The objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the fiel...
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The objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the field focused on solving the Simple Assembly Line Balancing problem, a basic simplified version of the general problem. As the production environment became more complex, several new ALB problem types appeared, and almost all ALB problems are NP-hard, meaning that finding a solution requires a lot of time, resources, and computational power. Methods with custom-made algorithms and generic approaches have been developed for solving these problems. While custom-made algorithms are generally more efficient, generic approaches can be more easily extended to cover other variations of the problem. Over the past few decades, automation has played an increasingly important role in various operations, although complete automation is often not possible. As a result, there is a growing need for partially automated assembly line balancing models. In these circumstances, the flexibility of a generic approach is essential. This paper compares two generic approaches: mixedintegerlinear programming (MILP) and constraint programming (CP), for two types of partially automated assembly line balancing problems. While CP is relatively slower in solving the simpler allocation problems, it is more efficient than MILP when an increased number of constraints is applied to the ALB and an allocation and scheduling problem needs to be solved.
Currently, the global aviation industry uses around 341 billion liters of jet fuel per year, with demand predicted to grow by 50% by the end of 2050. Renewable jet fuel (RJF) may cut greenhouse gas emissions (GHG), in...
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Currently, the global aviation industry uses around 341 billion liters of jet fuel per year, with demand predicted to grow by 50% by the end of 2050. Renewable jet fuel (RJF) may cut greenhouse gas emissions (GHG), increase fuel diversity for the aviation industry, and promote rural economies. The commercialization of RJF has been delayed due to a shortage of sustainable biomass resources. This study recommends using winter carinata crops as a reliable biomass feedstock in the southeastern US states, where the availability of resources will be investigated in each agricultural zone. RJF production is more expensive than traditional jet fuel production. Investors and legislators need to learn more about prospective federal financial initiatives, such as subsidies and grants, to help with RJF supply chain implementation. In this paper, using a mathematical programming approach, we designed an RJF supply chain and then investigated the effects of three direct monetary incentive programs, including producer credit program (PCP), biomass crop assistance program (BCAP), and biorefinery assistance program (BAP), to accelerate the commercialization of RJF manufacturing. According to the findings, the amount of incentives through PCP needed to fulfill 50% of the RJF demand was assessed to cover 16.70% of the total costs, while the BCAP could reach the commercialization threshold by receiving incentives for 22.84% of the biomass purchasing cost. Furthermore, having the BAP covering 89.39% of the annual capital and operating costs could help commercialize RJF production. This study also evaluated the effects of changes in renewable fuel prices, demand fulfillment rates, biomass yield rates, and the price of biomass feedstock and its resulting meal on the profitability of the supply chain. The study's findings will advise policymakers and investors on developing the RJF supply chain given various financial assistance programs and subsidies.
Purpose This study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccinatio...
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Purpose This study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccination station, assignment of demand group to vaccination station, allocation of the scarce medical professional teams to station and number of optimal days a vaccination station to be functional in a week. Design/methodology/approach The authors propose a mixed-integer nonlinear programming model. However, to handle nonlinearity, the authors devise a heuristic and then propose a two-stage mixed-integerlinear programming (MILP) formulation to optimize the allocation of vaccination centers or stations to demand groups in the first stage and the allocation of vaccination centers to cold storage links in the second stage. The first stage optimizes the cost and average distance traveled by people to reach the vaccination center, whereas the second stage optimizes the vaccine's holding and storage and transportation cost by efficiently allocating cold storage links to the centers. Findings The model is studied for the real-world case of Chandigarh, India. The results obtained validate that the proposed approach can immensely help government agencies and policymaking body for a successful vaccination drive. The model tries to find a tradeoff between loss due to underutilized medical teams and the distance traveled by a demand group to get the vaccination. Originality/value To the best of our knowledge, there are hardly any studies on a vaccination program at such a scale due to sudden outbreaks such as Covid-19.
Haze pollution poses significant health risks, driving organizations to mitigate its impact. Temporary safety zones are essential for protecting people during haze pollution incidents. However, current strategies may ...
