Order Batching Research Articles

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.

Joint pricing, ordering and order fulfillment decisions for a dual-channel supply chain with demand uncertainties: A distribution-free approach

A dual-channel supply chain is considered in which a manufacturer sells its product through a traditional retailer and an e-tailer, wherein the e-tailer may adopt two, i.e., the conventional batch ordering and the drop-shipping, order fulfillment policies. The problem is modeled as Stackelberg games in which the manufacturer is the leader and the retailer and the e-tailer are the followers competing on price. Optimization models are developed and equilibrium solutions are obtained for unknown distributions of the uncertain demands through a distribution-free approach. Analytical solutions of pricing and ordering decisions are also derived when the uncertain demand is assumed to follow known distributions to evaluate the performance of the distribution-free approach. Numerical studies are conducted to evaluate the effectiveness of the distribution-free approach and sensitivity analyses are performed to examine the effects of several important parameters on the equilibrium solutions. The choice of the e-tailer and the preference of the manufacturer for the order fulfillment policies are also analyzed through numerical studies when important parameters change. The results show that the e-tailer prefers the batch ordering policy if its market share is relatively large and its profit-sharing ratio is relatively low, the manufacturer prefers the batch ordering policy if the e-tailer’s market share is relatively small and the e-tailer’s profit-sharing ratio is relatively high, and both the e-tailer and the manufacturer prefer the drop-shipping policy for other situations. The drop-shipping policy benefits the manufacturer and the e-tailer when the channel substitutability is relatively low and the demand fluctuations are relatively high.

A heuristic approach for the online order batching problem with multiple pickers

The Online Order Batching Problem with Multiple Pickers (OOBPMP) consists of optimizing the operations related to the picking process of orders in a warehouse, when the picking policy follows an order batching strategy. In this case, this variant of the well-known Order Batching Problem considers the existence of multiple workers in the warehouse and an online arrival of the orders. We study three different objective functions for the problem: minimizing the completion time, minimizing the picking time, and minimizing the differences in the workload among the pickers. We have identified and classified all previous works in the literature for the OOBPMP. Finally, we propose a multistart procedure hybridized with a Variable Neighborhood Descent metaheuristic to handle the problem. We test our proposal over well-known instances previously reported in the literature by empirically comparing the performance of our proposal with previous methods in the state of the art. The statistical tests corroborated the significance of the results obtained.

QRobot: A Quantum Computing Approach in Mobile Robot Order Picking and Batching Problem Solver Optimization

This article aims to bring quantum computing to robotics. A quantum algorithm is developed to minimize the distance traveled in warehouses and distribution centers where order picking is applied. For this, a proof of concept is proposed through a Raspberry Pi 4, generating a quantum combinatorial optimization algorithm that saves the distance travelled and the batch of orders to be made. In case of computational need, the robot will be able to parallelize part of the operations in hybrid computing (quantum + classical), accessing CPUs and QPUs distributed in a public or private cloud. We developed a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket (D-Wave), and Pennylane locally or remotely. The proof of concept, when run in the above stated quantum environments, showed the execution time of our algorithm with different public access simulators on the market, computational results of our picking and batching algorithm, and analyze the quantum real-time execution. Our findings are that the behavior of the Amazon Braket D-Wave is better than Gate-based Quantum Computing over 20 qubits, and that AWS-Braket has better time performance than Qiskit or Pennylane.

Input-Dynamic Distributed Algorithms for Communication Networks

Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic CONGEST model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labelled graph under batch changes. We investigate, when a batch of alpha edge label changes arrive, - how much time as a function of alpha we need to update an existing solution, and - how much information the nodes have to keep in local memory between batches in order to update the solution quickly. Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. The diverse time complexity of our model spans from constant time, through time polynomial in alpha, and to alpha time, which we show to be enough for any task.

Improving Retail Warehouse Activity by Using Product Delivery Data

This paper presents a research study which is dedicated to the improvement in retail warehouse activity. This study aims to improve activity by identifying an efficient order picking strategy. (1) Background: The literature review shows the application of order picking strategies, but research related to their selection lacks an integrated approach. (2) Methods: The authors use the discrete event simulation method for the analysis of order picking strategies. The application of the discrete event simulation method enables various scenario tests in retail warehouses, allowing one to benchmark order picking strategies. By using the simulation model, experiments were designed to evaluate order picking strategies that are dependent on the delivery of the product distance variable. This research uses analysis of cost components and helps to identify the best possible order picking strategy to improve the overall warehouse performance. The authors benchmarked order picking strategies and presented constraints following product delivery data concerning their applications. (3) Results: The results presented show that the application of the order sorting strategy delivers 46.6% and the order batching strategy 6.7% lower costs compared to the single picking strategy. The results of the order batching strategy could be improved by 8.34% when the product clustering action is used. (4) Conclusions: The authors provide a theoretical framework which follows the application of order picking strategies using the product delivery data approach, which is the main scientific novelty of this paper. Recommendations are provided regarding the application of the proposed framework for the future improvement in retail warehouse activity.

