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.

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.

Multi-Depot Vehicle Routing Problem with Hybrid Genetic Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 555-560 ◽  
Author(s):  
Li Wei Dang ◽  
Xiao Ming Sun
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Genetic Algorithm ◽  
Routing Problem ◽  
Transport Distance ◽  
Delivery Costs ◽  
Two Factors

About the multi-depot vehicle routing problem, considering the transport distance and the number of dispatching vehicles together can effectively reduce the total delivery costs. Firstly establish the corresponding mathematical model by taking the two factors into account. Secondly solve the model by using hybrid genetic algorithms. Thirdly demonstrate the effectiveness of the model and algorithm by an example

Social Structure Discovery Using Genetic Algorithm

2017 ◽  
Vol 8 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Saeed Nasehi Moghaddam ◽  
Mehdi Ghazanfari ◽  
Babak Teimourpour
Keyword(s):  
Genetic Algorithm ◽  
Social Network ◽  
Search Procedure ◽  
Quality Of Results ◽  
Vector Fitting ◽  
Essential Components ◽  
Local Search Procedure ◽  
Multiple Samples ◽  
Interval Valued

As a way of simplifying, size reducing and making the structure of each social network be comprehensible, blockmodeling consists of two major, essential components: partitioning of actors to equivalent classes, called positions, and clarifying relations between and within positions. While actor partitioning in conventional blockmodeling is performed by several equivalence definitions, generalized blockmodeling, searches, locally, the best partition vector that best satisfies a predetermined structure. The need for known predefined structure and using a local search procedure, makes generalized blockmodeling be restricted. In this paper, the authors formulate blockmodel problem and employ a genetic algorithm for to search for the best partition vector fitting into original relational data in terms of the known indices. In addition, during multiple samples and situations such as dichotomous, signed, ordinal and interval valued, and multiple relations, the quality of results shows better fitness than classic and generalized blockmodeling.

Applications of JGA to Operations Management and Vehicle Routing

2007 ◽  
pp. 625-641 ◽  
Author(s):  
A. L. Medaglia
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Operations Management ◽  
Lot Sizing ◽  
Routing Problem ◽  
Operations Scheduling ◽  
Solution Methods ◽  
Capacitated Lot Sizing ◽  
Lot Sizing And Scheduling ◽  
Complex Activities

Two of the most complex activities in production and operations management (POM) are inventory planning and operations scheduling. This chapter presents two problems related to these activities, namely, the capacitated lot-sizing and scheduling problem and the capacitated vehicle routing problem. For each of these problems, the authors discuss several solution methods, present a competitive genetic algorithm, and describe its implementation in the Java Genetic Algorithm (JGA) framework. The purpose of this chapter is to illustrate how to use JGA to model and solve complex business problems arising in POM. The authors show that JGA-based solutions are quite competitive and easier to implement than widely used methods found in the literature.

scholarly journals Modeling Multi-Plant Capacitated Lot Sizing Problem with Interplant Transfer

2021 ◽  
Vol 6 (3) ◽  
pp. 961-974
Author(s):  
Amitkumar Patil ◽  
Gaurav Kumar Badhotiya ◽  
Bimal Nepal ◽  
Gunjan Soni
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Lot Sizing ◽  
Computation Time ◽  
Lot Sizing Problem ◽  
Hard Problems ◽  
Capacitated Lot Sizing ◽  
Tactical Decisions ◽  
Assembly Operations ◽  
Level Production

Lot sizing models involve operational and tactical decisions. These decisions may entail multi-level production processes such as assembly operations with multiple plants and limited capacities. Lot sizing problems are widely recognized as NP-hard problems therefore difficult to solve, especially the ones with multiple plants and capacity constraints. The level of complexity rises to an even higher level when there is an interplant transfer between the plants. This paper presents a Genetic Algorithm (GA) based solution methodology applied to large scale multi-plant capacitated lot sizing problem with interplant transfer (MPCLSP-IT). Although the GA has been a very effective and widely accepted meta-heuristic approach used to solve large scale complex problems, it has not been employed for MPCLSP-IT problem. This paper solves the MPCLSP-IT problem in large scale instances by using a genetic algorithm, and in doing so successfully obtains a better solution in terms of computation time when compared to the results obtained by the other methods such as Lagrangian relaxation, greedy randomized adaptive search procedure (GRASP) heuristics, and GRASP-path relinking techniques used in extant literature.

scholarly journals A New Improved Quantum Evolution Algorithm with Local Search Procedure for Capacitated Vehicle Routing Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Ligang Cui ◽  
Lin Wang ◽  
Jie Deng ◽  
Jinlong Zhang
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Quantum Evolution ◽  
Search Procedure ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Local Search Procedure ◽  
Evolution Algorithm ◽  
Capacitated Vehicle

The capacitated vehicle routing problem (CVRP) is the most classical vehicle routing problem (VRP); many solution techniques are proposed to find its better answer. In this paper, a new improved quantum evolution algorithm (IQEA) with a mixed local search procedure is proposed for solving CVRPs. First, an IQEA with a double chain quantum chromosome, new quantum rotation schemes, and self-adaptive quantum Not gate is constructed to initialize and generate feasible solutions. Then, to further strengthen IQEA's searching ability, three local search procedures 1-1 exchange, 1-0 exchange, and 2-OPT, are adopted. Experiments on a small case have been conducted to analyze the sensitivity of main parameters and compare the performances of the IQEA with different local search strategies. Together with results from the testing of CVRP benchmarks, the superiorities of the proposed algorithm over the PSO, SR-1, and SR-2 have been demonstrated. At last, a profound analysis of the experimental results is presented and some suggestions on future researches are given.

Sign in / Sign up

Export Citation Format

Share Document