Priority-based Genetic Algorithm Research Articles

Solving non-linear fixed-charge transportation problems using nature inspired non-linear particle swarm optimization algorithm

The main objective of this study is to formulate and solve the model of non-linear fixed-charge transportation problem (NFCTP), which is one of the NP-hard problems. This type of problem is challenging to solve using conventional methods because these methods are usually inefficient, costly to compute, and may stuck at the local optimum solution. To overcome this issue, a non-linear particle swarm optimization (NPSO) algorithm with new non-linear acceleration parameters is proposed in this study. Also, to preserve the feasibility condition (non-negative integer solution) of the transportation problem, two novel negative repair and fraction repair strategies have been incorporated into the proposed NPSO. The efficiency of the NPSO algorithm is tested on both the small-scale and large-scale NFCTPs. The dataset are considered from the existing studies. The obtained results are compared with those obtained by the spanning tree-based genetic algorithm (st-GA), priority-based genetic algorithm (pb-GA), and minimum cost flow-based genetic algorithm. The comparative study reveals that for all the considered problems, the proposed NPSO provides better feasible solutions in lesser computational time. Furthermore, the effectiveness of the NPSO algorithm is shown by comparing it to other seven existing variants of PSO and is found to outperformed these variants for the small scale as well as large scale NFCTPs.

A sustainable and resilient supply chain (RS-SCM) by using synchronisation and load-sharing approach: application in the oil and gas refinery

Nowadays sustainable and resilient supply chain management (R&S-SCM) is an interesting and in the meantime vital problem that has grabbed the attention of many researchers. This study is an effort to investigate the simultaneous design of sustainable and resilient flow in the supply chain. To achieve a sustainable and resilient supply chain, there is a wide variety of strategies depending on their impacts on different industries and the features of their supply chain. The fuzzy QFD method is designed to select the most appropriate strategy in the study. Two approaches are used for this purpose. Early planning in sustainable mode is formulated assuming that all members of the network are healthy and final planning in the reactive approach to destruction which formulates the return to the original state in the shortest possible time, cost and scenario-based status. A hybrid priority-based Genetic Algorithm (pb-GA) and simulated annealing algorithm (SA) is developed in two phases to find the optimal solutions. Response Surface Methodology (RSM) method is used to adjust significant parameters for the algorithm. Several test problems are generated showing that the proposed metaheuristic algorithm can find good solutions in reasonable time spans.

Probabilistic tree-based representation for solving minimum cost integer flow problems with nonlinear non-convex cost functions

The minimum cost flow problem (MCFP) is the most generic variation of the network flow problem which aims to transfer a commodity throughout the network to satisfy demands. The problem size (in terms of the number of nodes and arcs) and the shape of the cost function are the most critical factors when considering MCFPs. Existing mathematical programming techniques often assume the cost functions to be linear or convex. Unfortunately, the linearity and convexity assumptions are too restrictive for modelling many real-world scenarios. In addition, many real-world MCFPs are large-scale, with networks having a large number of nodes and arcs. In this paper, we propose a probabilistic tree-based genetic algorithm (PTbGA) for solving large-scale minimum cost integer flow problems with nonlinear non-convex cost functions. We first compare this probabilistic tree-based representation scheme with the priority-based representation scheme, which is the most commonly-used representation for solving MCFPs. We then compare the performance of PTbGA with that of the priority-based genetic algorithm (PrGA), and two state-of-the-art mathematical solvers on a set of MCFP instances. Our experimental results demonstrate the superiority and efficiency of PTbGA in dealing with large-sized MCFPs, as compared to the PrGA method and the mathematical solvers.

A hybrid priority-based genetic algorithm for simultaneous pickup and delivery problems in reverse logistics with time windows and multiple decision-makers

This article puts forward a hybrid priority-based nested genetic algorithm with fuzzy logic controller and fuzzy random simulation (hpn-GA with FLC–FRS) for solving a variant of the vehicle routing problem. To meet all the complex restrictions contained in practical reverse logistics, a new mathematical model is developed for simultaneous pickup and delivery problems with time windows and multiple decision-makers (SPDTW–MDM). Then, a hpn-GA with FLC–FRS is proposed, where the priority-based initializing method makes the initializing more applicable, a nested procedure structure handles multiple decision-makers, a fuzzy logic controller helps adjust the mutation rate, and a fuzzy random simulation is used to deal with uncertainties. Finally, in the case study, GA parameters are tuned by Taguchi method and result analyses are presented to highlight the performance of the optimization method for the SPDTW–MDM, while algorithm comparisons by instance applications in different scales show its efficiency and effectiveness.

