scholarly journals Application of nature inspired algorithms for multi-objective inventory control scenarios

2021 ◽  
pp. 91-114 ◽  
Author(s):  
Ferdous Sarwar ◽  
Mushaer Ahmed ◽  
Mahjabin Rahman
Keyword(s):  
Inventory Control ◽  
Heuristic Algorithm ◽  
Performance Metrics ◽  
Bat Algorithm ◽  
Raw Material ◽  
Test Problems ◽  
Multi Objective ◽  
Work In Process ◽  
Multi Objective Programming ◽  
Demand Rate

An inventory control system having multiple items in stock is developed in this paper to optimize total cost of inventory and space requirement. Inventory modeling for both the raw material storage and work in process (WIP) is designed considering independent demand rate of items and no volume discount. To make the model environmentally aware, the equivalent carbon emission cost is also incorporated as a cost function in the formulation. The purpose of this study is to minimize the cost of inventories and minimize the storage space needed. The inventory models are shown here as a multi-objective programming problem with a few nonlinear constraints which has been solved by proposing a meta-heuristic algorithm called multi-objective particle swarm optimization (MOPSO). A further meta-heuristic algorithm called multi-objective bat algorithm (MOBA) is used to determine the efficacy of the result obtained from MOPSO. Taguchi method is followed to tune necessary response variables and compare both algorithm's output. At the end, several test problems are generated to evaluate the performances of both algorithms in terms of six performance metrics and analyze them statistically and graphically.

Multi-Objective Cuckoo Search Under Multiple Archiving Strategies

2016 ◽  
Vol 15 (04) ◽  
pp. 1650020 ◽  
Author(s):  
Kamel Zeltni ◽  
Souham Meshoul
Keyword(s):  
Performance Metrics ◽  
Cuckoo Search ◽  
Nonparametric Test ◽  
Critical Issue ◽  
Test Problems ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Bounded Set ◽  
Recent Addition

Cuckoo Search (CS) is a recent addition to the field of swarm-based metaheuristics. It has been shown to be an efficient approach for global optimization. Moreover, its application for solving Multi-objective Optimization (MOO) shows very promising results as well. In multi-objective context, a bounded archive is required to store the set of nondominated solutions. But, what is the best archiving strategy to use in order to maintain a bounded set with good characteristics is a critical issue that may lead to a questionable choice. In this work, the behavior of the developed multi-objective CS is studied under several archiving strategies. An extensive experimental study has been conducted using several test problems and two performance metrics related to convergence and diversity. A nonparametric test for statistical analysis is performed. In addition, we used a Multi-Objective Particle Swarm Optimization (MOPSO) for further analysis and comparison. The results revealed that archiving strategies play an important role as they can impact differently on the quality of obtained fronts depending on the problem’s characteristics. Also, this study confirms that the proposed MOCS algorithm is a very promising approach for MOPs compared to the widely used MOPSO.

scholarly journals Multi-objective multi-factory scheduling

2020 ◽  
Author(s):  
J. Behnamian ◽  
S.M.T. Fatemi Ghomi
Keyword(s):  
Heuristic Algorithm ◽  
Parallel Machines ◽  
Mixed Integer ◽  
Test Problems ◽  
Scheduling Problem ◽  
Production Network ◽  
Mixed Integer Linear Program ◽  
Multi Objective ◽  
Transportation Times ◽  
Factory Scheduling

This paper introducesa multi-factory scheduling problem with heterogeneous factories and parallel machines. This problem, as a major part of supply chain planning, includes the finding of suitable factory for each job and the scheduling of the assigned jobs at each factory, simultaneously. For the first time, this paper studies multi-objective scheduling in the production network in which each factory has its customers and demands can be satisfied by itself or other factories. In other words, this paper assumes that jobs can transfer from the overloaded machine in the origin factory to the factory which has fewer workloads by imposing some transportation times. For simultaneous minimization of the sum of the earliness and tardiness of jobs and total completion time, after modeling the scheduling problem as a mixed-integer linear program, the existing multi-objective techniques are analyzed and a new one is applied to our problem. Since this problem is NP-hard, a heuristic algorithm is also proposed to generate a set of Pareto optimal solutions. Also, the algorithms are proposed to improve and cover the Pareto front. Computational experiences of the heuristic algorithm and the output of the model implemented by CPLEX over a set of randomly generated test problems are reported.

scholarly journals Optimized computational techniques-based inventory control process in supply chain management

2021 ◽  
Vol 12 (6) ◽  
pp. 374-382
Author(s):  
Hiba A. Tarish
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Inventory Control ◽  
Performance Metrics ◽  
Control Process ◽  
Raw Material ◽  
Computational Techniques ◽  
Management Process ◽  
Business Services ◽  
Chain Management

