With the advancement of global sustainable development goals, promoting sustainable supply chain management has become the key to enhance the competitiveness of enterprises. However, it is difficult for the existing m...
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With the advancement of global sustainable development goals, promoting sustainable supply chain management has become the key to enhance the competitiveness of enterprises. However, it is difficult for the existing management methods to deal with the balance between cost, efficiency and environmental impact, and the complexity of decision-making under uncertain conditions has increased significantly. Therefore, a mixed integer programming model based on the branch delimitation method and relaxation variables is proposed to transform the enterprise management problem of the sustainable supply chain into a mathematical programmingmodel. With the goal of minimizing the total cost, the effective search is carried out through boundary conditions and priority queues, and the relaxation variable technique is used to reduce the complexity of the problem and ensure that the model can still obtain a feasible solution when considering the uncertainty factors. The results showed that the hybrid integerprogrammingmodel had the smallest annual discount cost, the largest number of item searches, and an average processing time of less than 2 s, which effectively solved the problem between supplier selection and order allocation in the supply network, and showed good algorithm efficiency and application efficiency. The model can provide reference guidance for enterprise management decision-making and provide a reference value for the improvement of enterprise environmental, social and economic performance.
the deepening of globalization and increasing demands for environmental sustainability, modern supply chains are faced with increasingly complex management challenges. To reduce management costs and enhance efficiency...
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the deepening of globalization and increasing demands for environmental sustainability, modern supply chains are faced with increasingly complex management challenges. To reduce management costs and enhance efficiency, an experimental approach is proposed based on a mixed integer programming model, integrating heuristic algorithms with adaptive genetic algorithms. The objective is to improve both the efficiency and sustainability of supply chain management. Initially, the selection of suppliers within the supply chain is analyzed. Subsequently, heuristic algorithms and genetic algorithms are jointly employed to design, generate, and optimize initial solutions. Results indicate that during initial runs on training and validation sets, the fitness values of the research method reached as high as 99.67 and 96.77 at the 22nd and 68th iterations, respectively. Moreover, on the training set with a dataset size of 112, the accuracy of the research method was 98.56%, significantly outperforming other algorithms. With the system running five times, the time consumed for supplier selection and successful order allocation was merely 0.654s and 0.643s, respectively. In practical application analysis, when the system iterated 99 times, the research method incurred the minimum total cost of 962,700 yuan. These findings demonstrate that the research method effectively minimizes supply chain management costs while maximizing efficiency, offering practical strategies for optimizing and sustainably developing supply chain management.
In hospitals, the surgical ward is both a cost and revenue center. In this ward, hospitals face challenges such as increasing demand, limited resources, and rising costs. Consequently, the decisions made have an impli...
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In hospitals, the surgical ward is both a cost and revenue center. In this ward, hospitals face challenges such as increasing demand, limited resources, and rising costs. Consequently, the decisions made have an implications effect on the hospital's performance. Therefore, in this paper a robust mixed-integer binary programmingmodel is proposed with three objectives of maximizing the efficiency of available resources, minimizing the patients waiting time, and minimizing surgery costs that are formulated utilizing the augmented epsilon constraint approach. This model allocates the operating room to the patient and the surgeon and then obtains the required bed capacity inside the downstream units for stand-alone cardiac hospitals. This model includes different preferences for hospital, surgeon, and patient: waiting time, patient cancellations, tardiness, uncertainties in surgery durations, the patient operation start times, the overtime per working day, time windows, SICU beds, planning horizon, and the idle times of the surgeons, operating theater, and working day. The proposed model is solved using robust optimization to deal with stochastic. The proposed model is formulated on the stochastic programming method proposed by Bertsimas and Sim. In the proposed model, a rolling horizon method is used to reschedule the program after cancellation. The computational results illustrate that the rolling horizon method reduces waiting time and increases throughput. The results illustrate that the benefit obtained from the introduced model has improvements in reducing the surgery costs, and patient waiting time, and increasing the efficiency of available resources. This study has been performed at Shahid Rajaei Heart Hospital in Iran.