Recent advances in hybrid priority-based genetic algorithms for logistics and SCM network design

Abstract Design and optimization of logistics and supply chain management (SCM) network is very important issue, which plans, implements and controls the efficient, effective forward and reverse flow and storage of goods, services and related information between the point of origin and the point of consumption to meet customers’ requirements. A multistage based logistics or SCM network can be modeled by means of a sequence of stages, each consisting of a set of functions, i.e. the existing or potential facilities located in several countries or regions of the world where goods are transformed or manufactured or stocked and delivered: suppliers, plants, distribution centers (DCs) and customers. The structure of each stage of the supply chain is completed by a set of arcs connected to the nodes and representing the flow of goods: each arc has a weight proportional to the cost incurred in between the two nodes it connects together. In this paper, we survey a recent advance on hybrid priority-based genetic algorithms for solving a multistage logistics or SCM network problems. In particular, the following multistage based logistics or SCM network models will be introduced: (1) the sugarcane SCM network model, (2) multiobjective supply chain network model, (3) flexible multistage logistics network model, and (4) multiobjective reverse logistics network model. In each model, we summarize the background, mathematical model, hybrid priority-based genetic algorithm and numerical experiment. We also enhance a hybrid genetic algorithm (HGA) by combining a local search (LS) technique and tuning GA parameters by a fuzzy logic control (FLC) to fast the search ability of GA. Finally, numerical experiment of each case study is carried out to show the effectiveness of the proposed approach by the hybrid priority-based genetic algorithms.

A new multi-objective/product green supply chain considering quality level reprocessing cost

Green supply chain is among the hottest recent research subjects in supply chain management which not only optimises the costs and service levels during a time period all over the chain, but also considers effect of emission of CO2 greenhouse gas on the overall value of supply chain for sustainable development. Therefore, in this paper, a bi-objective, multi-stage, multi-product quadratic optimisation model is proposed by taking into account the quality level of the purchased materials plus their reprocessing extra costs to a system in stochastic mode and the environmental costs of CO2 emission. Here we also consider both straight and step by step transportation in supply chain network design. Since the problem is NP-hard, a priority-based genetic algorithm model is proposed. In this paper, three methods including BOM, LP-metric and elastic BOM are applied to solve small data, whereas, elastic BOM offers larger solution space and more justified solutions. Hence, for solving large data, three priority-based genetic algorithms corresponding to MODM techniques are utilised. Then, by using TOPSIS for small and ANOVA for medium and large problems, the optimum procedure for balancing the existing costs and CO2 emission in the chain is selected.

Optimizing Terminal Delivery of Perishable Products considering Customer Satisfaction

Freshness of products and timeliness of delivery are two critical factors which have impact on customer satisfaction in terminal delivery of perishable products. This paper investigates how to make a cost-saving vehicle scheduling for perishable products by maximizing customer satisfaction. Customer satisfaction is defined from the two aspects of freshness and time window. Then we develop a priority function based on customer satisfaction and use the hierarchical clustering method to identify customer service priority. Based on the priority, a multiobjective vehicle scheduling optimization model for perishable products is formulated to maximize customer satisfaction and minimize total delivery costs. To solve the proposed model, a priority-based genetic algorithm (PB-GA) is designed. Numerical experiments and sensitivity analysis are performed to show the validity and advantage of our approach. Results indicate that PB-GA can achieve better solutions than traditional genetic algorithm. The improvement of customer satisfaction is higher than the decrease rate of total costs within a certain shelf life range, which reveals that the proposed method is applicable to the terminal delivery of perishable products.

New Strategy to Optimize Lean Supply Chain Design by Meta-Heuristic

This paper aims at presenting one novel quantitative strategy of optimizing the design of Lean supply chain using Meta-Heuristics. While classifying Lean Manufacturing tools in two categories, namely Functional and Tier Lean tools, we propose a new framework to design the Lean supply chain by implementing the former into a 5-echelon Fat supply chain. As the following step, we investigate the effect of the latter on the mentioned Lean supply chain model. Then, we utilize the tight correlation of Tier Lean tools and priority-based Genetic Algorithm Meta-Heuristics in order to optimize the configuration of the Lean supply chain. Finally, these ideas are illustrated step by step in one numeral example.

Optimization and Simulation of Resource Constrained Scheduling Problem Using Genetic Algorithm

Due to the development of management idea and the scarcity of some resources, the lean management has become the necessary request to implement effective control of resource constrained project. Resource constrained project scheduling is the significant guarantee to attain the lean management. The resource constrained project scheduling problem (RCPSP), with the objective of minimizing project duration and with the precedence relations described by an activity-on-arrow (AOA) network, is formulated as a combination optimization problem and solved using the priority-based genetic algorithm (GA). The activity priorities are represented by chromosome and serial scheduling scheme (SSS) and parallel scheduling scheme (PSS) are developed and utilized to transform chromosome-represented priorities to an active schedule subject to the logic and resource constraints so that project duration corresponding to each chromosome can be evaluated. The overall framework of the GA for the RCPSP is developed and the basic components of the algorithm are designed. Simulation is provided so as to investigate the performance of the priority-based GA with SSS and PSS as decoding method, respectively. The optimal solution to a small-sized resource constrained benchmark instance is scheduled to find the shortest project duration. Comparative simulation results demonstrate not only the effectiveness and efficiency of GA with SSS or PSS as decoding methods in solution to RCPSP with precedence relation of activities diagramed as an AOA network but also the effect of different evolution parameter settings on solution quality of the problem.

