Power connectivity in the Greater Mekong Subregion (GMS) – The need for a wider discourse

Yang, Muyi; Sharma, Deepak; Shi, Xunpeng; Mamaril, Kristy; Jiang, Han; Candlin, Alison

doi:10.1016/j.enpol.2022.112994

Cited by 4 publications

(8 citation statements)

References 23 publications

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“…The likelihood of obtaining a better solution increases with the use of more efficient local search algorithms. As can be seen from the solution in Table 22, the AMIS provided a higher value for ARP than the GA or DE, as suggested in Yang et al [4] and Pitakaso et al [61], because it employed a more efficient local search method. This conclusion is in line with that of [59,[70][71][72][73][74][75], which apply various local search techniques, such as the best transition methods, scaling factors and random transition methods, to solve various types of problems.…”

Section: Amis-in Amis-at Amismentioning

confidence: 78%

“…The effectiveness of the suggested technique (AMIS) was evaluated by comparing it to two existing heuristics: the differential evolution algorithm (DE) and the genetic algorithm (GA), both of which were derived from previous works [4,61]. In this section, we ran two different sets of experiments.…”

Section: The Amis's Effectivenessmentioning

confidence: 99%

“…This is because the value of w 1 is the concentration value of the model, focused on maximizing the average profit rather than minimizing the overall number of containers used in the system. When focusing on the average profit gained using various solution methods, we found that the average profit of Lingo V.16 [62], DE [61], GA [4] and the AMIS is 398,999.98 THB, 381,223.19 THB, 386,127.32 THB and 398,999.98 THB, respectively. When compared to the results from Lingo V.16, which always finds the optimal solution, the AMIS can find the optimal solution 100% of the time, whereas Lingo V.16 is not applicable to problems with a large size within an acceptable computational time.…”

Section: The Amis's Effectivenessmentioning

confidence: 99%

“…When compared to the results from Lingo V.16, which always finds the optimal solution, the AMIS can find the optimal solution 100% of the time, whereas Lingo V.16 is not applicable to problems with a large size within an acceptable computational time. The AMIS can improve the solution quality of the GA proposed by [4] by 4.66% and the DE proposed by [61] by 3.33%.…”

Section: The Amis's Effectivenessmentioning

confidence: 99%

“…They are fueled by complex land use decision-making processes and have resulted in a diverse landscape mosaic throughout the region [3]. Yang et al [4] showed that GMS regional power connectivity is a complicated undertaking that is influenced by a variety of mutually reinforcing and unpredictable factors. In such circumstances, it makes sense for policymakers to focus their attention on specifically defined internal electricity system problems such as inadequate infrastructure, a lack of techn-ical expertise, and disorganized regulatory procedures in order to enhance regional power connectivity.…”

Section: Introductionmentioning

confidence: 99%

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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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