Minimising the total travel distance to pick orders on a unidirectional picking line

Order picking is the most important activity in distribution centres. It involves the process of retrieving products from storage in response to a specific customer request. The order picking system in a distribution centre used by Pep Stores Ltd. (Pep), located in Durban, South Africa, is considered. The order picking system in Pep utilises picking lines. The system requires that the pickers move in a clockwise direction around the picking line. The planning of picking lines may be divided into three tiers of decisions. The first tier determines which Stock Keeping Units (SKUs) should be allocated to which picking line and is known as the SKU to Picking Line Assignment Problem (SPLAP). The second tier, the SKU Location Problem (SLP), considers the positioning of the various SKUs in a picking line. The final tier considers the sequencing of the orders for pickers within a picking line and is referred to as the Order Sequencing Problem (OSP). Collectively, these three tiers aim to achieve the objective of picking all the SKUs for all the orders in the shortest possible time. The decisions associated with each tier are made sequentially during the planning of a picking line. Each problem therefore relies on the information generated by its predecessing tier(s). Initially the OSP is addressed. A number of heuristic and metaheuristic approaches are presented, together with an exact formulation to solve this tier. The size of the problem is reduced by using a relaxation of the problem that may be solved exactly. A number of greedy tour construction heuristics, a scope and ranking algorithm, methods based on awarding starting locations with respect to preference ratios and a modified assignment approach was used to solve the OSP. Furthermore, a tabu search, simulated annealing, genetic algorithm and a generalised extremal optimisation approach are used to solve the OSP. The solution quality and computational times of all the approaches are compared for the data provided by Pep, with the generalised extremal optimisation approach delivering the best solution quality. Two methods from the literature was used to model the SLP, whereafter an ant colony system was used to maximise the number of orders in common between adjacent SKUs. A number of agglomerative clustering algorithms were used from which dendrograms could be constructed. Two novel heuristic clustering algorithms were considered. The first heuristic calculates a distance between two clusters as the set of orders that have to collect all the SKUs in both clusters, whereas the second method is based upon the frequency of SKUs within a cluster. Little or no improvement was achieved in most cases. The SPLAP was introduced by means of a number of possibilities of how to formulate objectives. A possible exact formulation is presented, followed by a nearest neighbour search, which was initially used to construct new picking lines based on all data sets. A different approach was then taken by means of a tabu search where the waves of two or three picking lines were altered. Significant savings may be incurred for large data sets. iii Stellenbosch University http://scholar.sun.ac.za