Order Batching Research Articles

An advanced order batching approach for automated sequential auctions with forecasting and postponement

In sequential auctions, all the sub-orders from a buyer need to be sorted and consolidated within a short time window for shipping. Buyer demands and sub-order arrival times are uncertain. The current auction order fulfillment is facing several challenges. Based on a re-engineered Industrial Internet-of-Things (IIoT)-enabled automation system, this paper introduces an order batching approach with forecasting and postponement. Such an approach generates batches considering time interval and buyer completion rate to minimise the total processing time of the auction orders and system response time. The buyer completion rate refers to the ratio of current cumulative and predicted purchase quantity. We use the forecasting method proposed by Kong et al. (2021) to estimate the purchasing quantity. Through a series of computational experiments using real-life data, the proposed order batching method achieves a shorter order processing time and system response time. Results show that the number of auction buyers poses no effect on the performance of the proposed approach. Key parameters of order postponement rule influence on performance.

Optimal Order Batching in Warehouse Management: A Data-Driven Robust Approach

Optimizing warehouse processes has direct impact on supply chain responsiveness, timely order fulfillment, and customer satisfaction. In this work, we focus on the picking process in warehouse management and study it from a data perspective. Using historical data from an industrial partner, we introduce, model, and study the robust order batching problem (ROBP) that groups orders into batches to minimize total order processing time accounting for uncertainty caused by system congestion and human behavior. We provide a generalizable, data-driven approach that overcomes warehouse-specific assumptions characterizing most of the work in the literature. We analyze historical data to understand the processes in the warehouse, to predict processing times, and to improve order processing. We introduce the ROBP and develop an efficient learning-based branch-and-price algorithm based on simultaneous column and row generation, embedded with alternative prediction models such as linear regression and random forest that predict processing time of a batch. We conduct extensive computational experiments to test the performance of the proposed approach and to derive managerial insights based on real data. The data-driven prescriptive analytics tool we propose achieves savings of seven to eight minutes per order, which translates into a 14.8% increase in daily picking operations capacity of the warehouse.

Order batching and order picking with 3D positioning of the articles: solution through a hybrid evolutionary algorithm.

A critical factor in the logistic management of firms is the degree of efficiency of the operations in distribution centers. Of particular interest is the pick-up process, since it is the costliest operation, amounting to 50 and up to 75% of the total cost of the activities in storage facilities. In this paper we jointly address the order batching problem (OBP) and the order picking problem (OPP). The former problem amounts to find optimal batches of goods to be picked up, by restructuring incoming orders by either splitting up large orders or combining small orders into larger ones that can then be picked in a single picking tour. The OPP, in turn, involves identifying optimal sequences of visits to the storage positions in which the goods to be included in each batch are stored. We seek to design a plan that minimizes the total operational cost of the pick-up process, proportional to the displacement times around the storage area as well as to all the time spent in pick-ups and finishing up orders to be punctually delivered. Earliness or tardiness will induce inefficiency costs, be it because of the excessive use of space or breaches of contracts with customers. Tsai, Liou and Huang in 2008 have generated 2D and 3D instances. In previous works we have addressed the 2D ones, achieving very good results. Here we focus on 3D instances (the articles are placed at different levels in the storage center), which involve a higher complexity. This contributes to improve the performance of the hybrid evolutionary algorithm (HEA) applied in our previous works.

A Mixed Integer Linear Formulation and a Grouping League Championship Algorithm for a Multiperiod‐Multitrip Order Picking System with Product Replenishment to Minimize Total Tardiness

Order picking, which is collecting a set of products from different locations in a warehouse, has repeatedly been described as one of the most laborious and time‐consuming internal logistic processes. Each order is issued to pick some products located at given locations in the warehouse. In this paper, we consider an order picking problem, in which a number of orders with different delivery due dates are going to be retrieved by a limited number of order pickers in multiperiods such that the total tardiness is minimized. The aim is to determine a retrieval plan in terms of order batching and order picker multitrip routing as decision variables. Besides, products are arrived and replenished at the predetermined locations at different periods. Therefore, products sitting in those locations should be delivered soon to provide empty rooms for replenishment. A mixed integer linear programming formulation is proposed for this new problem. The model is optimally solved for small‐size problems. For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group‐based operators to generate reasonable batches of orders. Improvement heuristics are designed as well. The performance of the MILP formulation and metaheuristic algorithms is analyzed for different problem instances whose designs are based on real data gathered from an auto parts warehouse. Results indicate that our algorithms can stably solve large instances of the problem in a reasonable time.

