Purpose: ORDER PICKING operation is one of the most well-known labor and cost intensive internal logistics processes. Withdrawal of the ORDER in response to customer need is defined in ORDER to collect a set of ORDERs from storage zone in the shortest possible time. The purpose of this research is to provide a scientific and practical basis considering the constraints that enforce to achieve an acceptable level of performance in ORDER PICKING systems. This is done by building a Mixed Integer Linear Programming (MILP) formulation and developing an adapted solution method suited to the structure of the problem Methodology: First, by reviewing the literature in the field of ORDER PICKING systems, sufficient knowledge has been obtained at the operational level, and with emphasis on warehouse management constraints, a MILP formulation is proposed by integrating ORDER batching and picker routing. After validating the model and solving it through GAMS software, due to the nature of the problem, which is an NP-hard type, the problem is solved with an efficient algorithm, which is a grouping version of the league championship algorithm, and the results are compared. To develop the algorithm, operators are fit to the specific structure of the problem, i. e., the assignment of ORDERs (items) to ORDER pickers (groups) Findings: Developing a multi-period MILP formulation for multi-trip picker routing, assuming for the first time the possibility of product replenishment and limited access to pickers. For large-scale problem instances, the league championship algorithm is used. The results indicate the effective capability and efficiency of this algorithm for solving large test problem instances. Originality/Value: The issue of multi-period ORDER PICKING and multi-trip routing of pickers is considered for the first time ‎in this paper. Because of the limited number of pickers, this must be taken into account in modeling. ‎The assumption of product replenishment is also considered for the first time in this article and its ‎modeling has been done. In this way, ORDERs enter the warehouse over time, during different periods, ‎and are placed in a predetermined positions. The limited access to pickers in each period is also ‎discussed for the first time in this paper. Finally, the objective function of minimizing the total ‎tardiness, which is in line with the needs of the industry, is also introduced in this paper. Regarding the ‎solution method, a league championship metaheuristic algorithm is presented which takes into ‎account the problem structure (which corresponds to the structure of grouping problems) and ‎solution generation operators have been developed to maintain the new solution. ‎

In this paper, a mixed-integer linear programming model is proposed to integrate batch PICKING and distribution scheduling problems in ORDER to optimize them simultaneously in an ORDER PICKING warehouse. A tow-phase heuristic algorithm is presented to solve it in reasonable time. The first phase uses a genetic algorithm to evaluate and select permutations of the given set of customers. The second phase uses the route first-cluster method to obtain an effective schedule for a given permutation of customers. Computational experiments represent that integrated approach can lead to significant reduction in the makespan. Moreover, Empirical observations on the performance of the heuristic algorithm are reported.

Purpose: The ORDER-PICKING problem is important as one of the warehouse's logistics activities. This problem is defined as collecting ORDERs from different warehouse locations to respond to customers' ORDERs quickly. This paper aims to provide a multi-objective mathematical programming model for integrating the decisions of batching, routing, and scheduling of selectors with the packaging problem in a multi-warehouse environment. The objective functions include depreciation of the delivery times and total ORDER PICKING costs.Methodology: In this research, first, by reviewing the literature in the field of ORDER PICKING, the research gaps of the problem have been identified. Then, taking into account the main constraints of the problem, a multi-objective mathematical model has been formulated for the multi-warehouse ORDER-PICKING problem. The classic Benders decomposition algorithm and the accelerated Benders decomposition algorithm have been used to solve the problem. The data related to the warehouses of a company producing sanitary products in Iran was used as a case study to validate the applicability of the proposed model, and its results were reported in the article.Findings: The proposed model's results indicate that CPLEX can solve these problems up to small sizes in an acceptable time. Also, the numerical results show the performance of the Benders decomposition algorithm and the accelerated Benders algorithm as suitable alternatives for solving the model in large-sized problems. The calculation results obtained from the implementation of the solution methods for the proposed model showed that in terms of the number of iterations and the calculation time, the accelerated Benders algorithm had better results than the classic Benders algorithm.Originality/Value: In this research, the ORDER-PICKING problem with the integrity of operational decisions has been formulated as a multi-objective mathematical model for a multi-warehouse environment for the first time. Also, in this article regarding the solution method, exact solution approaches have been used for the first time considering the structure of the problem. The computation results show that the proposed algorithms are efficient and suitable methods for problem-solving.

