Order Batching Research Articles

Prediction of B2C e-commerce order arrival using hybrid autoregressive-adaptive neuro-fuzzy inference system (AR-ANFIS) for managing fluctuation of throughput in e-fulfilment centres

The complexity of today's e-commerce logistics environment compels practitioners to achieve a higher level of operating efficiency. As it is infeasible for operators to process a large number of discrete e-orders individually, warehouse postponement, that is, delaying the execution of a logistics process until the last possible moment, is essential. Yet the question remains as to how one can accurately identify the timing for consolidating e-orders, and subsequently releasing the grouped e-orders for batch order picking. This is a subject, lacking previous research, but is fundamentally crucial in today's e-commerce logistics environment. This paper introduces an integrated autoregressive-adaptive neuro-fuzzy inference system (AR-ANFIS) approach for forecasting e-commerce order arrivals. Two AR-ANFIS models are built for evaluating their prediction ability against ARIMA models. The experimental results confirm the suitability of the hybrid model for forecasting e-order arrivals. To make use of the model output, an algorithm is formulated to convert e-order arrival figures into cut-off time of order grouping. In this sense, this total solution, packaged as a decision support system, namely the E-order arrival prediction system, assists logistics practitioners in judging when to release the grouped e-orders for batch processing, and essentially improves their order handling capability.

An approach for the solution to order batching and sequencing in picking systems

This article discusses the use of Genetic Algorithms to solve a specific variation of the Order Batching and Sequencing Problem (OBSP) called Optimized Picking Sequence (OPS). Essentially, OPS is an optimization problem of the order picking in a typical Warehouse (WA) that employs a low-level picker-to-parts system with a pick-and-sort process. The OPS solution premise is to minimize the time and total cost of the picking processes in order to better adjust the trade-off between the level of customer service and the efficiency of this WA. To solve the OPS, we propose a computational tool called GA–OPS which is formulated by the iteration of two Genetic Algorithms (GABATCH and GATSP). GABATCH groups products from different picking orders into picking batches to prevent earliness and tardiness in order fulfilment and to minimize the number of picking travels. GATSP finds the best possible picking routes to reduce distance and picking total time for each batch. The OPS approach is an extension of the Optimal Billing Sequencing (OBS) problem and deals with practical dilemmas not addressed by the OBSP yet. The objective is to evaluate the GA–OPS performance in face of the conditions needed to adapt it to the real conditions of WAs. Experiments with problems of different complexity levels showed that the GA–OPS provides satisfactory quality solutions to any OPS instance. The iteration approach proposed to the OPS fills a gap in the literature and makes innovative contributions to advances towards developing and applying picking optimization methods which are more suitable to the reality of the mentioned WAs.

A Research on Order Batching Problem of Panel Furniture Enterprises

A Research on Order Batching Problem of Panel Furniture Enterprises

Batch replenishment planning under capacity reservation contract

We study the single-item Lot Sizing Problem (LSP) in a supply chain composed of a retailer and a supplier by integrating the buyback contract and the batch ordering. The purpose is to determine a replenishment planning for the retailer to satisfy his deterministic demands over a finite horizon, while minimizing the procurement and inventory costs. Regarding the procurement cost, we assume two different structures: FTL (Full Truck Load) and OFB (Only Full Batch). We consider three types of buyback contract: with fixed return periods, with a time limit on returns, and with returns permitted only in procurement periods. Each contract is characterized by the maximum return percentage being either equal to 100% (full return) or less than 100% (partial return). For the LSP under the buyback contract with fixed return periods, we assume the concept of lost sales. Another concept considered in the LSP's under the three types of buyback contract is the disposal of the unsold and unreturned quantities. We model these different LSP extensions as a Mixed Integer Linear Program (MILP). Thereafter, we develop exact polynomial time dynamic programming algorithms for some extensions and show the NP-hardness of others. For each problem solved in polynomial time, we compare the efficiency and the limits of the proposed algorithm with those of four MILP formulations by performing different tests. Finally, we propose mathematical models for the LSP's under other types of the capacity reservation contract in the deterministic and multi-period case.