The order batching problem: A state-of-the-art review

The Order Batching Problem (OBP) has received a lot of attention from researchers in recent years. Until now, this problem has been considered as a whole with different problems or it has been studied on its own, with different solution methods, in different warehouse layouts, with different data structures. The aim of this study is to provide the researchers with a general analysis for studies using the OBP. In accordance with this purpose; the OBP is analyzed by an extensive literature review that includes all relevant studies to date. This literature review is based on a specific method and the studies are grouped into specific classes. Accordingly, the most studied problem with the OBP is the Picker Routing Problem (PRP) with 17% and 51% of the studies were solved by metaheuristic algorithms. The most widely used metaheuristic algorithms are Neighborhood Search and Genetic Algorithms.

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

Novel hybrid discrete differential evolution algorithm for the multi-stage multi-purpose batch plant scheduling problem

The multi-stage multi-product batch plant scheduling problem is an important part of batch chemical industry scheduling problems. Different from the multi-purpose batch scheduling problem, this problem can be characterized by multiple stages with non-identical parallel units, and multiple batches of customer orders. Numerous methodologies for this problem have been investigated to addressing scheduling cases of different production systems in the past two decades. This paper focus on the large-scale batch plant scheduling problem by minimizing the make-span in the scheduling horizon. And a novel hybrid discrete differential evolutionary algorithm is proposed to handle this problem. First, a novel two-line encoding scheme is constructed based on discrete and continuous variables. The sequence of orders is represented by a time-based representation method. Second, two novel mutation methods are proposed within the framework of encoding method. Two methods provide multiple search directions which helps improve the exploration ability and diversity of the population. At last, two local permutation methods are applied to improve the local optimal for the algorithm. The proposed work is tested through several real industrial instances with different sizes and characteristics by comparing with mixed integer linear programming method and meta-heuristic algorithms. The improvement of proposed work is also analyzed by the instances. The results report the efficiency and effectiveness of the novel proposed evolutionary algorithm in solving large scale multi-stage multi-product batch plant scheduling problem.

Batch Order and Discrete Order Picking Integrated with Vehicle Routing Decisions

In a business-to-consumer (B2C) context, customers order more frequently and in smaller quantities, resulting in a high number of consignments. Moreover, online shoppers expect a fast and accurate delivery at low cost or even free. To survive in such a market, companies can no longer optimise individual supply chain processes, but need to integrate several activities. In this article, the integrated order picking-vehicle routing problem is analysed in an e-commerce environment. In previous research, a mathematical programming formulation has been formulated in literature but only small-size instances can be solved to optimality. Two picking policies are studied: discrete order picking and batch order picking. The influence of various problem contexts on the value of integration is investigated: a small picking time period, outsourcing to 3PL service providers, and a dynamic environment context.

Integrated scheduling of zone picking and vehicle routing problem with time windows in the front warehouse mode

This paper examines the optimal scheduling of order picking and delivery. We present a mixed-integer model for managing integrated scheduling of zone picking and vehicle routing problem with time windows (ISZPVRPTW) in the FWM and consider the real-world factors impactive in planning order fulfillment process, such as the fulfillment deadline, convey time between the picking zones, setup time and the variable driving speed. The decisions on order batching, picking sequencing, and vehicle routing are made together. A Two-stage Iterated Search (TIS) algorithm is developed to minimize operation costs and overdue penalty costs. With extensive numerical experiments, we demonstrate the effectiveness and efficiency of the proposed approach by comparing the results produced by TIS with that of the Traditional Sequential Scheduling (TSS) approach. The results show that the TIS algorithm outperforms the traditional approach in different evaluation indicators, and the time allocation as well as the caution intensity is significant for the fulfillment expense.

Drop-shipping or batch ordering: Contract choice in the presence of information sharing and quality decision

This study investigates the manufacturer's contract choice (drop-shipping or batch ordering) and retailer's information sharing strategy in the presence of product quality decisions. The equilibrium outcomes give strategic choices for both the manufacturer and retailer. Specifically, the retailer prefers to share demand information when the quality investment efficiency is high under batch ordering contract, while he always chooses to share information under drop-shipping contract. Given no information sharing, the manufacturer chooses the batch ordering contract if the revenue-sharing rate is high. Given information sharing, the manufacturer prefers batch ordering contract when either of the following conditions holds: a) when the revenue-sharing rate is low and quality investment efficiency is low; b) when the revenue-sharing rate is high and quality investment efficiency is high; otherwise, the manufacturer prefers drop-shipping contract. Additionally, our results indicate that there may exist a “prisoner's dilemma” when the drop-shipping contract is a dominant strategy for the manufacturer and sharing demand information is a dominant strategy for the retailer.

Efficient order consolidation in warehouses: The product-to-order-assignment problem in warehouses with sortation systems

To improve picking performance, many warehouses apply order batching and/or zoning in their picking areas. The former policy collects multiple customer orders jointly on a picker tour to increase picking density, and the latter partitions the picking area into smaller zones to enable a parallel order processing. Both picking policies require an additional consolidation stage, where bins filled with partial orders arriving from multiple zones are sorted according to customer orders. To connect both stages, a conveyor system is applied on which the picked products, each being a piece of a specific stock keeping unit (SKU), move from the picking area toward the consolidation stage. If multiple pieces of the product sequence, approaching the consolidation area on the conveyor, refer to the same SKU, these products are interchangeable among customer orders, and our product-to-order assignment problem arises: Given a product sequence where each product refers to some SKU, we assign products to customer orders, such that demands are fulfilled and order-related objectives, e.g., the sum of completion times, are optimized. We investigate different objectives for this very basic optimization task and show that some problem versions are solvable in polynomial time, whereas others turn out to be NP-hard. Furthermore, we provide exact and heuristic solution approaches. By applying these algorithms in a comprehensive simulation study, we show that our product-to-order assignment problem can be an impactful lever to improve consolidation performance.

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

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.

Multi-objective grouping genetic algorithm for the joint order batching, batch assignment, and sequencing problem

ABSTRACT This article solves the order batching, batch assignment, and sequencing problem (JOBASP) given multiple objectives and heterogeneous picking vehicles in multi-parallel-aisle warehouse systems. A multi-objective grouping genetic algorithm (GGA) is developed to minimize total travel time and total tardiness by implementing an encoding scheme where a gene represents orders grouped in a batch and the assignment of the batch to a picking vehicle. Computer simulations show that the proposed algorithm performs 25.4% better than a first come, first served (FCFS) rule–based heuristic and 10.2% better than an earliest due date (EDD) rule–based heuristic. The proposed GGA provides significant savings of up to 46.8% and 28.4% on travel time and tardiness, respectively, for these benchmark heuristics. Therefore, this article introduces a GGA to solve the JOBASP with a reasonable computing time, making this approach interesting for warehouse operators using heterogeneous picking vehicles and addressing multiple objectives.

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.

Order batching and sequencing for minimising the total order completion time in pick-and-sort warehouses

Under the pick-and-sort order batch processing strategy, orders successively go through an order picking process, a buffer area, and an order sorting process in the form of batches. Owing to the difference in order batches and the limitation of the buffer capacity, blocking in the picking and starving in the sorting often occurs. This leads to a longer order completion time. Therefore, to minimise the total order completion time, this study investigates the order batching and sequencing problem under this strategy to reduce or even eliminate the blocking and starving phenomena. We formulate this problem as a mathematical model. By analysing the model, two useful properties and a lower bound are obtained. Based on these properties, we propose a situation-based seed (SBS) algorithm to solve the problem, which provides the intelligence of coordination for the classical seed algorithm. Numerical experiments demonstrate the superiority, effectiveness and optimality of the proposed algorithm. The proposed algorithm can save 8.359% of the total order completion time compared with the real-life algorithm applied in pick-and-sort warehouses, and its performance is close to the lower bound (only 1.105% gap). In addition, we also provide some management insights on how to coordinate the picking and sorting processes in practical pick-and-sort warehouses.

Learning curve based integrated procurement and project scheduling of multiple sister ships project

Studies reported in extant literature confirm the importance of the considering learning curve effect in a project context. However, a study that incorporates the learning curve effect in an integrated procurement and project schedule has not been yet reported in the literature. The primary contribution of the present study is to address this research gap. It develops a learning curve based integrated procurement and project scheduling Mixed Integer Programming (MIP) model for a shipbuilding project involved in manufacturing of multiple sister ships.The present study makes its second contribution in the MIP model. It provides an innovative way to model decision variables corresponding to order quantity by defining order batching scenarios. This approach reduces the size of search space, thereby reducing the computational complexity of solution approach. Its third contribution is conceptualization of two new parameters, schedule compression percentage and batching degree, to analyze the effect of the learning curve on project schedule and the tradeoff between transportation cost and inventory holding cost.

