Location of urban distribution center (UDC) has played an important role in reducing distribution cost, environmental impact, and traffic congestion in urban areas. The article aims at evaluating potential location of...
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Location of urban distribution center (UDC) has played an important role in reducing distribution cost, environmental impact, and traffic congestion in urban areas. The article aims at evaluating potential location of UDC for sugar commodity using a combined approach that comprises two stages: spatial analysis and multi-objective mixed-integer linear programming. Results demonstrate that different locations of UDC correspond to different costs and environmental implications. It is suggested that a new UDC for the sugar commodity can be located at the east part of the city outskirt. Using more than two UDCs in the studied case appears to be not economically and environmentally beneficial.
For tire manufacturers to remain profitable while fulfilling environmental and social obligations such as producer responsibility, the opportunity lies in designing a tire Closed-Loop Supply Chain (CLSC) which combine...
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For tire manufacturers to remain profitable while fulfilling environmental and social obligations such as producer responsibility, the opportunity lies in designing a tire Closed-Loop Supply Chain (CLSC) which combines forward and reverse supply chains. In this paper, a new multi-objective mixed-integer linear programming model is proposed to configure and optimize a multi-echelon, multi-product, multi-period tire CLSC network based on multiple recovery options and markets. For one of the objectives of the model, the weighting factors (importance) of suppliers are determined according to a unique framework of qualitative criteria. In this respect, a novel decision-making method based on Spherical fuzzy logic is developed. Finally, the solution approach is devised based on the formulation of the augmented e-constraint method for finding efficient solutions. The application of the model is illustrated focusing on the region of Greater Toronto Area in Ontario, Canada. The optimal quantities for the flows of products, and number and locations of open facilities of the network are computed. The results show that the selected suppliers and allocated orders from them are impacted by considering multiple objectives. (c) 2022 Elsevier Inc. All rights reserved.
TA crucial aspect of the proper functioning of bikes and electric scooters sharing systems is the correct locationand dimensioning of the sharing stations. The resolution of the previous problem is carried out based o...
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TA crucial aspect of the proper functioning of bikes and electric scooters sharing systems is the correct locationand dimensioning of the sharing stations. The resolution of the previous problem is carried out based on the maximization of coverage or the minimization of costs, but the two objectives are not usually treated at the same *** this work, we propose a method based on the hybridization of the popular Elitist Non-Dominated Sorting Genetic Algorithm (NSGA- II) with a mixed-integerlinearprogramming (MILP) model to approximate the Pareto Frontier of the problem. This allows the decision-maker a greater understanding of the range of possible options. The NSGA-II plays the role of an outer block that deals with the selection and sizing of each of sharing stations. The MILP model is an inner block that calculates the associated coverage of that solution. The schema was compared with an adaptative-weighting algorithm, reaching the hybridization of NSGA-II and MILP a better coverage of the Pareto Frontier.
Nowadays the steel market is becoming ever more competitive for European steelworks, especially as far as flat steel products are concerned. As such competition determines the price products, profit can be increased o...
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Nowadays the steel market is becoming ever more competitive for European steelworks, especially as far as flat steel products are concerned. As such competition determines the price products, profit can be increased only by lowering production and commercial costs. Production yield can be significantly increased through an appropriate scheduling of the semi-manufactured products among the available sub-processes, to ensure that customers' orders are timely completed, resources are optimally exploited, and delays are minimized. Therefore, an ever-increasing attention is paid toward production optimization through efficient scheduling strategies in the scientific and industrial communities. This paper proposes a hybrid approach to improve the flexibility of production scheduling in steelworks producing flat steel products. Such approach combines three methods holding different scopes and modelling different aspects: an auction-based multi-agent system is applied to face production uncertainties, multi-objective mixed-integer linear programming is used for global optimal scheduling of resources under steady conditions, while a continuous flow model copes with long-term production scheduling. According to the obtained simulation results, the integration and combination of these three approaches allow scheduling production in a flexible way by providing the capability to adapt to different production conditions.
In supply chain management, selection of suitable suppliers and allocating corresponding orders are two essential strategic decisions. Making these decisions is a complex process due to some uncertain parameters, such...
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In supply chain management, selection of suitable suppliers and allocating corresponding orders are two essential strategic decisions. Making these decisions is a complex process due to some uncertain parameters, such as future demand. This study proposes a three-stage solution framework to solve problems with supplier selection & order allocation planning. In Stage 1, a new modified relational deep learning forecasting technique is developed to forecast the demands of products. In this part, the efficiency of this modified technique is compared with two well-known forecasting techniques, namely Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Light-Gradient Boosted Machine (LGBM). In Stage 2, a new hybrid principal component analysis method is used to generate suppliers' weights. The results from Stages 1 and 2 are used in the multiple objectives optimization model which is developed in Stage 3. The hybrid method is used to derive a set of efficient solutions. The developed framework is discussed using a real dataset from the Canadian meat industry. The results of forecasting models show that the developed deep learning network can reduce the forecasting error by 55.42% when compared to the SARIMA method, and 13.1% when compared to the LGBM method. It is also observed that the consideration of inter-product correlation functions can change the selected suppliers and the corresponding orders.
This study examines how a firm can improve its resilience to respond effectively to supply chain disruptions through investment in flexibility and innovation. There are several approaches and conceptualizations of fle...
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This study examines how a firm can improve its resilience to respond effectively to supply chain disruptions through investment in flexibility and innovation. There are several approaches and conceptualizations of flexibility and innovation in the operations and supply chain management literature. Nevertheless, how flexibility and innovation can enhance an organisational response to disruptions has not received much attention. We extend the research in supply chain resilience by investigating the relationships between flexibility, innovation, and resilience under disruption risk. The findings show that innovation and flexibility have a positive impact on resilience. A numerical example based on these relationships shows significant differences between optimum values of objective functions when we have high disruption risk versus low disruption risk imposed on the system. By developing a novel multi-objective mixed-integer linear programming, the findings reveal that disruption risks affect resilience and firms' ROI, so investing in improvements in flexibility and innovation decreases the impact of disruptions.
In the process of Purchasing, selectingsuppliers is an important mean and precondition for obtaining high-quality products. In this paper, we analyze some factors affecting supplier selection, such as product quantity...
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ISBN:
(纸本)9781450372947
In the process of Purchasing, selectingsuppliers is an important mean and precondition for obtaining high-quality products. In this paper, we analyze some factors affecting supplier selection, such as product quantity, production price, product quality and product delivery time, formulate a supplier selection model based on linear weighted multi-objectiveintegerprogramming, the objective function and constraints of the model are made and carried out, alongside a case study. The conclusions of the study can provide theoretical and methodological support for the practical work of selecting suppliers.
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