A Joint Comparative Analysis of Routing Heuristics and Paperless Picking Technologies Using Simulation and Data Envelopment Analysis

Recent literature demonstrates that warehouse order picking performance is reflected in the logistics performance of downstream retailers. Warehouse solutions and policies significantly contribute to the improvement of distribution and delivery to retailers. This paper therefore reports an analysis of the joint performance of routing policies and picking technologies, and provides insights into the best ways to combine routing strategies and paperless solutions in order to optimize cost efficiency. We follow a multistage approach that combines mixed integer linear programming algorithms, data envelopment analysis (DEA), and ranking and selection. The results show that traversal-voice picking and midpoint-voice picking combinations are equally distributed over the most efficient subsets and that superior technology can enhance picking efficiency only to a certain level. The study provides guidelines for logistics managers on ways to combine warehouse solutions and policies in order to better streamline the operations. It offers an original framework to analyze the joint performance of picking routing and picking solutions by considering the effect of picking errors.

Minimize the Route Length Using Heuristic Method Aided with Simulated Annealing to Reinforce Lean Management Sustainability

Cost reduction is a cornerstone of the Lean administration’s sustainability through modify its algorithms scheme to become multi-useful. This paper focuses on control “movement” waste, to minimize pipeline, cabling and sewerage network deployments time, to avoid demurrages (i.e., constructor sectors) and quickens planning through two stages. The first belongs to the build constrained hybridization of published heuristic routing methods (e.g., S-Shape, Mid-point, Largest-Gap, Return, Ascending, FLA-5, FLA-6 [Flow Line Analysis], and Composite) to select the shortest path that serves many locations (i.e., Plan-A), while allowing for the modification of these locations during service (i.e., Plan-B). The new locations are grouped into two clusters, the first of which lay on the shortest preferred path, while the second cluster contains locations that do not lay on the preferred path and are therefore moved on the backlogs-list, then use Simulated Annealing when to serve them. Finally, the impact of the selected performance is investigated after studying its correlation with another published effective one under cost considerations. The computational results of proposed Minimize-Route-Length aided with simulated annealing (MRL-SA) significantly outperform others in terms of the performance of the routing heuristics and total costs and develop the Last Planner System, which has a good reputation in construction projects and approve the proposed algorithm to maintain its competitiveness sustainability.

On Minimizing the Completion Times of Long Flows over Inter-Datacenter WAN

Long flows contribute huge volumes of traffic over inter-datacenter WAN. The Flow Completion Time (FCT) is a vital network performance metric that affects the running time of distributed applications and the users' quality of experience. Flow routing techniques based on propagation or queuing latency or instantaneous link utilization are insufficient for minimization of the long flows' FCT. We propose a routing approach that uses the remaining sizes and paths of all ongoing flows to minimize the worst-case completion time of incoming flows assuming no knowledge of future flow arrivals. Our approach can be formulated as an NP-Hard graph optimization problem. We propose BWRH, a heuristic to quickly generate an approximate solution. We evaluate BWRH against several real WAN topologies and two different traffic patterns. We see that BWRH provides solutions with an average optimality gap of less than 0.25%. Furthermore, we show that compared to other popular routing heuristics, BWRH reduces the mean and tail FCT by up to 1.46× and 1.53×, respectively.

Model for Order-Batching with Congestion Consideration

The paper presents the model for optimal number of merged orders that will reduce the average order-picking time. Two order batching policies were described: pick-then-sort and sort-while-pick. One-block rectangular warehouses were considered. The author studied two routing heuristics: s-shape and return for which the equations for average order-picking time can be designated based on probability distributions: binomial distribution and uniform distribution. The research takes into account the possible congestion problem. Based on simulations the analytical form of blocking time for pickers was proposed. The study shows that the advantages of each additional merged order is getting smaller. Using ABC classification for storage location assignment in warehouses with low number of aisles and large pick lists can extend the average order-picking time.