The commission sales form is a very significant channel of sales today, which is especially true in the field of dietary supplements. In parallel, the prevailing digitalization trends have opened up further new opportunities for this form of distribution. The multi-level optimization process presented in the publication makes it possible to optimize the distribution logistics processes of companies producing food supplements at a high level by exploiting these new possibilities. The operation of the procedure is also illustrated through a practical example.

In digital transformation and development, supply chain integration has become a key strategy to improve supply chain synergy efficiency and enhance enterprise competitiveness. Based on the survey data of 185 manufacturing enterprises in Tianjin, the fuzzy set qualitative comparative analysis (fs QCA) method is used to explore the synergistic mechanism of government policy, supply chain partnership, information sharing, risk avoidance, and intelligence degree on supply chain integration and the interaction among them. The results show that: (1) a single factor does not constitute a necessary condition for promoting supply chain integration, but the formation and development of supply chain partnership plays a universal role in promoting supply chain integration; (2) the “multiple concurrent” of five factors constitute the diversified configuration of a driving supply chain integration path, that is, the driving supply chain integration path has the characteristic of “all roads lead to... [more]

In this paper, we formulate and solve a novel real-life large-scale automotive parts paint shop scheduling problem, which contains color arrangement restrictions, part arrangement restrictions, bracket restrictions, and multi-objectives. Based on these restrictions, we construct exact constraints and two objective functions to form a large-scale multi-objective mixed-integer linear programming problem. To reduce this scheduling problem’s complexity, we converted the multi-objective model into a multi-level objective programming problem by combining the rule-based scheduling algorithm and the adaptive Partheno-Genetic algorithm. The rule-based scheduling algorithm is adopted to optimize color changes horizontally and bracket replacements vertically. The adaptive Partheno-Genetic algorithm is designed to optimize production based on the rule-based scheduling algorithm. Finally, we apply the model to the actual optimization problem that contained 829,684 variables and 137,319 constraints,... [more]

In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discr... [more]

The forecasting of univariate time series poses challenges in industrial applications if the seasonality varies. Typically, a non-varying seasonality of a time series is treated with a model based on Fourier theory or the aggregation of forecasts from multiple resolution levels. If the seasonality changes with time, various wavelet approaches for univariate forecasting are proposed with promising potential but without accessible software or a systematic evaluation of different wavelet models compared to state-of-the-art methods. In contrast, the advantage of the specific multiresolution forecasting proposed here is the convenience of a swiftly accessible implementation in R and Python combined with coefficient selection through evolutionary optimization which is evaluated in four different applications: scheduling of a call center, planning electricity demand, and predicting stocks and prices. The systematic benchmarking is based on out-of-sample forecasts resulting from multiple cross... [more]

Supply chains (SC) aim to provide products to the final customer at a certain service level. However, unforeseen events occur that impede supply chain objectives. SC Risk has been studied in the literature, providing frameworks and methodologies to manage SC failures. Nevertheless, more efforts are needed to prevent hazardous and disruptive risks and their consequences. These risks must be considered during the process of designing a supply chain. Some methodological contributions concerning risk in the supply chain network design (SCND) are conceptual frameworks for mitigating SC disruptions, which suggest strategies and measures for designing robust and resilient SCs. Although such contributions are valuable, they do not indicate how to cope with risk when designing a SC. The main objective of this research is to describe a methodology aimed at including risk considerations into the SCND. Our proposal aims to be, on the one hand, a comprehensive approach that includes a risk identifi... [more]

Range anxiety is a problem that restricts the development of pure electric vehicles. For this reason, much research starts from a shift schedule and strives to improve mileage. However, the proposed shift schedules have poor adaptive ability and are not suitable for dynamic conditions. In this paper, a shift schedule based on reinforcement learning (RL) is proposed, which uses Q-learning for optimization. However, the massive state variables and huge Q table in the state space put forward higher requirements on the computing power and storage space of the controller. Traditionally, the application of RL algorithms needs to rely on expensive GPU devices. To reduce high costs, we use an innovative treatment method, the optimal Latin hypercube design (Opt LHD), which is used for sampling, and state reduction is performed on the state space. Based on the above, the mileage is effectively improved by applying the shift schedule based on RL.

