Location Assignment Research Articles

Data-driven hierarchical learning and real-time decision-making of equipment scheduling and location assignment in automatic high-density storage systems

Automated high-density storage systems (AHDSS) have attracted widespread attention in recent years owing to their advantages of high throughput and space utilisation. However, owing to the characteristics of large-scale, multi-disturbance, and short-period task scenarios, a system is required to make instant and efficient decisions. To this end, this paper proposes a data-driven real-time decision-making method to solve the real-time equipment scheduling and dynamic location assignment problem in AHDSS. The proposed method comprises two phases: decision scheme learning and real-time decision-making. The operation state attribute features of the AHDSS were constructed to generate training data for equipment scheduling and location assignment scheme learning. Thereafter, a hierarchical learning and decision-making mechanism based on the deep belief network (DBN) is proposed. The integrated learning of better scheduling solutions was realised by establishing three-stage models of lift selection, shuttle selection, and location priority. Additionally, the Taguchi method was adopted to determine the best performance parameters for DBNs at different learning stages. Compared with other well-known machine learning algorithms, DBNs have a higher learning accuracy. Finally, a real-world AHDSS problem is studied, and the results demonstrate that the proposed approach outperforms existing dispatching rules.

Current studies on storage location assignment problem

Although operations seem more important than everything in a business operation, warehouse management has great importance to the success of a business operation. Storage location assignment is very important because of the uncertainty in the number of incoming products, the necessity of answering unknown customers’ demands and the speed rate of response to customer demands. Besides, several factors affect warehouse management. Assigning products to appropriate locations is one of the most important and complex. This study aims to present recent academic works on the storage location assignment problem published from 2018 to 2021. These problems are assigned accordingly. Methods of solution, goals and some related considerations are considered while investigating the works. Recommendations are provided in the conclusion. Based on the reviewed articles, warehouse capacity, market, products, logistics resources and operational configuration are taken into account as restrictions or considerations. Keywords: Location, logistics, storage, warehouse management;

Estimating optimal ABC zone sizes in manual warehouses

The ABC storage is the most popular class-based policy for the storage location assignment in warehouses. It divides a storage area into three zones and assigns the most demanded products to the best-located zone. Despite the policy’s popularity, arbitrary zone sizes are commonly used, which can lead to major efficiency losses. We investigate how several factors, such as the warehouse layout, the demand characteristics, and the storage and routing policies, impact the solutions for the zone sizing problem. We propose a new methodology to solve it using machine learning models to predict the optimal zone sizes considering the mentioned factors. We simulate many common manual warehouse settings, such as the multi-block layout, demand distributions, and several operating policies, to observe which zone sizes lead to the best performance in each one. The data generated is used to train four regression models – ordinary least squares, regression tree, random forest, and multilayer perceptron – to predict the optimal zone sizes from the best ones observed. Computational experiments show that zone sizes provided by all models significantly improve the order picking efficiency when compared to the arbitrary zone sizes commonly used, notably for the one-zone (random policy), the two-zone (20/80 rule), and the three-zone (20/30/50) systems. The proposed methodology is easily adaptable for different warehousing systems and problems when enough data is available to train the models. The resulting linear functions and decision trees are made available and can be used by practitioners for determining zone sizes for their particular warehouse.

A novel storage location assignment in multi-pickers picker-to-parts systems integrating scattered storage, demand correlation, and routing adjustment

Congestion always occurs in the multi-pickers picker-to-parts picking systems. In this paper, we propose a multi-pickers-based scattered storage location assignment (MPSSLA) strategy. To reduce the probability of congestion and working time of pickers, MPSSLA allows the same stock keeping units (SKUs) to be stored in multiple storage locations. Additionally, it assigns SKUs with high and middle demand correlations to storage locations that are as close as possible to each other. Moreover, a route adjustment method that considers congestion is proposed to adjust the working routes. This further improves the picking efficiency. The working process of pickers in MPSSLA is modelled as a space–time network and an algorithm is designed to determine the optimal solution. Through numerical experiments, the applicability and effectiveness of MPSSLA are analysed considering several main factors. The results show that the proposed algorithm is highly efficient and MPSSLA can achieve better optimisation results than the traditional centralised storage strategy in the multi-pickers picker-to-parts picking system. The proposed storage location assignment strategy can improve the warehouse operation efficiency of the multi-pickers picker-to-parts picking systems with high demand skewness and a large number of pickers.

