A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem

Green scheduling in the manufacturing industry has attracted increasing attention in academic research and industrial applications with a focus on energy saving. As a typical scheduling problem, the no-wait flow-shop scheduling has been extensively studied due to its wide industrial applications. However, energy consumption is usually ignored in the study of typical scheduling problems. In this article, a two-stage cooperative evolutionary algorithm with problem-specific knowledge called TS-CEA is proposed to address energy-efficient scheduling of the no-wait flow-shop problem (EENWFSP) with the criteria of minimizing both makespan and total energy consumption. In TS-CEA, two constructive heuristics are designed to generate a desirable initial solution after analyzing the properties of the problem. In the first stage of TS-CEA, an iterative local search strategy (ILS) is employed to explore potential extreme solutions. Moreover, a hybrid neighborhood structure is designed to improve the quality of the solution. In the second stage of TS-CEA, a mutation strategy based on critical path knowledge is proposed to extend the extreme solutions to the Pareto front. Moreover, a co-evolutionary closed-loop system is generated with ILS and mutation strategies in the iteration process. Numerical results demonstrate the effectiveness and efficiency of TS-CEA in solving the EENWFSP.

A Two-Stage Cooperative Evolutionary Algorithm With Problem-Specific Knowledge for Energy-Efficient Scheduling of No-Wait Flow-Shop Problem

The distributed production lines widely exist in modern supply chains and manufacturing systems. This paper aims to address the distributed no-wait flow shop scheduling problem (DNWFSP) with the makespan criterion by using proposed iterated greedy (IG) algorithms. Firstly, several speed-up methods based on the problem properties of DNWFSP are investigated to reduce the evaluation time of neighborhood with O(1) complexity. Secondly, an improved NEH heuristic is proposed to generate a promising initial solution, where the iteration step of the insertion step of NEH is applied to the factory after inserting a new job. Thirdly, four neighborhood structures (i.e. Critical_swap_single, Critical_insert_single, Critical_swap_multi, Critical_insert_multi) based on factory assignment and job sequence adjustment are employed to escape from local optima. Fourthly, four local search methods based on neighborhood moves are proposed to enhance local searching ability, which contains LS_insert_critical_factory1, LS_insert_critical_factory2, LS_swap, and LS_insert. Finally, to organize neighborhood moves and local search methods efficiently, we incorporate them into the framework of variable neighborhood search (VNS), variable neighborhood descent (VND) and random neighborhood structure (RNS). Furthermore, three variants of IG algorithms are presented based on the designed VNS, VND and RNS. The parameters of the proposed IG algorithms are tuned through a design of experiments on randomly generated benchmark instances. The effectiveness of the initialize phase and local search methods is shown by numerical comparison, and the comparisons with the recently published algorithms demonstrate the high effectiveness and searching ability of the proposed IG algorithms for solving the DNWFSP. Ultimately, the best solutions of 720 instances from the well-known benchmark set of Naderi and Ruiz for the DNWFSP are proposed.

Optimization of makespan for the distributed no-wait flow shop scheduling problem with iterated greedy algorithms

The no-wait flow shop scheduling problem (NWFSP) plays an essential role in the manufacturing industry. Inspired by the overall process of biogeography theory, the standard biogeography-based optimization (BBO) was constructed with migration and mutation operators. In this paper, a hybrid biogeography-based optimization with variable neighborhood search (HBV) is implemented for solving the NWFSP with the makespan criterion. The modified NEH and the nearest neighbor mechanism are employed to generate a potential initial population. A hybrid migration operator, which combines the path relink technique and the block-based self-improvement strategy, is designed to accelerate the convergence speed of HBV. The iterated greedy (IG) algorithm is introduced into the mutation operator to obtain a promising solution in exploitation phase. A variable neighbor search strategy, which is based on the block neighborhood structure and the insert neighborhood structure, is designed to perform the local search around the current best solution in each generation. Furthermore, the global convergence performance of the HBV is analyzed with the Markov model. The computational results and comparisons with other state-of-art algorithms based on Taillard and VRF benchmark show that the efficiency and performance of HBV for solving NWFSP.

A hybrid biogeography-based optimization with variable neighborhood search mechanism for no-wait flow shop scheduling problem

Inspired by the phenomenon of teaching and learning introduced by the teaching-learning based optimization (TLBO) algorithm, this paper presents a hybrid discrete optimization algorithm based on teaching-probabilistic learning mechanism (HDTPL) to solve the no-wait flow shop scheduling (NWFSSP) with minimization of makespan. The HDTPL consists of four components, i.e. discrete teaching phase, discrete probabilistic learning phase, population reconstruction, neighborhood search. In the discrete teaching phase, Forward-insert and Backward-insert are adopted to imitate the teaching process. In the discrete probabilistic learning phase, an effective probabilistic model is established with consideration of both job orders in the sequence and similar job blocks of selected superior learners, and then each learner interacts with the probabilistic model by using the crossover operator to learn knowledge. The population reconstruction re-initializes the population every several generations to escape from a local optimum. Furthermore, three types of neighborhood search structures based on the speed-up methods, i.e. Referenced-insert-search, Insert-search and Swap-search, are designed to improve the quality of the current learner and the global best learner. Moreover, the main parameters of HDTPL are investigated by the Taguchi method to find appropriate values. The effectiveness of HDTPL components is analyzed by numerical comparisons, and the comparisons with some efficient algorithms demonstrate the effectiveness and robustness of the proposed HDTPL in solving the NWFSSP.

