An analytics-based heuristic decomposition of a bilevel multiple-follower cutting stock problem

This paper presents a new class of multiple-follower bilevel problems and a heuristic approach to solving them. In this new class of problems, the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach. This approach uses Monte Carlo simulation and k-medoids clustering to reduce the bilevel problem to a single level, which can then be solved using integer programming techniques. The examples presented show that our approach produces better solutions and scales up better than the other approaches in the literature. Furthermore, for large problems, we combine our approach with the use of self-organising maps in place of k-medoids clustering, which significantly reduces the clustering times. Finally, we apply our approach to a real-life cutting stock problem. Here a forest harvesting problem is reformulated as a multiple-follower bilevel problem and solved using our approachThis publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/228

Measures of reconfigurability and its key characteristics in intelligent manufacturing systems

\In recent years, the fields of reconfigurable manufacturing systems, holonic manufacturing systems, and multi-agent systems have made technological advances to support the ready reconfiguration of automated manufacturing systems. While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, limited effort has been devoted to the measurement of reconfigurability in the resultant systems. Hence, it is not clear (1) to which degree these designs have achieved their intended level of reconfigurability, (2) which systems are indeed quantitatively more reconfigurable and (3) how these designs may overcome their design limitations to achieve greater reconfigurability in subsequent design iterations. Recently, a reconfigurability measurement process based upon axiomatic design knowledge base and the design structure matrix has been developed. Together, they provide quantitative measures of reconfiguration potential and ease. This paper now builds upon these works to provide a set of composite reconfigurability measures. Among these are measures for the key characteristics of reconfigurability: integrability, convertibility, and customization, which have driven the qualitative and intuitive design of these technological advances. These measures are then demonstrated on an illustrative example followed by a discussion of how they adhere to requirements for reconfigurability measurement in automated and intelligent manufacturing systems

Modeling and Solution Methodologies for Mixed-Model Sequencing in Automobile Industry

The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the MMS that minimizes work overload by controlling the sequence of models. In order to do that, CSP restricts the number of work-intensive options by applying capacity rules. Consequently, the objective is to find the sequence with the minimum number of capacity rule violations. In this dissertation, we provide exact and heuristic solution approaches to solve different variants of MMS and CSP. First, we provide five improved lower bounds for benchmark CSP instances by solving problems optimally with a subset of options. We present four local search metaheuristics adapting efficient transformation operators to solve CSP. The computational experiments show that the Adaptive Local Search provides a significant advantage by not requiring tuning on the operator weights due to its adaptive control mechanism. Additionally, we propose a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provide improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. We also provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances. To the best of our knowledge, this is the first study that considers stochastic failures of products in MMS. Moreover, we propose a two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. We present a bi-objective evolutionary optimization algorithm, a two-stage bi-objective local search algorithm, and a hybrid local search integrated evolutionary optimization algorithm to tackle the proposed problem. Numerical experiments over a case study show that while the hybrid algorithm provides a better exploration of the Pareto front representation and more reliable solutions in terms of waiting time of failed vehicles, the local search algorithm provides more reliable solutions in terms of work overload objective. Finally, dynamic reinsertion simulations are executed over industry-inspired instances to assess the quality of the solutions. The results show that integrating the reinsertion process in addition to considering vehicle failures can keep reducing the work overload by around 20\% while significantly decreasing the waiting time of the failed vehicles

Profitability of Mass Customization in Electrical Motor Manufacturing : Does Customization Improve Product Level Profitability