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Haze pollution poses significant health risks, driving organizations to mitigate its impact. Temporary safety zones are essential for protecting people during haze pollution incidents. However, current strategies may fall short in meeting demand under certain conditions. To address this challenge and aid decision-making, we propose a methodology for selecting temporary safety zone sites during haze pollution. The methodology integrates the Fuzzy Analytic Hierarchy Process (FAHP), the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), and a fuzzy mathematical model. Criteria for site selection are established through literature reviews and expert interviews and the FAHP assigns weights to each criterion. FTOPSIS evaluates candidate locations based on the criteria, generating scores for each site. Finally, a multi-objective fuzzy mathematical model identifies optimal locations, considering budget limits, capacity restrictions, and other constraints. We validated the approach in the Sarapee district in Thailand. The results demonstrate the model's ability to generate optimal solutions, identifying suitable locations for temporary safety zones during haze pollution crises. This research is significant for decision-makers and governments who need to strategically place temporary safety zones and safeguard communities from the hazards of haze pollution. By adopting this integrated approach, organizations can enhance their response to haze pollution incidents, protecting public health and well-being.
This paper investigates the problem of manufacturing processes by bar-turning machine. The scenario is modeled as a problem of scheduling jobs to non-related parallel machines with sequence dependent setup times apply...
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This paper investigates the problem of manufacturing processes by bar-turning machine. The scenario is modeled as a problem of scheduling jobs to non-related parallel machines with sequence dependent setup times applying batch splitting. In order to optimize this problem, we propose a mixedintegerlinear programming model (MILP). However, as shown in numerical result, it is limited to solve only small instances. Hence, a two-step approach is presented to solve real-size instances. This method uses a relaxed version of the MILP model proposed, however, allowing solutions with sub-tour. Afterwards, a heuristic algorithm is presented to adjust these solutions. The numerical results analyze 20 instances with different sizes, compositions of demand and set of machines. Some general rules to characterize good solutions were presented after investigating the results. These rules associate the quantity of pieces for each demand and the type of machine used, and the complexity of the piece demanded for the type of machine.
The collection and recycling of end-of-life photovoltaic modules is emerging as a challenge due to the dramatic growing installment of distributed PV systems worldwide. This paper studies the locational strategy for r...
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The collection and recycling of end-of-life photovoltaic modules is emerging as a challenge due to the dramatic growing installment of distributed PV systems worldwide. This paper studies the locational strategy for recycling facilities of end-of-life PV modules from the perspective of various stakeholders with a case in Zhejiang province, one of the leading regions in promoting distributed photovoltaic applications in China. The generation of end-of-life modules in the "Million Household Rooftop PV Project" in Zhejiang Province is estimated from 2041 to 2045. The regional reverse logistics network for collection of the retired PV modules is optimized using the mixed integer linear programing (MILP) model. The article draws on the PV recycling scheme of PV CYCLE in Europe, and proposes the following two scenarios for comparison: (1) Scenario A: Municipal recycling, in which the local government takes the responsibility to establish the collection and recycling infrastructures;and (2) Scenario B: Producer cooperative recycling under Extended Producer Responsibility scheme, in which the PV producers use their sales and customer services network to help collecting the end-of-life PV modules. The different roles of the stakeholders affect the overall locational strategy for recycling. The result shows that: (1) Both scenarios result in similar path of the utility of recycling facilities: when only one recycling facility is needed in 2041, the recycling plant in Hangzhou will be used to minimize the overall transportation cost for the whole province;while more facilities are needed due to increasing generation of end-of-life modules in the next 4 years, regional collection centers will be assigned to the nearest recycling facility, dividing the province into three regional recycling group: "North Group (including Hangzhou, Jiaxing, and Huzhou)", "South Group (including Jinhua, Lishui, and Quzhou)", and the "Coastal Group (including Wenzhou and Taizhou)". (2) Although the
This paper proposes and assesses three different control approaches for the hydrocarbon natural gas (HCNG) penetrated integrated energy system (IES). The three control approaches adopt mixed integer linear programing,...
