Scheduling Modeling
Morteza Farhadi Sartangi; Ali Husseinzadeh Kashan; Hassan Haleh; Abolfazl Kazemi
Abstract
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 ...
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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 problemMethodology: 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.
meta-heuristic algorithms
Ehsan Aghdaee; Ali Husseinzadeh Kashan
Abstract
In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of ...
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In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of the project has been used. However, using this method to valuate project time performance by utilizing the time performance index (SPI) by researchers and practitioners has been faced with serious criticism. Therefore, the present study proposes a framework for assessment and prediction of the temporal performance of each of the thread activities in project management. In this framework, using the multi objective league championship algorithm (MOLCA), the initial plan of the projects is optimized and then via using the Kalman Filter prediction method, project execution planning is done such that the projects in conditions of uncertainty could be forecasted and ahead horizon being demonstrated accurately with the least error for project managers. In this paper, in order to ensure the quality of the solutions, the output of the algorithm is compared with genetic algorithms (NSGII) and particle swarm optimization (MOPSO), where results demonstrate the superiority of the proposed algorithm.