The current market environment forces manufacturing companies to produce such customized products that are at the same time relatively cheap and finished with top quality to respond demanding requirements of customers. The concept of mass customization has been presented as a solution that offers economies of scale while producing customer tailored products. The aim of this research is to review the impact of mass customization on product level profitability in complex manufacturing environment. The theoretical framework is constructed on the main themes of mass customization, cost accounting, and product profitability analysis. All these mentioned topic areas are reviewed from the viewpoint of a manufacturing company that produces large variety of customized products with different order fulfillment methods. The research problem is divided into two separate questions of evaluating available product costing systems in complex manufacturing environment, and seeking evidence if mass customization is profitable in the case company that for this research is made as an assignment. Based on previous research, financial effects of mass customization are not sufficiently studied through empirical research. Furthermore, related research focuses mostly on product configurations and modules, and their effect on operative and technical development instead of financial measures. The empirical section of this research is conducted as a quantitative single-case study that aims to seek evidence if mass customization of electric motors is profitable for the case company. Operative data is collected from the case company’s ERP-system, and it is combined with financial information. This constructed data set is used for performing statistical analysis similar to methods that are applied in econometrics. The collected data set consists of 3900 statistical units thereby constructing a representative sample from the population. The findings show that mass customization is profitable for the case company when customization is measured through customer selected and otherwise optional variant codes, and by comparing profitability levels in between of different engineering groups. As a results, it was discovered that more customized statistical units were seen to be more profitable than those less customized units. This research contributes filling the recognized research gap of lacking empirical studies related to financial effects of mass customization. In addition, it also presents important information for the case company regarding of how different variant codes and engineering groups affect product level profitability in their manufacturing operations. Furthermore, the presented statistical method offers possibility to analyze and estimate how different product features influence product profitability levels based on statistical methods commonly used in econometrics. Therefore, this research can be seen to have central managerial and practical implications within management accounting practices in manufacturing environments.Kiristyvä kilpailutilanne markkinoilla sekä vaatimukset räätälöidyistä tuotteista ajavat yrityksiä tarjoamaan asiakaskohtaisia tuotteita saavuttaakseen kilpailuetua muihin kilpailijoihin nähden. Joustavan tuotevalikoiman lisäksi, asiakkaat odottavat samanaikaisesti edullisia hintoja, nopeita toimitusaikoja sekä hyvää laatua tuotteilta. Massakustomoinnin on esitetty tarjoavan mahdollisuuden hyödyntää suuruuden ekonomiaa samalla tarjoten asiakaskohtaisesti valmistettuja tuotteita, jotka täyttävät asiakkaiden erityiset vaatimukset. Tämän tutkimus tarkastelee massakustomoinnin vaikutusta tuotekohtaiseen kannattavuuteen korkean teknologian teollisuusympäristössä. Tutkimuksessa esitetty teoreettinen viitekehys muodostuu massakustomoinnin, kustannuslaskennan sekä kannattavuusanalyysin aihealueista, joita tarkastellaan erityisesti valmistavan tuotannon näkökulmasta. Tutkimuksen tavoitteena on luoda eheä kokonaisuus yhdistäen näitä mainittuja tutkimusaiheita sekä konkretisoida kustomoinnin taloudellisia vaikutuksia empiirisen tutkimuksen avulla. Tutkielman tutkimusongelma on jaettu kahteen erilliseen tutkimuskysymykseen. Ensimmäinen tutkimuskysymys tarkastelee kustannuslaskennan mahdollisuuksia tuotekustannusten määrittämiseksi ympäristössä, jossa tuotteiden määrä on suuri sekä valmistus monivaiheista. Toinen tutkimuskysymyksistä käsittelee massakustomoinnin vaikutusta kannattavuuteen kohdeyrityksessä. Aikaisempi tutkimus tunnistaa puutteet aikaisemmassa empiirisessä tutkimuksessa liittyen massakustomoinnin taloudelliseen vaikutuksiin sen keskittyessä yleisesti kustomoinnin operatiiviseen järjestämiseen sekä kehittämiseen tuotekonfigurointien ja -moduulien avulla. Tämän tutkielman empiirinen tutkimus on muodostettu hyödyntäen kvantitatiivista yksittäistapaustutkimusta, jonka tarkoituksena on tutkia tilastollisia menetelmiä hyödyntäen, miten massakustomointi vaikuttaa tuotekannattavuuteen kohdeyrityksen yhdessä tuote- ja kokokategoriassa. Aineisto on kerätty kohdeyrityksen toiminnanohjausjärjestelmästä sekä taloudellisista raporteista, joista on muodostettu yhtenäinen havaintoaineisto. Koottu havaintoaineisto muodostuu yhteensä 3900 havaintoyksiköstä, joiden voidaan nähdä kuvastavan yleistä tilannetta valitussa tapauksessa. Tulokset osoittavat, että massakustomointi parantaa keskimäärin kohdeyrityksen tuotteiden kannattavuutta, kun kustomoinnin mittana käytetään asiakkaiden valitsemien tuoteoptioiden määrää sekä insinööriprosessin muotoa. Tutkielma osallistuu tunnistetun tutkimusaukon täyttämiseen esittämällä empiirisiä tuloksia kustomoinnin taloudellisista vaikutuksista. Esitelty tilastollinen menetelmä esittää tavan yhdistää kustannuslaskentaa, kannattavuuden analysointia sekä tilastollisia menetelmiä johdon laskentatoimen menetelminä myös muilla massakustomointia hyödyntävillä teollisuudenaloilla lisäten tutkielman hyödyntämisen mahdollisuutta käytännön sekä liikkeenjohdon keinona

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems