The objective of this research is to develop metaheuristic methods by using the differential evolution (DE) algorithm for solving the U-shaped assembly line balancing problem Type 1 (UALBP-1). The proposed DE algorithm is applied for balancing the lines (manufacturing a single product within a fixed given cycle time), where the aim is to minimize the number of workstations. After establishing the method, the results from previous research studies were compared with the results from this study. For the UALBP, two groups of benchmark problems were used for the experiments: (1) For the medium-sized UALBP (21–45 tasks), it was found that the DE algorithm DE/best/2 to Exponential Crossover 1 produced better solutions when compared to the other metaheuristic methods: it could generate 25 optimal solutions from a total of 25 instances, and the average time used for the calculation was 0.10 seconds/instance; (2) for the large-scale UALBP (75–297 tasks), it was found that the basic DE algorithm and improved differential evolution algorithm generated better solutions, and DE/best/2 to Exponential Crossover 1 generated the optimal solutions and achieved the minimum solution search time when compared to the other metaheuristic methods: it could generate 36 optimal solutions from a total of 62 instances, and the average time used for the calculation was 4.88 seconds/instance. From the comparison of the DE algorithms, it was found that the improved differential evolution algorithm generated optimal solutions with a better solution search time than the search time of the basic differential evolution algorithm. The basic and improved DE algorithm are the effective methods for balancing UALBP-1 when compared to the other metaheuristic methods.

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

Background: This research aimed to establish a network linked to generation, for the transport route of tapioca starch products to a land port, serving as the logistics hub of Thailand’s Nakhon Ratchasima province. Methods: The adaptive large neighborhood search (ALNS) algorithm, combined with the differential evolution (DE) approach, was used for the problem analysis, and this method was named modified differential evolution adaptive large neighborhood search (MDEALNS) is a new method that includes six steps, which are (1) initialization, (2) mutation, (3) recombination, (4) updating with ALNS, (5) Selection and (6) repeat the (2) to (5) steps until the termination condition is met. The MDEALNS algorithm designed a logistics network linking the optimal route and a suitable open/close factory allocation with the lowest transport cost for tapioca starch. The operating supply chain of tapioca starch manufacturing in the case study. The proposed methods have been tested with datasets of the three groups of test instances and the case study consisted of 404 farms, 33 factories, and 1 land port. Results: The computational results show that MDEALNS method can reduced the distance and the fuel cost and outperformed the highest performance of the original method used by LINGO, DE, and ALNS. Conclusions: The computational results show that MDEALNS method can reduced the distance and the fuel cost and outperformed the highest performance of the original method used by LINGO, DE, and ALNS.

This research aimed to study the Differential Evolution (DE) for solving the Multi-floor Facility Layout Problem. (MFLP) with the target of minimize the transporting material cost.The DE algorithm had been evaluated and would be compared with MULTIPLE and SABLE algorithm. For MFLP, the Differential Evolution algorithm (DE) methods were tested with 6 data sets as following: 11-1, 11-2, 12, 21-1, 21-2, 21-3 by using DE/rand/1/bin and DE/rand/2/bin which found that all methods able to find the optimal solution better than the MULTIPLE. DE/rand/2/bin is having more effective than SABLE which calculate the comparison in percentage ratio as followings: 3.7, 11.5, 0.1 and 21.7% of problems 11-1, 21-1, 21-2 and 21-3, respectively. DE-rand/1-bin is having more effective than SABLE which calculate the comparison in percentage ratio as followings: 3.5, 4.5 and 22.3% of problems 11-1, 21-1 and 21-3, respectively. The result showed that the further performed DE by using basic DE were effective methods comparing to the other algorithms and other metaheuristic methods. Hence, they could be used to solve MFLP.