High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization

Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.

Modelling and optimisation of a product recovery network

An appropriate logistics network is an important element of the infrastructure of any product recovery company. Small and medium enterprises (SMEs) constitute a major fraction of the product recovery industry with a different business objective and scale of operation from those of original equipment manufacturers. This paper addresses the network design issues for SMEs involved in product recovery activities. A mathematical formulation is presented in an SME context and a subsequent simulation model is developed. A genetic algorithm approach is presented for optimising the network for a single product scenario

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

A novel optimization method on logistics operation for warehouse & port enterprises based on game theory

Purpose: The following investigation aims to deal with the competitive relationship among different warehouses & ports in the same company. Design/methodology/approach: In this paper, Game Theory is used in carrying out the optimization model. Genetic Algorithm is used to solve the model. Findings: Unnecessary competition will rise up if there is little internal communication among different warehouses & ports in one company. This paper carries out a novel optimization method on warehouse & port logistics operation model. Originality/value: Warehouse logistics business is a combination of warehousing services and terminal services which is provided by port logistics through the existing port infrastructure on the basis of a port. The newly proposed method can help to optimize logistics operation model for warehouse & port enterprises effectively. We set Sinotrans Guangdong Company as an example to illustrate the newly proposed method. Finally, according to the case study, this paper gives some responses and suggestions on logistics operation in Sinotrans Guangdong warehouse & port for its future development.Peer Reviewe

Facility location decisions within integrated forward/reverse logistics under uncertainty

In this paper, a stochastic mixed integer linear programming (SMILP) model is proposed to optimize the location and size of facilities and service centres in integrated forward and reverse streams under uncertainty. The objective of the model is to minimize establishment, transportation and inventory management costs and simultaneously maximize customer satisfaction with sustainable perspective. The model incorporates different elements and features of distribution networks including inventory management, transportation and establishment of new facilities as well as existing centres. The presented model is the streamlined approach for multi-objective, multi-period, multi-commodity distribution system, and it is supported by a real case study in automobile after sales network. Genetic algorithm is implemented to solve the model in reasonable time. The performance of the model and the effects of uncertainty on provided solution are studied under different cases. Competitive result of the stochastic model compared to deterministic model ensures that the proposed approach is valid to be applied for decision making under uncertainty.Scopu

Integrated and periodic relief logistics planning for reaction phase in uncertainty condition and model solving by particles swarm optimization algorithm

Disaster relief logistics is considered to be one of the major activities in disaster management. This research studies response phase of the disaster management cycle. To do so, a multi-purpose integrated model for a three-level relief cycle logistics is provided under an uncertainty condition and on a periodic basis. In this model, inventory transfer, vehicle routing, distribution and sending relief goods are modeled on a periodic basis. In addition, in order to solve the proposed mathematical model, ultra-initiative particles swarm algorithm in combination with variable neighborhood search based on Pareto archive is proposed. To prove the efficiency of the proposed particles swarm algorithm, several sample problems are randomly selected considering the solved problems in the literature and are solved by particles swarm algorithm. These problems are also solved by genetic algorithm and the results obtained from these two algorithms are compared in terms of quality, dispersion and integrity indices. The results show that compared to genetic algorithm, particles swarm algorithm is more capable of producing more integrated, qualified and dispersed responses. Moreover, the results show that the solution time of genetic algorithm is less than that of the proposed algorithm

Integrated and periodic relief logistics planning for reaction phase in uncertainty condition and model solving by particles swarm optimization algorithm

Disaster relief logistics is considered to be one of the major activities in disaster management. This research studies response phase of the disaster management cycle. To do so, a multi-purpose integrated model for a three-level relief cycle logistics is provided under an uncertainty condition and on a periodic basis. In this model, inventory transfer, vehicle routing, distribution and sending relief goods are modeled on a periodic basis. In addition, in order to solve the proposed mathematical model, ultra-initiative particles swarm algorithm in combination with variable neighborhood search based on Pareto archive is proposed. To prove the efficiency of the proposed particles swarm algorithm, several sample problems are randomly selected considering the solved problems in the literature and are solved by particles swarm algorithm. These problems are also solved by genetic algorithm and the results obtained from these two algorithms are compared in terms of quality, dispersion and integrity indices. The results show that compared to genetic algorithm, particles swarm algorithm is more capable of producing more integrated, qualified and dispersed responses. Moreover, the results show that the solution time of genetic algorithm is less than that of the proposed algorithm