Title: A Multiobjective Variable Neighborhood Strategy Adaptive Search to Optimize the Dynamic EMS Location—Allocation Problem
<br>Cover Date: 2022-06-01
<br>Cover Display Date: June 2022
<br>DOI: 10.3390/computation10060103
<br>Description: An aging society increases the demand for emergency services, such as EMS. The more often EMS is needed by patients, the more medical staff are needed. During the COVID-19 pandemic, the lack of medical staff became a critical issue. This research aims to combine the allocation of trained volunteers to substitute for medical staff and solve the EMS relocation problem. The objective of the proposed research is to (1) minimize the costs of the system and (2) maximize the number of people covered by the EMS within a predefined time. A multiobjective variable neighborhood strategy adaptive search (M-VaNSAS) has been developed to solve the problem. From the computational results, it can be seen that the proposed method obtained a better solution than that of current practice and the genetic algorithm by 32.06% and 13.43%, respectively.
<br>Citations: 9
<br>Aggregation Type: Journal
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<br><br><br>Title: A new optimization technique for the location and routing management in agricultural logistics
<br>Cover Date: 2020-03-01
<br>Cover Display Date: 1 March 2020
<br>DOI: 10.3390/joitmc6010011
<br>Description: This paper aims to solve the location and routing problem (LRP) in the agricultural sector with the objective function of fuel cost minimization. Many farmers may have problems when transporting and selling products because of high costs and unfair prices. The proper location of standardized procurement centers and suitable routes will relieve farmers' problems. This paper includes a realistic constraint that a farm can be visited to collect product more than once. A mathematical model was formulated to be solved by Lingo software, but when the problem size was larger, Lingo was unable to solve the problem within a reasonable processing time. The variable neighborhood strategy adaptive search (VaNSAS) is proposed to solve this LRP. The main contributions of this paper are a real case study problem and the first introduction of VaNSAS. Furthermore, the different combinations of the solution approach are proposed to prove which combination is the best algorithm. The computational results show that VaNSAS can find the solutions for all problem sizes in much less processing time compared to Lingo. In medium and large-sized instances, the VaNSAS can reduce processing times by 99.91% and 99.86%, respectively, from solutions obtained by Lingo. Finally, the proposed VaNSAS has been deployed in a case study problem to decide the best locations and transportation routes with the lowest fuel cost.
<br>Citations: 8
<br>Aggregation Type: Journal
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<br><br><br>Title: Simple assembly line balancing problem type 2 by variable neighborhood strategy adaptive search: A case study garment industry
<br>Cover Date: 2020-01-01
<br>Cover Display Date: 2020
<br>DOI: 10.3390/JOITMC6010021
<br>Description: This article aims to minimize cycle time for a simple assembly line balancing problem type 2 by presenting a variable neighborhood strategy adaptive search method (VaNSAS) in a case study of the garment industry considering the number and types of machines used in each workstation in a simple assembly line balancing problem type 2 (SALBP-2M). The variable neighborhood strategy adaptive search method (VaNSAS) is a new method that includes five main steps, which are (1) generate a set of tracks, (2) make all tracks operate in a specified black box, (3)operate the black box, (4) update the track, and (5) repeat the second to fourth steps until the termination condition is met. The proposed methods have been tested with two groups of test instances, which are datasets of (1) SALBP-2 and (2) SALBP-2M. The computational results show that the proposed methods outperform the best existing solution found by the LINGO modeling program. Therefore, the VaNSAS method provides a better solution and features a much lower computational time.