Optimal inventory strategies for two-echelon supply chain system involving carbon emissions and fuzzy deterioration

The industry sector is an vital direct and indirect source of carbon emissions. Today industries are looking for solutions to reduce carbon emissions associated with their operations. Operational adjustments, such as modifications in batch sizes or order quantities, have proven to be an effective way to decrease emissions. This paper provides a new mathematical model which integrate cost and emissions in transportation and storage to execute optimal operational adjustments for deteriorating products under the framework of two-echelon inventory system. The deterioration rate is treated as the triangular fuzzy number, and we use the extension principle to find the membership function of the fuzzy total cost and defuzzify by the centroid to find the estimate of the total cost in the fuzzy sense. Numerical example is performed to show the effectiveness of the proposed model, and several managerial insights are observed from sensitive analyses which may help both the government and the industry to adopt appropriate carbon reduction regulations.

Supplier selection problem in IoT solutions

PurposeThe use of Internet of Things (IoT) and networks has built a potential impact on the product cost and time in a company’s manufacturing process. These IoT solutions provide end-to-end visibility and faster introduction of merchandise and supplier in the market. The main aim of this research paper is to supply products with improved quality and cheaper price, whereas the rising response and quality of the client service.Design/methodology/approachThis paper designs and develops two cases for selecting the most efficient vendor while keeping in mind the profit and cost constraints in optimization.FindingsOutsourcing is a vital parameter to cut back the price and maximize the profit of the manufacturer. Therefore, the integration of supply chain with IoT can provide a solution to the cost optimization and supplier/vendor selection problems in supply chain management.Research limitations/implicationsThe results show that the models are quite realistic and can help the IoT-based manufacturing units to make strategic decisions regarding product manufacturing and distribution.Practical implicationsThe authors can further extend the model to derive the retailer’s profit function and develop the end product cost to the consumers and hence make it a n-level multi-vendor selection model for IoT-based systems.Originality/valueThe right choice of vendor for IoT-enabled business is a crucial concern. In this paper, the authors designed and developed multi-vendor models with in-house production and outsourcing decisions to meet the demand along with the vendor selection. The variable demands and designed variable unit cost function and batch order are set to make vendor selection more realistic.

Genetic algorithms applied to integration and optimization of billing and picking processes

This article intends to provide a computational tool that integrates and provides optimized solutions to two interdependent problems called Optimized Billing Sequencing (OBS) and Optimized Picking Sequence (OPS). These problems are addressed separately by the existing literature and refer respectively to the optimization of billing and picking processes in a typical warehouse with low-level picker-to-parts system. Integration literature is, therefore, limited and there is a demand for more robust OBS/OPS optimization methods. This approach will deal with practical dilemmas that have not been addressed by researchers yet to propose an extension to the OBS model by Pinto et al. (J Intell Manuf 29(2):405–422, 2018) along with a specific variation of the Order Batching and Sequencing Problem. The premise is to prove to managers the possibility of making more consistent decisions about the trade-off between the level of customer service and the warehouse efficiency. The proposed tool is formulated by the integration of two Genetic Algorithms called GA-OBS and GA-OPS where GA-OBS maximizes the order portfolio billing and generates the picking order to the OPS, whereas GA-OPS comprises the iteration of batch and routing algorithms to minimize picking total time and cost to the OPS. Experiments with problems with different complexity levels showed that the proposed tool produces solutions of satisfactory quality to OBS/OPS. The approach proposed fills a gap in the literature and makes innovative contributions to the development of more suitable optimization methods to the reality of warehouses.