Bi-objective operation optimization in multi-shuttle automated storage and retrieval systems to reduce travel time and energy consumption

The automated warehouses currently in widespread use maintain highly efficient operations at the cost of consuming a large amount of energy. This study first considers the problem of operation optimization in multi-shuttle automated storage and retrieval systems (AS/RSs) to reduce both time and energy consumption. A time and energy bi-objective integer programming model is proposed to jointly optimize the storage/retrieval (S/R) location assignment and S/R scheduling for a multi-shuttle AS/RS. The adaptive variable neighbourhood search algorithm and Lagrangian relaxation algorithm are developed to tackle large-size instances. The results demonstrate that the bi-objective model is effective at finding the compromise solution considering travel time and energy consumption simultaneously. The decrease in travel time is obtained at the expense of increasing energy consumption and vice versa. The study provides valuable insights for warehouse managers to operate a time-efficient and energy-efficient automated warehouse.

A Network-Based Model for Optimization of Container Location Assignment at Inland Terminals

The development of inland terminals helps seaports mitigate inevitable storage capacity problems, extending their gravitational field and strengthening their competitive advantage. Optimizing the container storage, efficiency, and productivity of seaports and inland terminals is becoming increasingly important. The present paper develops a network-based mathematical model to optimize the location assignment of inbound containers at inland terminals. The model’s assumptions are based on the analysis of criteria at representative European inland terminals, and the model aims to reduce unproductive manipulations. The present study is one of the few to develop a single model that integrates all important criteria for container location assignment, including a container’s dimensions, its occupancy, cargo type, the container’s owner, and distance to the final user. These criteria are important for the terminal operator so that they can determine container locations in a way that minimizes unproductive manipulations. Data from a single inland terminal were used to validate the model within the FlexSim CT simulation environment. Our results suggest that the model can reduce unproductive manipulations and associated costs at inland terminals.

전자 제조 산업에서의 스마트팩토리를 위한 자재 배치 최적화연구

In this paper, we address a material location assignment problem in the electronics manufacturing process. We define two typical types of material supply processes and formulate a mathematical optimization model for the material location assignment problem involved in each type of process. We propose a solution approach based on genetic algorithm to efficiently solve the location assignment problems. Finally, we present two types of case studies on the material location assignment in a global leading electronics manufacturer in Korea. We describe the practical aspects of the problems and provide experimental results comparing the performance of the proposed algorithm and the company’s legacy method using a real-world data. The results demonstrate that the proposed approach is feasible and efficient to obtain material location assignment solutions to both types of material supply processes.

Analysis and Design of Rack-Climbing Robotic Storage and Retrieval Systems

Warehouses are becoming increasingly robotized. Autonomous rack-climbing robots have recently been introduced in e-commerce fulfillment centers. The robots not only retrieve loads from any level in a rack but also, roam the warehouse and bring the loads to order picking stations without using conveyors or lifts. This paper models and analyzes this system under both single and dual commands with different robot assignment (dedicated versus shared) and storage location assignment (class-based and random) policies. We study these policies in the presence of robot congestion. We evaluate the impact of two blocking protocols, a wait-outside-aisle policy and a block-and-recirculate policy, on the order throughput time. The system is modeled using semiopen queuing networks (SOQNs) for the different operating policies. The analytical models are validated using simulation. We also use this model to compare this system with a shuttle-based system. The results show that (1) the choice of the wait-outside-aisle policy or the block-and-recirculate policy mainly depends on the number of the robots in the system and the throughput requirement and that (2) the dedicated robot assignment policy can be an attractive policy, especially for a large system.

