This research article aims to solve the special case of the location routing problem (SLRP) when the objective function is the fuel consumption. The fuel consumption depends on the distance of travel and the condition of the road. The condition of the road causes the vehicle to use a different speed, which affects fuel usage. This turns the original LRP into a more difficult problem. Moreover, the volume of the goods that are produced in each node could be more or less than the capacity of the vehicle, and as the case study requires the transportation of latex, which is a sensitive good and needs to be carried within a reasonable time so that it does not form solid before being used in the latex process, the maximum time that the latex can be in the truck is limited. All of these attributes are added into the LRP and make it a special case of LRP: a so-called SLRP (a special case of location routing problem). The differential evolution algorithms (DE) are proposed to solve the SLRP. We modified two points in the original DE, which are that (1) the mutation formula is introduced and (2) the new rule of a local search is presented. We call this the modified differential evolution algorithm (MDE). From the computational result, we can see that MDE generates a 13.82% better solution than that of the original version of DE in solving the test instances.

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.objective of this research is to develop an appropriate method for solving a simple assembly line balancing problem type 2, presenting a case study of the garment industry.Chesbrough [1] said that large organizations work together to drive innovation and create sustainable growth by using the concept of open innovation, which is considered to be related to industry that has adopted "technology, tools, or methods" for the industry in production systems, as well as making products according to the various needs of consumers. However, the industry still has to maintain production efficiency in order to meet the required quality. In addition, new technology, tools, and methods must be also studied and selected to be suitable for the production system and the current knowledge. Many industries still rely on human labor for production, especially the garment industry. When a problem occurs, it will be solved immediately without applying technology or any new methods to resolve it, which causes production efficiency to be at a low level. Therefore, to increase the capacity of the production system, in order to compete with other industries, it is necessary to use the concept of open innovation to help in production planning, reduce the time required for industry operations, and increase production efficiency.The assembly line balancing problem is a form of production planning used for task assignments [2] or to assign work to each station to let each work station operate with the same average production time and allow the process flow system to be flexible and eliminate delay or bottlenecks in order to be able to produce products correctly and eliminate mistakes during production. The precedence diagram or table of relationships determines the operation according to the workflow that is clearly specified in the production process. There are different objectives for problem solving, such as reducing production time, reducing the number of workstations, determining the efficiency of assembly line bal...

This study aims to solve the real-world multistage assignment problem. The proposed problem is composed of two stages of assignment: (1) different types of trucks are assigned to chicken farms to transport young chickens to egg farms, and (2) chicken farms are assigned to egg farms. Assigning different trucks to the egg farms and different egg farms to the chicken farms generates different costs and consumes different resources. The distance and the idle space in the truck have to be minimized, while constraints such as the minimum number of chickens needed for all egg farms and the longest time that chickens can be in the truck remain. This makes the problem a special case of the multistage assignment (S-MSA) problem. A mathematical model representing the problem was developed and solved to optimality using Lingo v.11 optimization software. Lingo v.11 can solve to optimality only small- and medium-sized test instances. To solve large-sized test instances, the differential evolution (DE) algorithm was designed. An excellent decoding method was developed to increase the search performance of DE. The proposed algorithm was tested with three randomly generated datasets (small, medium, and large test instances) and one real case study. Each dataset is composed of 12 problems, therefore we tested with 37 instances, including the case study. The results show that for small- and medium-sized test instances, DE has 0.03% and 0.05% higher cost than Lingo v.11. For large test instances, DE has 3.52% lower cost than Lingo v.11. Lingo v.11 uses an average computation time of 5.8, 103, and 4320 s for small, medium and large test instances, while DE uses 0.86, 1.68, and 8.79 s, which is, at most, 491 times less than Lingo v.11. Therefore, the proposed heuristics are an effective algorithm that can find a good solution while using less computation time.

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.

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.