Formulating and solving the integrated batching, routing, and picker scheduling problem in a real-life spare parts warehouse

New market developments increase the complexity of managing order picking operations. Integrating order picking planning problems enables warehouse managers to organize order picking operations more efficiently. This paper provides a decision support tool that integrates and solves the three main operational order picking planning problems (i.e., order batching, picker routing, and picker scheduling). Different from other studies, the objective is to increase order picking efficiency while ensuring a high customer service level. The new integrated planning problem accounts for order due times, a limited availability of order pickers, as well as a high-level storage locations, to ensure the applicability in practice. An iterated local search algorithm is introduced to solve the problem effectively and efficiently. Moreover, a real-life case shows the substantial performance benefits gained from integrating batching, routing, and picker scheduling.

Mathematical programming modeling for joint order batching, sequencing and picker routing problems in manual order picking systems

This article aims to introduce mathematical programming models for the joint order batching and picker routing problem (JOBPRP) and the joint order batching, sequencing and routing problem (JOBSPRP). For this purpose, we present formulations for the traveled distance and travel time between picking positions in low-level and high-level picker-to-part systems (2D and 3D warehouses) and single-block and multiple-block warehouses. Likewise, we formulate Steiner traveling salesman problem (STSP) models considering multiple pickers, heterogeneous picking vehicles, multiple objectives and due windows. We calculate the number of binary variables, continuous variables and constraints for the proposed models, in order to show the complexity of solving these order picking problems using exact solution methods. As a result, we introduce several mathematical models for manual order picking systems, which could serve as references for researchers interested in finding optimal or high-quality solutions to joint order picking problems, considering realistic warehouse and distribution center environments.

Production of an endopolygalacturonase from Wickerhanomyces anomalus with disintegration activity on plant tissues

Abstract The objective of the present study was to produce an endo-polyglacturonase by Wickerhanomyces anomalus, in a low cost fermentation medium, by batch and fed-batch cultures in order to be used in the enzyme. The effect of several nutrients on PGase production, were evaluate at shakes flasks, in a reference fermentation medium, using one factor at a time method, Plackett-Burman design and response surface methodology. The optimized fermentation medium was used to evaluate the growth and production of the enzyme by batch and fed-batch cultures, at a lab scale bioreactor. PGase activity obtained in the RF medium was ∼20 U/mL. The absence of trace element solution had a repressive effect on the enzyme synthesis. The addition of yeast extract, instead of vitamins and amino acids, in the culture medium, improved the production of the enzyme. Plackett-Burman design determined that only pectin and yeast extract had significant and positive effect on PGase production. The Doehlert design determined that maximum PGase synthesis was obtained with 6.0 and 0.8 g/L of pectin and yeast extract, respectively. The final optimized fermentation medium included glucose, pectin, urea, yeast extract and salts. In this medium, PGase synthesis reached ∼25 U/mL, and ∼49 U/mL, in batch and fed-batch cultures, respectively, at lab scale bioreactor. In this study, it was able to obtain enzymatic extract with high PGase activity, by the growth of W. anomalus in a low cost culture medium, by fed-batch system, for its future use in the enzymatic cassava starch extraction.

An efficient two-stage method for solving the order-picking problem

With the rapid development of online shopping and the expansion of convenience stores in recent years, physical retail stores have successively launched fast online delivery services. To cut costs, hypermarket vendors usually use their existing physical stores as the distribution centers, leading to excessively long total walking distances in the ordering–picking process. In this paper, we propose an online two-stage method to optimize the order picking, specifically, an order batching stage and a picking-path planning stage. The empirical results indicate that the proposed method effectively calculates picking paths and that order pickers efficiently complete their picking tasks by these paths.

