Cold-chain products are time-sensitive and perishable and pose the risk of failure if they are transported to a distant location. Thus, there is a need to analyze their quality during distribution so that the customers may receive optimal-quality products. To address this issue, this study integrates inspection strategies with the sustainable distribution system of multi-quality multiple-cold-chain products. A bi-objective model of cost and emission is proposed under the constraints of heterogeneous vehicle and time window. Furthermore, this study intends to address the following questions: which inspection strategy helps to ensure the potency of delivered products, and what is the impact of quality differentiation on the value of objective functions? A set of meta-heuristics is used for implementing the model using a rich panel of experiments. The results reveal that the quality conditions of different products impact the solutions of cost and emissions. Moreover, the conformity strat... [more]

Occupant behavior and business processes in a building environment constitute an inseparable set of important factors that drives energy consumption. Existing methodologies for building energy management lag behind in addressing these core parameters by focusing explicitly on the building’s structural components. Additional layers of information regarding indoor and outdoor environmental conditions and occupant behavior patterns, mostly driven by everyday business processes (schedules, loads, and specific business activities related to occupancy patterns and building operations), are necessary for the effective and efficient modeling of building energy performance in order to establish a holistic energy efficiency management framework. The aim of this paper was to develop a context-driven framework in which multiple levels of information regarding occupant behavior patterns resulting from everyday business processes were incorporated for efficient energy management in buildings. A prel... [more]

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is ben... [more]

Due to rapid economic development and population growth, environmental pollution problems such as urban pollution and depletion of natural resources have become increasingly prominent. Municipal solid waste is part of these problems. However, waste is actually an improperly placed resource. As a part of green supply chain management, remanufacturing can turn waste products into remanufactured products for resale. Based on the development status of China’s remanufacturing industry, this paper establishes three Stackelberg game models, namely the free recycling model (model N), the government regulation model based on the reward−penalty mechanism (model G), and the government dual-intervention model (model GF). In this study, the standard solution method for the Stackelberg game method, namely the backward induction method, is applied to solve the dynamic game equilibrium. For comparison, a further numerical analysis is also carried. The research results show that: (1) in the closed-loop... [more]

This work presents a review that aims to characterize the policy evaluation practices regarding the public policies on energy, with a focus on the metrics: concerns, objectives, and indicators. As key novelty, emphasis was put into finding attributes and metrics that can be used to assess effectiveness, not only efficacy or efficiency. The concerns and objectives were organized into four categories: Institutional, Environmental, Economic, and Social. For every category, detailed and condensed concerns were identified. It was attempted to find indicators for every condensed concern, which resulted in 15 core indicators.

A model developed at the University of Tomsk, Russia, for high latitudes (over 55° N) is proposed and applied to the analysis and observation of the solar resource in the state of Sonora in the northwest of Mexico. This model utilizes satellite data and geographical coordinates as inputs. The objective of this research work is to provide a low-cost and reliable alternative to field meteorological stations and also to obtain a wide illustration of the distribution of solar power in the state to visualize opportunities for sustainable energy production and reduce its carbon footprint. The model is compared against real-time data from meteorological stations and satellite data, using statistical methods to scrutinize its accuracy at local latitudes (26−32° N), where a satisfactory performance was observed. An annual geographical view of available solar radiation against maximum and minimum temperatures for all the state municipalities is provided to identify the photovoltaic electricity g... [more]

This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for... [more]

The Time-Stepped Linear Superposition Method (TLSM) has been used previously to model and analyze the propagation of multiple competitive hydraulic fractures with constant internal pressure loads. This paper extends the TLSM methodology, by including a time-dependent injection schedule using pressure data from a typical diagnostic fracture injection test (DFIT). In addition, the effect of poro-elasticity in reservoir rocks is accounted for in the TLSM models presented here. The propagation of multiple hydraulic fractures using TLSM-based codes preserves infinite resolution by side-stepping grid refinement. First, the TLSM methodology is briefly outlined, together with the modifications required to account for variable time-dependent pressure and poro-elasticity in reservoir rock. Next, real world DFIT data are used in TLSM to model the propagation of multiple dynamic fractures and study the effect of time-dependent pressure and poro-elasticity on the development of hydraulic fracture n... [more]

