Average Flow Time Estimation and Its Application for Storage Relocation in an Order Picking System

Pick-To-Light Order Picking System is the operational process when an operator begins to pick the parts in a sequential manner by which the quantity of the parts is recorded. The design of an effective hybrid order picking process in an assembly line is assisted by an intelligent sensing system to improve pick efficiency, accuracy and increase productivity. This research compares between intelligent hybrid order picking versus order picking with pick confirmation system at parts assembly line. The results show that by our proposed system with the elimination of certain steps within the picking process, the better efficiency, accuracy, fewer miss-picks will occur in the system and the operator can perform more intelligently with required picking quantities. The development of this system can provide a low-cost solution with an intelligent order picking system for small and medium-sized enterprises (SMEs) and a fast-moving production assembly line in manufacturing.

An order-picking system (OPS) is essential to meet customer needs and orders in inbound logistics. Failure to perform the process properly results in high costs and customer dissatisfaction. To properly execute the OPS and to be able to meet the needs of customers with maximum efficiency, different aspects of the order picking process should be carefully considered. Research works have been conducted to improve OPS. This article attempted to review study components, study methods, and results in internal logistics while focusing on OPS. We also examined the composition of different aspects of OPS, including cost, equipment, warehousing, assignment, routing, batching, sequencing, and tardiness in 157 articles performed from 1958 to 2020 and studies on the process of time (Makespan). The reviewed studies were classified based on the simultaneous use of batching variables, assignment, routing, sequencing, and tardiness, the methods used in the research work, and finding research gaps for future investigations to clarify the relevance and application of routing, assignment, batching, sequencing, and tardiness aspects in the order picking process.

The performance of a distribution center is typically judged on throughput-based criteria. Order picking consumes 30% to 40% of operation time in a typical distribution center. To effectively execute an order picking operation in a distribution center depends upon coordinating the formulation of the storage strategy, order processing, and planning the order picking route. However, it is usually too complicated to employ traditional optimization methods, such as linear programming, to solve this kind of problem. As a result, we applied the Particle Swarm Optimization (PSO) Algorithm to schedule order picking routes. PSO is one of the latest swarm intelligence algorithms; consequently, when compared to previous sophisticated algorithms such as genetic algorithms and simulated annealing, the study of its properties and applications is still in its infancy. This research considers the convergence rate, the convergent reliability (i.e., solution precision), and the performance test function (i.e., fitness function) in scheduling order picking routes. We apply genetic algorithms to determine the initial solution in order to locate the optimal solution faster by PSO. This paper also compares the effects of different parameters on particle swarm optimization. In order to verify the result, we also made a comparison with the Ant system in finding the optimal solution in order route planning. Overall, the research result will enhance the system of order picking in distribution centers and improve the efficiency of order picking operations.

The Order-Picking System (OPS) is vital for inbound logistics, ensuring efficient customer order fulfillment and minimizing costs. Efficient execution and implementation of OPS are critical to meeting customer demands and reducing dissatisfaction, necessitating a thorough examination of the process of order picking features. The order picking, a cornerstone of warehouse operations, involves meticulous selection and gathering of items from designated storage locations, unfolding through stages like order assignment and order batching. In order assignment, specific orders are methodically delegated to pickers or teams, considering factors like urgency, order size, item location, and picker availability. The overarching goal is to optimize resource utilization and simultaneously reduce the time needed for order fulfillment, ensuring a streamlined and efficient approach. Conversely, order batching strategically groups multiple orders for concurrent picking, aiming to minimize trips and enhance overall efficiency and productivity. Throughout the order-picking process, pickers utilize tools like pick lists, barcode scanners, and automated storage and retrieval systems (AS/RS) for precise item location and retrieval. Post-collection, items are transported to a dedicated packing area for meticulous preparations before shipping to the customer. Orchestrating the order-picking process requires careful planning, coordination, and execution for punctual and precise customer order fulfillment. This paper highlighted a systematic reviewing process which analyzed relevant research papers, with a primary focus on the problems of order assignment and batching—a key area within the order-picking process. The objective was to provide a comprehensive overview of hybrid Genetic Algorithm solutions for these challenges, achieved through a systematic review from 2018 to 2023 using Web of Science and Scopus databases. After screening, the relevant references were selected, focusing on terms like storage assignment problems. A thorough examination delved into various subcategories, encompassing recent approaches of genetic algorithms and openly accessible datasets. The resulting review offers a concise summary, highlighting key findings, challenges, and potential directions associated with hybrid genetic algorithms, specifically in relation to storage assignment, storage location assignment problems, and order batching issues.