Catalyst deactivation, ash accumulation and bio-oil deoxygenation during ex situ catalytic fast pyrolysis of biomass in a cascade thermal-catalytic reactor system

In this work, we investigated the deactivation of a commercial ZSM-5 based catalyst during ex situ catalytic fast pyrolysis (CFP). The experimental runs were carried out in a novel cascade dual fluidized bed reactor system where thermal pyrolysis of biomass was carried out in the first reactor, while ex situ catalytic conversion of the pyrolysis vapours was carried out in the second reactor. Consecutive reaction-regeneration cycles were realised using the same catalyst batch in order to evaluate catalyst deactivation over time. A comparison between in situ and ex situ CFP revealed that in contrast to what was observed in the case of in situ CFP, no accumulation of biomass-derived metals on the catalyst was observed during ex situ CFP. Both acidity and surface area were less affected compared to in situ CFP and the catalyst maintained higher activity. Product distribution and composition exhibited some variation over time which was attributed to the accumulation of biomass ash in the pyrolysis reactor and not to the poisoning of the catalyst bed. These results clearly demonstrated that ex situ CFP is an effective way to avoid catalyst poisoning during the CFP of biomass and to prolong catalyst lifetime.

Dynamic order batching in bucket brigade order picking systems with consecutive batch windows and non-identical pickers

The blocking mitigation mechanism contained in a batching and sequencing model improves order picking performance in bucket brigade order picking systems by adjusting the batch formation in a given batch window. The batch formation of a given batch window affects the transition to the next batch window and limits the batch formation’s structure in the next window. In addition, imbalance in picker capability increases the variation of batch formation while mitigating blocking delay. Our paper proposes a dynamic indexed batching model to smooth the transition between consecutive batch windows and give a better approximation of non-identical pickers’ capability. We develop a mixed integer programming solution for a dynamic indexed batching model in a bucket brigade order picking system (DIBMB). Simulation experiments show that the DIBMB smooths the transition between batch windows as measured in the delay experienced by the first batch in each batch window. Comparisons to the available batching models show that under DIBMB, the blocking delay gradually increases throughout the sequence of batches.

Dual-index Policies for Serial Systems with Dual Delivery Modes and Batch Orders

In this paper, we consider a stochastic serial inventory system with dual delivery/supply modes for each echelon stage. The two modes in each stage take different lead times, incur different ordering costs and require different base order batch sizes. We study a class of dual-index echelon-(R,nQ) policies which is a nature extension of the well-studied dual-index policies (Veeraraghavan and Scheller-Wolf 2008, Zhou and Yang 2016) and echelon-(R,nQ) policy (Chen 2000) that have been proved to be near-optimal/optimal for the single-echelon dual-sourcing system and the batch-ordering serial system, respectively. Based on the concept of strong Q-jump-convexity, we prove that this policy is indeed optimal for the case in which the lead time difference between the two delivery modes in each stage is one period and the order batch sizes satisfy an integer-ratio constraint. For the general case, we analyze the system dynamics under this policy and show separability results for the policy parameters. Based on these properties, we provide a simulation-based optimization procedure for policy parameters. Three simple heuristics are then provided to find the near-optimal policy parameters, which are shown to be very efficient by the numerical studies.We also numerically investigate the value of the dual delivery modes and observe that as long as the regular delivery mode of the upstream echelon is only marginally less favorable than the regular delivery mode of the downstream echelon, the downstream echelon should be given priority by firms thinking of adding an expedited mode to the system.

An embedded Markov chain approach to stock rationing under batch orders

We provide an extension to the embedded Markov chain approach of Fadiloglu and Bulut (2010) for the analysis of lot-per-lot inventory systems with backorders under rationing. This extension generalizes the method so that it can be used for the analysis under (Q,R) policy. We introduce a new embedded Markov chain of higher dimensionality that keeps track of the ordering process. We finally provide a recursive procedure to generate the steady-state probabilities from the chain and obtain the steady-state distribution.

