Modelling of transport operations in supply chains in obedience to “just-in-time” conception

Transportation is a key logistics function, which determines the dynamic nature of material flows in logistics systems. At the same time, transportation is a source of uncertainty of logistics operations performance in the supply chain. Obviously, the development of a new approach for evaluation of the duration of delivery “Just-In-Time” (JIT) will improve the efficiency of supply chains in accordance with one of the major criteria, namely customer satisfaction. One of the basic approaches to make effective management decisions in transportation and other logistic operations is the JIT concept. In the majority of examined sources the JIT concept is described on the verbal level without any usage of calculation dependences. The paper is devoted to the formation of analytical and simulation models, which allow obtaining the probabilistic evaluation of the implementation of unimodal and multimodal international transportation JIT. The first model where the order of the operations implementation does not affect final result is formed on the basis of the probability theory: distribution laws composition, theorems of numerical characteristics of random variables, formula of complete probability. The second model accounts the impact of operations implementation order in transportation and their interconnection and is based on the simulation (the method of statistic experiments) and shown as a corresponding algorithm, which allows to consider different limitations (technical, organizational and so on). Considered analytical dependences give the possibility to obtain the necessary estimations of the transport operations implementation according to JIT: mean transportation time, delivery implementation probability by the set moment or the delivery time with the set probability. To carry out some comparative calculations and clarify the algorithm, two international routes have been chosen: the first one is a unimodal road transportation, the second one is a multimodal transportation (road and marine transport). All the data, which is necessary for calculation has been collected on the basis of official information (in particular, the data of tachograph, special questionnaires filled in by the drivers, the survey results of the managers). For unimodal transportations analytical dependences and modelling results give close results. For the combined multimodal transportations taking into account various limitations the preference must be given to the simulation. The modelled indexes take into consideration their intercommunication and definitely estimate the supply chains reliability, and this allows decreasing the uncertainty of the logistic system

PPC Task Plan Sourcing - Synchronization of Procurement and Production. A Model-based Observation

As companies continue to globalize, manufacturers face the challenge of strategically adjusting their vertical integration and restructuring production and supply chains. This leads manufacturers to increasingly pursue two strategies of restructuring. On the one hand, in the form of outsourcing value-adding activities, the focus is being placed on the core competencies of the company's own production. As a result, the vertical range of manufacture within the company is decreasing, while outsourcing is becoming more and more important. On the other hand, companies are also pursuing the strategy of least possible dependency to secure production through regional procurement of resources and expansion of the necessary competencies by means of increased vertical integration. In order to understand the consequences and effects of these changes at the level of production planning and control (PPC), a model-based view is necessary for an expanded understanding of the processual context of these changes. The PPC is the essential steering instance of production. It combines long-term tasks, e.g. Plan Sales or Roughly Plan Resources, with short-term tasks, e.g. Schedule Throughput or Plan Resources in Detail. The main PPC task, Plan Sourcing, is an essential link with its tasks and procedures between the core processes of procurement and production in the company's internal supply chain. In the context of this paper, the PPC main task Plan Sourcing is to be considered in a model-based manner, which focuses on the selection and connection of suppliers as well as the general view of the supplier management of manufacturers. For this purpose, the effect on the PPC and the production logistic objectives variables is presented by means of the consideration of the tasks and possible procedures for the fulfilment of these PPC tasks. Utilizing collected findings, a process-related derivation for the synchronization of the affected areas of procurement and production is presented

Simulation of production scheduling in manufacturing systems

Research into production scheduling environments has been primarily concerned with developing local priority rules for selecting jobs from a queue to be processed on a set of individual machines. Most of the research deals with the scheduling problems in terms of the evaluation of priority rules with respect to given criteria. These criteria have a direct effect on the production cost, such as mean make-span, flow-time, job lateness, m-process inventory and machine idle time. The project under study consists of the following two phases. The first is to deal with the development of computer models for the flow-shop problem, which obtain the optimum make-span and near-optimum solutions for the well-used criteria in the production scheduling priority rules. The second is to develop experimental analysis using a simulation technique, for the two main manufacturing systems, 1. Job-shop 2. Flexible Manufacturing System The two manufacturing types were investigated under the following conditions i. Dynamic problem conditions ii. Different operation time distributions iii. Different shop loads iv. Seven replications per experiment with different streams of random number v. The approximately steady state point for each replication was obtained. In the FMS, the material handling system used was the automated guided Vehicles (AGVs), buffer station and load/ unload area were also used. The aim of these analyses is to deal with the effectiveness of the priority rules on the selected criteria performance. The SIMAN software simulation was used for these studies