Order batching and picker scheduling in warehouse order picking

This article focuses on the integration of order batching and picker scheduling decisions while taking into account two objectives that have been considered in the literature, namely the minimization of both total travel time to collect all items and makespan of the pickers. This integrated problem not only occurs naturally in wave picking systems in which the latest picking time of orders becomes the key performance metric, but also arises when there is a limit on the picker operating time. We present models that result from combining these objectives and analyze their relationship through bounds. We propose a column generation-based exact algorithm for the integrated problem. The novelty of the proposed approach lies in the ability of efficiently solving the integrated order batching and picker scheduling problem to optimality by designing a column generation subproblem based on the set of batches, which makes it a challenging optimization problem due to its size. We alleviate this difficulty by reformulating this subproblem, which allows efficient implicit enumeration of its variables. We have also devised a Variable Neighborhood Search algorithm used as a subprocedure within the proposed exact solution algorithm. Finally, we conduct experiments on randomly generated instances and show that the proposed algorithms are capable of solving instances with up to 100 orders.

Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.

Order batching and batch sequencing in an AMR-assisted picker-to-parts system

Efficient order fulfillment processes in warehouses are a key success factor in times of increasing global retail sales volumes and same-day deliveries. To streamline order fulfillment processes, warehouse managers frequently rely on autonomous mobile robots (AMRs). By supporting human order pickers with AMRs, unproductive picker walking time can be reduced, which is essential for increasing the performance of a traditional picker-to-parts system. We study an AMR-assisted picker-to-parts system in which the warehouse is partitioned into disjoint zones, each with one order picker assigned to it. A set of customer orders is given, each associated with a due date until which its items are to be collected. Each AMR of a given fleet is responsible for transporting the items of a single batch of customer orders from the picking area to the depot. An order picker collects those batch items that are stored in her zone and transports them to a handover location. The AMR visits the handover locations and returns to the depot after collecting all batch items. The objective is to minimize the total tardiness of all customer orders. We define the resulting problem as mixed integer program and provide an efficient heuristic solution approach. Furthermore, we investigate the influence of increasing the AMR fleet size and varying travel and walking speed ratios between AMRs and order pickers. We find that a slight increase in the speed ratio leads to a larger reduction of the total tardiness compared to increasing the AMR fleet size.

A Two-Stage Path Planning Method for the Rearrangement Problem in Modular Puzzle-Based Storage System

The modular puzzle-based storage system consists of multiple storage modules. The system has multiple input/output (IO) points which can simultaneously deal with multiple orders in one batch. When a batch of orders comes in advance, it is necessary to rearrange the items near each IO point to reduce the picking time of customers. To complete the rearrangement quickly, this paper proposes a two-stage path planning method considering the simultaneous movement of multiple items. This method includes two stages: planning single moves and merging single moves. In the first stage, the sequence of single moves of each module needs to be obtained so as to convert the system from an initial state to a target state. In the second stage, the single moves in the sequence are merged into block moves and parallel moves to reduce the steps of movement. The simulation results show that the single move planning method can be used to solve the rearrangement problem stably and effectively and that the single move merging method can greatly optimize the experimental results with the optimization rate more than 50% in different configurations.