This paper considers the integrated ORDER PICKING (joint ORDER batching and picker routing) and delivery problem in a manual picker-to-parts and multi-block 3D warehouse with considering overbooking and delivery-delay allowed strategies. Received ORDERs by the customers are grouped into the batches, assigned to the pickers with horizontal and vertical velocities to compute the travel time, picked up from the shelves of the warehouse, and delivered to customers’ community. The warehouse’s policy is to accept ORDERs for a certain number of unavailable products in addition to the available products. Thus, the concept of the overbooking strategy for supplying unavailable products and the delivery postponed strategy for delayed delivery is applied. Hence, this study introduces a novel mathematical model to deal with such a system, where the objective aims to minimize the cost of the completion time of all batches, the purchasing of the unavailable products and the return time of all vehicles to the depot. To solve this model, four new heuristic algorithms are devised, a broad range of numerical experiments is investigated to illustrate the validity and applicability of the proposed model and solution approaches.

Every company's success depends on the efficiency levels of the implemented processes, therefore the operation chain's value-adding activities are highlighted. It is relevant to the operations’ proper management for resource control, boost competitiveness, reduce costs, inspire staff, and improve customer satisfaction. This research aims to optimize operation management, internal logistic flows, and process times within an Ecuadorian hardware and construction material distribution center. The company's current problem is the long time for ORDER PICKING process, since standardized techniques that adapt the process to the operators’ needs are missing. To solve the problem, the Action Research (AR) methodology was proposed, along with tools such as the Traveling Salesman Problem (TSP) algorithm and programming languages (JAVA). First it was used the TSP algorithm to choose a practical path, so the average ORDER PICKING distance and logistical flows were reduced. Then, by establishing it as a standard for product relocation and emphasizing high-turnover items, it was suggested a sale-by-package output approach instead of a unit sale. From both methods, the company’s operations improved satisfactorily. As a result of using the TSP algorithm, 70, Stock Keeping Unit (SKU) were relocated, all based on their turnover. These SKUs accounted for 41. 65% of sales during the period analyzed. This relocation resulted in a 7. 57% reduction in the average travel distance per invoice, as well as a reduction in the workload for operators. The overall travel distance in the invoice pick-up operation was reduced to 47%, giving the possibility to comply with 65% of all ORDERs, due to the optimization of the movement within the warehouses and with the possibility to further minimize unnecessary travel, improving operational efficiency and reducing both time and costs associated with product collection.

Intelligent agents are considered as significant means towards realizing the semantic web vision. On the Semantic Web, integrating ontologies and rules enables software agents to interoperate between them, however, this leads to a problem, that no studies have focused on effective distributed reasoning for integrating ontologies and rules in multiple knowledge-bases. The methods that have been presented for distributed reasoning not only get a lot of times and memory, but also do not lead to a complete and sound reasoning. In this paper, to solve this problem, we present a distributed reasoning system that deals with the representation of the knowledge-base of ORDER sorted logic. This logic is able to describe the hierarchy of predicates and inheritance of expressions that there are in our natural language. To have a distributed reasoning, our proposed method uses the expansion of rigid and valid-non-rigid properties between knowledge-bases. Furthermore, with considering time and the situation of properties for reasoning, the non-rigid properties have not been ignored, in fact, in their valid time and situation, they are used. With this method, we achieve a complete reasoning and, moreover, the extracted knowledge is completely considered in the knowledge-bases and we have a distributed reasoning with high efficiency and sound without missing any information.

Warehouses are used in many factories from the moment of receiving raw materials to the moment of transmitting final products. In this paper, a bi-objective mathematical model is proposed for ORDER PICKING problem in warehouses and delivery of ORDERs to production/assembly lines. The first considered objective is to minimize the total cost of ORDER PICKING in warehouse, and the second objective is to minimize the average tardiness of delivering ORDERs to production/assembly lines. The proposed model is a mixed integer linear programming problem. Since the under study problem is proved in literature to be a NP-Hard problem, two multi-objective meta-heuristic algorithms are proposed entitled non-dominated sorting genetic algorithm (NSGA-II), and nondominated ranking genetic algorithm (NRGA). Since the optimality of solutions for meta-heuristic algorithms depends on the parameters of algorithms, the Taguchi method is utilized to tune the parameters of algorithms. Finally, computational results from solving different numerical examples with different sizes illustrate the performance of the proposed method.