A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

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.

SIMAID: a rapid development methodology for the design of acyclic, bufferless, multi-process and mixed model agile production facilities for spaceframe vehicles

The facility layout problem (FL) is a non-linear, NP-complete problem whose complexity is derived from the vast solution space generated by multiple variables and interdependent factors. For reconfigurable, agile facilities the problem is compounded by parallelism (simultaneity of operations) and scheduling issues. Previous work has either concentrated on conventional (linear or branched) facility layout design, or has not considered the issues of agile, reconfigurable facilities and scheduling. This work is the first comprehensive methodology incorporating the design and scheduling of parallel cellular facilities for the purpose of easy and rapid reconfiguration in the increasingly demanding world of agile manufacturing. A novel three-stage algorithm is described for the design of acyclic (asynchronous), bufferless, parallel, multi-process and mixed-model production facilities for spaceframe-based vehicles. Data input begins with vehicle part processing and volume requirements from multiple models and includes time, budget and space constraints. The algorithm consists of a powerful combination of a guided cell formation stage, iterative solution improvement searches and design stage scheduling. The improvement iterations utilise a modified (rules-based) Tabu search applied to a constant-flow group technology, while the design stage scheduling is done by the use of genetic algorithms. The objective-based solution optimisation direction is not random but guided, based on measurement criteria from simulation. The end product is the selection and graphic presentation of the best solution out of a database of feasible ones. The case is presented in the form of an executable program and three real world industrial examples are included. The results provide evidence that good solutions can be found to this new type and size of heavily constrained problem within a reasonable amount of time

a hybrid metaheuristic approach for minimizing the total flow time in a flow shop sequence dependent group scheduling problem

Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs) and Biased Random Sampling (BRS) search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation

Trends and topics in IJPR from 1961 to 2017:a statistical history

This paper studies the history of the International Journal of Production Research (IJPR) by analysing the topics that have received the most attention in each of the journal’s publication years. Text mining exposed for scrutiny the most frequently mentioned and cited terms contained in the titles, abstracts and keywords of IJPR papers. Analyses suggest that the triad of scheduling/optimisation/simulation and supply-chain-related topics have been IJPR’s mainstays, but valuable opportunities remain for relevant topics that have not yet been concurrently and frequently studied. Results also show that terms related to sustainability and risk management topics have gained recent relevance. In addition, IJPR appears to complement its modelling technique focus with empirical methodological approaches to provide a well-balanced perspective, since the ‘case study’ term is common. Finally, a linear relationship is found between the number of papers that have covered certain topics and the number of citations those topics have received, highlighting which topics had fewer or more citations than expected, given the number of papers that covered those topics. IJPR stands as one of the most prestigious and established journals in its field and the results from this study indicate the evolving interests of the field for over half a century