A priority-based genetic algorithm for a flexible job shop scheduling problem

This research is partially supported by LATNA Laboratory, NRU HSE, RF government grant, ag. 11.G34.31.0057.

Optimization of Closed-Loop Supply Chain Problem for Calculation Logistics Cost Accounting

This paper aims to build closed loop supply chain model (CLSCM) and propose a multi-objective genetic algorithm. This paper designs the method of calculation for a solution using optimization algorithms with the priority-based genetic algorithm (priGA), and Adaptive Weight Approach (AWA).In this paper, we present a multi-objective closed loop supply chain model in integrated logistics system. We formulated a mathematical model with two objectives functions: (1) minimize transportation cost, open cost, inventory cost, purchase cost, disposal cost and saving cost of integrated facilities of CLSCM, (2) minimize the delivery time tardiness in all periods. Finally, a simulation is investigated to demonstrate the applicability of the proposed multi-objective closed loop supply chain model (CLSCM) and solution approaches.

A Straight Priority-Based Genetic Algorithm for a Logistics Network

Closed-loop logistics (forward and reverse logistics) has re- ceived increased attention of late due to customer expectations, greater environmental concerns, and economic aspects. Unlike previous works, which consider single products or single periods in multi-objective func- tion problems, this paper considers a multi-product multi-period closed- loop logistics network with regard to facility expansion as a facility location-allocation problem, which is closer to real-world scenarios. A multi-objective mixed integer nonlinear programming formulation is developed to minimize the total cost, the product delivery time, and the used product collection time. The model is linearized by defining new variables and adding new constraints to the model. Then, to solve the model, a priority-based genetic algorithm is proposed that uses straight encoding and decoding methods. To assess the performance of the above algorithm, its final solutions and CPU times are compared to those generated by an initial priority-based genetic algorithm from the recent literature and the lower bound obtained by CPLEX. The numerical results show that the straight priority-based genetic algo- rithm outperforms the initial priority-based genetic algorithm at least in terms of obtaining a reasonable quality of final solutions for closed- loop logistics problems.

순환형 공급체인 환경에서 시설 통합에 의한 물류원가 절감 및 재고관리시스템 모델구축에 관한 연구

순방향물류와 역방향물류의 두 물류 체인을 통합해서 자원 순환형 공급체인 (CLSC: closed-loop supply chain) 을 구성하는 것은 기업의 비용절감을 위해서 반드시 필요한 요소이다. 본 연구에서는 순환형 공급체인의 환경에서 순방향물류의 도매점과 역방향물류의 처리센터의 재고를 통합하는 재고관리를 제안한다. JIT(Just-in-Time)배송이 고려된 새로운 CLSC 재고관리모델은, 회수센터에서 처리센터로 배송된 사용이 끝난 제품이 도매업자의 수요에 미치지 못할 때 제조사에게 필요한 제품을 재발주 할 것이지 사용이 끝난 제품의 회수를 기다릴 것이지를 선택함으로써 비용을 절감한다. 제안 모델의 유효성을 검증하기 위하여 최적화 기법중 하나인 유전자 알고리즘(Genetic Algorithm: GA)을 이용하였다. 파라미터가 해에 미치는 영향을 알아보기 위해서 세 가지 파라미터 조건에서 우선 순위형 GA (priority-based GA: priGA)와, 각 세대마다 파라미터가 조정되는 개량형 하이브리드 GA (modified Hybrid Genetic Algorithm: mhGA)를 사이즈가 다른 4가지 수치 예에 적용하여 시뮬레이션을 실시하였다. It is an element certainly required for the cost reduction of a company that forward and reverse logistics chain are unified and constitutes a resource closed-loop supply chain (CLSC). In this study, the inventory control which unifies inventory of distribution centers (DCs) of forward logistics and processing center of reverse logistics in the CLSC environment is proposed. The inventory system model for newly-constructed CLSC considers the JIT(Just-In-Time) delivery from the processing center to the manufacturer, including the making of decisions on whether to wait for the arrival of end-of-life products or to back-order necessary products for manufacturer when the supply of end-of-life products at the processing center via the returning center is insufficient for the demands of the manufacturers. The validity of the proposed model was verified using the genetic algorithm (GA). In order that a parameter might investigate the effect which it has on a solution, the simulation was carried out for priGA(priority-based GA) on three kinds of parameter conditions. Moreover, mhGA(modified hybrid GA) to which a parameter is adjusted for every Study on Reducing Logistics Costs and Inventory Control System according to facilities integration in the Closed-Loop Supply Chain Environment generation, the simulation was carried out to a four-kind numerical example.