Industry 4.0 houses diverse technologies including wireless communication and shared networks for internal and external operations. Due to the wireless nature and remote operability, the exposure to security threats is high. Cyber risk detection and mitigation are prominent for secure industrial operations and planned outcomes. In addition, the system faces the threat of intelligence attacks, security standards issues, privacy concerns and scalability problems. The cyber risk related research problems influence overall data transmission in industry wireless communication networks. For augmenting communication security through cyber risk detection, this article introduces an Explicit Risk Detection and Assessment Technique (ERDAT) for cyber threat mitigation in the industrial process. A fuzzy harmony search algorithm powers this technique for identifying the risk and preventing its impact. The harmony search algorithm mimics the adversary impact using production factors such as process... [more]

Aiming at the intelligent production line scheduling problem, a production line scheduling method considering multiple constraints was proposed. Considering the constraints of production task priority, time limit, and urgent task insertion, a production process optimization scheduling calculation model was established with the minimum waiting time and minimum completion time as objectives. The BAS was used to solve the problem, and a fast response mechanism for emergency processing under multiple constraints was established. Compared with adaptive particle swarm optimization (APSO) and non-dominated sorting genetic algorithm-II (NSGA-II) operation, this algorithm showed its superiority. The practical application in garment processing enterprises showed that the method was effective and can reduce the completion time and waiting time.

In this paper, we address a multiload AGVs workshop scheduling problem with limited buffer capacity. This has important theoretical research value and significance in the manufacturing field in considering the efficient multiload AGVs widely used today, and in the limited buffer area in production practice. To minimize the maximum completion time, an improved ant colony optimization-simulated annealing algorithm based on a multiattribute dispatching rule is proposed. First, we introduce a multiattribute dispatching rule, which combines two attributes, delivery completion time and input queue through dynamic weights that are determined by the information about the system, using the multiattribute dispatching rule to construct the initial solution. Then, with the ant colony optimization-simulated annealing algorithm as the basic framework, we propose a method for calculating transfer probability based on the multiattribute dispatching rule, which obtains heuristic information through the... [more]

Biomasses are renewable sources used in energy conversion processes to obtain diverse products through different technologies. The production chain, which involves delivery, logistics, pre-treatment, storage and conversion as general components, can be costly and uncertain due to inherent variability. Optimization methods are widely applied for modeling the biomass supply chain (BSC) for energy processes. In this qualitative review, the main aspects and global trends of using geographic information systems (GISs), linear programming (LP) and neural networks to optimize the BSC are presented. Modeling objectives and factors considered in studies published in the last 25 years are reviewed, enabling a broad overview of the BSC to support decisions at strategic, tactical and operational levels. Combined techniques have been used for different purposes: GISs for spatial analyses of biomass; neural networks for higher heating value (HHV) correlations; and linear programming and its variatio... [more]

In recent years, the stability of the distribution network has declined due to the large proportion of the uses of distributed generation (DG) with the continuous development of renewable energy power generation technology. Meanwhile, the traditional distribution network operation mode cannot keep the balance of the source and load. The operation mode of the active distribution network (ADN) can effectively reduce the decline in operation stability caused by the high proportion of DG. Therefore, this work proposes a bi-layer model for the planning of the electricity−hydrogen hybrid energy storage system (ESS) considering demand response (DR) for ADN. The upper layer takes the minimum load fluctuation, maximum user purchase cost satisfaction, and user comfort as the goals. Based on the electricity price elasticity matrix model, the optimal electricity price formulation strategy is obtained for the lower ESS planning. In the lower layer, the optimal ESS planning scheme is obtained with t... [more]