Wave order picking under the mixed-shelves storage strategy: A solution method and advantages

Order picking (OP) is a time and cost consuming operation in many warehouses. The optimization of order picking planning is crucial to speed up customers’ delivery process and reduce warehouse expenses. A commonly adopted strategy is called mixed-shelves storage strategy (MSSS) that is highly recommended for e-commerce warehouses. Applying this, the units of the same stock keeping unit (SKU) are scattered to various storage locations which provides more improvement room for OP. However, consolidated methods have not been sufficiently discussed when OP is under the MSSS. In this paper, we study the problem of wave picking systems to jointly address order batching, batch assignment, and picker routing (BAR) in a MSSS-based warehouse. We propose the decomposition of OP into BAR subproblems (DOPBAR) for solving such a wave OP problem. We examine our approach on a large set of instances, validate the effectiveness of our method, and point out the advantages of the MSSS in OP. Moreover, we analyze the trade-offs between two conflicting objectives that practitioners aim to optimize: customer service level (makespan) and workforce level (labor cost). Finally, a few insightful managerial recommendations are proposed that support the decision making in choosing a storage location assignment strategy.

An evaluation of several combinations of routing and storage location assignment policies for the order batching problem

In this work, we address the Order Batching Problem (OBP) and we devise tailor-made Mixed Integer Linear Programming (MILP) formulations of the OBP considering composite, largest gap and optimum routing policies, for the first time. We also introduce a novel picker routing policy named as mixed policy which combines the properties of both midpoint and return routing policies. Then, we perform extensive computational experiments with MILP formulations of the OBP considering several combinations of routing and storage policies under different operating conditions on randomly generated test instances. Finally, we present several managerial insights based on the computational results.

АНАЛИЗ ОТКАЗОВ ПОЛИМЕРНЫХ ДЕТАЛЕЙ СЕЛЬСКОХОЗЯЙСТВЕННОЙ ТЕХНИКИ

Since the last century, polymer materials have been widely used in industry. They are increasingly being used in the construction of agricultural machinery. Simultaneously with the spread of polymer parts, there is a need to develop technologies for repairing agricultural machinery containing such parts. (Research purpose) The research purpose is in analyzing the failures of polymer parts of agricultural machinery and equipment. (Materials and methods) The article presents a study of cases of failures of polymer parts of agricultural machinery based on data received from the enterprise LLC "Rassvet" of the Rostov region, the production cooperative "Koelginskoe" of the Chelyabinsk region for 2019 and summary information from various suppliers of spare parts of agricultural machinery. (Results and discussion) The parts of the industrial water treatment filter, turbine water meters and the sprayer housing failed at LLC "Rassvet" during the period under review. In the production cooperative "Koelginskoe" the gear of the servo drive of the grain flow and the oil seal of the hydraulic cylinder of the tractor K-744 failed, and therefore it was necessary to replace the entire unit. It is unprofitable for dealers of agricultural machinery to supply parts in a single copy due to their low cost. (Conclusions) The nomenclature of polymer parts of agricultural machines that fail is quite diverse, and cases of their failures are not rare. In some cases, the replacement of these parts is impossible due to their absence from sale, and in other cases, it turns out to be unprofitable due to long delivery times or a large minimum order batch. In this regard, agricultural producers suffer significant losses, overpaying for a whole node instead of replacing one part.

Multi-Product Multi Echelon Measurements of Perishable Supply Chain: Fuzzy Non-Linear Programming Approach

The perishable milk products industry has to deal with multiple pressures such as demand forecasting, price fluctuations, lead time, order batching, and inflated orders along with difficulties of climatic and traffic conditions, storage areas and shipment in unfavorable circumstances. The Indian dairy industry faces immense wattage issue due to improper infrastructure for the cold chain storage facilities, resulting in unsatisfied customers. A study is undertaken to comprehend the supply chain framework that handles perishability issues in production and distribution. Researchers propose a multi-objective mixed-integer non-linear supply chain coordination model under uncertain environments to minimize the cost of transportation, offset wastage of products and neutralize the losses due to insufficiencies of transit and storage amenities. The proposed model is meant for managing the delivery with lesser deterioration losses for producers, warehouses, and retailers. The model considers various costs for holding, halting, discounts on purchased cost, transportation cost for truckload policy under regular and unforeseen circumstances of curfew, and identify the rate of deterioration to know the impact on the cost for all players involved in the SCM framework. To handle uncertainty of objective functions, fuzzy set concepts and the defuzzification method are imposed, and fuzzy non-linear programming algorithms are used to get the single objective function from the defuzzified multi-objective functions. Data analysis is done on Lingo 18.0 software. Rate of deterioration is highest for the warehouse, which indicates that efforts should be made to augment warehouse facilities for less spoilage to reduce losses in cost. Finally, the study ends with main findings, conclusions, limitations and future scopes.