A fuzzy-random programming for integrated closed-loop logistics network design by using priority-based genetic algorithm

A B S T R A C T Recovery of used products has steadily become interesting issue for research due to economic reasons and growing environmental or legislative concern. This paper presents a closed-loop logistics network design based on reverse logistics models. A mixed integer linear programming model is implemented to integrate logistics network design in order to prevent the sub-optimality caused by the separate design of the forward and reverse networks. The study presents a single product and multi-stage logistics network problem for the new and return products not only to determine subsets of logistics centers to be opened, but also to determine transportation strategy, which satisfies demand imposed by facilities and minimizes fixed opening and total shipping costs. Since the deterministic estimation of some parameters such as demand and rate of return of used products in closed loop logistics models is impractical, an uncertain programming is proposed. In this case, we assume there are several economic conditions with predefined probabilities calculated from historical data. Then by means of expert's opinion, a fuzzy variable is offered as customer's demand under each economic condition. In addition, demand and rate of return of products for each customer zone is presented by fuzzy-random variables, similarly. Therefore, a fuzzy-random programming is used and a priority-based genetic algorithm is proposed to solve large-scale problems.

Multiobjective Genetic Algorithm for Scheduling Problems in Manufacturing Systems

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic populationbased metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

Priority-Based Genetic Algorithm for Conflict-Free Automated Guided Vehicle Routing

Priority-Based Genetic Algorithm for Conflict-Free Automated Guided Vehicle Routing

A genetic algorithm based approach for simultaneously balancing and sequencing of mixed-model U-lines with parallel workstations and zoning constraints

This paper presents a Priority-Based Genetic Algorithm ( PGA) based method for the simultaneously tackling of the mixed-model U-shape assembly line ( MMUL) line balancing/model sequencing problems ( MMUL/ BS) with parallel workstations and zoning constraints and allows the decision maker to control the process to create parallel workstations and to work in different scenarios. In the presented method, simulated annealing based fitness evaluation approach ( SABFEA) is developed to be able to make fitness function calculations easily and effectively. A new fitness function is adapted to MMULs for aiming at minimizing the number of workstations as primary goal and smoothing the workload between-within workstations by taking all cycles into consideration. A numerical example to clarify the solution methodology is presented. Performance of the proposed approach is tested through sets of test problem with randomly generated minimum part sets. The results of the computational experiments indicate that SABFEA works with PGA very concordantly; and it is an effective method in solving MMUL/ BS with parallel workstations and zoning constraints.

A study on facility location–allocation problem in mixed environment of randomness and fuzziness

In logistics system, facility location---allocation problem, which can be used to determine the mode, the structure and the form of the whole logistics system, is a very important decision problem in the logistics network. It involves locating plants and distribution centers, and determining the best strategy for allocation the product from the plants to the distribution centers and from the distribution centers to the customers. Often uncertainty may be associated with demand, supply or various relevant costs. In many cases, randomness and fuzziness simultaneously appear in a system, in order to describe this phenomenon; we introduce the concept of hybrid variable and propose a mixed-integer programming model for random fuzzy facility location---allocation problem. By expected value and chance constraint programming technique, this model is reduced to a deterministic model. Furthermore, a priority-based genetic algorithm is designed for solving the proposed programming model and the efficacy and the efficiency of this method and algorithm are demonstrated by a numerical example. Till now, few has formulated or attacked the FLA problems in the above manner. Furthermore, the techniques illustrated in this paper can easily be applied to other SCN problems. Therefore, these techniques are the appropriate tools to tackle other supply chain network problems in realistic environments.

Solution Method of Multi-Product Two-Stage Logistics Problem with Constraints of Delivery Course

The logistics network design is one of the important phase of Supply Chain Management (SCM) and it is the problem that should be optimized for long-term promotion of efficiency of the whole supply chain. Usually a plant produces different type of products. Even if it is a factory of the same company, delivery is different by a kind of a produced product. The restrictions which this model has are deeply concerned with TP in the real world. In this paper, we consider the logistics network design problems with multi-products and constraints for delivery course. To solve the problem, we used a hybrid priority-based Genetic Algorithm (h-priGA), and we tried the comparison experiment with priority-based Genetic Algorithm (priGA)and h-priGA, we show it about the effectiveness of h-priGA.

A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm

Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers.In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.