A memetic algorithm for the integral OBP/OPP problem in a logistics distribution center

Abstract

In this paper, we present a new decision-making tool aimed at improving the efficiency of the operational planning of pickup processes in logistic distribution centers. It is based on a memetic algorithm (MA) solving both the Order Batching Problem (OBP) and the Order Picking Problem (OPP). The result yields a sequence of simultaneous pick up operations of lots for different clients in a storing facility, satisfying a previously defined distribution plan. The objective is the minimization of the operational cost of the entire process, which is directly proportional to the time spent on different activities involved. The failure to satisfy the conditions, either leads to overstocking, delays in delivery or creates inefficiency costs. The analysis of the results obtained with our algorithmic tool indicates that it has a good performance in comparison with other known algorithms used to solve this kind of problem.

Multi-objective Batch Scheduling in Collaborative Multi-product Flow Shop System by using Non-dominated Sorting Genetic Algorithm

“…It follows permutation rule where batch www.ijacsa.thesai.org overlapping is not permitted. The batch scheduling is formalized by using ( 14) to (16). Equation ( 14) and ( 15) is used to determine the batch start time.…”

“…Equation ( 14) and ( 15) is used to determine the batch start time. Meanwhile, the batch end time is determined by using (16).…”

“…Like the flow shop scheduling problem, batch scheduling problem is also a combinatorial optimization problem. There are many methods or algorithms to solve the batch scheduling problem, such as genetic algorithm (GA) [8,13], mixed integer linear programming (MILP) [15], shortest processing time (SPT) [10], variable neighborhood descent (VND) [10], backward dynamic programming [5], memetic algorithm [16], back-off decomposition algorithm [17], longest due date (LDD) [9], adjacent pairwise interchange (API) [9], permutation method (PM) [9], and so on. There are several parameters that were used as objective in a batch scheduling studies.…”

“…The most common parameter is make-span [14,15]. Other parameters are total actual flow time (TAFT) [9,10], total tardiness [18], total earliness [18], revenue [17], overstocking [16], delay [16], inefficiency cost [16], total completion time [5,12], total energy cost [19], number of late jobs [6], run time [20], and so on.…”

Comparison of MOEAs in an Optimization-Decision Methodology for a Joint Order Batching and Picking System

“…The interest in the operation of distribution centers has grown with the increasing e-commerce deliveries of small-size packages in a shipping supply chain [1]. The efficiency of the operational process in storing facilities is critical for the overall performance of a firm [2][3][4][5]. The preparation of batches amounts to between 50% and 70% of the operational costs in distribution centers, being labor-intensive activities in manual systems and capitalintensive activities in automatized ones.…”