In recent years, the customized bus (CB) has been introduced and popularized in China to improve the attraction and service level of public transportation. A key point of the CB system, the route design problem, is al...
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In recent years, the customized bus (CB) has been introduced and popularized in China to improve the attraction and service level of public transportation. A key point of the CB system, the route design problem, is always formulated as a vehicle routing problem with pickup and delivery (VRPPD). However, VRPPD cannot sufficiently describe the in-vehicle passengers of multiple vehicles involved. In this paper, a mixed integer programming model is developed to formulate a multivehicle routing problem, with suggestions for bus stop locations and routes. Meanwhile, the model can determine passenger-to-vehicle assignment based on a series of constraints, like operation standard and number of stations. In solving the problem, a numerical example is used to compare a genetic algorithm (GA) and branch-andcut algorithm. The comparison results illustrate that GA is more efficient with lower complexity. Finally, in order to apply and evaluate the proposed model, a real-world case study using smartcard data is conducted to compare the approach with the current CB route design method in Beijing. (C) 2018 American Society of Civil Engineers.
In a high speed rail network,there are usually two types of trains running on a single rail line,which are intra-trains and *** stopping schedules for both types of trains may cause negative influence on train operati...
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In a high speed rail network,there are usually two types of trains running on a single rail line,which are intra-trains and *** stopping schedules for both types of trains may cause negative influence on train operation,e.g.,stopping schedules lack regularity,inter-trains consume too much route capacity of intra-trains,and intra-passengers are transported by inter-trains *** paper applies a mixed integer programming model in coordinating stopping schedules of intra-and inter-trains for a high speed rail *** goal is to increase regularity of both types of trains on a main rail line,in the meanwhile,to rationalize the division of labor of intra-and inter-trains in term of transporting intra-and inter-passengers.A numerical experiment is implemented based on a real-world *** results show that a series of coordination indicators are improved compared to those of an original train stopping schedule,which demonstrates the effectiveness of our application approach.
作者:
Hnaien, F.Yalaoui, F.Mhadhbi, A.Nourelfath, M.ICD-LOSI
UMR-CNRS 6281 University of Technology of Troyes 12 rue Marie Curie Troyes10010 France Finaxys
27 Rue des Poissonniers Neuilly-sur-Seine92200 France
Department of Mechanical Engineering Laval University QuebecQCG1V 0A6 Canada
This paper deals with the production planning and preventive maintenance scheduling on a single machine multi-product capacitated lot-sizing problem (CLSP). The machine is assumed to be subject to random failures. Pre...
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Background: The majority of curative health care is organized in hospitals. As in most other countries, the current 94 hospital locations in the Netherlands offer almost all treatments, ranging from rather basic to ve...
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Background: The majority of curative health care is organized in hospitals. As in most other countries, the current 94 hospital locations in the Netherlands offer almost all treatments, ranging from rather basic to very complex care. Recent studies show that concentration of care can lead to substantial quality improvements for complex conditions and that dispersion of care for chronic conditions may increase quality of care. In previous studies on allocation of hospital infrastructure, the allocation is usually only based on accessibility and/or efficiency of hospital care. In this paper, we explore the possibilities to include a quality function in the objective function, to give global directions to how the 'optimal' hospital infrastructure would be in the Dutch context. Methods: To create optimal societal value we have used a mathematical mixedintegerprogramming (MIP) model that balances quality, efficiency and accessibility of care for 30 ICD-9 diagnosis groups. Typical aspects that are taken into account are the volume-outcome relationship, the maximum accepted travel times for diagnosis groups that may need emergency treatment and the minimum use of facilities. Results: The optimal number of hospital locations per diagnosis group varies from 12-14 locations for diagnosis groups which have a strong volume-outcome relationship, such as neoplasms, to 150 locations for chronic diagnosis groups such as diabetes and chronic obstructive pulmonary disease (COPD). Conclusions: In conclusion, our study shows a new approach for allocating hospital infrastructure over a country or certain region that includes quality of care in relation to volume per provider that can be used in various countries or regions. In addition, our model shows that within the Dutch context chronic care may be too concentrated and complex and/or acute care may be too dispersed. Our approach can relatively easily be adopted towards other countries or regions and is very suitable to perform a 'w
The escalating severity of global warming has drawn worldwide attention to ecological problems. Nevertheless, with the introduction of environmental policies, biomass resources have been effectively developed as a ren...
