Solving Biobjective Distributed Flow-Shop Scheduling Problems With Lot-Streaming Using an Improved Jaya Algorithm.

A distributed flow-shop scheduling problem with lot-streaming that considers completion time and total energy consumption is addressed. It requires to optimally assign jobs to multiple distributed factories and, at the same time, sequence them. A biobjective mathematic model is first developed to describe the considered problem. Then, an improved Jaya algorithm is proposed to solve it. The Nawaz-Enscore-Ham (NEH) initializing rule, a job-factory assignment strategy, the improved strategies for makespan and energy efficiency are designed based on the problem's characteristic to improve the Jaya's performance. Finally, experiments are carried out on 120 instances of 12 scales. The performance of the improved strategies is verified. Comparisons and discussions show that the Jaya algorithm improved by the designed strategies is highly competitive for solving the considered problem with makespan and total energy consumption criteria.

Language-based Opacity Verification and Enforcement in the Framework of Labeled Petri Nets.

This work deals with the language-based opacity verification and enforcement problems in discrete event systems modeled with labeled Petri nets. Opacity is a security property that relates to privacy protection by hiding secret information of a system from an external observer called an "intruder". A secret can be a subset of a system's language. In this case, opacity is referred to as language-based opacity. A system is said to be language-based opaque if an intruder, with a partial observation on the system's behavior, cannot deduce whether the sequences of events corresponding to the generated observations are included in the secret language or not. We propose a novel and efficient approach for language-based opacity verification and enforcement, using the concepts of basis markings and basis partition. First, a sufficient condition is formulated to check language-based opacity for labeled Petri nets by solving an integer-programming problem. A unique graph, called a modified basis reachability graph (MBRG), is then derived to verify different language-based opacity properties. The proposed method relaxes the acyclicity assumption of the unobservable transition subnet thanks to the basis partition notion. A new embedded insertion function technique is also provided to deal with opacity enforcement. This technique ensures that no new observed behavior is created. A verification algorithm is developed to check the enforceability of a system. Finally, once a system is proved to be enforceable, an algorithm is given to construct a new structure, called an insertion automaton, which synthesizes all possible insertion functions that ensure opacity.

SMSPL: Robust Multimodal Approach to Integrative Analysis of Multiomics Data.

With the recent advancement of technologies, it is progressively easier to collect diverse types of genome-wide data. It is commonly expected that by analyzing these data in an integrated way, one can improve the understanding of a complex biological system. Current methods, however, are prone to overfitting heavy noise such that their applications are limited. High noise is one of the major challenges for multiomics data integration. This may be the main cause of overfitting and poor performance in generalization. A sample reweighting strategy is typically used to cope with this problem. In this article, we propose a robust multimodal data integration method, called SMSPL, which can simultaneously predict subtypes of cancers and identify potentially significant multiomics signatures. Especially, the proposed method leverages the linkages between different types of data to interactively recommend high-confidence samples, adopts a new soft weighting scheme to assign weights to the training samples of each type, and then iterates between weights recalculating and classifiers updating. Simulation and five real experiments substantiate the capability of the proposed method for classification and identification of significant multiomics signatures with heavy noise. We expect SMSPL to take a small step in the multiomics data integration and help researchers comprehensively understand the biological process.

Current-state opacity verification in discrete event systems using an observer net.

Due to the proliferation of contemporary computer-integrated systems and communication networks, there is more concern than ever regarding privacy, given the potential for sensitive data exploitation. A recent cyber-security research trend is to focus on security principles and develop the foundations for designing safety-critical systems. In this work, we investigated the problem of verifying current-state opacity in discrete event systems using labeled Petri nets. A system is current-state opaque provided that the current-state estimate cannot be revealed as a subset of secret states. We introduced a new sub-model of the system, named an observer net. The observer net have the same structure as the plant, but it is distinguished by the use of colored markers as well as simultaneous and recursive transition enabling and firing, which offer an efficient state estimation. We considered two settings of the proposed approach: an on-line setting, in which a current-state opacity algorithm is proposed. The algorithm waits for the occurrence of an observable event and determines if the current observation of a plant reveals the secret behaviour, as well as, an off-line setting, where the verification problem is solved based on a state estimator called a colored estimator. In this context, necessary and sufficient conditions for verifying opacity are developed with illustrative examples to demonstrate the presented approach.

Repercussions of COVID-19 pandemic on solid waste generation and management strategies.