Solid oxide fuel cells are an emerging energy conversion technology suitable for high-temperature power generation with proper auxiliary heat. Combining SOFCs and polygeneration has produced practical applications for modern energy system designs. Even though many researchers have reviewed these systems’ technologies, opportunities and challenges, reviews regarding the optimal strategy for designing and operating the systems are limited. Polygeneration is more complicated than any other energy generation type due to its ability to generate many types of energy from various prime movers. Moreover, integration with other applications, such as vehicle charging and fueling stations, increases the complication in making the system optimally serve the loads. This study elaborates on the energy planning and guidelines for designing a polygeneration system, especially for residential applications. The review of polygeneration technologies also aligns with the current research trend of developi... [more]

Globally, wind power plays a leading role in the renewable energy industry. In order to ensure the normal operation of a wind farm, the staff will regularly check the equipment of the wind farm. However, manual inspection has some disadvantages, such as heavy workload, low efficiency and easy misjudgment. In order to realize automation, intelligence and high efficiency of inspection work, inspection robots are introduced into wind farms to replace manual inspections. Path planning is the prerequisite for an intelligent inspection robot to complete inspection tasks. In order to ensure that the robot can take the shortest path in the inspection process and avoid the detected obstacles at the same time, a new path-planning algorithm is proposed. The path-planning algorithm is based on the chaotic neural network and genetic algorithm. First, the chaotic neural network is used for the first step of path planning. The planning results are encoded into chromosomes to replace the individuals w... [more]

With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production co... [more]

Parallel Machine Scheduling (PMS) is a well-known problem in modern manufacturing. It is an optimization problem aiming to schedule n jobs using m machines while fulfilling certain practical requirements, such as total tardiness. Traditional approaches, e.g., mix integer programming and Genetic Algorithm (GA), usually fail, particularly in large-size PMS problems, due to computational time and/or memory burden and the large searching space required, respectively. This work aims to overcome such challenges by proposing a heuristic-based GA (DAS/GA). Specifically, a large-scale PMS problem with n independent jobs and m identical machines with a single server is studied. Individual heuristic algorithms (DAS) and GA are used as benchmarks to verify the performance of the proposed combined DAS/GA on 18 benchmark problems established to cover small, medium, and large PMS problems concerning standard performance metrics from the literature and a new metric proposed in this work (standardized... [more]

Currently, practical optimisation models and intelligent solution algorithms for solving disassembly sequence planning are attracting more and more attention. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this paper proposes a new optimisation model for the energy-efficient disassembly sequence planning. The minimum energy consumption is used as the evaluation criterion for disassembly efficiency, so as to minimise the energy consumption during the dismantling process. As the proposed model is a complex optimization problem, called NP-hard, this study develops a new extension of the whale optimisation algorithm to allow it to solve discrete problems. The whale optimisation algorithm is a recently developed and successful meta-heuristic algorithm inspired by the behaviour of whales rounding up their prey. We have improved the whale optimisation algorithm for predation behaviour and added a local search strategy to improve... [more]

Sustainable closed-loop supply chain (SCLSC) network design and decision-making is a critical problem for enterprises and organizations’ operations because of its excellent economic, environmental, and social performance. This article proposes a multi-objective mixed-integer programming model with targets for minimum total cost, reduction in environmental damage, and maximum social responsibility. In order to deal with the uncertainty caused by the dynamic business environment, a fuzzy robust programming (FRP) approach is applied. Furthermore, an efficiency-oriented optimization methodology, hybridizing meta-heuristics and efficiency evaluation, is proposed to solve the developed multi-objective model and functions as auxiliary decision-making. Data envelopment analysis is applied to evaluate the sustainability performance of feasible solutions and calculate their efficiency. The efficiency can comprehensively reflect the sustainability performance and guide the evolution process of me... [more]

