خطاب به تعیین اندازه دسته تولید و مشکل برنامه ریزی انبارداری در محیط تولید

In recent years, lot sizing issues have gained attention of researchers worldwide. Previous studies devoted on lot sizing scheduling problems were primarily focused within the production unit in a manufacturing plant. In this article lot sizing concept is explored in the context of warehouse management. The proposed formulation helps manufacturer to decide the effective lot-size in order to meet the due dates while transferring the product from manufacturer to retailer through warehouse. A constrained based fast simulated annealing (CBFSA) algorithm is used to effectively handle the problem. CBFSA algorithm encapsulates the salient features of both genetic algorithm (GA) and simulated annealing (SA) algorithms. This hybrid solution approach possesses the mixed characteristics of both of the algorithms and determines the optimal/near optimal sequence while taking into consideration the lot-size. Results obtained after implementing the proposed approach reveals the efficacy of the model over various problem dimensions and shows its superiority over other approaches (GA and SA).

Uncertain demand pattern and customized product design are some of the key challenges for companies in current competitive market scenario. Moreover, providing best customer service and growing the customer base are increasingly becoming tougher for manufacturing enterprises. In order to meet these challenges warehouse management plays a key role in the manufacturing supply chain. The competitive warehousing scenario compels the manufacturing firms to deal with the issues such as selection of appropriate warehouses and its efficient management. The product delivery from the manufacturing to warehouse and then from warehouse to retailer is a very crucial issue. This is a complex decision making process, therefore, to ease the complexity nowadays lot-sizing of the product in warehouse scheduling is becoming more common. Sometimes manufacturing companies strive hard to find a place for a complete lot in warehouses. In such circumstances the lot-sizing helps to deal with the availability issues in the warehouse. The lot-size concept also helps the firms to meet the due-date of the retailers. Proper management and efficient use of warehouses are critical for manufacturing industries to performance well in current competitive business scenario. An inefficient warehouse can lead to excess inventory and deadlock of manufacturing order while transferring products from manufacturers to customers. In this research, warehouse scheduling problem has been considered with the prime objective to search for an optimal schedule, such that the total tardiness and number of tardy orders are minimized. The paper also addresses lot sizing scheduling issues in warehousing environment where the shipment of goods from the manufacturers to warehouses and then to retailers has been considered. The paper also assumes that the number of vehicles in the warehouse is fixed for the time period. The present research also considers the precedence relationship among the products along with non-uniform starting time of warehouse to match the real-life scenario as closely as possible. This non-uniform starting time of warehouse arises due to the non-uniform ending time of previous customer order. Subsequently, a mathematical model is developed to address the dynamic nature of this problem. Warehouse scheduling problem is well known for its computational complexities (Byung-In, Heragu, Graves, & Onge, 2003). Since complexities grow exponentially searching an effective solution is difficult even with the help of a state-of-art optimization tools. Nevertheless, the need of coping up with increasingly higher computational complexity has stimulated research in optimization algorithms. This is particularly true in context of evolutionary algorithms, particularly in case of genetic algorithm (GA) and simulated annealing (SA) which showed better performance than their predecessors in solving complex optimization problems (Xu, Wei, & Wang, 2009). Therefore, this paper focuses on the hybrid form of both the approaches and termed as, constraint based fast simulated annealing (CBFSA) algorithm (Tiwari, Kumar, Prakash, Kumar, & Shankar, 2006) to address the warehouse scheduling problem. This algorithm addresses the possible synergy between GA and SA and converges quickly toward better solutions in minimum number of generations. Rest of the paper is organized as follows: Section 2 discusses the literature in warehouse management together with highlighting lot-sizing aspects. Section 3 presents the problem environment with detailed mathematical modeling for the generation of alternative warehouse sequence as well as lot sizing. Section 4 covers the background of constraint based fast-simulated annealing (CBFSA) algorithm. Section 5 puts forward the solution approach based on CBFSA algorithm pertaining to warehouse scheduling problems. In Section 6 illustrative examples are presented to demonstrate the efficiency of proposed CBFSA algorithm and the corresponding results are discussed in Section 7. Finally, Section 8 summarizes the paper with a note about the future scope of this paper.

Most of the research in past are mainly focused on addressing the dynamic nature of customer demand in product development and manufacturing. However, the warehouse scheduling problem has been ignored which is an important factor for the better performance of the supply chain. Also the lot sizing issues in warehousing environment has not been extensively researched in past. Realizing this research gap the present paper addresses lot sizing and dynamic scheduling issues in warehouse scheduling environment and proposes the CBFSA algorithm to solve the complex scheduling problem under such scenario. This paper considers an important aspect of lot sizing issue. The paper emphasizes that the products need to broken into different lot–sizes taking into consideration the due date and space availability in the warehouse. The model successfully addresses the lot-sizing concept while minimizing the total tardiness. Certainly, this approach will pave the way to tackle the warehouse scheduling problems more efficiently and effectively. Results obtained by the CBFSA algorithm outperformed the other existing approaches (GA and SA). In future, the algorithm can be tested on number of other scheduling problems with increased complexity and can also be compared with other meta-heuristics such as Ant Colony Optimization, Particle Swarm Optimization, and other hybrid algorithms to further justify its robustness. The paper also strongly emphasize that breaking the orders into lot sizes helps manufacturing firms to deliver products within the due dates. Future work should be aimed at considering more warehouse constraints to bring the problem scenario more realistic to the manufacturing environment.