Inventory turnover is a simple accounting analysis method seldom used by the user of Indonesian governmental organization’s financial statement due to lack of accrual information in the past. The opportunity for the application of this analysis arrived in 2015 along with the implementation of accrual accounting basis by the Indonesian government. This study observes the Indonesian ministry/agency (MA) inventory management focusing on the inventory average flow time, during the 2017 and 2018 period. The study shows that among 86 MAs, 62 MAs have desired average flow time. The other 24 MAs have high average flow time with 12 of them also have unstable average flow time. While several MAs disclosed information related to their average flow time, most MAs did not. Thus, they failed to communicate their conditions to their stakeholders. The average flow time can be used as a starting point for the managers, auditors, and other stakeholders to further analyze government inventory management. Better inventory management can reduce costs and improve the government planning process. Therefore, the government may be capable of allocating their budget for other programs or activities to deliver higher value for the public.

Efficient warehouse management is essential for optimizing inventory, minimizing transportation costs, and enhancing overall performance. This research introduces a novel Mixed-Integer Nonlinear Programming (MINLP) model to address the Storage Location Assignment Problem (SLAP) in warehouse management. Integrating multi-criteria decision-making with strategic production planning, our model advances warehouse operations by allocating storage locations to products strategically, focusing on reducing transportation distances and maximizing storage efficiency. The distinctive innovation of this study is the nuanced application of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) results to strategic storage location assignments, enhancing the model's capability to consider a comprehensive evaluation of inventory attributes, including physical characteristics and perishability. This approach evolves TOPSIS's application in warehouse management, enabling it to consider both physical characteristics and perishability of products. The outcomes of TOPSIS, including product classifications and preferences, serve as vital inputs to the mathematical model, facilitating a comprehensive evaluation of storage locations that encompasses spatial, demand-related, and physical aspects of inventory. Additionally, our research introduces a versatile decision support system, adaptable to various operational requirements. This system enhances practical decision-making in warehouse management, accommodating scenarios based on single or multiple criteria, including the cube-per-order index (COI). The research results highlight the significant impact of this innovative approach in enhancing warehouse management. By addressing the complexities of storage location assignment and integrating multiple criteria, we achieve more efficient and cost-effective warehouse operations. The approach has been shown to be adaptable and practical, making it a valuable contribution to the field of logistics and warehouse management.

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area.

In the aircraft manufacturing industry, the manual retrieval of heavy round metallic bars poses a significant risk of physical strain for order pickers. We study a rectangular warehouse with several vertical levels and aisles to mitigate this concern. Initially, we undertook a Time study using the MATLAB learner application to derive prediction equations. Subsequently, we developed a mathematical model aimed at optimizing time, distance, and energy expenditure (EE) in the retrieval process of these bars. According to our research, order pickers’ energy consumption is considerably decreased in all simulated scenarios when ergonomic considerations are taken into account. Warehouse managers can make well-informed decisions about layout parameters like slot height, aisles, and vertical levels with the help of our suggested model. However, a limited number of assignments can be solved using the exact solution method. We address this by introducing heuristic-based assignments for energy optimization. These heuristic-based methods provide near-optimal results and solve large storage location assignments in significantly less time. This study offers meaningful contributions to warehouse design and order picking by jointly addressing economic efficiency and ergonomic well-being. The insight can guide managers in structuring layouts that not only enhance worker comfort and safety but also strengthen overall operational performance.

