Design-balanced capacitated multicommodity network design with heterogeneous assets

Li, Xiangyong; Wei, Kai; Aneja, Y. P.; Tian, Peng

doi:10.1016/j.omega.2016.05.001

Cited by 29 publications

(11 citation statements)

References 26 publications

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“…Transported agricultural products pass through different actors in the supply chain, which form a transportation or service network. The service network design (SND) problem has been modeled and presented by Pedersen et al [36], Andersen et al [37][38][39], Moccia et al [40], Thiongane et al [41] and Li et al [42].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

et al. 2022

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In recent years, agriculture products have contributed to 28.75% of Thailand’s GDP. China, Vietnam, Myanmar, Cambodia, Laos and Vietnam are the main markets for agricultural products. The annual export volume exceeds 119,222 million THB. The majority of them are shipped over Thailand’s land borders to its neighbors. Small and medium-sized farmers make up more than 85% of those who produce agricultural items. Numerous scholars have studied the transportation methods used by the Greater Mekong Subregion (GMS) nations along the economic corridor, but the majority of them have concentrated on import–export operations involving sizable firms, which are not applicable to the transportation of agricultural products, particularly when attention is paid to small and medium-sized farmers. In this study, mixed-integer programming (MIP) is presented to design an agricultural product logistics network. In order to prolong the lifespan of the container used, the MIP’s primary goal is to maximize the total chain profit while maintaining the lowest container usage possible. The approach was developed to increase small and medium-sized farmers’ ability to compete. Small and medium-sized farmers bring their products to an agricultural product collecting center, also known as a container loading facility. After that, skilled logistics companies distribute the goods. In order to convey the goods to the final clients in neighboring nations, the proper locations of the containing loading centers, the correct transportation option and the borders must be decided. The issue was identified as multi-echelon location–allocation sizing (MELLS), an NP-hard problem that cannot be handled in an efficient manner. To solve a real-world problem, however, efficient techniques must be supplied. AMIS, an artificial multiple intelligence system, was created to address the suggested issue. AMIS was developed with the goal of leveraging a variety of methods for local search and development. There are several well-known heuristics techniques employed in the literature, including the genetic algorithm (GA) and the differential evolution algorithm (DE). With respect to the improved solutions obtained, the computational results show that AMIS exceeds the present heuristics, outperforming DE and GA by 9.34% and 10.95%, respectively. Additionally, the system’s farmers made a total of 15,236,832 THB in profit, with an average profit per container of 317,434 THB and an average profit per farmer of 92,344.44 THB per crop. The container loading center uses 48 containers, with a 5.33 container average per container loading center (CLC). The farmers’ annual revenues were previously less than 88,402 THB per family per year, so we can predict that the new network may increase customers’ annual income by 4.459% for each crop.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

et al. 2022

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“…The performances of these algorithms are not suitable for solving large-scale instances because either the solution quality cannot meet the requirement, or they fail to obtain solutions. A tabu-search-based algorithm for large-scale SND problem is proposed, and the largest instance that can be solved includes 25 nodes, 50 commodities, and 215 arcs [42]. In our work, large-scale instances which include 30 nodes, 870 commodities, and 8700 arcs are solved efficiently.…”

Section: Literature Reviewmentioning

confidence: 99%

An Iterative Backbone Algorithm for Service Network Design Problems

2022

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Service network design problems arise at airlines, trucking companies, and railroads wherever there is a need to determine cost-minimizing routes and schedules, given resource availability and service constraints. In recent years, the application of consolidation-based service network design in the express service has attracted lots of academic attention due to the rapid growth of the express industry. This paper studies the consolidation-based service network design problem, which jointly determines the commodity flow, vehicle dispatching, and fleet sizing. We propose a mixed-integer optimization model to address the problem and design an efficient iterative backbone algorithm to solve large-scale real-world problems. The numerical results of large-scale instances confirmed that the solution obtained by our proposed algorithm is better than that of the primal model, and the running time taken is less than half that of the general solution approach. The computational study confirmed the effectiveness and efficiency of the proposed algorithm.

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“…Different from previous contributions [42][43][44], the current work focuses on the planning of a WHN in a real scenario, including restrictions related to the wireless technology capacity and the flexibility to incorporate different wireless technologies. This proposal also finds a set of routes that could link each smart meter, based on the chargeability, the flows within the routing tree, and the alternatives to minimize the total cost of information transported by the paths that exist in the feasible set of solutions; all based on a real georeferenced scenario.…”

Section: Related Workmentioning

confidence: 99%

Capacitated Multicommodity Flow Problem for Heterogeneous Smart Electricity Metering Communications Using Column Generation

2019

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This paper addresses the planning and deployment of wireless heterogeneous networks (WHNs) for smart metering, based on a cross-layer solution. We combine the constraints of the network layer that considers routing and flow demands at each link in the WHN, while at the same time, we account for the restrictions of the physical layer referred to the capacity of a short range technology when used in a multi-hop fashion. We propose a model based on a column generation approach to solve the capacitated multicommodity flow problem (CMCF); the model includes wireless links capacities, coverage, and cost. The work integrates the multi-hop routing of packets in a mesh network formed by smart meters and concentrators connected to a cellular network via base stations. The traffic of each link is represented in a multigraph with the occupation percentage, and we build a scalable routing tree on a georeferenced map to represent a real deployment. The results describe the behavior of the proposed model in terms of the traffic load per concentrator, the network coverage, and the reduction of energy consumption. We demonstrate that an infrastructure cost reduction is achieved with the inclusion of multi-hop short range technology, which reduces the number of smart meters that require a direct connection to cellular technology. The model guarantees 100% coverage of the smart meters analyzed in each scenario. The calculation time of the CMCF for advanced metering infrastructure (CMCF-AMI) based on the column generation algorithm as the population increases is reduced by 10%, and this is the expected return when the population is considerable.

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Design-balanced capacitated multicommodity network design with heterogeneous assets

Cited by 29 publications

References 26 publications

A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

An Iterative Backbone Algorithm for Service Network Design Problems

Capacitated Multicommodity Flow Problem for Heterogeneous Smart Electricity Metering Communications Using Column Generation

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