A Novel Enhanced Differential Evolution Algorithm for Outbound Logistics of the Poultry Industry in Thailand

Abstract

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.

The Novel Strategy of Differential Evolution for Multi-Fleet Size and Vehicle Routing Problem in Logistics Service Providers

“…They developed a modified DE algorithm to minimize total costs and truck usage, showing considerable improvements. Later, Moonsri et al (2022) [7] proposed a Hybrid and Self-Adaptive DE algorithm (HSADE) to solve the multi-depot vehicle routing problem in Thailand's egg distribution. HSADE demonstrated an average total cost improvement of 14.13%, suggesting potential efficiency enhancements in agricultural logistics.…”

“…HSADE demonstrated an average total cost improvement of 14.13%, suggesting potential efficiency enhancements in agricultural logistics. Lastly, Moonsri et al (2022) [8] utilized an enhanced DE algorithm, called Reinitialization Differential Evolution (RI-DE), for logistics planning in Thailand's poultry industry. The algorithm outperformed standard ones in terms of cost efficiency and computational time.…”

Comparative Study of Swarm‐Based Algorithms for Location‐Allocation Optimization of Express Depots

“…A supported vector machine was introduced to distinguish the state of a particle, and the state will determine whether or not a group of particles will be updated. Moonsri et al [5] discussed the poultry logistics planning problem, which routes vehicles to each established depot. A new mutation formula is developed in the reinitialization phase of differential evolution (DE) to protect the local structure of the solution, and a location search of partial variables is used to enhance the exploitation ability.…”

“…Step 1: obtain the number of distribution centers N by Equation (19) Step 2: obtain the position of N distribution centers by Equation ( 20) 0.6 × 120 72, c 1 0.7/0.7 + 0.9 × 500 218.75, and c 2 0.9/0.7 + 0.9 × 500 281.25. e sorting index of the ascending order of [0.4, 0.95, 0.25, 0.55, 0.6] is [1][2][3][4][5].…”

“…However, for a city-level LAP, the model and solution representation may vary because the available options for distribution centers can be many, and the setup cost and capacity of di erent options have a signi cant impact on the total cost. On the other hand, the NP-hard nature of LAP makes heuristic algorithms favorable, among which swarm-based algorithms are representative [3,5,6]. e solution representation plays a vital role in applying the algorithm because an e cient encoding scheme may reduce the length of the solution string, smooth the solution space, or eliminate the constraints.…”