A Heuristic-Based Adaptive Iterated Greedy Algorithm for Lot-Streaming Hybrid Flow Shop Scheduling Problem with Consistent and Intermingled Sub-Lots.

Owing to the different quantities and processing times of sub-lots, intermingling sub-lots with each other, instead of fixing the production sequence of sub-lots of a lot as in the existing studies, is a more practical approach to lot-streaming flow shops. Hence, a lot-streaming hybrid flow shop scheduling problem with consistent and intermingled sub-lots (LHFSP-CIS) was studied. A mixed integer linear programming (MILP) model was established, and a heuristic-based adaptive iterated greedy algorithm (HAIG) with three modifications was designed to solve the problem. Specifically, a two-layer encoding method was proposed to decouple the sub-lot-based connection. Two heuristics were embedded in the decoding process to reduce the manufacturing cycle. Based on this, a heuristic-based initialization is proposed to improve the performance of the initial solution; an adaptive local search with four specific neighborhoods and an adaptive strategy has been structured to improve the exploration and exploitation ability. Besides, an acceptance criterion of inferior solutions has been improved to promote global optimization ability. The experiment and the non-parametric Kruskal-Wallis test (p = 0) showed the significant advantages of HAIG in effectiveness and robustness compared with five state-of-the-art algorithms. An industrial case study verifies that intermingling sub-lots is an effective technique to enhance the utilization ratio of machines and shorten the manufacturing cycle.

Cross-dimensional formation control of second-order heterogeneous multi-agent systems.

This paper is concerned with the cross-dimensional formation control of a second-order multi-dimensional heterogeneous multi-agent system. Agents are first separated into several groups according to their position/velocity vector dimensions. Then the cross-dimensional formation control problem is formulated such that agents in the same group form a time-varying formation in their own dimension and agents in different groups cooperatively move in multiple dimensions. This can make follower agents in different dimensions cooperatively track a leader. Moreover, a cross-dimensional formation protocol is designed based on full or partial information of neighboring agents. For higher-dimensional agents, full information of lower-dimensional neighbors is adopted. For lower-dimensional ones, only partial information of higher-dimensional neighbors is used. Furthermore, a necessary and sufficient condition for the second-order heterogeneous multi-agent system to achieve cross-dimensional formation is provided. Accordingly, a criterion for designing cross-dimensional formation protocol is further derived. Finally, under an undirected graph, a lower-dimensional protocol design criterion is obtained if there is no data exchange between lower- and higher-dimensional followers. The effectiveness of the obtained results is demonstrated through cross-dimensional target enclosing performance analysis for multiple robots and quadrotors.

Evolutionary Optimization Under Uncertainty: The Strategies to Handle Varied Constraints for Fluid Catalytic Cracking Operation.

This article studies an operational optimization problem of the fluid catalytic cracking (FCC) unit under uncertainty. The objective of this problem is to quickly reoptimize the operating variables that control the operational condition of the FCC unit when fossil fuel yield constraints or prices change. To solve this problem, based on the challenges caused by the varied constraints, we establish a mathematical model and propose a fast adaptive differential evolution algorithm with an adaptive mutation strategy, a parameter adaptation strategy, a repaired strategy, and an enhanced strategy. In the proposed algorithm, we integrate the status information of each solution into the mutation strategy and parameter adaptation scheme to search for the best solution in the irregular feasible region of the operating variables. In addition, a repaired strategy is proposed to repair the infeasible operating variables with unknown bounds, and an enhanced strategy is presented to further improve the objective function value of the best solution. The experimental results on ten test scenarios with different fossil fuel yield constraints and prices demonstrate the robustness of the proposed algorithm for optimizing the operating variables of the FCC unit under uncertainty.

An Effective Cooperative Co-Evolutionary Algorithm for Distributed Flowshop Group Scheduling Problems.