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This paper proposes and assesses three different control approaches for the hydrocarbon natural gas (HCNG) penetrated integrated energy system (IES). The three control approaches adopt mixed integer linear programing, conditional value at risk (CVaR), and robust optimization (RO), respectively, aiming to mitigate the renewable generation uncertainties. By comparing the performance and efficiency, the most appropriate control approach for the HCNG penetrated IES is identified. The numerical analysis is conducted to evaluate the three control approaches in different scenarios, where the uncertainty level of renewable energy (within the HCNG penetrated IES) varies. The numerical results show that the CVaR-based approach outperforms the other two approaches when renewable uncertainty is high (approximately 30%). In terms of the cost to satisfy the energy demand, the operational cost of the CVaR-based method is 8.29% lower than the RO one, while the RO-based approach has a better performance when the renewable uncertainty is medium (approximately 5%) and it is operational is 0.62% lower than that of the CVaR model. In both evaluation cases, mixed integer linear programing approach cannot meet the energy demand. This paper also compares the operational performance of the IES with and without HCNG. It is shown that the IES with HCNG can significantly improve the capability to accommodate renewable energy with low upgrading cost.
Electrical distribution systems are facing new challenges mainly due to the growing penetration of distributed generation of mainly intermittent nature such as wind and solar PV. As a result, these systems need to und...
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ISBN:
(纸本)9781538651865
Electrical distribution systems are facing new challenges mainly due to the growing penetration of distributed generation of mainly intermittent nature such as wind and solar PV. As a result, these systems need to undergo massive transformations in terms of operational scheme. In other words, new operational strategies, which increase the flexibility of distribution systems, have to be put in place. This is highly required if distribution systems are to support large quantities of variable RES power. One plausible strategy worth considering relates to the meshed operation of such systems. The main topic of this paper revolves around the prospects of operating distribution grids in a meshed manner. The benefits are quantified in terms of added flexibility to the system, and vRES utilization levels. A mixedintegerlinear programming model is employed to perform the required analysis, and a 119-bus distribution system is used for this purpose. The analysis of the results generally shows the strong viability of the new operation strategy in terms of adding flexibility and scaling up the utilization level of variable RES power in the considered system. This strategy can be considered as a viable flexibility option that enables further integration of intermittent power sources.
We present the challenges of environmental management and underline the importance of an energy saving policy for companies. We propose a model to determine the energy balance of manufacturing by integrating the diffe...
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We present the challenges of environmental management and underline the importance of an energy saving policy for companies. We propose a model to determine the energy balance of manufacturing by integrating the different productive and non-productive phases. We define two purposes for minimizing production time and energy consumption. We apply this model to the scheduling of flexible job-shop workshops. To determine the optimal solution we use two types of methods: The first is genetic algorithms. We propose different types of algorithms to solve this multi- criteria problem. For example, we propose to develop two populations to minimize the energy consumed and the production time, and to cross them to achieve the overall objective. The second is constraint programming. We propose to find the optimal solution by developing a double tree to evaluate the energy consumed and the production time. We build our algorithm starting from the tasks to be performed on the machines or from the machines that will perform the tasks. We discuss the construction of the Pareto front to get the best solution. We finish by comparing the different approaches and discussing their relevance to deal with problems of different sizes. We also offer several improvements and some leads for future research.
Electrical distribution system operators (DSOs) are facing an increasing number of challenges, largely as a result of the growing integration of distributed energy resources (DERs), such as photovoltaic (PV) and wind ...
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Electrical distribution system operators (DSOs) are facing an increasing number of challenges, largely as a result of the growing integration of distributed energy resources (DERs), such as photovoltaic (PV) and wind power. Amid global climate change and other energy-related concerns, the transformation of electrical distribution systems (EDSs) will most likely go ahead by modernizing distribution grids so that more DERs can be accommodated. Therefore, new operational strategies that aim to increase the flexibility of EDSs must be thought of and developed. This action is indispensable so that EDSs can seamlessly accommodate large amounts of intermittent renewable power. One plausible strategy that is worth considering is operating distribution systems in a meshed topology. The aim of this work is, therefore, related to the prospects of gradually adopting such a strategy. The analysis includes the additional level of flexibility that can be provided by operating distribution grids in a meshed manner, and the utilization level of variable renewable power. The distribution operational problem is formulated as a mixedintegerlinear programming approach in a stochastic framework. Numerical results reveal the multi-faceted benefits of operating distribution grids in a meshed manner. Such an operation scheme adds considerable flexibility to the system and leads to a more efficient utilization of variable renewable energy source (RES)-based distributed generation.
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