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.

Companies are looking for e ective strategies to improve warehouse performance quality due to customers dissatisfaction of service. The Order picking process is one of the main warehouse management strategies. As the inventory of stored items and the number of Orders increased, the picking process and response time became more important. E ective coordination between Order batching and Order picking process is essential to improve the e ciency of the warehouse management system. In this paper, a novel MixedInteger Nonlinear Programming (MINLP) model for on-line Order batching is proposed for improving the warehouse performance, which in turn results in the reduction of the response and idle times. The proposed method takes aim at the investigation of Order classi cation for the  rst time in the picker-to-part system as a manual picking system and an online Order batching system, with the intent of minimizing the turnover time and idle time. Besides, an Order batching model in a blocked warehouse using a zoning system is proposed which is called Online Order batching in Blocked Warehouse with One Picker for each Block (OOBBWOPB). The mentioned model is solved by two algorithms: Arti cial BeeColony (ABC) algorithm and Ant-Colony (ACO) algorithm. Two numerical case studies are de ned and analyzed using MATLAB software. According to the results compared with the results of Zhang et al. (2017) the proposed model shows better performance and the average customer Order response time is signi cantly reduced (2017) and the ACO yields better results than ABC.

In this paper the problem of serial batch scheduling in a two-stage hybrid flow shop environment with minimizing Makesapn is studied. In serial batching it is assumed that jobs in a batch are processed serially, and their completion time is defined to be equal to the finishing time of the last job in the batch. The analysis and implementation of the prohibited transference of jobs among the machines of stage one in serial batch is the main contribution of this study. Machine set-up and ready time for all jobs are assumed to be zero and no Preemption is allowed. Machines may not breakdown but at times they may be idle. As the problem is NP-hard, a genetic algorithm is developed to give near optimal solutions. Since this problem has not been studied previously, therefore, a lower bound is developed for evaluating the performance of the proposed GA. Many test problems have been solved using GA and results compared with lower bound. Results showed GA can obtain a near optimal solution for small, median and large size problems in reasonable time.

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.

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.

This paper tackles the problem of batching scheduling on a multi-operational parallel machine with precedence constraint. Based on the literature, batching accelerates the production process and decreases the transportation costs. Therefore, it is mentioned as a very important subject in scheduling. On the other hand, most of researches consider the processing time as a predefined and constant parameter that is not reasonable in real industries.In this paper a mathematical model is presented in Order to minimize the makespan in which the processing times of jobs are considered as a function of their batch size. The proposed model is known to be NP-hard, therefore two Mehta-heuristic algorithms including genetic algorithm (GA) and simulated annealing (SA) are offered so as to find near optimal solutions in reasonable run time. In Order to check the verification of proposed model, the problem is solved optimally for small scales and the results are compared with the solutions of GA and SA. Computational study demonstrates that the proposed methods can found the solutions with very low gap in a suitable run time.

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.

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. ‎

Background: The importance of clean air in industrial workplaces is well known. Cement dust, due to its silica content, is very dangerous. The aims of this study were to assess and control the release of cement dust from silos of concrete batching units during charging and production processes in a dam project.Methods: In this study, dust particule mass released from the cement silos were investigated by performing environmental sampling and personal sampling from 15 operators of batching machine. Samples were analyzed by gravimetric method. Control methods were used to control the released dust. Data collected before and after the intervention were analyzed through SPSS19 and using paired t-test and one sample t- test.Results: Mean concentrations of dust particles masses in personal and environmental samples were respectively 15.56 and 30.77mg/m3 and both were higher than the standard exposure limit values in Iran. After implementation of dust control methods, mean concentrations of dust in personal and environmental samples decreased respectively to 4.1 and 5.2 mg/m3 that show the efficiency of control methods.Conclusion: The control method applied in the present study is a novel and cost-effective method to control the released particles from batching units of cement silos. It is possible to decrease the dust concentration in industrial workplaces to the standard levels, by performing simiar methods.