This article addresses a novel scheduling problem, a distributed flowshop group scheduling problem, which has important applications in modern manufacturing systems. The problem considers how to arrange a variety of jobs subject to group constraints at a number of identical manufacturing cellulars, each one with a flowshop structure, with the objective of minimizing makespan. We explore the problem-specific knowledge and present a mixed-integer linear programming model, a counterintuitive paradox, and two suites of accelerations to save computational efforts. Due to the complexity of the problem, we consider a decomposition strategy and propose a cooperative co-evolutionary algorithm (CCEA) with a novel collaboration model and a reinitialization scheme. A comprehensive and thorough computational and statistical campaign is carried out. The results show that the proposed collaboration model and reinitialization scheme are very effective. The proposed CCEA outperforms a number of metaheuristics adapted from closely related scheduling problems in the literature by a significantly considerable margin.

Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs.

In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns.

In various flow shop scheduling problems, it is very common that a machine suffers from breakdowns. Under this situation, a robust and stable suboptimal scheduling solution is of more practical interest than a global optimal solution that is sensitive to environmental changes. However, blocking lot-streaming flow shop (BLSFS) scheduling problems with machine breakdowns have not yet been well studied up to date. This paper presents, for the first time, a multiobjective model of the above problem including robustness and stability criteria. Based on this model, an evolutionary multiobjective robust scheduling algorithm is suggested, in which solutions obtained by a variant of single-objective heuristic are incorporated into population initialization and two novel crossover operators are proposed to take advantage of nondominated solutions. In addition, a rescheduling strategy based on the local search is presented to further reduce the negative influence resulted from machine breakdowns.The proposed algorithm is applied to 22 test sets, and compared with the state-of-the-art algorithms without machine breakdowns. Our empirical results demonstrate that the proposed algorithm can effectively tackle BLSFS scheduling problems in the presence of machine breakdowns by obtaining scheduling strategies that are robust and stable.

Flexible Job-Shop Rescheduling for New Job Insertion by Using Discrete Jaya Algorithm.

Rescheduling is a necessary procedure for a flexible job shop when newly arrived priority jobs must be inserted into an existing schedule. Instability measures the amount of change made to the existing schedule and is an important metrics to evaluate the quality of rescheduling solutions. This paper focuses on a flexible job-shop rescheduling problem (FJRP) for new job insertion. First, it formulates FJRP for new job insertion arising from pump remanufacturing. This paper deals with bi-objective FJRPs to minimize: 1) instability and 2) one of the following indices: a) makespan; b) total flow time; c) machine workload; and d) total machine workload. Next, it discretizes a novel and simple metaheuristic, named Jaya, resulting in DJaya and improves it to solve FJRP. Two simple heuristics are employed to initialize high-quality solutions. Finally, it proposes five objective-oriented local search operators and four ensembles of them to improve the performance of DJaya. Finally, it performs experiments on seven real-life cases with different scales from pump remanufacturing and compares DJaya with some state-of-the-art algorithms. The results show that DJaya is effective and efficient for solving the concerned FJRPs.

An Improved Artificial Bee Colony Algorithm for Solving Hybrid Flexible Flowshop With Dynamic Operation Skipping.

In this paper, we propose an improved discrete artificial bee colony (DABC) algorithm to solve the hybrid flexible flowshop scheduling problem with dynamic operation skipping features in molten iron systems. First, each solution is represented by a two-vector-based solution representation, and a dynamic encoding mechanism is developed. Second, a flexible decoding strategy is designed. Next, a right-shift strategy considering the problem characteristics is developed, which can clearly improve the solution quality. In addition, several skipping and scheduling neighborhood structures are presented to balance the exploration and exploitation ability. Finally, an enhanced local search is embedded in the proposed algorithm to further improve the exploitation ability. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed DABC algorithm is favorably compared against several presented algorithms, both in solution quality and efficiency.

Hybrid particle swarm optimization for hybrid flowshop scheduling problem with maintenance activities.

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.