It has been over ten months since the beginning of the 2019 coronavirus disease (COVID-2019), and its impact on solid waste management, especially medical waste, is becoming clearer. This study systematically reviews the potential influences of the COVID-19 pandemic on medical waste, personal protection equipment waste and municipal solid waste (MSW), and discusses the corresponding measures and policies of solid waste management in typical countries. The results show that the generation of medical waste from the pandemic increased significantly, with 18%-425% growth. It is estimated that the daily output of COVID-19 medical waste increased from 200 t/d on Feb. 22 to over 29000 t/d at the end of September 2020 throughout the world. The use of personal protective equipment will continue to grow in the long-term, while the blockade and isolation measures greatly reduced the volume of commercial waste, especially for tourist cities, and part of this waste was transferred to household waste. Residents' attitudes and behavior toward food waste have changed due to the COVID-19 pandemic. In response to the pandemic, international organizations and several countries have issued new policies and guidelines and adjusted their management strategies for medical waste and MSW treatment. The pandemic has brought specific challenges to the disposal capacity of medical waste worldwide. It has also brought about the stagnation of policies related to the reduction of plastic products and waste recycling. This study will provide some useful information for managers and governmental officials on effective solid waste management during and after the COVID-19 pandemic.

On a maximally permissive deadlock prevention policy for automated manufacturing systems by using resource-oriented Petri nets.

It is theoretically and practically significant to synthesize a maximally permissive (optimal) controller to prevent deadlocks in an automated manufacturing system (AMS). With an AMS being modeled with Petri nets, by the existing methods, integer linear programming (ILP) problems are usually formulated and solved to obtain optimal policies by forbidding illegal markings at the same time no legal marking is excluded. Without an efficient technique for solving an ILP, such a method is usually computationally prohibitive. A resource-oriented Petri net (ROPN) is employed to model a class of AMS for resolving the deadlock control problem with maximal permissiveness in this paper. Efficient methods are developed to figure out the key structures in an ROPN model for deadlock prevention. Based on the structural properties of ROPN models, this work explores several types of illegal markings that can be prohibited optimally by structural analysis. For these markings, a deadlock prevention policy can be derived in an algebraic way without solving a notorious ILP problem. For the other markings, linear programming (LP), instead of ILP, approaches are developed to forbid them optimally. Thus, a maximally permissive controller can be developed while the computational cost is reduced greatly. The proposed methods are verified by typical examples in the literature.

Most permissive liveness-enforcing Petri net supervisors for discrete event systems via linear monitors.

This paper proposes a deadlock prevention method to design a maximally permissive liveness-enforcing pure Petri net supervisor for a discrete event system, if such a supervisor exists; otherwise, it obtains the most permissive one in the sense that no other pure liveness-enforcing supervisors via linear monitors can be more permissive than it. This paper exploits an iterative method. At each iteration, a first-met bad marking (FBM) is singled out and an integer linear programming problem (ILPP) is configured. If a feasible solution can be found for the ILPP, then a place invariant (PI) is designed to prohibit the FBM from being reached while no legal marking is forbidden. If the ILPP has no solution, we collect all these FBMs that cannot be optimally controlled. For each of such FBMs, another ILPP is designed to find the least number of legal markings whose reachability conditions contradict the current considered FBM and enumerate all the optimal solutions of this ILPP. Based on it, we develop a 0-1 linear programming problem to find the maximal number of legal markings after removing all the contradictory legal markings. Then, the new sets of legal markings and FBMs are obtained, and we return to the iteration stage to redesign a PI to control each FBM if the ILPP has a feasible solution. Repeat the above process until no FBM can be reached. Finally, a most permissive pure liveness-enforcing supervisor via linear monitors is derived. Two Petri net models are used to illustrate the proposed method.

Modeling and deadlock avoidance of automated manufacturing systems with multiple automated guided vehicles.

An automated manufacturing system (AMS) contains a number of versatile machines (or workstations), buffers, an automated material handling system (MHS), and is computer-controlled. An effective and flexible alternative for implementing MHS is to use automated guided vehicle (AGV) system. The deadlock issue in AMS is very important in its operation and has extensively been studied. The deadlock problems were separately treated for parts in production and transportation and many techniques were developed for each problem. However, such treatment does not take the advantage of the flexibility offered by multiple AGVs. In general, it is intractable to obtain maximally permissive control policy for either problem. Instead, this paper investigates these two problems in an integrated way. First we model an AGV system and part processing processes by resource-oriented Petri nets, respectively. Then the two models are integrated by using macro transitions. Based on the combined model, a novel control policy for deadlock avoidance is proposed. It is shown to be maximally permissive with computational complexity of O (n2) where n is the number of machines in AMS if the complexity for controlling the part transportation by AGVs is not considered. Thus, the complexity of deadlock avoidance for the whole system is bounded by the complexity in controlling the AGV system. An illustrative example shows its application and power.