A hybrid discrete optimization algorithm based on teaching-probabilistic learning mechanism for no-wait flow shop scheduling

This paper proposes a Block-shifting Simulated Annealing (BSA) algorithm for the no-wait flowshop scheduling problem (NWFSP) to minimize makespan. The proposed algorithm makes use of the objective incremental properties of NWFSP and embeds a block-shifting operator based on k-insertion moves into the algorithm framework of simulated annealing. A major advantage of the BSA algorithm lies in its easy implementation since it does not involve sophisticated evolutionary strategy and parameter tuning process. In addition to its simplicity, BSA is shown to be very effective. Through experimental comparisons, the effectiveness of the block-shifting operator is clearly revealed. In addition, the BSA algorithm is proved to be more effective and robust than the state-of-the-art algorithms for solving the NWFSP.

A novel Block-shifting simulated annealing algorithm for the no-wait flowshop scheduling problem

In order to satisfy the confidentiality and integrity of the communication link data in the wireless sensor networks, and solve the key escrow problem of the identity-based signcryption scheme, a certificateless broadcast signcryption scheme is proposed. The scheme has dynamically adjusting authorization set without updating private key. In the encryption stage, since part of the operation is outsourced to a trusted third party to relieve the computational burden of the broadcaster, the scheme achieves lightweight broadcast encryption, which is suitable for resource-constrained devices in wireless sensor networks. The partial key is hidden in the secret value sequence to disguise the recipient’s identity and enhance the security of ciphertext. Lastly, we make the comparisons of the proposed work with the existing works in terms of functions and computational cost. In addition, performance evaluation shows that the scheme has higher efficiency in the signcryption and unsigncryption phase and is more suitable for practical broadcast service applications. 

Broadcast signcryption scheme based on certificateless in wireless sensor network

With the wide application of 5G network, much research has been carried out in recent years toward the 5G network slicing technology in order to save 5G network resources and satisfy the service requirements of different users. Different public key cryptosystems may be deployed in different 5G network slicings. Therefore, heterogeneous signcryption is required to achieve secure communications between different 5G network slices. The existing scheme is a one-to-one communication between 5G network slicings. To obtain one-to-many and many-to-one secure communications, two new privacy-preserving heterogeneous signcryption schemes have been proposed in this article based on 5G network slicings, which can accomplish mutual communications between the public key infrastructure (PKI) and the certificateless public-key cryptography. In this work, we present PKI $\to $ CLC completely anonymous multireceiver signcryption (PMRCHS) and CLC $\to $ PKI heterogeneous aggregate signcryption (CPHAS) schemes based on the 5G Internet of Things (IoT) slicing and the 5G Internet of Vehicle (IoV) slicing. Under the random oracle model (ROM), the proposed schemes have proved to satisfy confidentiality and unforgeability under the computational Diffie–Hellman problem and discrete logarithm problem (DLP). Furthermore, we make comparisons of the proposed work with the existing works in terms of computational cost, communication cost, anonymity, and communication direction. The results show that the proposed schemes are more secure and effective.

https://www.researchsquare.com/article/rs-1152978/latest.pdf

Privacy-Preserving Mutual Heterogeneous Signcryption Schemes Based on 5G Network Slicing

The development of the Internet has dramatically facilitated the present society. While a large amount of data is produced, private data security involves users' data rights. Traditionally, attribute-based encryption (ABE) still needs to improve efficiency in the face of a large amount of data. The introduction of edge computing environments reduces the operating costs of lightweight devices by outsourcing some encryption and decryption operations. Therefore, we design an efficient attribute-based encryption scheme under edge computing that supports keyword search. Data owners and users do not disclose private information to the cloud during the search phase. Our work considers the computational efficiency of ordinary and lightweight users, and partial operations can be outsourced according to the demand during decryption, which is more flexible and applicable to practical applications. The scheme also supports efficient attribute revocation and ciphertext update in the cloud environment. Moreover, the proposed algorithm has been proven to be IND-sCP-CPA security and IND-CKA security. The work compares our algorithm with some typical schemes in terms of storage overhead and computation cost in the end. The performance analysis results indicate that the proposed work is efficient and suitable for edge computing. 

Siwei Zhou

Papers

A novel Block-shifting simulated annealing algorithm for the no-wait flowshop scheduling problem

Broadcast signcryption scheme based on certificateless in wireless sensor network

Privacy-Preserving Mutual Heterogeneous Signcryption Schemes Based on 5G Network Slicing

Attribute-Based Searchable Encryption in Edge Computing for Lightweight Devices

Attribute-based searchable encrypted scheme with edge computing for Industrial Internet of Things