Now-a-days, supply chain management is one of the crucial factors in business for identifying the flow of goods, raw material storage, movement of products and inventory process so on. The main aim of the supply chain management process is to provide the valuable business services, infrastructure, logistic control and inventory control to the business for improving the business. Among the various supply chain factors, inventory control is one of the difficult tasks in supply chain management process because of high cost inventory, consistent stockouts, low rate of inventory turnover, high value of obsolete inventory, maximum working capital, huge cost for storage and lot customers. Then, several supply chain management techniques are introduced in traditional to control the inventory process but they are failing to detect with greatest accuracy due to the low convergence rate in computation techniques. So, in this paper introduce the effective and optimized neural network computational approach for managing the inventory control. In addition to this, the system ability to select the product based on the few factors that improve the inventory control system accuracy. Then the excellence of the system is evaluated using experimental analysis and respective performance metrics.

scholarly journals A new balanced direction for multi-objective evolutionary algorithms

2016 ◽  
Author(s):  
Nguyen Long ◽  
Nguyen Xuan Hung ◽  
Nguyen Thi Hien ◽  
Bui Thu Lam
Keyword(s):  
Evolutionary Algorithms ◽  
Performance Metrics ◽  
Test Problems ◽  
Evolutionary Processes ◽  
Multi Objective

This paper suggests to use a new improvement direction for multi-objective evolutionaryalgorithms. In DMEA-II, two improvement directions(convergence and spread) are used for the guidanceduring evolutionary processes. Based on those directions and the balance between exploration andexploitation, we determined a new improvement direction to keep DMEA-II to be better on the balanceof convergence and diversity.To validate the performance of the new improvedversion of DMEA-II, we carried out a case studyon several test problems and comparison with wellknown MOEAs, it obtained quite good results onprimary performance metrics, namely the generationdistance, inverse generation distance and hypervolume. Our analysis on the results indicates that,the usage of proposed direction may make DMEAII to be improved in keeping balanced betweenconvergence and diversity at each generation duringthe search

Multi-Objective Model and Heuristic Algorithm for Billet Stacking Problem in Steel Production

2014 ◽  
Vol 889-890 ◽  
pp. 606-611
Author(s):  
Can Tao Shi ◽  
Guang Jing Dong ◽  
Yi Wei Ma
Keyword(s):  
Heuristic Algorithm ◽  
Calculation Method ◽  
Rolling Mill ◽  
Clustering Algorithm ◽  
Programming Model ◽  
Steel Production ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Model ◽  
Best Fit

Aiming at billet stacking problem of the rolling mill, a multi-objective programming model is established. Billets on the roller are batched by clustering algorithm and then assigned to the warehouse stack positions by improved Clustering-based Best Fit algorithm (CBF). The utilization of non-empty warehouse stack positions and shuffles are considered in the algorithm. Compared to existing artificial calculation method, the proposed algorithm can not only reduce the shuffles significantly, but also improve the space utilization of warehouse stacking positions.

scholarly journals Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3956
Author(s):  
Khaled Guerraiche ◽  
Latifa Dekhici ◽  
Eric Chatelet ◽  
Abdelkader Zeblah
Keyword(s):  
Power Systems ◽  
Electrical Power ◽  
Bat Algorithm ◽  
Moment Generating Function ◽  
Electrical Power System ◽  
System Availability ◽  
Reliability Indices ◽  
Multi Objective ◽  
Investment Cost ◽  
Modified Bat Algorithm

The design of energy systems is very important in order to reduce operating costs and guarantee the reliability of a system. This paper proposes a new algorithm to solve the design problem of optimal multi-objective redundancy of series-parallel power systems. The chosen algorithm is based on the hybridization of two metaheuristics, which are the bat algorithm (BA) and the generalized evolutionary walk algorithm (GEWA), also called BAG (bat algorithm with generalized flight). The approach is combined with the Ushakov method, the universal moment generating function (UMGF), to evaluate the reliability of the multi-state series-parallel system. The multi-objective design aims to minimize the design cost, and to maximize the reliability and the performance of the electric power generation system from solar and gas generators by taking into account the reliability indices. Power subsystem devices are labeled according to their reliabilities, costs and performances. Reliability hangs on an operational system, and implies likewise satisfying customer demand, so it depends on the amassed batch curve. Two different design allocation problems, commonly found in power systems planning, are solved to show the performance of the algorithm. The first is a bi-objective formulation that corresponds to the minimization of system investment cost and maximization of system availability. In the second, the multi-objective formulation seeks to maximize system availability, minimize system investment cost, and maximize the capacity of the system.

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