scholarly journals Population Based Metaheuristic Algorithm Approach for Analysis of Multi-Item Multi-Period Procurement Lot Sizing Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Prasanna Kumar ◽  
Mervin Herbert ◽  
Srikanth Rao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Inventory Management ◽  
Lot Sizing ◽  
Population Based ◽  
Optimization Approach ◽  
Time Data ◽  
Significant Difference ◽  
Lot Sizing Problem ◽  
Order Quantities

This research study focuses on the optimization of multi-item multi-period procurement lot sizing problem for inventory management. Mathematical model is developed which considers different practical constraints like storage space and budget. The aim is to find optimum order quantities of the product so that total cost of inventory is minimized. The NP-hard mathematical model is solved by adopting a novel ant colony optimization approach. Due to lack of benchmark method specified in the literature to assess the performance of the above approach, another metaheuristic based program of genetic algorithm is also employed to solve the problem. The parameters of genetic algorithm model are calibrated using Taguchi method of experiments. The performance of both algorithms is compared using ANOVA analysis with the real time data collected from a valve manufacturing company. It is verified that two methods have not shown any significant difference as far as objective function value is considered. But genetic algorithm is far better than the ACO method when compared on the basis of CPU execution time.

scholarly journals Optimization of a Two-Echelon Location Lot-Sizing Routing Problem with Deterministic Demand

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Laila Kechmane ◽  
Benayad Nsiri ◽  
Azeddine Baalal
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Inventory Management ◽  
Lot Sizing ◽  
Search Procedure ◽  
Strategic Decisions ◽  
Routing Problem ◽  
Deterministic Demand ◽  
Local Search Procedure ◽  
Tactical Decisions

This paper aims to solve a multiperiod location lot-sizing routing problem with deterministic demand in a two-echelon network composed of a single factory, a set of potential depots, and a set of customers. Solving this problem involves making strategic decisions such as location of depots as well as operational and tactical decisions which include customers’ assignment to the open depots, vehicle routing organization, and inventory management. A mathematical model is presented to describe the problem and a genetic algorithm combined with a local search procedure is proposed to solve it and is tested over three sets of instances.

Designing an uncertain bi-objective green leagile capacitated lot sizing problem considering FM/M/C queue system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Masoud Rabbani ◽  
Soroush Aghamohamadi Bosjin ◽  
Neda Manavizadeh ◽  
Hamed Farrokhi-Asl
Keyword(s):  
Mathematical Model ◽  
Solution Method ◽  
Lot Sizing ◽  
Test Problems ◽  
Content Type ◽  
Queue System ◽  
Proposed Model ◽  
Lot Sizing Problem ◽  
Capacitated Lot Sizing ◽  
To Receive

Purpose This paper aims to present a novel bi-objective mathematical model for a production-inventory system under uncertainty. Design/methodology/approach This paper addresses agile and lean manufacturing concepts alongside with green production methods to design an integrated capacitated lot sizing problem (CLSP). From a methodological perspective, the problem is solved in three phases. In the first step, an FM/M/C queuing system is used to minimize the number of customers waited to receive their orders. In the second step, an effective approach is applied to deal with the fuzzy bi-objective model and finally, a hybrid metaheuristic algorithm is used to solve the problem. Findings Some numerical test problems and sensitivity analyzes are conducted to measure the efficiency of the proposed model and the solution method. The results validate the model and the performance of the solution method compared to Gams results in small size test problems and prove the superiority of the hybrid algorithm in comparison with the other well-known metaheuristic algorithms in large size test problems. Originality/value This paper presents a novel bi-objective mathematical model for a CLSP under uncertainty. The proposed model is conducted on a practical case and several sensitivity analysis are conducted to assess the behavior of the model. Using a queue system, this problem aims to reduce the items waited in the queue to receive service. Two objective functions are considered to maximize the profit and minimize the negative environmental effects. In this regard, the second objective function aims to reduce the amount of emitted carbon.

A hybrid invasive weed optimization for an imperfect, two-warehouse, lot-sizing problem

2020 ◽  
Vol 15 (4) ◽  
pp. 1363-1387
Author(s):  
Mohammad Saeid Atabaki ◽  
Seyed Hamid Reza Pasandideh ◽  
Mohammad Mohammadi
Keyword(s):  
Inventory Management ◽  
Programming Model ◽  
Lot Sizing ◽  
Unconstrained Minimization ◽  
Computational Time ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Content Type ◽  
Imperfect Quality ◽  
Lot Sizing Problem

Purpose Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs. Design/methodology/approach The problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO. Findings Taking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible. Originality/value Developing a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.

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