A Heuristic Solution Approach to Order Batching and Sequencing for Manual Picking and Packing Lines considering Fatiguing Effect

In this research, we study an extended version of the joint order batching and scheduling optimization for manual vegetable order picking and packing lines with consideration of workers’ fatiguing effect. This problem is faced by many B2C fresh produce grocers in China on a daily basis which could severely decrease overall workflow efficiency in distribution center and customer satisfaction. In this order batching and sequencing problem, the setup time for processing each batch is volume-dependent and similarity dependent, as less ergonomic motion is needed in replenishing and picking similar orders. In addition, each worker’s fatiguing effect, usually caused by late shift and repetitive operation, which affects order processing times, is assumed to follow a general form of logistic growth with respect to the start time of order processing. We develop a heuristic approach to solve the resultant NP-hard problem for minimization of the total completion time. For order batching, a revised similarity index takes into account not only the number of common items in any two orders but also the proportion of these items based on the vegetable order feature. To sequence batches, the genetic algorithm is adapted and improved with proposed several efficient initialization and precedence rules. Within each batch, a revised nondecreasing item quantity algorithm is used. The performance of the proposed heuristic solution approach is evaluated using numerical instances generated from practical warehouse operations of our partnering B2C grocer. The efficiency of the proposed heuristic approach is demonstrated.

Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse.

The utilization of a storage space can be considerably improved by using dense mobile racks. However, it is necessary to perform an optimisation study on the order picking to reduce the time cost as much as possible. According to the channel location information that needs to be sorted, the multiple orders are divided into different batches by using hierarchical clustering. On this basis, a mathematical model for the virtual order clusters formed in the batches is established to optimize the order cluster picking and rack position movement, with the minimum picking time as the objective. For this model, a hybrid genetic algorithm is designed, and the characteristics of the different examples and solution algorithms are further analysed to provide a reference for the solution of the order picking optimisation problem in a dense mobile rack warehouse.

Integrated Remanufacturing and Opportunistic Maintenance Decision-Making for Leased Batch Production Lines

Abstract With the development of the product-service paradigm, many original equipment manufacturers (OEMs) have begun to provide remanufacturing services besides maintenance services as the lessors. An integrated contract including remanufacturing and maintenance service brings extra leasing profits and complex scheduling challenges as well. The use of machine remanufacturing in conjunction with system-level opportunistic maintenance (OM) has not been previously studied. This paper tries to integrate remanufacturing planning into maintenance optimizations for improved system performance in terms of saved leasing profits. An integrated remanufacturing and opportunistic maintenance (R&OM) policy is developed and applied to a leased manufacturing line in batch production. The system-level saved profit achieved via the R&OM policy is estimated and maximized through a sequential decision-making programming. Performing this integrated policy, it is possible to utilize the sequential R&OM opportunities raised from the random batch orders. In addition, preventive maintenance (PM) and preventive remanufacturing (PR) can be dynamically optimized to form profit-effective schemes. Numerical examples based on the collected information from a leased batch production line of engine crankshaft demonstrate that this proposed R&OM policy could efficiently achieve saved leasing profit maximization, reduce joint decision complexity, and expand OM theory.

Development of solar power system for Sarawak peat water continuous electrocoagulation treatment process

Sarawak state government has established Sarawak Alternative Rural Water Supply (SAWAS) programme in order to serve as a purpose of providing safe and clean water to the rural communities not connected to municipal clean water supply. In the rural areas of Sarawak, particularly on the coastal region where municipal water supply is not available, the villagers are normally resorted to utilize rainwater and peat water for daily usage. Some of these rural areas are even not connected to electricity grid. Subsequently, one of the proposed methods to eradicate these problems in supplying clean water without electricity supply grid is to implement stand-alone water treatment system with solar power system. As such, the main aim of the study is to design a solar power system to support Sarawak peat water electrocoagulation treatment process. The study is divided into two stages. In the first stage, the study designs a solar power system to support the treatment process of peat water for both batch and continuous electrocoagulation systems. This includes designing and fabrication of a small-scale solar power system. The second stage of the study involved experimental studies on both batch and continuous electrocoagulation systems in order to study the effectiveness of solar power system to supply electricity for the electrocoagulation systems. Overall, the study has developed a solar power system for both batch and continuous electrocoagulation of peat water system. From the experiments conducted, the developed systems are capable to reduce 18.8% and 46.15% of peat water turbidity for batch and continuous electrocoagulation systems respectively. However, in order to meet a more stringent drinking water standards, some improvements on the designed systems are indispensable.