<br>Citations: 26
<br>Aggregation Type: Journal
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<br><br><br>Title: Location decision making and transportation route planning considering fuel consumption
<br>Cover Date: 2019-06-01
<br>Cover Display Date: 1 June 2019
<br>DOI: 10.3390/joitmc5020027
<br>Description: This study presents the Location Routing Problem (LRP) for which we have created a model for the integration of locating facilities and vehicle routing decisions to solve the problem. The case study is the Palm Oil Collection Center, which is also important for the supply chain system. A mathematical model was made to minimize the total cost of a facility-opening cost, fixed cost of vehicle uses and fuel consumption cost. The fuel consumption cost relies on the distance and road conditions, in case of poor physical condition of a road, and its width, which can be affected the speed of the vehicle as well as the used fuel. Thus, we propose an Adaptive Large Neighborhood Search (ALNS) based on heuristic for solving the LRP. The ALNS method was tested with three datasets of samples divided into small, medium and large problems. Then, the results were compared with the results from the exact method by the Lingo program. The computational study indicated that the ALNS algorithm was competitive to the results of the Lingo for all instance sizes. Moreover, the ALNS was more effective than the exact method; approximately 99% in terms of processing time. We extended this approach to solve the case study, which was considered to be the largest problem, and the ALNS algorithm was efficient with acceptable solutions and short processing time. Therefore, the proposed method provided an effective solution to manage location routing decision of the palm oil collection center.
<br>Citations: 19
<br>Aggregation Type: Journal
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<br><br><br>Title: Adaptive large neighborhood search for a production planning problem arising in pig farming
<br>Cover Date: 2019-06-01
<br>Cover Display Date: 1 June 2019
<br>DOI: 10.3390/joitmc5020026
<br>Description: This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS.
<br>Citations: 7
<br>Aggregation Type: Journal
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<br><br><br>Title: Methodology to solve the combination of the generalized assignment problem and the vehicle routing problem: A case study in drug and medical instrument sales and service
<br>Cover Date: 2019-03-01
<br>Cover Display Date: March 2019
<br>DOI: 10.3390/admsci9010003
<br>Description: This article presents algorithms for solving a special case of the vehicle routing problem (VRP). We define our proposed problem of a special VRP case as a combination of two hard problems: the generalized assignment and the vehicle routing problem. The different evolution (DE) algorithm is used to solve the problem. The recombination process of the original DE is modified by adding two more sets of vectors—best vector and random vector—and using two other sets—target vector and trial vector. The linear probability formula is proposed to potentially use one out of the four sets of vectors. This is called the modified DE (MDE) algorithm. Two local searches are integrated into the MDE algorithm: exchange and insert. These procedures create a DE and MDE that use (1) no local search techniques, (2) two local search techniques, (3) only the exchange procedure, and (4) only the insert procedure. This generates four DE algorithms and four MDE algorithms. The proposed methods are tested with 15 tested instances and one case study. The current procedure is compared with all proposed heuristics. The computational result shows that, in the case study, the best DE algorithm (DE-4) has a 1.6% better solution than that of the current practice, whereas the MDE algorithm is 8.2% better. The MDE algorithm that uses the same local search as the DE algorithms generates a maximum 5.814% better solution than that of the DE algorithms.
<br>Citations: 4
<br>Aggregation Type: Journal
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<br><br><br>Title: Adaptive large neighborhood search to solve multi-level scheduling and assignment problems in broiler farms
<br>Cover Date: 2019-01-01
<br>Cover Display Date: 2019
<br>DOI: 10.3390/JOITMC5030037
<br>Description: This research aimed to present a solution to the problem of production scheduling and assignment in broiler farms, which thus enabled the farms to achieve maximum profit. In the operation of farms, there are many factors that affect profits, such as the number of broilers being consistent with the demand of production plants, including profits from the sales and transportation costs. Therefore, we formulated a mathematical model and tested it while using three problem groups through the Lingo v.11 program. The results indicated that this mathematical model could find a suitable solution. However, finding the best solution had time constraints, which resulted in various other problems that prevented a search for an optimal solution due to time consumption exceeding 72 h. We developed an algorithm using the Adaptive Large Neighborhood Search (ALNS) method in order to find another possible solution using a shorter time period, which consisted of ALNS1, ALNS2, and ALNS3. These algorithms are based on a combination of the method of destruction solutions and methods accepting different solutions. We aimed to effectively solve the problems and ensure that they are appropriate for the case study, a broiler farm in Buriram. When comparing the algorithm efficiency with the Lingo v.11 program, it was found that the ALNS1 algorithm was the most suitable for finding the optimal solution in the shortest time, which resulted in a 5.74% increase in operating profits.
