In a warehouse, efficient product transportation is required for high productivity. In general, it is recognized that mixed-load transportation can decrease the moving time to some extent. However, many scheduling methods without regard to mixed-load transportation have recently been proposed. In this respect, we propose a design for scheduling problems with mixed-load transportation handled by vehicles. The proposed method uses two well-known dispatching rules and a local search method based on a simulated annealing algorithm. Two rules are applied to obtain the initial solution while ignoring the mixed-load transportation, and the local search method is applied to improve the initial solution to obtain a semi-optimal solution in consideration of mixed-load transportation. To evaluate the applicability of the proposed system, we implemented our system with several product transportation environment. The simulation results verify the effectiveness of the proposed method.

In this paper, the final stage of a multiphase approach for solving the picking problem in a warehouse is addressed. Given a number of agents, each with its own set of picking sequences (trips or routes) to accomplish, a dispatching problem is described and shown to have a non-polynomial search space with respect to the number of agents and number of routes. A simulation-based scheduling procedure is proposed to solve the problem. The aim is to reduce potential delays induced by agent queues. Extensive statistical simulations on a realistic warehouse operating at varying conditions are conducted to show that the said dispatching procedure is able to make significant improvements with respect to minimizing operating time, on the average, over the case when no dispatching policy is applied to the agents

In this paper, fast heuristics for a centralized multi-agent route planner are presented and computationally evaluated. We solve a sub-problem of warehouse scheduling involving the routing of intelligent agents as a preliminary step in optimizing the total schedule. The problem involves the generation of routes for automated agents tasked with the transfer of items within a warehouse from storage pallets to a common loading shed. The goal is to minimize the total distance of the routes and the number of routes generated. This constitutes a multiple-objective optimization problem which is NP-hard and hence can take a prohibitively long time to solve using existing search-based techniques. The approach adapted here is to model the system as a split delivery vehicle routing problem (SDVRP) with grid distances and to solve it using heuristics based on tested operations research concepts. Twenty-two such heuristics are tested including the well-known greedy nearest-neighbor (NN) heuristic, and the established savings heuristic of Clarke and Wright. The author introduces two SDVRP variations of the NN algorithm, namely the nearest-fill (NF) and nearest-fill farthest-start (NFFS) heuristics. Existing SDVRP improvement procedures are also considered and generalized to produce numerous heuristic variations. This novel approach of applying fast vehicle routing heuristics to multi-agent routing has the advantage of yielding good quality results within a very short period of time. The results of the study show that the greedy NFFS heuristic combined with the improvement procedures, consistently produces superior results with regard to minimization of distance and the number of routes in an the instances tested.

Logistics network design problems are usually solved through experimental and intuitional approaches. These approaches are taken because of the complicated conditions that accompany problems associated with layout planning, stock analysis, network flow. In this study, the problem is divided into two parts: material flow and layout planning. An algorithm is examined in this study for a design that involves the formulation of material flow into the shape of mixed integer problems.