Stochastic modeling of multiline orders in integrated storage‐order picking system

AbstractDue to demanding service levels in e‐commerce order fulfillment, modeling and analysis of integrated storage and order picking processes in warehouses deserve special attention. The upstream storage system can have a significant impact on the performance of the downstream order picking process. With a particular focus on multiline e‐commerce orders, we develop an analytical modeling framework for integrated analysis of upstream (shuttle‐based storage and retrieval system) and downstream (pick system) networks. To capture the consolidation delays in fulfilling multiline orders, the downstream pick system is modeled with a closed queuing network that includes synchronization nodes. The configuration of the synchronization station is adapted to model the variety of order profiles handled at the pick station. For the downstream closed queuing network, we propose a decomposition‐based solution methodology that results in good solution accuracy. The resulting semi‐open queuing network (SOQN) of the integrated system is analyzed using the matrix‐geometric method (MGM). To improve the accuracy of analytical estimates of the measures, we propose a hybrid simulation/analytical framework, where the performance measures of complex subnetworks are obtained from simulation. We also develop a detailed simulation model of the physical system for validating the analytical and hybrid estimates of the performance measures. The results from experiments indicate that the hybrid simulation/analytical approach reduces the error in the throughput time estimates to 3% from 18% obtained from the analytical model. Then, we investigate the effect of the upstream network configuration (such as the number of storage aisles) and the downstream network configuration (such as the mixed vs. dedicated picking, CONWIP control for orders, order batching) on the order throughput times. Our analysis provides a threshold on the maximum numbers of allowable orders (CONWIP control) and number of aisles beyond which the improvement in average throughput time of the integrated system is marginal. Numerical experiments with high‐order arrivals also highlight that mixed picking in the downstream network can result in significant throughput time reduction in comparison to dedicated picking.

Input-Dynamic Distributed Algorithms for Communication Networks

Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic \congest model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labeled graph under batch changes. We investigate, when a batch of α edge label changes arrive, \beginitemize \item how much time as a function of α we need to update an existing solution, and \item how much information the nodes have to keep in local memory between batches in order to update the solution quickly. \enditemize Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. In particular, we derive non-trivial upper bounds for two selected, contrasting problems: maintaining a minimum spanning tree and detecting cliques.

Assessment of scatter on material properties and its influence on formability in hole expansion

The plastic deformation included in the technological processes of sheet metal forming allows the production of different components with different geometries. Therefore, the formability of metallic materials is an essential feature in stamping processes. One of the inevitable scattering sources when considering these processes is the variability of material properties, which can change from coil to coil, from batch to batch and from supplier to supplier. This constitutes a significant obstacle in improving the quality of any manufacturing process. The current study investigates the formability and the mechanical characterization of a defined advanced high strength steel grade, considering different suppliers, coils, and batches in order to evaluate its influence on the results of the sheet metal forming process. A microstructure analysis is also considered to understand the impact of micro-scale variables (e.g. grain size) in macro-scale behavior. Additionally, numerical simulations were taken into consideration to evaluate the influence of mechanical characterization in the results.

A Developed Sensor for Color Identification, Sorting and Counting Automation Control System

Knowing how to manipulate the data from environment to automation control systems in industry can innovate and improve a quality of the products effectively. In plastic manufacturing, there is a need of automated control system that can recognize color and sort, count and sequentially control processing since the certain quantity of color billets mix into a batch in order to produce desired colorful products. In this paper, a color recognition sensor based on the principle of LEDs energy harvest corresponding actuator has been designed. The derivational voltage due to red, green and blue color shift in color recognition, sorting and counting automation control system is measured as a signal input of the controller that works with the reflected light properties such as the reflective harmonic energy. Sequentially, the automation control system with human machine interface (HMI) and supervisory control and data acquisition (SCADA) will monitor the quantity of diversity color products in intuitive and remote supervisory, customization or adjustments in automation processing. Furthermore, the designed device will integrate into artificial intelligent (AI) and unmanned aerial vehicle (UAV).

A Developed Sensor for Color Identification, Sorting and Counting Automation Control System

Knowing how to manipulate the data from environment to automation control systems in industry can innovate and improve a quality of the products effectively. In plastic manufacturing, there is a need of automated control system that can recognize color and sort, count and sequentially control processing since the certain quantity of color billets mix into a batch in order to produce desired colorful products. In this paper, a color recognition sensor based on the principle of LEDs energy harvest corresponding actuator has been designed. The derivational voltage due to red, green and blue color shift in color recognition, sorting and counting automation control system is measured as a signal input of the controller that works with the reflected light properties such as the reflective harmonic energy. Sequentially, the automation control system with human machine interface (HMI) and supervisory control and data acquisition (SCADA) will monitor the quantity of diversity color products in intuitive and remote supervisory, customization or adjustments in automation processing. Furthermore, the designed device will integrate into artificial intelligent (AI) and unmanned aerial vehicle (UAV).