families can not be processed in the same batch.The objective function is minimizing maximum lateness.For the problem with fixed number of m families and n jobs,a polynomial time algorithm based on dynamic programming with time complexity of O(n(n/m+1)m)was presented.

A Note on DP Algorithm for Batching Scheduling to Minimize Maximum Lateness

In parallel-batching machine scheduling, all jobs in a batch start and complete at the same time, and the processing time of the batch is the maximum processing time of any job in it. For the unbounded parallel-batching machine scheduling problem of minimizing the maximum lateness, denoted 1|p-batch|L_(max), a dynamic programming algorithm with time complexity O(n^2) is well known in the literature.Later, this algorithm is improved to be an O(n log n) algorithm. In this note, we present another O(n log n) algorithm with simplifications on data structure and implementation details.

Two-Agent Scheduling on a Bounded Parallel-Batching Machine with Makespan and Maximum Lateness Objectives

This paper studies the two-agent scheduling on a bounded parallel-batching machine.In the problem,there are two agents A and B each having their own job sets with the restriction that the processing times of jobs of agent B are equal.The jobs of different agents can be processed in a common batch.Moreover,each agent has its own objective function to be minimized.The objective function of agent A is the makespan of its jobs,and the objective function of agent B is the maximum lateness of its jobs.We present a polynomial-time algorithm for finding all Pareto optimal solutions of this two-agent parallel-batching scheduling problem.