30,837 research outputs found
Discrete Warehouse Problem
Coordinated Science Laboratory was formerly known as Control Systems LaboratoryJoint Services Electronics Program / N00014-84-C-0149National Science Foundation / MIP 87-09074 and ECS 84-1090
A Discrete Particle Swarm Optimization Algorithm for Bi-Criteria Warehouse Location Problem
The uncapacitated warehouse location problem (UWLP) is one of the widely studied discrete location problems, in which the nodes (customers) are connected to a number (w) of warehouses in such a way that the total cost, yields from the dissimilarities (distances) and from the fixed costs of the warehouses is minimized. Despite w is considered as fixed integer number, the UWLP is NP-hard. If the UWLP has two or more objective functions and w is an integer variable, the UWLP becomes more complex. Large size of this kind of complex problems can be solved by using heuristic algorithms or artificial intelligent techniques. Itâs shown that Particle Swarm Optimization (PSO) which is one of the technique of artificial intelligent techniques, has achieved a notable success for continuous optimization, however, PSO implementations and applications for combinatorial optimization are still active research area that to the best of our knowledge fewer studies have been carried out on this topic. In this study, the bi-criteria UWLP of minimizing the total distance and total opening cost of warehouses. is presented and itâs shown that promising results are obtained.Warehouse Location Problem, Particle Swarm Optimization, Discrete Location Problems, Bi-criteria.
Configuring Traditional Multi-Dock, Unit-Load Warehouses
The development of expected-distance formulas for multi-dock-door, unit-load warehouse configurations is the focus of the dissertation. From formulations derived, the width-to-depth ratios minimizing expected distances are obtained for rectangle-shaped, unit-load warehouse configurations. Partitioning the storage region in the warehouse into three classes, the performance of a multi-dock-door, unit-load warehouse is studied when storage regions can be either rectangle-shaped or contour-line-shaped. Our first contribution is the development of formulas for expected distance traveled in storing and retrieving unit loads in a rectangle-shaped warehouse having multiple dock doors along one warehouse wall and storage racks aligned perpendicular to that wall. Two formulations of the optimization problem of minimizing expected distance are considered: a discrete formulation and a continuous formulation with decision variables being the width and depth of the warehouse for single- and dual-command travel. Based on dock door configurations treated in the literature and used in practice, three scenarios are considered for the locations of dock doors: 1) uniformly distributed over the entire width of a wall; 2) centrally located on a wall with a fixed distance between adjacent dock doors; and 3) not centrally located on a wall, but with a specified distance between adjacent dock doors. Our second contribution is the investigation of the effect on the optimal width-to-depth ratio (shape factor) of the number and locations of dock doors located along one wall or two adjacent walls of the warehouse. Inserting a middle-cross-aisle in the storage area, storage racks are aligned either perpendicular or parallel to warehouse walls containing dock doors. As with the warehouse having storage racks aligned perpendicular to the warehouse wall, discrete and continuous formulations of the optimization problem are developed for both single- and dual-command travel and three scenarios for dock-door locations are investigated.Our final contribution is the analysis of the performance of a unit-load warehouse when a storage region or storage regions can be either rectangle-shaped or contour-line-shaped. Particularly, we consider two cases for the locations of dock doors: equally spaced over an entire wall of the warehouse and centrally located on a wall, but with a specified distance between adjacent dock doors. Minimizing expected distance, the best rectangle-shaped configuration is determined and its expected distance is compared with the expected distance in its counterpart contour-line-shaped configuration
Better Approximation Bounds for the Joint Replenishment Problem
The Joint Replenishment Problem (JRP) deals with optimizing shipments of
goods from a supplier to retailers through a shared warehouse. Each shipment
involves transporting goods from the supplier to the warehouse, at a fixed cost
C, followed by a redistribution of these goods from the warehouse to the
retailers that ordered them, where transporting goods to a retailer has
a fixed cost . In addition, retailers incur waiting costs for each
order. The objective is to minimize the overall cost of satisfying all orders,
namely the sum of all shipping and waiting costs.
JRP has been well studied in Operations Research and, more recently, in the
area of approximation algorithms. For arbitrary waiting cost functions, the
best known approximation ratio is 1.8. This ratio can be reduced to 1.574 for
the JRP-D model, where there is no cost for waiting but orders have deadlines.
As for hardness results, it is known that the problem is APX-hard and that the
natural linear program for JRP has integrality gap at least 1.245. Both results
hold even for JRP-D. In the online scenario, the best lower and upper bounds on
the competitive ratio are 2.64 and 3, respectively. The lower bound of 2.64
applies even to the restricted version of JRP, denoted JRP-L, where the waiting
cost function is linear.
We provide several new approximation results for JRP. In the offline case, we
give an algorithm with ratio 1.791, breaking the barrier of 1.8. In the online
case, we show a lower bound of 2.754 on the competitive ratio for JRP-L (and
thus JRP as well), improving the previous bound of 2.64. We also study the
online version of JRP-D, for which we prove that the optimal competitive ratio
is 2
Variables, Generality and Existence: considerations on the notion of a concept-script
A defense of the Frege / Russell idea of logic as a 'concept=script' or 'ideal language', and a discussion of the relationship of this project to the formalisation of mass nouns or non-count noun
Task Assignment and Path Planning for Autonomous Mobile Robots in Stochastic Warehouse Systems
The material handling industry is in the middle of a transformation from manual operations to automation due to the rapid growth in e-commerce. Autonomous mobile robots (AMRs) are being widely implemented to replace manually operated forklifts in warehouse systems to fulfil large shipping demand, extend warehouse operating hours, and mitigate safety concerns. Two open questions in AMR management are task assignment and path planning. This dissertation addresses the task assignment and path planning (TAPP) problem for autonomous mobile robots (AMR) in a warehouse environment. The goals are to maximize system productivity by avoiding AMR traffic and reducing travel time. The first topic in this dissertation is the development of a discrete event simulation modeling framework that can be used to evaluate alternative traffic control rules, task assignment methods, and path planning algorithms. The second topic, Risk Interval Path Planning (RIPP), is an algorithm designed to avoid conflicts among AMRs considering uncertainties in robot motion. The third topic is a deep reinforcement learning (DRL) model that is developed to solve task assignment and path planning problems, simultaneously. Experimental results demonstrate the effectiveness of these methods in stochastic warehouse systems
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