Computing replenishment cycle policy parameters for a perishable item

In many industrial environments there is a significant class of problems for which the perishable nature of the inventory cannot be ignored in developing replenishment order plans. Food is the most salient example of a perishable inventory item. In this work, we consider the periodic-review, single-location, single-product production/inventory control problem under non-stationary stochastic demand and service level constraints. The product we consider can be held in stock for a limited amount of time after which it expires and it must be disposed of at a cost. In addition to wastage costs, our cost structure comprises fixed and unit variable ordering costs, and inventory holding costs. We propose an easy-to-implement replenishment cycle inventory control policy that yields at most 2N control parameters, where N is the number of periods in our planning horizon. We also show, on a simple numerical example, the improvement brought by this policy over two other simpler inventory control rules of common use

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

Defining and characterising structural uncertainty in decision analytic models

An inappropriate structure for a decision analytic model can potentially invalidate estimates of cost-effectiveness and estimates of the value of further research. However, there are often a number of alternative and credible structural assumptions which can be made. Although it is common practice to acknowledge potential limitations in model structure, there is a lack of clarity about methods to characterize the uncertainty surrounding alternative structural assumptions and their contribution to decision uncertainty. A review of decision models commissioned by the NHS Health Technology Programme was undertaken to identify the types of model uncertainties described in the literature. A second review was undertaken to identify approaches to characterise these uncertainties. The assessment of structural uncertainty has received little attention in the health economics literature. A common method to characterise structural uncertainty is to compute results for each alternative model specification, and to present alternative results as scenario analyses. It is then left to decision maker to assess the credibility of the alternative structures in interpreting the range of results. The review of methods to explicitly characterise structural uncertainty identified two methods: 1) model averaging, where alternative models, with different specifications, are built, and their results averaged, using explicit prior distributions often based on expert opinion and 2) Model selection on the basis of prediction performance or goodness of fit. For a number of reasons these methods are neither appropriate nor desirable methods to characterize structural uncertainty in decision analytic models. When faced with a choice between multiple models, another method can be employed which allows structural uncertainty to be explicitly considered and does not ignore potentially relevant model structures. Uncertainty can be directly characterised (or parameterised) in the model itself. This method is analogous to model averaging on individual or sets of model inputs, but also allows the value of information associated with structural uncertainties to be resolved.

Global supply chains of high value low volume products

Imperial Users onl

A framework for the design and analysis of incentive systems for food safety control in supply chains

Since 2005 the EU food industry has primary legal responsibility for food safety control. This requires new responsibilities and relationships between government and industry, and between companies. This research presents a framework for incentive systems for food safety control in supply chains. It emphasizes key elements of food safety control from multiple perspectives and provides insights for the design and analysis of incentive systems for food safety control. An incentive system combines inter-company incentive mechanisms with intra-company decision making processes to control a hazard within the legal environment. Incentive mechanisms, which consist of a performance measure and a performance reward, induce companies to use control measures. The framework can be used to analyze the effectiveness and efficiency of alternative incentive systems in which companies have to cooperate with partners from other stages of the supply chain.Incentive mechanism, food safety, supply chain control., Agricultural and Food Policy,

Spurious complexity and common standards in markets for consumer goods

It has been argued that cognitively constrained consumers respond sub-optimally to complex decision problems, and that firms can exploit these limitations by introducing spurious complexity into tariff structures, weakening price competition. We model a countervailing force. Restricting one's choices to the most easily comparable options is a psychologically well-attested heuristic. Consumers who use this heuristic favour firms that follow common conventions about tariff structures. Because a 'common standard' promotes price competition, a firm's use of it signals that it offers value for money, validating the heuristic. This allows an equilibrium in which firms use common standards and set competitive prices.common standard, spurious complexity, cognitive limitations

Assessing the sustainability of biomass supply chains for energy exploitation

Biomass use has increased significantly lately, partly due to conventional fuels price increase. This trend is more evident in rural areas with significant local biomass availability. Biomass may be used in various ways to generate heat. In this work, the focus is on comparing two different biomass energy exploitation supply chains that provide heat at a specific number of customers at a specific cost. The first system is pellets production from biomass and distribution of the pellets to the final customers for use in domestic pellet boilers. The second option is centralized energy co-generation, which entails simultaneous electricity and heat generation. In the latter case, heat is distributed to the customers via a district heating network whereas electricity is fed to the electricity grid. The biomass source examined is locally available agricultural residues and the model is applied to a case study region in Greece. The aim of this work is to determine how these two different biomass exploitation options perform in sustainability terms, including the economic, environmental and social dimensions of sustainability. The effect of trying to optimise separately the economic and environmental dimensions of sustainability on the system design is examined, while at the same time taking into account the social dimension. Furthermore, a bi-objective optimisation is employed, to overcome the limitations of the single-objective optimisation. Both the upstream and the downstream supply chains of the pelletizing/CHP units are modelled