Dynamic order acceptance and capacity planning within a multi-project environment.

We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for regular as well as nonregular capacity.We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as an optimal capacity allocation for accepted projects, both with an eye on maximizing the expected revenues within the problem horizon.We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selectionand capacity usage.Capacity planning; multi-project; Order acceptance; Stochastic dynamic programming;

Integrating the Core: A New Management Curriculum to Empower our Students

This paper follows Kennesaw State University\u27s (KSU) faculty journal in developing a new integrated core curriculum for their Management majors that will empower the students and meet the needs of today\u27s employers. Curriculums must change to stay current. Depending on the amount of change, this can be a huge undertaking for a department ensconced in an existing curriculum paradigm, and can be met with resistance. In this paper we look for answers to: 1) Why is the change necessary? 2) What are we changing to? We will follow up with some thoughts about 3) how will we make these changes

Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area

MEĐUOVISNOST ZASTUPLJENOSTI PROBLEMA U PLANIRANJU KAPACITETA I PROFITABILNOST I KONKURENTNOSTI PROIZVODNIH PODUZEĆA

U ovom radu provedeno je istraživanje o utjecaju stupnja zastupljenosti problema u planiranju kapaciteta na profitabilnost i konkurentnost na primjeru poduzeća strojogradnje. Rezultati istraživanja pokazali su da poduzeća koja imaju niži stupanj zastupljenosti problema u planiranju kapaciteta nemaju veću profitabilnost i konkurentnost od poduzeća s višim stupnjem zastupljenosti istih. Osim toga, rezultati istraživanja pokazali su da srednja poduzeća imaju nešto veću zastupljenost problema u planiranju kapaciteta od velikih, odnosno da nema značajne razlike između srednjih i velikih poduzeća s obzirom na stupanj zastupljenosti problema u planiranju kapaciteta. S druge strane, kada se analizira tip proizvodnog procesa rezultati su pokazali da između tipova proizvodnog procesa i stupnjeva zastupljenosti problema u planiranju kapaciteta postoji statistički značajna razlika čime se može prihvatiti tvrdnja da poduzeća koja imaju prekidani tip procesa imaju viši stupanj zastupljenosti problema u planiranju kapaciteta od poduzeća s linijskim i projektnim tipom procesa

Available-to-promise (ATP) systems: a classification and framework for analysis

Available-to-promise (ATP) systems deal with a number of managerial decisions related to order capture activities in a company, including order acceptance/rejection, due date setting, and resource scheduling. These different but interrelated decisions have often been studied in an isolated manner, and, to the best of our knowledge, no framework has been presented to integrate them into the broader perspective of order capture. This paper attempts to provide a general framework for ATP-related decisions. By doing so, we: (1) identify the different decision problems to be addressed; (2) present the different literature-based models supporting related decisions into a coherent framework; and (3) review the main contributions in the literature for each one of these. We first describe different approaches for order capture available in the literature, depending on two parameters related to the application context of ATP systems, namely the inclusion of explicit information about due dates in the decision model, and the level of integration among decisions. According to these parameters, up to six approaches for ATP-related decisions are identified. Secondly, we show the subsequent decision problems derived from the different approaches, and describe the main issues and key references involving each one of these decision problems. Finally, a number of conclusions and future research lines are discussed.Ministerio de Ciencia e Innovación DPI2007-6134

A Capacity Allocation Planning Model for Integrated Care and Access Management

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146954/1/poms12941-sup-0001-AppendixS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146954/2/poms12941_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146954/3/poms12941.pd

Desenvolvimento de um sistema de apoio à decisão para a programação de sistemas de produção do tipo Job-Shop

Mestrado em Engenharia Electrotécnica e de ComputadoresO job shop é um sistema produtivo comum nas Pequenas e Médias Empresas (PMEs). Estas operam no sector da produção por encomenda. São sistemas que produzem uma grande variedade de produtos, em pequenas quantidades, de acordo com as especificidades de cada cliente. Num sistema produtivo tão dinâmico, é evidente a importância do controlo da actividade de produção. O controlo de carga é um conceito do controlo da actividade de produção e com origem no conceito controlo de entradas/saídas (I/OC). O trabalho visa estudar um sistema múltiplo de apoio à decisão e tem como principais objectivos o planeamento e controlo de operações num sistema de produção do tipo Job-Shop. O esquema é assente em quatro etapas de decisão e a cada uma está associado uma decisão de controlo: aceitar, rejeitar ou negociar uma encomenda; definir a data de entrega da encomenda; dar a ordem de lançamento de uma encomenda para o shop floor; definir a sequência na qual as ordens serão processadas no shop-floor. A cada etapa de decisão são consideradas regras de benchmark (por exemplo, total acceptance, immediate release), assim como algumas regras consideradas noutros estudos (backward infinite loading, total work content, earliest due date). Estas regras tendem a garantir os prazos estabelecidos e uma situação estável e controlável no chão de fábrica. O sistema foi desenvolvido em VBA – Excel. A fim de avaliar o impacto das regras de decisão no desempenho do sistema foram seleccionados determinados critérios de optimalidade (por exemplo, atraso médio, tempo médio em sistema, utilização das máquinas).Job shop is a common productive system in Small and Medium Enterprises (SMEs). These are make-to-order companies. They produce many different products, manufactured usually in small batches, according to specifications that are generally customer specific. In such dynamic productive system, is obvious the importance of the Production Planning and Control (PPC). Workload Control is a PPC concept, originates from the concept of input–output control that attempts to manage manufacturing lead times. This report aims at studying a multiple decision-making scheme for planning and controlling operations in a general job-shop. The decision-making scheme includes four main decisions: accept, negotiate or reject an in-coming order; define the order’s due date; release the accepted jobs to the shop floor or keep them in the pre-shop pool; dispatch the jobs at the shop floor. At each decision stage benchmark rules are considered (e.g. total acceptance, immediate release), as well as some rules proposed in previous studies (e.g. backward infinite loading, total work content, earliest due date). These rules tend to guarantee due dates and a steady and controllable situation in the shop floor. The system was developed in VBA – Excel. In order to judge the impact of these decision rules in the performance of the systems there has been selected performance measures (e.g. mean tardiness, mean total time in the system, machine utilization)

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation

Order Assignment and Resource Reservation: An Optimization Model and Policy Analysis

To maintain a competitive edge, companies today must be able to efficiently allocate resources to optimally commit and fulfill requested orders. As such, order processing and resource allocation models have become increasingly sophisticated to handle the complexity of these decisions. In our research, we introduce a model that integrates production scheduling, material allocation, delivery scheduling, as well as functions involving commitment of forecast demand for configure-to-order (CTO) and assemble-to-order (ATO) business environments. The model is formulated as a Mixed Integer Program (MIP) and seeks to maximize revenue by trading off commitment of higher profit forecast orders with the production and delivery schedule of lower profit accepted orders. Our model is particularly useful for testing different policies relating to order commitment, delivery mode selection and resource allocation