A New Approach to Model and Optimize the Order Sequence of a Two-stage Storage and Order Picking System with Particular Attention to Restrictions of the Material Flow

Supermarkets typically order their goods from a centrally located distribution center (warehouse). Each order that the warehouse receives is characterized by the requested items, the location of the respective supermarket and a due date by which the items have to be delivered. For processing an order, a human operator (order picker) retrieves the requested items from their storage locations in the warehouse first. The items are then available for shipment and loaded on the vehicle which performs the tour including the respective location of the supermarket. Whether and to which extent a due date is violated (tardiness) depends on the composition of the tours, the corresponding routes and the start dates of the tours (vehicle routing subproblem). The start date of a tour, however, is also affected by the assignment of orders to pickers and the sequence according to which the orders are processed by the pickers (order picking subproblem). Although both subproblems are closely interconnected, they have not been considered simultaneously in the literature so far. In this paper, an iterated local search algorithm is designed for the simultaneous solution of the subproblems. By means of extensive numerical experiments, it is shown that the proposed approach is able to generate high-quality solutions even for large instances. Furthermore, the economic benefits of an integrated solution are investigated. Problem classes are identified, where the sequential solution of the subproblems leads to acceptable results, and it is pointed out in which cases an integrated solution is inevitable.

[EMBARGOED UNTIL 8/1/2024] Order picking, which involves retrieving items from storage locations for an internal or external customer, is a core function in warehouses and accounts for up to 65 percent of the total operating cost. It is considered as a crucial driver for supply chain performance as improper planning of picking operations leads to inefficient asset utilization and delayed deliveries, which, in turn, adversely affects customer satisfaction, operating cost, and competitiveness. A majority of warehouses adopt a picker-to-parts system (workers travel through a warehouse to retrieve and transport items from storage to packing station), and the fulfillment speed of such order picking system (OPS) depends on the following key decisions - (i) set of orders to be picked in a tour (order batching decision), (ii) assignment of a batch to a picker and order in which their a processed (batch assignment and sequencing decisions), and (iii) route followed x by the picker to collect the orders in each batch (picker routing decision). However, with the growing customer demand and global labor shortage, warehouses are seeking efficient and less labor-intensive order picking systems. Autonomous mobile robots (AMRs) or collaborative robots (cobots) have the potential to alleviate the strain on human workers and expedite order picking operations. However, there are several key operational challenges to address for ensuring an effective collaborative human-robot order-picking system (CHR-OPS), where humans perform item retrieval tasks, and AMRs handle item transportation to the depot. This research aims to improve the fulfillment efficiency of a picker-to-parts CHROPS by optimizing key decisions associated with two warehouse picking strategies, namely, static picking and dynamic picking. In the case of static picking, the items to be retrieved from storage for a given day are known as apriority. On the other hand, the dynamic picking strategy allows for orders to arrive over time (e.g., e-commerce warehouses), and the pick cycle (or picking plan) can be updated in real time. First, we address the problem of optimizing the following key subproblems of a CHR-OPS with a static picking strategy: (i) order batching (how many items should be collected in each AMR tour?), (ii) batch assignment and sequencing (how to assign batches to AMRs, and in what order should they be processed?), and (iii) picker-robot routing (how should the AMR and picker be routed to coordinate the picking process?). Existing literature has not dealt with the three subproblems, and this work is the first to address them for a picker-to-parts CHR-OPS system employing multiple pickers and AMRs. A mixed integer linear programming model is developed to jointly optimize the three subproblems with the objective of minimizing the total tardiness of all orders. The MILP model is validated and solved to optimality for small instances. However, since the problem under consideration is NP-hard, it is computationally intractable for larger instances. To efficiently handle large instances, we proposed deterministic and stochastic local search algorithms, namely variable neighborhood descent and a new variant of simulated annealing. The numerical experiments demonstrate the superior performance of the proposed solution approach compared to existing methods. Besides, our results also show that the picking efficiency is impacted by human{robot team composition, AMR speed, AMR capacity and warehouse layout. Subsequently, we address the CHR-OPS with dynamic picking and developing interventionist picking algorithms for a multi-robot multi-human setting. Specifically, we propose two interventionist policies for dynamic collaborative order picking, namely, the collaborative human-robot interventionist picking algorithm (CHRIPA) and the collaborative human-robot rule-based interventionist picking algorithm (CHR-RIPA). The evaluation of the policies demonstrates that the proposed rules can improve the overall performance of the system compared to benchmark approaches (human-only dynamic picking and collaborative picking with no intervention). In addition, results indicate CHR-IPA outperforms CHR-RIPA in terms of average tar diness and order completion time, albeit with a slightly higher travel distance for human workers. The results have led to several managerial implications for a collaborative order picking system. Further, the proposed models and algorithms are modular and can be adapted to any warehouse setting by accounting for the relevant parameters such as warehouse layout, AMR capacity and human-robot composition. Finally, the directions for future research have been identified and summarized.