Intake valve parameters significantly affect the performance of the piston-type expander (PTE). To improve compressed energy utilization efficiency, intake valve parameters need to be regulated according to load. In this paper, an electro-pneumatic variable valve actuation (EPVVA) system was proposed for independent control distributing valve parameters. The trajectory planning for the intake valve was proposed to obtain good mechanical properties. Then, the intake valve duration angle was optimized, and the optimum intake valve lift curves were obtained at different rotational speeds. Results show that the energy efficiency decreased with the intake valve duration angle increasing. The output power ascended sharply with increasing intake valve duration angle, but the amplitude of power growth decreased. The output power had a maximum value at a specific intake valve duration angle. The gray relation analysis (GRA) method was applied to obtain the optimum intake duration angle based on... [more]

Due to the current complexity of the supply chain, multi-echelon inventory management has become challenging while also being an interesting field of research as it allows efficient control of supply chain interdependencies. It became clear to many researchers that analytical models are no longer effective for addressing the multi-echelon inventory management problem. Simulation can be used to assess and quantify the impact of each inventory strategy on a supply chain performance. Our paper aims to provide a simulation-based approach to guide decision makers select and validate a multi-echelon distribution inventory system. The proposed approach is composed of four major steps that involve characterization of the current supply chain, conceptual modeling of the multi-echelon inventory system alternatives, and finally, simulation modeling using appropriate simulation software to compare and test different options. The approach was also tested and validated through an application to the... [more]

Due to the increasing level of customization and globalization of competition, rescheduling for distributed manufacturing is receiving more attention. In the meantime, environmentally friendly production is becoming a force to be reckoned with in intelligent manufacturing industries. In this paper, the energy-efficient distributed hybrid flow-shop rescheduling problem (EDHFRP) is addressed and a knowledge-based cooperative differential evolution (KCDE) algorithm is proposed to minimize the makespan of both original and newly arrived orders and total energy consumption (simultaneously). First, two heuristics were designed and used cooperatively for initialization. Next, a three-dimensional knowledge base was employed to record the information carried out by elite individuals. A novel DE with three different mutation strategies is proposed to generate the offspring. A local intensification strategy was used for further enhancement of the exploitation ability. The effects of major paramet... [more]

In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology transformation, to optimize the thermoelectric coupling relationship and improve the regulation capacity of the CEHS. Firstly, the electric and heat output models of CHP units considering the optimal control process, were constructed and used to analyze the electric−thermal characteristics and the impact of unit pressure safety under variable load input. On this basis, CHP units, electric boilers, wind power units, and thermal power units are optimally scheduled to minimize system operating costs. Finally, a simultaneous method of “discrete first, optimize later” is proposed to solve the dynamic optimal scheduling problem. The simulation results verify that the optimal scheduling considering the optimal control... [more]

Over the past 20 years, civil aviation has substantially reduced its environmental impact to augment sustainable transportation. In Spain, the domestic market has been habitually characterized by a few enterprises providing air transport services linked to scheduled flights on domestic corridors. Because of geographic diversity and the highly concentrated population characterizing this southern European country, many of them could not be supplied by alternative transport modes in terms of both time and distance by comparison with air transportation. For air quality monitoring from 139 national corridors, this paper aims to study related aviation emissions to conduct an economic analysis in terms of positive or negative externalities. For such purposes, the study focused on these domestic routes served by the five most important Spanish airports, specifically on the number of passengers transported from 2011 to 2020. Up to 10 aircraft types representing no more than 89% of regular opera... [more]