<br>Citations: 10
<br>Aggregation Type: Journal
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<br><br><br>Title: An algorithm to manage transportation logistics that considers sabotage risk
<br>Cover Date: 2018-09-01
<br>Cover Display Date: September 2018
<br>DOI: 10.3390/admsci8030039
<br>Description: This paper presents an algorithm to solve the multilevel location–allocation problem when sabotage risk is considered (MLLAP-SB). Sabotage risk is the risk that a deliberate act of sabotage will happen in a living area or during the transportation of a vehicle. This can change the way decisions are made about the transportation problem when it is considered. The mathematical model of the MLLAP-SB is first presented and solved to optimality by using Lingo v. 11 optimization software, but it can solve only small numbers of test instances. Second, two heuristics are presented to solve large numbers of test instances that Lingo cannot solve to optimality within a reasonable time. The original differential evolution (DE) algorithm and the extended version of DE—the modified differential evolution (MDE) algorithm—are presented to solve the MLLAP-SB. From the computational result, when solving small numbers of test instances in which Lingo is able to find the optimality, DE and MDE are able to find a 100% optimal solution while requiring much lower computational time. Lingo uses an average 96,156.67 s to solve the problem, while DE and MDE use only 104 and 90 s, respectively. Solving large numbers of test instances where Lingo cannot solve the problem, MDE outperformed DE, as it found a 100% better solution than DE. MDE has an average 0.404% lower cost than DE when using a computational time of 90 min. The difference in cost between MDE and DE changes from 0.08% when using 10 min to 0.54% when using 100 min computational time. The computational result also explicitly shows that when sabotage risk is integrated into the method of solving the problem, it can reduce the average total cost from 32,772,361 baht to 30,652,360 baht, corresponding to a 9.61% reduction.
<br>Citations: 5
<br>Aggregation Type: Journal
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<br><br><br>Title: A novel and simple management system of interactive system using google street view
<br>Cover Date: 2015-01-01
<br>Cover Display Date: 2015
<br>DOI: 10.1007/978-3-319-10774-5_17
<br>Description: The Google street View (GSV) is used for many applications such as using for driver decision support system, collecting and merging the image for special purpose. The interactive system can be created by GSV. However, the most difficulty is the great image volumes. Moreover, for the reality, the map of interactive system ought to like with the physical place. This research proposed the management system created by the simple concept of database and designed by the simple color menu concept. Additionally, the map is constructed by the adjacent in the directed graph theory. This concept will demonstrate with the interactive system of the small pagoda in Thailand. The administrator management is easy and simple to manage this system, and the user or visitor feels like stay in the physical place and attracts to go to that place with more satisfied contentment.
<br>Citations: 0
<br>Aggregation Type: Book Series
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<br><br><br>Title: The most frequent closed sequence mining
<br>Cover Date: 2010-11-01
<br>Cover Display Date: 2010
<br>DOI: N/A
<br>Description: Frequent sequence mining is an important data mining task with broad applications. To mine frequent sequences, a minimum support threshold is required as a filter. If a very small support threshold value is given in the mining process, a large number of resulting sequences may be produced. Consequently, users have to find useful sequences from the large number of resulting sequences and make tasks of analyze complicated. To avoid this problem, the most frequent sequences should be mined instead of mining of all frequent sequences because they are always the useful sequences. Therefore, this paper is proposed to mine the most frequent sequences. Moreover, the most frequent sequences are produced in form of closed sequences, called the most frequent closed sequences, to avoid generation of redundant sequences. In addition, an efficient method is presented in this paper for mining the most frequent closed sequences.
<br>Citations: 1
<br>Aggregation Type: Conference Proceeding
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