scholarly journals A Survey of the Literature on Order-Picking Systems by Combining Planning Problems

2021 ◽  
Vol 11 (22) ◽  
pp. 10641
Author(s):  
Amir Reza Ahmadi Keshavarz ◽  
Davood Jaafari ◽  
Mehran Khalaj ◽  
Parshang Dokouhaki
Keyword(s):  
Customer Service ◽  
Order Picking ◽  
Logistics Systems ◽  
Systemic Analysis ◽  
Customer Dissatisfaction ◽  
Operating Strategies ◽  
Order Assignment ◽  
Warehouse Operations ◽  
Planning Problems ◽  
Main Operating

Companies have been trying continuously to reduce their logistics costs in the current competitive markets. Warehouses are important components of the logistics systems and they must be managed effectively and efficiently to reduce the production cost as well as maintain customer satisfaction. Order-picking is the core of warehouse operations and an order-picking system (OPS) is essential to meet customer needs and orders. Failure to perform the OPS process properly results in high costs and customer dissatisfaction. This research aims to investigate the state of the art in the adoption of OPS and provide a broad systemic analysis on main operating strategies such as simultaneous consideration of order assignment, batching, sequencing, tardiness, and routing need. This study reviews 92 articles, classifies combinations of tactical and operational OPS problems, and provides guidelines on how warehouse managers can benefit from combining planning problems, in order to design efficient OPS and improve customer service. Combining multiple order-picking planning problems results in substantial efficiency benefits, which are required to face new market developments.

scholarly journals Optimization of Warehouse Operations with Genetic Algorithms

2020 ◽  
Vol 10 (14) ◽  
pp. 4817
Author(s):  
Mirosław Kordos ◽  
Jan Boryczko ◽  
Marcin Blachnik ◽  
Sławomir Golak
Keyword(s):  
Genetic Algorithms ◽  
Product Placement ◽  
Order Picking ◽  
Crossover Operator ◽  
Proper Number ◽  
Random Product ◽  
Fully Automatic ◽  
The Cost ◽  
The Given ◽  
Warehouse Operations

We present a complete, fully automatic solution based on genetic algorithms for the optimization of discrete product placement and of order picking routes in a warehouse. The solution takes as input the warehouse structure and the list of orders and returns the optimized product placement, which minimizes the sum of the order picking times. The order picking routes are optimized mostly by genetic algorithms with multi-parent crossover operator, but for some cases also permutations and local search methods can be used. The product placement is optimized by another genetic algorithm, where the sum of the lengths of the optimized order picking routes is used as the cost of the given product placement. We present several ideas, which improve and accelerate the optimization, as the proper number of parents in crossover, the caching procedure, multiple restart and order grouping. In the presented experiments, in comparison with the random product placement and random product picking order, the optimization of order picking routes allowed the decrease of the total order picking times to 54%, optimization of product placement with the basic version of the method allowed to reduce that time to 26% and optimization of product placement with the methods with the improvements, as multiple restart and multi-parent crossover to 21%.

Improvement of the Optimization of an Order Picking Model Associated With the Components of a Classic Volkswagen Beetle Using an Ant Colony Approach

2019 ◽  
pp. 189-210
Author(s):  
Jared Olmos ◽  
Rogelio Florencia ◽  
Francisco López-Ramos ◽  
Karla Olmos-Sánchez
Keyword(s):  
Mathematical Modeling ◽  
Ant Colony Optimization ◽  
Subject Matter ◽  
Operational Research ◽  
Order Picking ◽  
Ant Colony ◽  
Operational Costs ◽  
Auto Parts ◽  
Warehouse Operations

Warehouse operations, specifically order picking process, are receiving close attention of researches due to the need of companies in minimizing operational costs. This chapter explains an ant colony optimization (ACO) approach to improve the order picking process in an auto parts store associated with the components of a classic Volkswagen Beetle car. Order picking represents the most time-consuming task in the warehouse operational expenses and, according to the scientific literature, is becoming a subject matter in operational research. It implements a low-level, picker-to-part order picking using persons as pickers with multiple picks per route. The context of the case study is a discrete picking where users' orders are independent. The authors use mathematical modeling to improve de ACO metaheuristic approach to minimize the order-picking cost.

