A petrochemical smart factory is a green, efficient, safe and sustainable modern factory that combines cutting-edge information and communication technology with petrochemical advanced technology and equipment. A Cyber-physical System (CPS) is the infrastructure of a petrochemical smart factory. Based on the future challenges of the petrochemical industry, this paper proposes the definition, connotation and framework of a petrochemical CPS and constructs a CPS system at the enterprise, unit and field levels, respectively. Furthermore, the paper provides theoretical support and implementation reference of a CPS in the petrochemical industry and other industries by investigating the construction practice of a multi-level CPS in the China Petrochemical Corporation (SINOPEC).

Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network. Tactical decisions are mid-term plans to allocate the production and distribution of materials, while operational decisions are related to the daily planning of the execution of manufacturing operations. These planning processes are conducted independently with minimal exchange of information between them. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas. We propose a network representation for the multilevel decision structure and analyze the components that are involved in finding integrated solutions that maximize the sum of the benefits of all nodes of the decision network. Although such task is very challenging, significant research progress has been made in each component of this structure. An overview of strategic models, mid-term planning models, and scheduling models is presented to address the solution of each node in the decision network. Coordination mechanisms for converging the integrated solutions are also analyzed, including solving large-scale models, multiobjective optimization, bi-level programming, and decomposition. We conclude by summarizing the challenges that hinder the full integration of multilevel decision making in supply chain management.

Boosting the resilience of power systems is a core requirement of smart grids. In fact, resilience enhancement is crucial to all critical infrastructure systems. In this study, we review the current research on system resilience enhancement within and beyond smart grids. In addition, we elaborate on resilience definition and resilience quantification and discuss several challenges and opportunities for system resilience enhancement. This study aims to deepen our understanding of the concept of resilience and develop a wide perspective on enhancing the system resilience for critical infrastructures.

L^♮-convexity, one of the central concepts in discrete convex analysis, receives significant attentions in the operations literature in recent years as it provides a powerful tool to derive structures of optimal policies and allows for efficient computational procedures. In this paper, we present a survey of key properties of L^♮-convexity and some closely related results in lattice programming, several of which were developed recently and motivated by operations applications. As a new contribution to the literature, we establish the relationship between a notion called m-differential monotonicity and L^♮-convexity. We then illustrate the techniques of applying L^♮-convexity through a detailed analysis of a perishable inventory model and a joint inventory and transshipment control model with random capacities.

This paper presents a review of methodologies for analyzing stochastic manufacturing and service systems. On the basis of the scale and level of details of operations, we can study stochastic systems using micro-, meso-, and macro-scopic models. Such a classification unifies stochastic modeling theory. For each model type, we highlight the advantages and disadvantages and the applicable situations. Micro-scopic models are based on quasi-birth-and-death process because of the phase-type distributed service times and/or Markov arrival processes. Such models are appropriate for modeling the detailed operations of a manufacturing system with relatively small number of servers (production facilities). By contrast, meso-scopic and macro-scopic models are based on the functional central limit theorem (FCLT) and functional strong law of large numbers (FSLLN), respectively, under heavy-traffic regimes. These high-level models are appropriate for modeling large-scale service systems with many servers, such as call centers or large service networks. This review will help practitioners select the appropriate level of modeling to enhance their understanding of the dynamic behavior of manufacturing or service systems. Enhanced understanding will ensure that optimal policies can be designed to improve system performance. Researchers in operation analytics and optimization of manufacturing and logistics also benefit from such a review.

In practice, an energy consumer often consists of a set of residential or commercial buildings, with individual units that are expected to cooperate to achieve overall optimization under modern electricity operations, such as time-of-use price. Global utility is decomposed to the payoff of each player, and each game is played over a prediction horizon through the design of a series of sliding window games by treating each building as a player. During the games, a distributed learning algorithm based on game theory is proposed such that each building learns to play a part of the global optimum through state transition. The proposed scheme is applied to a case study of three buildings to demonstrate its effectiveness.

