Solving a Floral Order-Picking Model Using a Metaheuristic to Seven Societies of Central Asia

Introduction

The main problem is stated by inadequate location of order picking algorithm demands or management. This ill management of resources is translated in high costs of maintenance of order picking algorithm, as is possible see in Figure 1. Another situation is the prolonged time workers in a bouquet factory spend in the order picking algorithm while being taken to require components or back to another assembly section of a complete floral design, already with the components in their integrating part; the region of study is shown in Figure 1 based on a specialized research presented by Ho and Lin (2017), where they stated a variation of a Vehicle Routing Problem (VRP) called Order Picking Algorithm (OPA). In it is stated that the problem can be divided in five subproblems.

• 1.

  Data Preparation: It defines the data required to set up the stop allocation, the route definition and the scheduling.

• 2.

  Stop Selection: Consists in allocating all the workers in a bouquet factory in different areas to the nearest assembly section.

• 3.

  Route Generation: It covers the decision of an order in which the order picking algorithm will visit each step.

• 4.

  Arrival Time: It covers the setting of time for which the workers in a bouquet factory must arrive in a section with more components to organize.

• 5.

  Schedule of Route: Deals with allocation of order picking algorithm routes that best fit, taking under consideration the time for which workers in a bouquet factory must arrive at next section of assembly, while establishing plans to allow each order picking algorithm transport workers in a bouquet factory from any stock of components to their particular section of assembly.

We consider that order picking is a process of retrieving items from storage locations in response to specific customer request (Hong & Kim, 2017); in addition, we propose the next situations:

• 1.

  Most laborious job in a typical warehouse.

• 2.

  Up to 55% of warehouse operating cost.

• 3.

  Direct influence on order accuracy and delivery time.

Furthermore, Mahid, Bishop and Blackwell (2011) they add more restrictions in their work:

• 1.

  Order Picking Algorithm Capacity: The maximum number of workers a bouquet factory may contain.

• 2.

  Maximum Travel Time: The maximum time workers in a bouquet factory can stay in the truck.

• 3.

  Time Window: The time it takes to arrive.

• 4.

  Homogenous Capacity: Exist order picking algorithm with different capacity.

• 5.

  Mixed Load: Different workers in a bouquet factory from different section of assembly.

We are considering a novel order picking algorithm based on the study of Scholz and Wäscher (2017) determining that different eight components have different color, tonality, additional components, variations in design, section to assemble with other components, shape and thickness for the adjustment of section as it occurs with the piece called “soul” that connects and assembles the rest, as can be seen in Figure 2(a) and Figure 2(b).

Figure 1.

Societies analyzed in this study