Kit Preparation with Cobot-supported Sorting in Mixed Model Assembly

Kitting is a common approach of materials supply with mixed-model assembly, by which components are supplied to the assembly process in pre-sorted kits. With kitting, the kit preparation is a labour intensive process and order batching is often applied to enhance efficiency. Here, improved quality and efficiency by means of automation is desirable, but knowledge of the potential of collaborative robots to support kit preparation with order batching is lacking. The purpose of this paper is to identify the potential of cobots to support time-efficient kit preparation with order batching, when the pick task is performed manually and a cobot carries out the sort task. A modelling approach is applied with experimental data from laboratory experiments to compare the cycle time between fully manual and cobot-supported kit preparation with order batching. The findings suggest that a cobot-supported sort task leads to a comparable average cycle time, with less variability of the cycle time, when compared to the fully manual setup. The paper contributes several insights on the application of cobots to support kit preparation, and the model developed in the paper can be used by practitioners to assess the potential of cobots to support their processes for kit preparation.

Research on AGV Path Planning under “Parts-to-Picker” Mode

This paper aims to optimize the “Parts to Picker” picking system with order sorting and AGV path planning as its priorities according to AGV-based “Parts to Picker” sorting mode and the order picking procedures. And this paper builds an intelligent order batching model with the minimum number of AGV handling shelves as the objective function. In addition, this paper also introduces the concept of order proximity coefficient, realizes reasonable and effective batching of orders. Based on the results of the order batching, this paper presents a goods-picking list in the target shelves after collecting various order batches, and proposes a TSP-based and “S-type” shelves handling strategy. In addition, this paper builds a path planning model with the shortest AGV total handling distance. The path planning under multiple AGVs parallel situation is realized. Finally, this paper proves the efficiency of AGV path planning strategy based on the results of orders batching by listing practical examples from enterprise.

Order Batching Optimization for Warehouses with Cluster-Picking

This paper focuses on optimizing the static order batching problem (OBP) with multiple pickers. This problem involves optimal assignment of a set of customer orders to batches, given capacity constraints, in order to minimize the total distance travelled. The order picking strategy considered is cluster picking. We assume that each picker is assigned one batch and all pickers begin the pick tour simultaneously. The aim is to minimize the total distance travelled by all pickers, which is calculated based on the S-shape routing strategy. A mathematical model is developed to solve the OBP for small-scale instances. For larger instances, a simplified version of an order batching heuristic proposed by DHL Supply Chain is utilized and experimented with for different-sized problems. Both approaches are compared to the conventional first come first serve (FCFS) method of order batching.

A Markovian Approach for Time Series Prediction for Quality Control

In this work we aim to predict quality levels of incoming batches of a selected product type to a white goods manufacturer from a third party supplier. We apply a Markov Model that captures the quality level of the incoming batch in order to predict the quality status of the future arrivals. The ultimate aim is to generate reliable predictions for the future incoming batches, so that the manufacturing company could warn its supplier if the predictions indicate a significant deterioration in the quality. Applied methodology is compared to several benchmark approaches and its superior performance is shown using a benchmark dataset from the literature and the dataset provided by the manufacturing company. Proposed algorithm performs better compared to benchmarks in detecting the instances with quality level falling outside the tolerances in the validation data; and proves itself as a promising approach for the company.

Adaptive Storage Reassignment in Order Picking Systems to Picker Learning and Change of Demand

In today’s logistical environment, companies must quickly deliver many kinds of products to customers, owing to intensifying competition, shortening product lifecycle. Mid-supply-chain logistical warehouses play an important role of adjusting the difference between manufacturer supply and customer demand. At the warehouses, most of the work comprises picking. Thus, it is imperative to make the picking process more efficient. The working time for picking is determined by a combination of elements, such as warehouse layout, storage assignment, order batching, and routing. And, adaptive storage assignments to change of demand have been effective. In practice, if the storage assignment changes, the picker will have a learning curve negatively affecting picking time. A model handling such effects has already been proposed. However, it is assumed all of the picker learning is reset. This is impractical. Our study proposes a new model in which the picker only relearns the shelf for which a storage assignment has been changed. Furthermore, pickers’ learning grows with the number of storage-shelf accesses, the degree of picker’s learning affect the probability of picking errors and search time. Our study specifies the effectiveness of the storage-shelf learning method for handling adaptive storage assignments.