21,571 research outputs found
Datasets on mathematical modeling of multi-product multi-stage production to analyze the relationship between production yield, demand, and costs
The data presented in this article are related to the research article âOptimal procurement and production planning for multi-product multi-stage production under yield uncertaintyâ (Talay and Ăzdemir-Akyıldırım, in press) [1]. The data includes: 1) the input parameters (production yield, demand, and costs) collected through comprehensive review of the literature and diversified further to enrich the analytical results, and 2) results from mathematical modeling and analysis on the optimal procurement and semi-processed material allocation decisions for different parameter sets. The dataset is particularly constructed for a production system with two stages and three final products.No sponso
Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach
Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution
Recommended from our members
Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
Evaluation of sales and operations planning in a process industry
Cette thĂšse porte sur la planification des ventes et des opĂ©rations (S±&OP) dans une chaĂźne d'approvisionnements axĂ©e sur la demande. L'objectif de la S±&OP, dans un tel contexte, est de tirer profit de l'alignement de la demande des clients avec la capacitĂ© de la chaĂźne d'approvisionnement par la coordination de la planification des ventes, de la production, de la distribution et de l'approvisionnement. Un tel processus de planification exige une collaboration multifonctionnelle profonde ainsi que l'intĂ©gration de la planification. Le but Ă©tant d'anticiper l'impact des dĂ©cisions de vente sur les performances de la chaĂźne logistique , alors que l'influence de la dynamique des marchĂ©s est prise en compte pour les dĂ©cisions concernant la production, la distribution et l'approvisionnement. La recherche a Ă©tĂ© menĂ©e dans un environnement logistique manufacturier multi-site et multi-produit, avec un approvisionnement et des ventes rĂ©gis par des contrats ou le marchĂ©. Cette thĂšse examine deux approches de S±&OP et fournit un support Ă la dĂ©cision pour l'implantation de ces mĂ©thodes dans une chaĂźne logistique multi-site de fabrication sur commande. Dans cette thĂšse, une planification traditionnelle des ventes et de la production basĂ©e sur la S±feOP et une planification S±fcOP plus avancĂ©e de la chaĂźne logistique sont tout d'abord caractĂ©risĂ©es. Dans le systĂšme de chaĂźne logistique manufacturiĂšre multi-site, nous dĂ©finissons la S±&OP traditionnelle comme un systĂšme dans lequel la planification des ventes et de la production est effectuĂ©e conjointement et centralement, tandis que la planification de la distribution et de l'approvisionnement est effectuĂ©e sĂ©parĂ©ment et localement Ă chaque emplacement. D'autre part, la S±fcOP avancĂ©e de la chaĂźne logistique consiste en la planification des ventes, de la production, de la distribution et de l'approvisionnement d'une chaĂźne d'approvisionnement effectuĂ©e conjointement et centralement. BasĂ©s sur cette classification, des modĂšles de programmation en nombres entiers et des modĂšles de simulation sur un horizon roulant sont dĂ©veloppĂ©s, reprĂ©sentant, respectivement, les approches de S±&OP traditionnelle et avancĂ©e, et Ă©galement, une planification dĂ©couplĂ©e traditionnelle, dans laquelle la planification des ventes est effectuĂ©e centralement et la planification de la production, la distribution et l'approvisionnement est effectuĂ©e sĂ©parĂ©ment et localement par les unitĂ©s d'affaires. La validation des modĂšles et l'Ă©valuation prĂ©-implantation sont effectuĂ©es Ă l'aide d'un cas industriel rĂ©el utilisant les donnĂ©es d'une compagnie de panneaux de lamelles orientĂ©es. Les rĂ©sultats obtenus dĂ©montrent que les deux mĂ©thodes de S±feOP (traditionnelle et avancĂ©e) offrent une performance significativement supĂ©rieure Ă celle de la planification dĂ©couplĂ©e, avec des bĂ©nĂ©fices prĂ©vus supĂ©rieurs de 3,5% et 4,5%, respectivement. Les rĂ©sultats sont trĂšs sensibles aux conditions de marchĂ©. Lorsque les prix du marchĂ© descendent ou que la demande augmente, de plus grands bĂ©nĂ©fices peuvent ĂȘtre rĂ©alisĂ©s. Dans le cadre de cette recherche, les dĂ©cisions de vente impliquent des ventes rĂ©gies par des contrats et le marchĂ©. Les dĂ©cisions de contrat non optimales affectent non seulement les revenus, mais Ă©galement la performance manufacturiĂšre et logistique et les dĂ©cisions de contrats d'approvisionnement en matiĂšre premiĂšre. Le grand dĂ©fi est de concevoir et d'offrir les bonnes politiques de contrat aux bons clients de sorte que la satisfaction des clients soit garantie et que l'attribution de la capacitĂ© de la compagnie soit optimisĂ©e. Ăgalement, il faut choisir les bons contrats des bons fournisseurs, de sorte que les approvisionnements en matiĂšre premiĂšre soient garantis et que les objectifs financiers de