In the face of the challenges posed by the need to drastically decrease carbon emissions, all agents in the supply chain need to strengthen low-carbon collaboration with the support of digital transformation. This study sets up a low-carbon collaboration evolutionary game model of the supply chain based on benefit sharing by introducing digital transformation. The equilibrium-point stability of the supply chain is then analyzed under two separate conditions—i.e., less and more government rewards and punishments compared to supply-chain agents’ strategic risk cost. Furthermore, based on the evolutionary game model, this study draws the system dynamics (SD) flow diagram to analyze the research problem quantitatively. The main results show that: (1) low-carbon benefit-driven effects promotes collaboration benefit sharing, thereby increasing the probability of low-carbon collaboration; (2) digital transformation is an essential regulator of low-carbon collaboration in the supply chain and... [more]

As low-carbon and sustainable manufacturing becomes the mainstream development direction of the current manufacturing industry, the traditional heavy industry manufacturing enterprises in China urgently need to transform. For the heavy cement equipment manufacturing enterprise investigated here, there is a large amount of energy waste during the manufacturing operation due to scheduling confusion. In particular, the multispeed, multi-function machining and the transportation of multiple automated guided vehicles (multi-AGV) are the main influencing factors. Therefore, this paper addresses a novel low-carbon scheduling optimization problem that integrated multispeed flexible manufacturing and multi-AGV transportation (LCSP-MSFM & MAGVT). First, a mixed-integer programming (MIP) model is established to minimize the comprehensive energy consumption and makespan in this problem. In the MIP model, a time-node model is built to describe the completion time per workpiece, and a comprehensive... [more]

This paper studies the production process of a shipbuilding enterprise. The company suffers from long manufacturing cycle, low utilization rate of personnel and an unbalanced production line. To solve these problems, the lean shipbuilding mode, mainly divided into shipbuilding work breakdown, production plan and virtual flow operation in this paper, is put forward, which combines the lean production and modern information management technology with shipbuilding. Supported by the theory of work breakdown structure and task package scheduling, the shipbuilding task package is reasonably divided. The priority of task package manufacturing is determined by calculating the task package manufacturing sequence coefficient, and a reasonable number of operators is calculated to ensure the continuity of segmented manufacturing. After determining the manufacturing priority of the task pack and the number of allocable personnel, the corresponding work can be scheduled. Production planning drives a... [more]

In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance... [more]

Copper losses during the Peirce-Smith converter (PSC) operation is of great concern in the copper smelting process. Two primary objectives of the PSC are to produce blister copper with a shorter batch time and to keep the copper losses at a minimum level. Due to the nature of the process, those two objectives are contradictory to each other. Moreover, actions inside the PSC are subject to several operational constraints that make it difficult to develop a scheduling framework for its optimal operation. In this work, a basic but efficient linear multi-period scheduling framework for the PSC is presented that finds the optimal timings of the PSC operations to keep the copper losses and the batch time at a minimum level. An industrial case study is used to illustrate the effectiveness of the proposed framework. This novel solution can be implemented in other smelting processes and used for the design of an inter-PSC scheduling framework.

In recent decades, more and more consumers—referred to as “green consumers”—are willing to incorporate environmentally responsible decisions into their purchasing behaviors. This tendency is particularly notable in the European Union, the USA, and China. From a research perspective, while recent studies on remanufacturing have investigated optimal practices in regard to green consumerism, they have failed to address the flexibility manufacturers are afforded to outsource remanufacturing operations to third parties. In practice, some brand-name manufacturers—such as IBM, Land Rover, and Sun—do indeed outsource their remanufacturing operations. To further our understanding of the implications of differentiated structures for remanufacturing operations under green consumerism, we developed two models: one for a manufacturer undertaking remanufacturing themselves (Model M), and one for a manufacturer outsourcing it to a third-party remanufacturer (Model O). Our results indicate that, for m... [more]

