Outbound logistics is a crucial field of logistics management. This study considers a planning distribution for the poultry industry in Thailand. The goal of the study is to minimize the transportation cost for the multi-depot vehicle-routing problem (MDVRP). A novel enhanced differential evolution algorithm (RI-DE) is developed based on a new re-initialization mutation formula and a local search function. A mixed-integer programming formulation is presented in order to measure the performance of a heuristic with GA, PSO, and DE for small-sized instances. For large-sized instances, RI-DE is compared to the traditional DE algorithm for solving the MDVRP using published benchmark instances. The results demonstrate that RI-DE obtained a near-optimal solution of 99.03% and outperformed the traditional DE algorithm with a 2.53% relative improvement, not only in terms of solution performance, but also in terms of computational time.

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This paper presents the Hybrid and Self-Adaptive Differential Evolution algorithms (HSADE) to solve an egg distribution problem in Thailand. We introduce and formalize a model for a multi-product, multi-depot vehicle routing problem with a time window, a heterogeneous fleet and inventory restrictions. The goal of the problem is to minimize the total cost. The multiple products comprise customers’ demands with different egg sizes. This paper presents a Mixed Integer Linear Programming (MILP) model, an initial solution-based constructive heuristic, a new self-adaptive mutation strategy, and a neighborhood search structure with the probability to improve DE. The two measurements of criteria are the heuristic performance (HP) compared with the solution obtained by MILP and the relative improvement (RI) of the solution compared with Thailand’s current egg distribution practice. The computational results show that the performance of HSADE is better than the current practice, and HSADE can provide on average a 14.13% improvement in total cost. Additionally, our proposed algorithm can be applied to similar agriculture logistics in Thailand and worldwide.

This paper focuses on optimizing scheduling solutions for the flexible flow shop problem, with tooling constraints and machine eligibility, to minimize makespan for cultivating sugarcane. Normally, preparing the soil for planting sugarcane requires six steps: 1) 7 power harrow and rototiller, 2) rotary mini combine, 3) 22/24 disc harrow, 4) rotary mini combine, 5) sugarcane plantation, and 6) sugarcane sprayer. Each of these steps requires a variety of tools. With limited availability of tools and equipment, resource allocation is important. The objective of this research was to minimize the makespan. For optimal convergence, meta-heuristics, such as a Differential Evolution algorithm, a ParticleSwarm optimization algorithm, and a Hybrid DEPSO algorithm were developed to solve the problem. Experimental results showed that all three methods efficiently solved flexible flow shop problems.