The purpose of this paper is to explore the impact of inventory turnover on the profitability level of Saudi manufacturers. The data comprises 78 manufacturers listed on the Saudi Stock Exchange and was used to test the research hypothesis. The related data over the 2017−2021 period were collected from annual reports and the Datastream database. After running a multiple regression analysis with a fixed effects model, findings showed that the higher the inventory turnover ratio, the higher the cost which could be suppressed, and the greater the profitability of a company. The outcomes of this study have significant implications for managerial accounting issues in the setting of Saudi Arabia. Further, they provide policy recommendations to decision makers and assist managers in enhancing sustainability in the manufacturing sector. This research is the first to investigate this relationship including the impact of COVID-19 among Saudi companies in several industries, thus filling a gap in... [more]

A public health emergency of international concern, such as a pandemic, disrupts the normal operation of the global supply chain, which necessitates in-depth research on supply chain management. In this paper, we used bibliometric and content analysis to provide a systematic analysis of the supply chain industry from this background. The descriptive analysis provides insights into the publication growth trajectory, in terms of the contributing authors, countries, and subject categories, which presents an intuitive display of previous research. In addition, the existing research mainly covers three dimensions of supply chain disruption, strategies, and sustainability, which can be clustered into supply chain disruption, disruption recovery, reconfiguration, digital intelligence, optimization, and sustainability. By revisiting the supply chain industry, we explored the transformation of its characteristics in the pandemic, covering themes ranging from expansion to contraction, from tradi... [more]

The automobile industry is the pillar industry of the national economy. The good operation of the automobile supply chain is conducive to the sustainable development of the economy and social economy. In recent years, the popular research of automotive supply chain disruption risk management has been widely of concern by both business and academic practitioners. It is observed that most of the literature has focused only on a particular journal or field; there is a distinct lack of comprehensive bibliometric review of two decades, of research on automotive supply chain disruption risk management. This paper delivers a comprehensive bibliometric analysis that provides a better understanding not previously fully evaluated by earlier studies in the field of automotive supply chain disruption risk management. We used the 866 journal article during the period between 2000 and 2022 from the WOS database as sample data. Highlights research topics and trends, key features, developments, and po... [more]

This study aims to examine how the bilateral matching decision making of manufacturing enterprises that are seeking partners in the manufacturing supply chain can be improved by taking into consideration evaluation criteria for organizational quality-specific immunity. This study constructs an evaluation indicator system to measure organizational quality-specific immunity based on immune theory. The system’s evaluation criteria are based on the key components of organizational quality-specific immunity. We also construct bilateral matching evaluation and decision-making models using interval-valued hesitant fuzzy information and bidirectional projection technology (BMIHFIBPT). The interval-valued bilateral fuzzy bidirectional projection technology is applied to solve a combination satisfaction and matching optimization model. Empirical analysis is carried out to assess both the supply and demand sides of representative manufacturing enterprises in the manufacturing supply chain, match... [more]

Although mature industrial wireless sensor network applications increasingly require low-power operations, deterministic communications, and end-to-end reliability, it is very difficult to achieve these goals because of link burstiness and interference. In this paper, we propose a novel link quality estimation mechanism named the burstiness distribution metric, which uses the distribution of burstiness in the links to deal with variations in wireless link quality. First, we estimated the quality of the link at the receiver node by counting the number of consecutive packets lost in each link. Based on that, we created a burstiness distribution list and estimated the number of transmissions. Our simulation in the Cooja simulator from Contiki-NG showed that our proposal can be used in scheduling as an input metric to calculate the number of transmissions in order to achieve a reliability target in industrial wireless sensor networks.

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover,... [more]

Electricity distribution network optimisation has attracted attention in recent years due to the widespread penetration of distributed generation. A considerable portion of network optimisation algorithms rely on an initial solution that is supposed to bypass the time-consuming steps of optimisation routines. The aim of this paper is to present a nature inspired algorithm for initial network generation. Based on slime mold behaviour, the algorithm can generate a large-scale network in a reasonable computation time. A mathematical formulation and parameter exploration of the slime mold algorithm are presented. Slime mold networks resemble a relaxed minimum spanning tree with better balance between the investment and loss costs of a distribution network. Results indicate lower total costs for suburban and urban networks.