Adaptive Large Neighborhood Search for a Production Planning Problem Arising in Pig Farming

Abstract

This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480–620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS.

Optimizing vertically integrated pork production supply chain: A Lagrangian heuristic approach

“…Similarly [ 8 ], focused on pig fattening farms and pork processing units, dealing with multi-period planning problems for specific pig sizes through dynamic programming techniques. Further investigations, such as pig farm scheduling and worker assignment problems [ 9 ], utilized an Adaptive Large Neighborhood Search (ALNS) heuristic to optimize profits over finite periods.…”

“…The empirical data used in this study is derived from extensive research conducted on real-world pig supply chains in Thailand, as documented in Refs. [ 7 , 8 ], and [ 9 ].…”

Modified Differential Evolution Algorithm for a Transportation Software Application

“…Theeraviriya et al [42] proposed the differential evolution (DE) algorithm to solve the location and routing problem; the innovation proposed in that study is that while considering the location and routing problem, fuel consumption is taken into account and DE is proposed to solve a special case of the generalized assignment problem [43]. Praseeratasan et al [44] proposed ALNS to solve a real-world production planning problem. The authors used pig farming as a case study.…”

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

“…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).…”