Balancing Accuracy of Promised Ship Data and IT Costs

2009 ◽  
pp. 620-634
Author(s):  
Young M. Lee
Keyword(s):  
Real Time ◽  
Customer Service ◽  
Service Level ◽  
Business Environment ◽  
Order Fulfillment ◽  
Business Decision ◽  
Customer Order ◽  
Customer Dissatisfaction ◽  
Time Availability ◽  
Static View

In an ideal e-business environment, when a customer order is scheduled and a ship date is computed, the availability should immediately be reserved and not be available for future orders. However, in reality the availability data that are used for the scheduling the orders are not real time availability (physical availability), but they are availability information stored in an IT system (system availability). The availability data in the IT system (static view of availability) is typically refreshed (synchronized with real time availability) only periodically since it is very expensive to update the database in real time. Due to this potentially inaccurate view of the availability, some orders cannot be shipped on the promised ship date. Therefore, for certain customer orders, products are shipped later than the promised ship date resulting in customer dissatisfaction. Therefore, one of key decisions in order fulfillment process is to properly balance IT system (e.g., IT expense) and accuracy of promised ship date. In this work, we study how availability fresh rate (IT system) impacts customer service level. The simulation model we develop helps making critical business decision on refresh rate of availability, and avoiding expensive IT investment.

Customer dissatisfaction among older consumers: a mixed-methods approach

2014 ◽  
Vol 36 (2) ◽  
pp. 420-441 ◽  
Author(s):  
MICHAEL P. CAMERON ◽  
MARGARET RICHARDSON ◽  
SIALUPAPU SIAMEJA
Keyword(s):  
Mixed Methods ◽  
Older People ◽  
Customer Service ◽  
Service Providers ◽  
Mixed Methods Approach ◽  
Face To Face ◽  
Factors Associated ◽  
Older Consumers ◽  
Customer Dissatisfaction ◽  
Research Questions

ABSTRACTWorldwide, populations are ageing and consequently so are the consumer profiles for most organisations. Understanding how best to ensure satisfaction in interactions with older customers, patients, members of organisations, and so on is therefore increasingly important. This paper examines two research questions: (a) How satisfied or dissatisfied are older people with their customer service experience, and what are the factors associated with dissatisfaction? and (b) What prompts older people to want to change service providers? The research questions are addressed using a mixed-methods approach – quantitative analysis of observation logs, supported by illustrative quotes from focus groups. We find that on the whole older people are generally satisfied with their interactions with organisations, although a substantial minority of interactions lead to dissatisfaction or lower-than-expected satisfaction. Dissatisfaction with interactions is mostly associated with impersonal communications, including interactions that are not conducted face to face, and those that are one-off interactions rather than repeated interactions. Media and communications firms prompted the greatest levels of dissatisfaction among older consumers. Dissatisfied older consumers from our sample are more than 12 times more likely to report an intention to switch providers than satisfied consumers. This highlights the potential costs to organisations of poor customer interactions with older people.

scholarly journals An Augmented Reality Assisted Order Picking System using IoT

2019 ◽  
Vol 8 (3) ◽  
pp. 744-749
Keyword(s):  
Augmented Reality ◽  
Relevant Information ◽  
Order Picking ◽  
Major Bottleneck ◽  
Cost Efficient ◽  
Control And Management ◽  
Warehouse Operations ◽  
The Internet Of Things ◽  
Level Order ◽  
Picking System

It is widely recognized that order picking is the most complicated and time-consuming task in warehouse operations and often termed as the major bottleneck in warehouse workflow. Over the years the process of order picking has been extensively studied and many methods have been proposed to deal with its challenges. However, most of these solutions involve complex and expensive components with elaborate setups. In this paper, we propose RASPICK a modular, robust and cost-efficient order picking system that is scalable and can be used in warehouses of all sizes. The proposed system aims to reduce the cognitive load on the picker by providing crucial and relevant information for each item on the picking list. For a baseline, the proposed system is also compared to manual paper-based picking and shows significant improvements in average trip-time for lists of different sizes. The system combines the convenience of Augmented Reality with the power of the Internet of things to facilitate central control and management of pickers and attempts to address the low-level order picking bottlenecks.

Increasing Warehouse Productivity With an Ergonomic Handheld Scanner

2018 ◽  
Vol 26 (3) ◽  
pp. 23-31
Author(s):  
Chandra Nair ◽  
Konstantinos Tsiopanos ◽  
Richard Martin ◽  
Graham Marshall
Keyword(s):  
Value Added ◽  
Order Picking ◽  
Line Of Sight ◽  
Increase Productivity ◽  
Mobile Computers ◽  
Handheld Scanner ◽  
Warehouse Operations

Rugged handheld scanners, known in the industry as rugged mobile computers, are used for critical warehouse operations, such as receiving, order picking, and put-away. The form of rugged scanners has not fundamentally changed since it was introduced to replace pen and clipboard. Warehouses have extracted the maximum available efficiency increases available through today’s handheld rugged scanners, but new operational challenges require new ways to further increase productivity and accuracy. The “line of sight” rugged handheld scanner concept described in this article is designed to enhance the user’s efficiency by eliminating non-value-added wrist motions.

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