A New Optimization Technique for the Location and Routing Management in Agricultural Logistics

Abstract

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.

Variable Neighborhood Strategy Adaptive Search to Solve Parallel-Machine Scheduling to Minimize Energy Consumption While Considering Job Priority and Control Makespan

“…This paper presents a novel method called variable neighborhood strategy adaptive search (VaNSAS) to solve the parallel-machine-scheduling problem in order to minimize energy consumption while considering job priority and makespan control. Although VaNSAS successfully improved solution-search performance in previous studies [17,22,[36][37][38], none had accounted for energy consumption, late delivery charge, and production overhead. The advantage of applying VaNSAS in this study was that its algorithms search for the best possible solution in many different areas by using several searching approaches, thereby moving to find more diversification and intensification at all times depending on the designed blackbox methods.…”

“…Variable neighborhood strategies adaptive search (VaNSAS) is a new type of metaheuristics that was first introduced by Theeraviriya et al [36], with the concept of solving combinatorial optimization problems. The primary idea of the VaNSAS is to allow for algorithms to search in many different areas to obtain the best possible solution by using several searching methods.…”

“…VaNSAS is very flexible to use in many optimization problems, such as the location and routing problem (LRP). Theeraviriya et al [36] studied the LRP in the Thai rubber industry, and proposed VaNSAS to solve the problem with the objective function of fuel-cost minimization, including a realistic constraint that allowed for a vehicle to collect products by visiting rubber farms more than once in cases where they had more rubber than vehicle capacity. Computation results showed that VaNSAS could find solutions for all problem sizes in much less processing time than that needed by the exact method.…”

“…The differential evolutionary (DE) algorithm is suitable to solve this kind of problem because it can obtain non-dominated solutions in a single run and has been successfully applied to optimization problems [17,20,24,[27][28][29]. In addition, metaheuristics based on local search methods, such as the variable neighborhood strategies adaptive search (VaNSAS), have been successfully applied to solve many combinatorial optimizations problems [30][31][32][33][34][35][36][37][38][39][40], which inspired us to develop a parallel-machine-scheduling model, and to propose VaNSAS and new neighborhood strategies: (1) solution destroy and repair (SDR); (2) track-transition method (TTM); and (3) multiplier factor (MF).…”

Method for Calculating the Required Number of Transport Vehicles Supplying Aviation Fuel to Aircraft during Combat Tasks

“…As a rule, publications in the area of linear programming present new methods [36,37], techniques [38], algorithms [39], or improvements [40] to existing methods. On the other hand, solutions that could be defined as innovative are less common [41].…”