Hyperconnected fulfillment and inventory allocation and deployment models

Consumption patterns have been changed dramatically over the past decades, notably by the growth of e-commerce. With the prevalence of e-commerce and home delivery, customer expectations for a faster, punctual, and cheap delivery are increasing. In fact, many customers are expecting for same-day or x-hour deliveries now and offering fast delivery becomes more and more critical for e-retailers to survive in a fierce market competition. However, many companies are simply lacking financial, physical, and/or operational resources to increase their responsiveness. Focusing on solving the challenges in the perspective of fulfillment and inventory, we aim to find a breakthrough from a recently emerging logistics innovation movement induced by the introduction of the Physical Internet (PI). PI can potentially enable responsive yet affordable fulfillment for companies of any size through open asset utilization and multi-player operations. The key of PI innovation is transforming asset-driven logistics operations to service-driven logistics operations. This thesis provides an academic foundation for hyperconnected fulfillment to effectively satisfy the growing customer expectations on responsive deliveries. We first present a comprehensive design and evaluation of a hyperconnected fulfillment system. Then, we focus on providing inventory operations models, inventory allocation and deployment respectively, which maximally utilize the key features of hyperconnected fulfillment system: connectivity, flexibility, and decentralization. In Chapter 2, a hyperconnected fulfillment and delivery system is designed in the context of the last-mile operations in urban areas. A comprehensive system and decision architecture of the hyperconnected system is modeled. We carefully design the scenarios to show a gradual transformation from dedicated to hyperconnected system in each thread of delivery and fulfillment so as to reveal the marginal impact of each step of transformation. We conduct a scenario analysis using a simulation platform built upon the system and decision architecture where autonomous agents are optimizing their decisions and interact with the environment. The experimental results clearly demonstrate the potential benefit of hyperconnected urban fulfillment and delivery system by concurrently improving often opposing performance criteria: economic efficiency, service capability and sustainability. Chapter 3 tackles an optimal inventory allocation problem among multiple sales outlets. Specifically, we analyze a case where a dropshipper allocates availability to multiple e-retailers via availability promising e-contracts (APCs). Under the APC, the e-retailers do not observe actual availability and this information asymmetry leads them to pose a promised availability threshold (PAT). PAT is a threshold on remaining promised availability set by an e-retailer for a product of a dropshipper, below which the e-retailer unlists the product and thus does not accept any more orders from customers, until the promised availability is climbed above the threshold by the dropshipper. The dropshipper's APC problem with PAT is modeled as 2-stage stochastic program with two stochastic parameters: demand and PAT. We design and evaluate three contract policies differentiated by the allowance level for overpromising: guaranteed fulfillment, controlled fillrate, and penalty-driven fillrate policies. We also present a modeling approach to convert the endogenous demands, per-retailer-distribution of which is affected by the APCs, to exogenous demands with linear substitution constraints. The numerical results show the penalty-driven fillrate policy is the dominating strategy for dropshippers especially under a lean availability. Chapter 4 tackles an inventory deployment problem under the context of open asset utilization and responsive fulfillment. When it comes to very responsive deliveries, such as X-hour deliveries, the physical availability of inventories near the delivery locations becomes necessary, which requires a broad and dense fulfillment network. The open asset utilization and service-driven fulfillment operations of the PI can enable affordable access to such decentralized fulfillment network comprised of the open fulfillment centers. We evaluate the benefit of such decentralized fulfillment network for a responsive fulfillment and develop an appropriate inventory deployment model, which possesses a partially pooled demand and inventory structure induced by responsiveness requirements, as a variant of Newsvendor. We derive a pragmatic heuristic inventory solution, W-solution, and present an efficient binary search based solution heuristic, W-heuristic. Then, via numerical experiments over both theoretical and empirical demand distributions, we demonstrate the advantage of decentralized network and w-solution over centralized network and allocation-based inventory model, pre-allocation model, respectively. We also report rather counter-intuitive observations that the w-solution which accounts for pooling leads to more inventory than pre-allocation model which does not account for pooling under low sales margin.Ph.D

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005.

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005

Proactive management of uncertainty to improve scheduling robustness in proces industries

