Solving multi-objective master production schedule problem using memetic algorithm

Abstract

<span>A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to perform optimization on production planning problems based on, for instance, linear and non-linear programming, dynamic-lot sizing and meta-heuristics. In particular, several meta- heuristics have been successfully used to solve MPS problems such as genetic algorithms (GA) and simulated annealing (SA). This paper proposes a memetic algorithm to solve multi-objective master production schedule (MOMPS). The proposed memetic algorithm combines the evolutionary operations of MA (such as mutation and Crossover) with local search operators (swap operator and inverse movement operator) to improve the solutions of MA and increase the diversity of the population). This algorithm has proved its efficiency in solving MOMPS problems compared with the genetic algorithm and simulated annealing. The results clearly showed the ability of the algorithm to evaluate properly how much, when and where extra capacities (overtime) are permitted so that the inventory can be lowered without influencing the level of service. </span>

Formulations for joint order picking problems in low-level picker-to-part systems

“…Likewise, other nature-based metaheuristics could be adapted to solve the JOPP formulated in this study to provide efficient solutions in short computing times [23]. Therefore, it is expected that studies and investigations in joint order picking problems will increase, including one or several features of realistic warehouse environments mentioned in Table 1, such as online customer order arrivals (time windows approaches), multiple pickers, pickers with different skills, multi-block warehouses, non-conventional layouts, 3D warehouses, due dates, and approaches considering conflicting objectives and a tradeoff between them [29], thus, providing solutions that allow to improve both the operative efficiency (operative costs) and the customer service (on-time deliveries). Likewise, it is expected that the proposed models for the JOPP are extended to be more realistic wh en considering multiple pickers and congestion in narrow-aisle warehouses, and considering multiple pickers and splitting to assign a single batch among several pickers, minimizing makespan [30], tardiness, earliness, and total traveled distance.…”

“…Constraints (26) show that a picking location can be passed only once by the customer order k in batch b. Constraints (27)(28)(29) are typical constraints in TSP ensuring the solution represents a Hamilton cycle [13]. Constraints (29) ensure a complete picking route, avoiding sub -tours in the TSP.…”

Birds Sound Classification Based on Machine Learning Algorithms

“…False alarm rate: It's also known as the false positive, and it's characterized as the ratio of incorrectly predicted Attack samples to all Normal samples [62].…”

Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

“…The order batching problem is considered NP-Hard when the number of customer orders per batch is greater than two [15], therefore any extension of this problem is considered NP-Hard as well [13,27,28]. Then, the OBSPMP is considered an NP-hard problem, so it cannot be solved using exact solution methods at least for large instances [25], thus metaheuristics like genetic algorithms can provide satisfactory solutions in short computing times for combinatorial problems related to logistics and operations management [29,30]. In this study, we introduce a GGA to provide high-quality solutions in short computing times for the OBSPMP, satisfying the operating requirements of real warehouses.…”