As part of China’s “the Belt and Road” strategy, China Railway Express provides alternative shipping routes and transportation modes from Asia to Europe and creates new opportunities for intermodal transportation in the shipping industry. A time–distance-based cost (time cost) function was proposed to compare China Railway Express with traditional transportation modes. Time cost was related to different types of cargoes, which exhibit distinct sensitivity to time. Using the proposed cost function as basis, we identified the cost indifference area where total costs are equal. Further analysis was performed for selecting the transportation mode and supply area for a specific cargo. This study provides various parties, such as business owners, the government, and the shipping industry, with many valuable insights.

An increasing number of novel and highly specialized computer-aided decision-making technologies for short-term production scheduling in oil refineries has emerged and evolved over the past two decades, thereby encouraging refiners to permanently rethink the way the refining business is operated and managed. In this report, we discuss the key lessons learned from one of the pioneering, yet daring, enterprise-wide programs entirely implemented in an energy company devoted to developing and implementing an advanced refinery production scheduling (RPS) technology, i.e., the RPS system of Petrobras. Apart from mathematical and information technology issues, the long-term sustainability of a successful RPS project is, we argue, the outcome of a virtuous cycle grounded on permanent actions devoted to improving technical education inside the organization, reinspecting organizational cultures and operational paradigms, and developing working processes.

This study aims to solve a typical long-term strategic decision problem on supply chain network design with consideration to uncertain demands. Existing methods for these problems are either deterministic or limited in scale. We analyze the impact of uncertainty on demand based on actual large data from industrial companies. Deterministic equivalent model with nonanticipativity constraints, branch-and-fix coordination, sample average approximation (SAA) with Bayesian bootstrap, and Latin hypercube sampling were adopted to analyze stochastic demands. A computational study of supply chain network with front-ends in Europe and back-ends in Asia is presented to highlight the importance of stochastic factors in these problems and the efficiency of our proposed solution approach.

Petrochemical industry plays an important role in the development of the national economy. Purified terephthalic acid (PTA) is one of the most important intermediate raw materials in the petrochemical and chemical fiber industries. PTA production has two parts: p-xylene (PX) oxidation process and crude terephthalic acid (CTA) hydropurification process. The CTA hydropurification process is used to reduce impurities, such as 4-carboxybenzaldehyde, which is produced by a side reaction in the PX oxidation process and is harmful to the polyester industry. From the safety and economic viewpoints, monitoring this process is necessary. Four main faults of this process are analyzed in this study. The common process monitoring methods always use T ² and SPE statistic as control limits. However, the traditional methods do not fully consider the economic viewpoint. In this study, a new economic control chart design method based on the differential evolution (DE) algorithm is developed. The DE algorithm transforms the economic control chart design problem to an optimization problem and is an excellent solution to such problem. Case studies of the main faults of the hydropurification process indicate that the proposed method can achieve minimum profit loss. This method is useful in economic control chart design and can provide guidance for the petrochemical industry.

The global production of bio-based chemical products, particularly biofuel products, has tremendously increased over the last decade. Driven largely by a new legislation, this increase has generated the commercialization of new products and processes. Unfortunately, alongside these developments were a significant number of accidents and explosions at biofuel facilities, entailing property damage, injury, and even deaths. The aim of this current study is to draw attention to incidents that occurred in biofuel facilities and clarify the misconceptions that cause people to ignore safety in bio-refineries. A process hazard analysis (PHA) method, namely the hazard and operability study (HAZOP), is first used in biofuel production. This method is an ethanol distillation and dehydration process. Through the HAZOP analysis, 36 recommended action items are proposed, and all recommendations are accepted. The case study reveals that potential high-level risks exist in the current biofuel process design and operating procedures, and these risks can be better controlled if they can be previously identified.

This study addresses the problem of two-stage scheduling on batch and single machines with limited waiting time constraint; thus, the makespan is minimized. A mixed-integer linear programming model is proposed for this problem. Three tight lower bounds and a heuristic algorithm are developed. The worst-case performance of the proposed algorithm is discussed. A hybrid differential evolution algorithm is also developed to improve the solution quantity. Numerical results show that the hybrid algorithm is capable of obtaining high-quality solutions and exhibits a competitive performance