In recent years, mobile racks or auto robots have been widely used in e-commerce warehouses where storage location assignment is a fundamental problem in the order picking process. The present storage location assignment strategies mainly allocate stocks into various racks according to a specific objective function or the relationships between stocks. These strategies include the random storage assignment strategy (RAS) and the good- clustering storage location assignment strategy (GCAS). In this paper, we first analyze the key factors that affect the efficiency of the order picking system.The results show that the rack- moved-number (RMN) is a significant factor in the order picking process. Then, we propose a genetic- algorithm (GA) based method for the storage location assignment problem which adopts RMN as its fitness function. To find a better solution, we take the natural deduplicated stock sequence of history orders (NDSSHO) as a seed to initialize the population of chromosomes. We also define a specific cross mutation strategy to avoid checking the validity of chromosomes by exchanging selected genes and adjusting new generated chromosomes. At last, we compare the RMN of our proposed method with RAS and GCAS. The experimental results show that the RMN of our proposed method is about 50% less than RAS and GCAS.

Picker routing and storage-assignment strategies for precedence-constrained order picking

The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country’s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important. It affects the normal operation of the pipeline by plugging it. Under high pressure and low temperature conditions, gas hydrate is a crystalline structure consisting of a network of hydrogen bonds between host molecules of water and guest molecules of the incoming gases. Industry uses different types of chemical inhibitors in pipeline to suppress hydrate formation. To overcome this problem, machine learning algorithm has been introduced as part of risk management strategies. The objective of this paper is to utilize Machine Learning (ML) model which is Gaussian Process Regression (GPR). GPR is a new approach being applied to mitigate the growth of gas hydrate. The input parameters used are concentration and pressure of Carbon Dioxide (CO2) and Methane (CH4) gas hydrates whereas the output parameter is the Average Depression Temperature (ADT). The values for the parameter are taken from available data sets that enable GPR to predict the results accurately in terms of Coefficient of Determination, R2 and Mean Squared Error, MSE. The outcome from the research showed that GPR model provided with highest R2 value for training and testing data of 97.25% and 96.71%, respectively. MSE value for GPR was also found to be lowest for training and testing data of 0.019 and 0.023, respectively.

This article examines the effect of different product location strategies on the distance that order pickers must cover to do their job. This distance is an important cost component in warehousing activities. Our empirical study is based on a real industrial setting, in which the products are located on both sides of a conveyor belt. We show that choosing the right product location strategy allows the current picking distance to be reduced more than 10%. We also propose a post-optimization procedure that can further reduce picking distances—up to 20% of the current distances. Through a study of the routing strategy used to dispatch pickers, we demonstrate that solving a simple travelling salesman problem can further reduce distances up to 13%, compared with the distances incurred using a predetermined route. We show that reductions of up to 27% compared to current picking distances are possible if our product location and routing methods are combined.