la compagnie soient atteints. Dans cette thĂšse, un modĂšle coordonnĂ© d'aide Ă la dĂ©cision pour les contrats e dĂ©veloppĂ© afin de fournir une aide Ă l'intĂ©gration de la conception de contrats, de l'attribution de capacitĂ© et des dĂ©cisions de contrats d'approvisionnement pour une chaĂźne logistique multi-site Ă trois niveaux. En utilisant la programmation stochastique Ă deux Ă©tapes avec recours, les incertitudes liĂ©es Ă l'environnement et au systĂšme sont anticipĂ©es et des dĂ©cisions robustes peuvent ĂȘtre obtenues. Les rĂ©sultats informatiques montrent que l'approche de modĂ©lisation proposĂ©e fournit des solutions de contrats plus rĂ©alistes et plus robustes, avec une performance prĂ©vue supĂ©rieure d'environ 12% aux solutions fournies par un modĂšle dĂ©terministe
Recommended from our members
Decision support for build-to-order supply chain management through multiobjective optimization
This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF
Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies
Abstract This paper develops a supply chain (SC) model by integrating raw material ordering and production planning, and production capacity decisions based upon two case studies in manufacturing firms. Multiple types of uncertainties are considered; including: time-related uncertainty (that exists in lead-time and delay) and quantity-related uncertainty (that exists in information and material flows). The SC model consists of several sub-models, which are first formulated mathematically. Simulation (simulation-based stochastic approximation) and genetic algorithm tools are then developed to evaluate several non-parameterised strategies and optimise two parameterised strategies. Experiments are conducted to contrast these strategies, quantify their relative performance, and illustrate the value of information and the impact of uncertainties. These case studies provide useful insights into understanding to what degree the integrated planning model including production capacity decisions could benefit economically in different scenarios, which types of data should be shared, and how these data could be utilised to achieve a better SC system. This study provides insights for small and middle-sized firm management to make better decisions regarding production capacity issues with respect to external uncertainty and/or disruptions; e.g. trade wars and pandemics.</jats:p
Recommended from our members
Modeling Bioenergy Supply Chains: Feedstocks Pretreatment, Integrated System Design Under Uncertainty
Biofuels have been promoted by governmental policies for reducing fossil fuel dependency and greenhouse gas emissions, as well as facilitating regional economic growth. Comprehensive model analysis is needed to assess the economic and environmental impacts of developing bioenergy production systems. For cellulosic biofuel production and supply in particular, existing studies have not accounted for the inter-dependencies between multiple participating decision makers and simultaneously incorporated uncertainties and risks associated with the linked production systems.This dissertation presents a methodology that incorporates uncertainty element to the existing integrated modeling framework specifically designed for advanced biofuel production systems using dedicated energy crops as feedstock resources. The goal of the framework is to support the bioenergy industry for infrastructure and supply chain development. The framework is flexible to adapt to different topological network structures and decision scopes based on the modeling requirements, such as on capturing the interactions between the agricultural production system and the multi-refinery bioenergy supply chain system with regards to land allocation and crop adoption patterns, which is critical for estimating feedstock supply potentials for the bioenergy industry. The methodology is also particularly designed to incorporate system uncertainties by using stochastic programming models to improve the resilience of the optimized system design.The framework is used to construct model analyses in two case studies. The results of the California biomass supply model estimate that feedstock pretreatment via combined torrefaction and pelletization reduces delivered and transportation cost for long-distance biomass shipment by 5% and 15% respectively. The Pacific Northwest hardwood biofuels application integrates full-scaled supply chain infrastructure optimization with agricultural economic modeling and estimates that bio-jet fuels can be produced at costs between 4 to 5 dollars per gallon, and identifies areas suitable for simultaneously deploying a set of biorefineries using adopted poplar as the dedicated energy crop to produce biomass feedstocks. This application specifically incorporates system uncertainties in the crop market and provides an optimal system design solution with over 17% improvement in expected total profit compared to its corresponding deterministic model
- âŠ