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

Inventory drivers in a pharmaceutical supply chain

In recent years, inventory reduction has been a key objective of pharmaceutical companies, especially within cost optimization initiatives. Pharmaceutical supply chains are characterized by volatile and unpredictable demands –especially in emergent markets-, high service levels, and complex, perishable finished-good portfolios, which makes keeping reasonable amounts of stock a true challenge. However, a one-way strategy towards zero-inventory is in reality inapplicable, due to the strategic nature and importance of the products being commercialised. Therefore, pharmaceutical supply chains are in need of new inventory strategies in order to remain competitive. Finished-goods inventory management in the pharmaceutical industry is closely related to the manufacturing systems and supply chain configurations that companies adopt. The factors considered in inventory management policies, however, do not always cover the full supply chain spectrum in which companies operate. This paper works under the pre-assumption that, in fact, there is a complex relationship between the inventory configurations that companies adopt and the factors behind them. The intention of this paper is to understand the factors driving high finished-goods inventory levels in pharmaceutical supply chains and assist supply chain managers in determining which of them can be influenced in order to reduce inventories to an optimal degree. Reasons for reducing inventory levels are found in high inventory holding and scrap related costs; in addition to lost sales for not being able to serve the customers with the adequate shelf life requirements. The thesis conducts a single case study research in a multi-national pharmaceutical company, which is used to examine typical inventory configurations and the factors affecting these configurations. This paper presents a framework that can assist supply chain managers in determining the most important inventory drivers in pharmaceutical supply chains. The findings in this study suggest that while external and downstream supply chain factors are recognized as being critical to pursue inventory optimization initiatives, pharmaceutical companies are oriented towards optimizing production processes and meeting regulatory requirements while still complying with high service levels, being internal factors the ones prevailing when making inventory management decisions. Furthermore, this paper investigates, through predictive modelling techniques, how various intrinsic and extrinsic factors influence the inventory configurations of the case study company. The study shows that inventory configurations are relatively unstable over time, especially in configurations that present high safety stock levels; and that production features and product characteristics are important explanatory factors behind high inventory levels. Regulatory requirements also play an important role in explaining the high strategic inventory levels that pharmaceutical companies hold

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

Restless bandit marginal productivity indices II: multiproject case and scheduling a multiclass make-to-order/-stock M/G/1 queue

This paper develops a framework based on convex optimization and economic ideas to formulate and solve approximately a rich class of dynamic and stochastic resource allocation problems, fitting in a generic discrete-state multi-project restless bandit problem (RBP). It draws on the single-project framework in the author's companion paper "Restless bandit marginal productivity indices I: Single-project case and optimal control of a make-to-stock M/G/1 queue", based on characterization of a project's marginal productivity index (MPI). Our framework significantly expands the scope of Whittle (1988)'s seminal approach to the RBP. Contributions include: (i) Formulation of a generic multi-project RBP, and algorithmic solution via single-project MPIs of a relaxed problem, giving a lower bound on optimal cost performance; (ii) a heuristic MPI-based hedging point and index policy; (iii) application of the MPI policy and bound to the problem of dynamic scheduling for a multiclass combined MTO/MTS M/G/1 queue with convex backorder and stock holding cost rates, under the LRA criterion; and (iv) results of a computational study on the MPI bound and policy, showing the latter's near-optimality across the cases investigated

Modelling environmental effects of agriculture : the case of organic rye and grey partridge

Our optimal control model identifies economic reasons as to why farmland bird populations have dramatically declined in modern agricultural landscapes. By integrating recreational wildlife values into farm level decision-making on arable crop choice and herbicide use, we derive those economic instruments needed for creating suitable conditions for game bird species on farmland. Based on the Finnish data available on the grey partridge (Perdix perdix), we illustrate how the optimal acreage subsidy for organically-grown areas, herbicide tax rates and the hunting licence fee could be estimated in monetary terms. Finally, we discuss the benefits and costs of cultivating organic cereals which will enhance preservation of the grey partridge

Optimal growth under military threat

National Defense is a public good that requires resources for its production and its availability affects the economic behavior of private agents. A major policy problem of the government is to find an optimal allocation of resources between private use and national defense. It is shown that, in a simple optimal growth framework, a government's solution may not be the one that satisfies the military authority's objective. Attention is drawn to the need of cooperation between these two bodies and to the importance of the transparency of military expenditures in reaching a compromise that satisfies the public

Lowland farming system inefficiency in Benin (West Africa):

This paper uses a directional distance function and a single truncated bootstrap approach to investigate inefficiency of lowland farming systems in the Benin Republic. First, we employed a dual approach to estimate and decompose short-run profit inefficiency of each farming system into pure technical, allocative and scale inefficiency and also into input and output inefficiency. Second, an econometric analysis of factors affecting the inefficiency was generated using a single truncated bootstrap procedure to improve inefficiency analysis statistically and obtain consistent estimates. In the short run, scale, allocative and output inefficiency were found to be the main sources of inefficiency. Based on inefficiency results, the inefficiency of lowland farming systems is the most diverse. Compared to a vegetable farming system, technical inefficiency is significantly higher if farmers switch to a rice farming system. Scale, allocative, output, and input inefficiency are significantly lower with an integrated ricevegetable farming system and there was high prevalence of increasing returns to scale in the integrated rice-vegetable farming system. Water control and lowland farming systems are complements and play a significant role in the level of inefficiency. Input inefficiency shows the difficulty that the producers face in adjusting the quality and quantity of seeds and fertilizers. The paper provides empirical support for efforts to promote an integrated rice-vegetable farming system in West Africa lowlands to increase food security. Keywords Lowlands . Inefficiency . Bootstrap . Beni

Biodiversity and economic incentives in agriculture : integrating bird fauna values into decision-making

Our optimal control model identifies economic reasons as to why several farmland bird populations have dramatically declined in modern agricultural landscapes. By integrating bird fauna values into decision-making on cereal crop choice, herbicide use and hunting bag size, we derive those economic instruments needed for enhancing biodiversity on farmland and reversing the decline of grey partridge (Perdix perdix) populations. Based on the Finnish data available, we illustrate how the optimal acreage subsidy for organically-grown rye areas, the herbicide tax rate and the grey partridge hunting licence fee could be estimated in monetary terms. The procedure to derive and value the first-best policy instruments is applicable for various components of agri-environmental schemes implemented throughout the European Union

On-line Non-stationary Inventory Control using Champion Competition

The commonly adopted assumption of stationary demands cannot actually reflect fluctuating demands and will weaken solution effectiveness in real practice. We consider an On-line Non-stationary Inventory Control Problem (ONICP), in which no specific assumption is imposed on demands and their probability distributions are allowed to vary over periods and correlate with each other. The nature of non-stationary demands disables the optimality of static (s,S) policies and the applicability of its corresponding algorithms. The ONICP becomes computationally intractable by using general Simulation-based Optimization (SO) methods, especially under an on-line decision-making environment with no luxury of time and computing resources to afford the huge computational burden. We develop a new SO method, termed "Champion Competition" (CC), which provides a different framework and bypasses the time-consuming sample average routine adopted in general SO methods. An alternate type of optimal solution, termed "Champion Solution", is pursued in the CC framework, which coincides the traditional optimality sense under certain conditions and serves as a near-optimal solution for general cases. The CC can reduce the complexity of general SO methods by orders of magnitude in solving a class of SO problems, including the ONICP. A polynomial algorithm, termed "Renewal Cycle Algorithm" (RCA), is further developed to fulfill an important procedure of the CC framework in solving this ONICP. Numerical examples are included to demonstrate the performance of the CC framework with the RCA embedded.Comment: I just identified a flaw in the paper. It may take me some time to fix it. I would like to withdraw the article and update it once I finished. Thank you for your kind suppor