The combination of RPLC-ESI-FTMS/MS and m-CPBA epoxidation for the location and geometry assignment of double bonds in unsaturated fatty acyl chains of drying oils

Abstract Oil painting is the process of painting with drying oils as binders. Such oils mainly consist of triacylglycerols characterized by high levels of unsaturated fatty acyl chains. For instance, poppy seed oils have received much attention for their use as oil paints since very ancient time. Upon exposure to oxygen, not only do drying oils harden to a densely cross-linked polymer network through radical reactions, but their fatty acids also interact with cations of some pigments forming the related carboxylate salts. These so-called metal soaps lead to severe degradation phenomena because they can migrate and aggregate, breaking through the surface of the paint. Since the fatty-acidome of a drying oil is involved in these processes, the current study is chiefly devoted to the identification of both free and total fatty acids in poppy seed oil by reversed-phase liquid chromatography coupled with high-resolution mass spectrometry by an electrospray ionization source (RPLC-ESI-FTMS). Total fatty acids (TFA) were investigated by enzymatic hydrolysis and comparisons were made between the free fatty acid (FFA) profile and the total one resulting from the digestion on the same poppy seed oil. A key component of our approach was the double bond (C=C) epoxidation reaction, which was carried out on unsaturated fatty acids by meta-chloroperoxybenzoic acid (m-CPBA). A chromatographic set up was developed to separate such oxidized lipids, that were identified as deprotonated molecules [epoM-H]− by accurate mass matching in full mass spectra. Their epoxide ring was subsequently cleaved upon tandem mass fragmentation to yield a pair of diagnostic product ions for C=C double bond localization. Interestingly, the double bond geometry (i.e., cis/trans) was also assigned based on the retention times of derivatized-FA. The present approach opens new avenues for the analysis of artworks whether affected by metal soap-related degradation phenomena.

Storage location assignment of steel coils in a manufacturing company: an integer linear programming model and a greedy randomized adaptive search procedure

Storage location assignment of steel coils in a manufacturing company: an integer linear programming model and a greedy randomized adaptive search procedure

Toward a Reference Experimental Benchmark for Solving Hub Location Problems

Our study provides an experimental benchmark for state-of-the-art solution algorithms with hub location problems. Such problems are fundamental optimization problems in location science with widespread application areas, such as transportation, telecommunications, economics, and geography. Given they combine aspects of facility location and quadratic assignment problems, the majority of hub location problems are NP-hard and, accordingly, several solution techniques have been proposed for solving these problems. In this study, we report on the results of a large benchmark and reproduction effort to investigate 12 fundamental hub location problems that combine single or multiple allocation, a p-hub median objective or fixed hub set-up costs, capacitated or uncapacitated hubs, and complete or incomplete networks. We implemented four standard exact algorithms on these 12 problems as proposed in the literature. Algorithms are evaluated on subsets of three standard data sets in the field (CAB, TR, and AP); we computed more than 5,000 optimal solutions for these data sets. We report comparisons of solution techniques regarding wall clock time, convergence speed, memory use, and the impact of data features. In addition, we identify patterns in optimal solutions across these 12 problems, extracting insights regarding solution similarity, hub set candidates, and economies of scale. All results and programs are being made available to the public for free academic use.

Industrial internet of things-driven storage location assignment and order picking in a resource synchronization and sharing-based robotic mobile fulfillment system

In this paper, we intend to address the value creation of utilizing the Industrial Internet of Things (IIoT)-driven resource synchronization and sharing-based robotic mobile fulfillment system (RMFS) to enhance the overall operational effectiveness and efficiencies during information transfer and synchronization of resources. With the advent of IIoT, a graph theory-based heuristic under the multi-deep RMFS is used for computing the shortest path. A-star, Dijkstra, and genetic heuristic algorithms are applied for comparison. A simulation with a consideration of the different types of collisions is conducted for different algorithms. By providing a new three-tier IIoT architecture which includes the suppliers, RMFS, and the disposal center, a model is developed with different storage location assignment rules and strategies under the particular parties to minimize the operation costs. IIoT enables resource synchronization and information sharing, and the path will be generated under different order scenarios with different algorithms. The results show that different storage assignment rules and strategies may lead to 30% cost differences compared to the company’s current practice with random storage.

A quantitative approach to investigate natal philopatry in Hickory Shad (Alosa mediocris) using otolith chemistry

The goal of this study was to understand if Hickory Shad exhibit natal homing. Hickory Shad were captured in eighteen major rivers along the known spawning range. LA-ICP-MS was used to quantify seven elements (Mg, Mn, Cu, Zn, Sr, Ba, and Pb), along a continuous transect that ran from the ventral edge of the otolith through the core to the dorsal edge of each otolith, resulting in a time resolved model of the environmental exposure history of each fish. Hidden Markov models were used to estimate a natal river element signature for each Hickory Shad. The true number of natal rivers present in the dataset were unknown, so a Gaussian mixture model was used to estimate the number of mixture distributions and classify hickory shad based on natal river signatures. Five clusters were identified; Hickory Shad captured in the majority of rivers were assigned to one of three or more clusters, while those captured in a few rivers had unique element profiles and were assigned the same cluster at high rates (70%–90% of samples per river). A Chi-Square test confirmed that there was a significant relationship (p < 0.01) between capture location and cluster assignment. Overall, the results of this study suggest a high rate of straying from natal rivers through most of the Hickory Shad spawning range, and that some localized populations may return to natal rivers at a higher rate. This study provides the first piece of quantitative information about the rate at which Hickory Shad exhibit natal homing, and provides an important inferential baseline for future studies to understand their life history strategies and population dynamics.

Improved efficiency of warehouse picking by co-optimization of order batching and storage location assignment

Improved efficiency of warehouse picking by co-optimization of order batching and storage location assignment

Multiple-Rack Strategies Using Optimization of Location Assignment Based on Mrcga in Miniload Automated Storage and Retrieval System

Multiple-Rack Strategies Using Optimization of Location Assignment Based on Mrcga in Miniload Automated Storage and Retrieval System

The Storage Location Assignment Problem of Automated Drug Dispensing Machines

The Storage Location Assignment Problem of Automated Drug Dispensing Machines

Assigning and sequencing storage locations under a two level storage policy: Optimization model and matheuristic approaches

We deal with a problem which combines storage location assignment with sequencing decisions about the assigned storage locations. Given a set of different product types, with the corresponding storage demand, a set of capacitated storage locations has to be assigned to each product type for the corresponding storing operations. In addition, a suitable sequencing of the assigned storage locations must be devised for each product type, i.e., it has to be decided the ordering with which the storage locations will be filled up during the storing operations. A motivation is that a First-In First-Out (FIFO) picking criterion among storage locations is required per product type. The sequencing established for the assigned storage locations will therefore allow to easily implement the FIFO policy in the successive order picking. Moreover, the selected sequencing also determines the availability of extra storage per product type, on top of pairs of consecutive storage locations along the sequence. The goal is to maximize the storage capacity which remains available after the assignment of the storage locations. We prove the NP-Hardness of the problem and we model it as a constrained multicommodity flow problem on an auxiliary graph. We then propose a Mixed-Integer Linear Programming (MILP) model, with some valid inequalities, based on the multicommodity flow formulation. Two relaxations are proposed as well to estimate the quality of the model solutions. Two matheuristic approaches are then designed starting from the MILP model. The proposed methodology is applied to a case study related to a large warehouse with a high stock rotation index in tissue logistics, which motivated our study. Computational results on a wide test bed related to such a real application context show the efficiency and the efficacy of the presented approaches.