Dinamisme, capacitat de resposta i flexibilitat són característiques essencials en el desenvolupament de la societat actual. Les noves tendències de globalització i els avenços en tecnologies de la informació i comunicació fan que s'evolucioni en un entorn altament dinàmic i incert. La incertesa present en tot procés esdevé un factor crític a l'hora de prendre decisions, així com un repte altament reconegut en l'àrea d'Enginyeria de Sistemes de Procés (PSE). En el context de programació de les operacions, els models de suport a la decisió proposats fins ara, així com també software comercial de planificació i programació d'operacions avançada, es basen generalment en dades estimades, assumint implícitament que el programa d'operacions s'executarà sense desviacions. La reacció davant els efectes de la incertesa en temps d'execució és una pràctica habitual, però no sempre resulta efectiva o factible. L'alternativa és considerar la incertesa de forma proactiva, és a dir, en el moment de prendre decisions, explotant el coneixement disponible en el propi sistema de modelització.Davant aquesta situació es plantegen les següents preguntes: què s'entén per incertesa? Com es pot considerar la incertesa en el problema de programació d'operacions? Què s'entén per robustesa i flexibilitat d'un programa d'operacions? Com es pot millorar aquesta robustesa? Quins beneficis comporta? Aquesta tesi respon a aquestes preguntes en el marc d'anàlisis operacionals en l'àrea de PSE. La incertesa es considera no de la forma reactiva tradicional, sinó amb el desenvolupament de sistemes proactius de suport a la decisió amb l'objectiu d'identificar programes d'operació robustos que serveixin com a referència pel nivell inferior de control de planta, així com també per altres centres en un entorn de cadenes de subministrament. Aquest treball de recerca estableix les bases per formalitzar el concepte de robustesa d'un programa d'operacions de forma sistemàtica. Segons aquest formalisme, els temps d'operació i les ruptures d'equip són considerats inicialment com a principals fonts d'incertesa presents a nivell de programació de la producció. El problema es modelitza mitjançant programació estocàstica, desenvolupant-se finalment un entorn d'optimització basat en simulació que captura les múltiples fonts d'incertesa, així com també estratègies de programació d'operacions reactiva, de forma proactiva. La metodologia desenvolupada en el context de programació de la producció s'estén posteriorment per incloure les operacions de transport en sistemes de múltiples entitats i incertesa en els temps de distribució. Amb aquesta perspectiva més àmplia del nivell d'operació s'estudia la coordinació de les activitats de producció i transport, fins ara centrada en nivells estratègic o tàctic. L'estudi final considera l'efecte de la incertesa en la demanda en les decisions de programació de la producció a curt termini. El problema s'analitza des del punt de vista de gestió del risc, i s'avaluen diferents mesures per controlar l'eficiència del sistema en un entorn incert.En general, la tesi posa de manifest els avantatges en reconèixer i modelitzar la incertesa, amb la identificació de programes d'operació robustos capaços d'adaptar-se a un ampli rang de situacions possibles, enlloc de programes d'operació òptims per un escenari hipotètic. La metodologia proposada a nivell d'operació es pot considerar com un pas inicial per estendre's a nivells de decisió estratègics i tàctics. Alhora, la visió proactiva del problema permet reduir el buit existent entre la teoria i la pràctica industrial, i resulta en un major coneixement del procés, visibilitat per planificar activitats futures, així com també millora l'efectivitat de les tècniques reactives i de tot el sistema en general, característiques altament desitjables per mantenir-se actiu davant la globalitat, competitivitat i dinàmica que envolten un procés.Dynamism, responsiveness, and flexibility are essential features in the development of the current society. Globalization trends and fast advances in communication and information technologies make all evolve in a highly dynamic and uncertain environment. The uncertainty involved in a process system becomes a critical problem in decision making, as well as a recognized challenge in the area of Process Systems Engineering (PSE). In the context of scheduling, decision-support models developed up to this point, as well as commercial advanced planning and scheduling systems, rely generally on estimated input information, implicitly assuming that a schedule will be executed without deviations. The reaction to the effects of the uncertainty at execution time becomes a common practice, but it is not always effective or even possible. The alternative is to address the uncertainty proactively, i.e., at the time of reasoning, exploiting the available knowledge in the modeling procedure itself. In view of this situation, the following questions arise: what do we understand for uncertainty? How can uncertainty be considered within scheduling modeling systems? What is understood for schedule robustness and flexibility? How can schedule robustness be improved? What are the benefits? This thesis answers these questions in the context of operational analysis in PSE. Uncertainty is managed not from the traditional reactive viewpoint, but with the development of proactive decision-support systems aimed at identifying robust schedules that serve as a useful guidance for the lower control level, as well as for dependent entities in a supply chain environment. A basis to formalize the concept of schedule robustness is established. Based on this formalism, variable operation times and equipment breakdowns are first considered as the main uncertainties in short-term production scheduling. The problem is initially modeled using stochastic programming, and a simulation-based stochastic optimization framework is finally developed, which captures the multiple sources of uncertainty, as well as rescheduling strategies, proactively. The procedure-oriented system developed in the context of production scheduling is next extended to involve transport scheduling in multi-site systems with uncertain travel times. With this broader operational perspective, the coordination of production and transport activities, considered so far mainly in strategic and tactical analysis, is assessed. The final research point focuses on the effect of demands uncertainty in short-term scheduling decisions. The problem is analyzed from a risk management viewpoint, and alternative measures are assessed and compared to control the performance of the system in the uncertain environment.Overall, this research work reveals the advantages of recognizing and modeling uncertainty, with the identification of more robust schedules able to adapt to a wide range of possible situations, rather than optimal schedules for a hypothetical scenario. The management of uncertainty proposed from an operational perspective can be considered as a first step towards its extension to tactical and strategic levels of decision. The proactive perspective of the problem results in a more realistic view of the process system, and it is a promising way to reduce the gap between theory and industrial practices. Besides, it provides valuable insight on the process, visibility for future activities, as well as it improves the efficiency of reactive techniques and of the overall system, all highly desirable features to remain alive in the global, competitive, and dynamic process environment

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms