A Pareto-Based Adaptive Variable Neighborhood Search for Biobjective Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Abstract

Different from most researches focused on the single objective hybrid flowshop scheduling (HFS) problem, this paper investigates a biobjective HFS problem with sequence dependent setup time. The two objectives are the minimization of total weighted tardiness and the total setup time. To efficiently solve this problem, a Pareto-based adaptive biobjective variable neighborhood search (PABOVNS) is developed. In the proposed PABOVNS, a solution is denoted as a sequence of all jobs and a decoding procedure is presented to obtain the corresponding complete schedule. In addition, the proposed PABOVNS has three major features that can guarantee a good balance of exploration and exploitation. First, an adaptive selection strategy of neighborhoods is proposed to automatically select the most promising neighborhood instead of the sequential selection strategy of canonical VNS. Second, a two phase multiobjective local search based on neighborhood search and path relinking is designed for each selected neighborhood. Third, an external archive with diversity maintenance is adopted to store the nondominated solutions and at the same time provide initial solutions for the local search. Computational results based on randomly generated instances show that the PABOVNS is efficient and even superior to some other powerful multiobjective algorithms in the literature.

A Hybrid Pareto Based Multi Objective Evolutionary Algorithm for a Partial Flexible Open-Shop Scheduling Problem

“…This is attempted by developing a HPBO approach. 5 problem sets are taken from "Taillard" [2] benchmark problem and are solved for different sizes. A total of 35 problems is illuminated.…”

“…I.e., the time required to finish every one of the jobs. This problem is observed to be NP-hard [2].The meta heuristics can be partitioned into either helpful heuristics or improvement meta heuristics, the previous are heuristics that assemble a partial feasible schedule from scratch and the last are heuristics that attempt to improve a previously generated schedule by typically applying some form of specific problem knowledge.…”

Restricted Enumeration and Machine Grouping Based Approach for Hybrid Flexible Flow Shop Scheduling Problems With Sequence-Dependent Setup Times

“…However, it should be noted that the size of the problems solved through B&B is relatively small. For this reason, many researchers turned to develop dispatching rules and constructive heuristics to quickly obtain a near-optimal solution for complex HFS problems (Tian, Li, and Lius, 2016).…”

Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

“…Pareto-based metaheuristic is an effective approach for the multiobjective scheduling problem [11]. For the unrelated parallel machine environment, Lin et al [12] proposed two heuristics and a Pareto-based genetic algorithm for unrelated parallel machine scheduling problem to minimize the makespan, total weighted completion time, and total weighted tardiness.…”