Smart stacking for import containers using customer information at automated container terminals

Motivated by the practical import free-flow program that aims to expedite the container retrieval process, we conceptualize a new container stacking strategy, termed Smart Stacking (SS) strategy. The SS strategy aims to create relocation-free stacks (smart stacks) by utilizing customer information. The Storage Location Assignment Problem (SLAP) under the SS strategy is addressed. The problem is to determine the smart customers/containers, and the number and locations of smart stacks, when assigning a batch of import containers to a yard block at an automated container terminal to minimize the total retrieval time. Two variants of SLAP are investigated under the non-split policy and the split policy, depending on whether the containers from the same customer are allowed to be split between smart stacks and non-smart stacks. For the non-split variant, a mixed-integer programming (MIP) model is formulated first. By analyzing the properties of the optimal solution, an improved formulation with enhanced computational performance is then proposed. Based on the structure of the model, a divide-and-conquer heuristic is designed to solve the non-split variant more efficiently. For the split variant, a MIP model under the optimal partitions of the non-split model is developed. We theoretically prove that the split variant yields better results than the non-split variant. Extensive experiments are carried out to illustrate the effectiveness of smart stacking. It is found that customer information and yard utilization rate have a significant influence on the effectiveness of smart stacking.

Retrieval-oriented storage relocation optimization of an automated storage and retrieval system

Business-to-consumer platforms (e.g., Tmall.com and JD.com) demand the efficient retrieval handling of storage systems. The amount of a certain item to be retrieved is often less than a pallet load; thus, dynamically re-assigning the storage locations of nonempty pallets after each retrieval may improve retrieval operational efficiency. We call such a dynamic decision on the location for the returned pallets relocation. We treat the relocation of each non-empty pallet as an operational decision rather than a tactical decision. Due to its dynamic nature, we formulate relocation as a dynamic program that minimizes the total crane travel time. To overcome the computational curse of dimensionality, we propose several heuristic relocation policies that are inspired by conventional storage location assignment policies. Based on the findings from preliminary numerical experiments, we design an approximate dynamic programming-based relocation policy that achieves a greater operational efficiency improvement than the aforementioned heuristics. Through a more extensive set of numerical experiments, we find that random relocation is generally no better than no relocation, and that closest-open relocation is effective across different rack shapes, crane velocity configurations, and retrieval characteristics. Our experiments also show that the approximate dynamic programming-based relocation policy consistently yields further improvement over closest-open relocation, at the expense of higher (but still affordable) computational complexity.

Site-independent confirmation of primary site-based PANSS ratings in a schizophrenia trial

Blinded, site-independent (remote) ratings from audio-digital recordings of site-based Positive and Negative Syndrome Scale (PANSS) interviews were obtained in a 5-week, randomized, double-blinded study assessing the safety, tolerability, and efficacy of KarXT (a fixed combination of xanomeline and trospium chloride) in hospitalized adults with schizophrenia experiencing an acute exacerbation of psychosis (EMERGENT-1; ClinicalTrials.gov identifier: NCT3697252). The blinded site-independent raters had no knowledge of site location, study visit, drug vs. placebo assignment, or any treatment emergent adverse events (TEAEs). Concordance analyses of 561 paired site-based and site-independent PANSS ratings across all visits revealed a high correlation (ICC = 0.775). Paired scoring differences were positively correlated with the PANSS total score (Spearman's rho = 0.37, p < 0.0001). Paired PANSS scores were available from 148 subjects at both the baseline and end of study visits (KarXT = 72, Placebo = 76). Site-based PANSS total scores (primary aim) revealed a significantly greater improvement from baseline in the KarXT group compared to the placebo group (p < 0.0001). The blinded site-independent PANSS total scores derived from listening to and scoring the recorded site-based PANSS interviews replicated this finding (p < 0.001) and yielded an overall predictive value of 85.1% for matching the site-based response/non-response outcomes. TEAE's have the potential to “unblind” site-based ratings. In this study, the site-independent raters were blinded to TEAEs, affirmed the site-based PANSS ratings, and mitigated concerns about possible functional unblinding of site-based raters. This method of blinded assessment via audio-digital recordings may have utility for other studies concerned with ratings precision and/or functional unblinding.