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The escalating severity of global warming has drawn worldwide attention to ecological problems. Nevertheless, with the introduction of environmental policies, biomass resources have been effectively developed as a renewable energy source. This paper investigates an advanced biomass supply chain design wherein biomass resources are initially converted into bio-oil through widely distributed fast pyrolysis facilities, and are subsequently transported to a centralised biorefinery for further refining into biofuels. This novel biomass supply chain addressed three key issues: (1) The number of fast pyrolysis facilities, (2) The allocation of resources, and (3) The routes of resources transport. In respect of these problems, a two-stage stochastic mixed integer programming model is established to minimise the total cost of the biomass supply chain considering the uncertainty collection price of fast pyrolysis facilities. A hybrid simulated annealing algorithm which incorporates the sample average approximation method is proposed to solve the stochastic model and is effectiveness for large-scale examples. Finally, a sensitivity analysis is performed using the proposed algorithm and the results show that the proposed stochastic model outperforms the deterministic model under uncertain collection price. The model allows optimising the biomass supply chain economic performances and minimise financial risk on investment by determining the fast pyrolysis facility locations, reasonable resource allocation and optimal transport routes under uncertain collection price.
Tourism generates huge amounts of waste. It has been estimated that about half of the waste generated by hotels is food and garden bio-waste. This bio-waste can be used to make compost and pellets. In turn, pellets ca...
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Tourism generates huge amounts of waste. It has been estimated that about half of the waste generated by hotels is food and garden bio-waste. This bio-waste can be used to make compost and pellets. In turn, pellets can be used as an absorbent material in composters and as an energy source. In this paper, we consider the problem of locating composting and pellet-making facilities so that the bio-waste generated by a chain of hotels can be managed at or close to the generation points. The general objective is twofold: i) to avoid waste transportation from generation to treatment points and product transportation from production to demand points, and ii) to implement a circular model in which the hotels themselves become the suppliers of the products they need (compost and pellets) by transforming the bio-waste that they generate. Any bio-waste not processed by the hotels has to be treated at private or state-run plants. A mathematical optimization model is presented to locate the facilities and allocate the waste and products. The application of the proposed location-allocation model is illustrated with an example.
This paper introduces a multi-period, two-dimensional vehicle loading and dispatching problem, called Two-Dimensional Vehicle Loading and Dispatching Problem with Incompatibility Constraints (VLDP). The problem concer...
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This paper introduces a multi-period, two-dimensional vehicle loading and dispatching problem, called Two-Dimensional Vehicle Loading and Dispatching Problem with Incompatibility Constraints (VLDP). The problem concerns preparing a single-origin single-destination transportation plan of loading required orders to vehicles at the origin and dispatching the vehicles to deliver the orders to the destination within their due dates. The decision maker uses their own fleet of vehicles, with each vehicle having a fixed transportation cost per trip, and may outsource additional vehicles at a higher cost. VLDP involves constraints regarding the due dates of the orders, pairwise incompatibility of orders packed in the same vehicle, incompatibility of orders and vehicles, as well as area and weight capacity of the vehicles. An order can be delivered earlier than its due date, incurring an earliness penalty due to storage requirements at the destination. The objective is to minimize the total vehicle usage and earliness penalty costs. A mixed-integer Linear programmingmodel (MILP) is provided, as well as an Adaptive Large Neighbourhood Search (ALNS) algorithm. Results of computational experiments on instances derived from real-world data show the effectiveness of the ALNS algorithm. (C) 2022 Elsevier B.V. All rights reserved.
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