To counteract climate change, the application of renewable energy sources and their efficient use are of crucial importance. In this context, wastewater has also gained increased attention in recent years. For decades, wastewater treatment plants have applied the heat from digester gas combustion to supply internal demands. However, in the context of efficient energy use the question arises: can using high temperature heat for supplying low temperature demand still be considered the best option? This article presents an innovative approach to covering wastewater treatment plant (WWTP) internal demand with low temperature wastewater heat recovery, making thermal energy from digester gas combustion available for feed-in to a local high temperature district heating network. The presented feasibility study was carried out in an Austrian municipality and investigates the heat balance, the economic risk, climatic benefits and the social aspects of the suggested approach. The practical implem... [more]

In view of the current situation that the maturity of enterprise intelligent manufacturing capability is generally low and the information asymmetry in the upstream and downstream of the supply chain is high, taking any supply and demand link in the supply chain as an example, a group of initial demand signals that change nonlinearly over time are divided into intrinsic mode functions and noise residuals with different data characteristics by means of the variational modal decomposition (VMD) algorithm. On the basis of signal denoising and reconstruction, the support vector machine (SVM) algorithm is used to make regression prediction of the reconstructed signal with each intrinsic mode function as sample attribute. Compared with the regression prediction results of the original demand signal, it is verified that the VMD-SVM bullwhip effect weakening model can effectively filter the demand noise generated by each link in the supply chain and improve the accuracy of demand information t... [more]

This paper presents a roadmap performed in 2010 as part of a European project for the modelling of carbon capture and storage technology, and various scenarios with different taxations and permit prices for the CO2 emissions considering the Greek national plans, then the gradual decommissioning of various lignite or other units of electricity power plants. In addition, this study presents a first check, 10 years after its writing, of the current situation of the Greek energy system, regarding the correspondence of the roadmap designed in 2010 to what has been finally executed during this period, including the possibility of other energy sources complimenting or substituting the national strategic energy plans. For this purpose, the integrated MARKAL-EFOM system (TIMES) was employed to model the Greek energy system and evaluate its development over time, until 2040, by analyzing three different scenarios with respect to taxation and permit prices for carbon emissions. The results obtain... [more]

Oil and Gas plants consist of a set of heat exchangers, which are used in recovering the waste heat from product streams to preheat the oil. The heat transfer coefficient of exchangers declines considerably during the operation period due to fouling. Fouling in heat exchangers is a complex phenomenon due to the acceleration of many layers of chemical substances across tubes of heat exchangers resulting from chemical reactions and surface roughness. In this paper, the fouling process was determined as a critical failure in the heat exchanger. Failure is an accelerated fouling layer across the heat exchanger tubes, which can be the reason for the clogging of tubes. Hence, a risk assessment was conducted using the Risk-Based Inspection (RBI) approach to estimate the probability of fouling in heat exchangers. The results showed that the RBI approach can be used successfully to predict the suitable time to shut down the plant and conduct the fouling cleaning process.

Transition from the status quo to more sustainable supply chain management (SSCM) practices is a highly complex and non-linear process with multiple drivers, but also obstacles, on the way. The impending strict regulatory framework, particularly in terms of the environmental dimensions of sustainability development (SD), is single-handedly opening the door to rapid and potentially disruptive change. The research literature on SSCM has increased exponentially over the last decade to meet the mounting demand for information on how to tackle often conflicting sustainability-related requirements while satisfying all internal and external stakeholders. Due to the continuously evolving and wicked nature of SSCM, a limited number of scholars have approached the issue with design thinking problem solving methodologies (DTPSMs). The results of a systematic literature review (SLR) were mirrored with the Evolved Double Diamond (EDD) design process model to formulate a design thinking overview and... [more]

This paper proposes and implements a long-term deterministic capacity expansion model for the co-optimization of electric and natural gas infrastructures. It determines the required investments in generation units, transmission lines and pipelines for meeting future demands, while representing electricity and natural gas flows using DC Power Flow and Weymouth equations, respectively. A Mixed Integer Nonlinear Programming (MINLP) problem is developed, from which a linearized version is derived. A 26 node integrated gas-electric system for the Eastern Region of the United States is used to demonstrate the model’s capabilities. Results show that the model provides an accurate operational representation of the integrated system, and, therefore, enhances the expansion planning process.