Reinforcement learning for Order Acceptance on a shared resource

Order acceptance (OA) is one of the main functions in business control. Basically, OA involves for each order a reject/accept decision. Always accepting an order when capacity is available could disable the system to accept more convenient orders in the future with opportunity losses as a consequence. Another important aspect is the availability of information to the decision-maker. We use the stochastic modeling approach, Markov decision theory and learning methods from artificial intelligence to find decision policies, even under uncertain information. Reinforcement learning (RL) is a quite new approach in OA. It is capable of learning both the decision policy and incomplete information, simultaneously. It is shown here that RL works well compared with heuristics. Finding good heuristics in a complex situation is a delicate art. It is demonstrated that a RL trained agent can be used to support the detection of good heuristics

On the Nonlinearity of Modern Shock-Capturing Schemes

The development is reviewed of shock capturing methods, paying special attention to the increasing nonlinearity in the design of numerical schemes. The nature is studies of this nonlinearity and its relation to upwind differencing is examined. This nonlinearity of the modern shock capturing methods is essential, in the sense that linear analysis is not justified and may lead to wrong conclusions. Examples to demonstrate this point are given

On a number theoretic property of optimal maintenance grouping

In this paper we consider the problem of preventive maintenance of a failure prone system, for which a number of maintenance actions has to be executed on a regular basis. For each action i the frequency is prescribed. Between consecutive actions of type i there is an integer interspacing of T(i) time units. The set-up costs are activity dependent. The set-up structure is supposed to be tree-like and additive over the set-up nodes involved in the action or group of actions. Hence, for different activities with common setup nodes joint execution leads to set-up costs reduction. The question is how the actions should be arranged in time in order to exploit this set-up costs reduction effect maximally. It is shown that the time averaged set-up costs are minimal if a main peak clustering property is satisfied: all maintenance actions are combined at one moment in time. Intuitively, this property is appealing, but it asks for some interesting and non-trivial applications of number theory and inductive reasoning, to prove it

Mixed policies for recovery and disposal of multiple-type consumer products

New European government policies aim at the closure of material flows as part of integrated chain management (ICM). One of the main implementation instruments is extended producer responsibility, which makes original equipment manufacturers (OEMs) formally responsible for take-back, recovery, and reuse of discarded products. One of the key problems for OEMs is to determine a recovery strategy, i.e., determine to what extent return products must be disassembled and which recovery and disposal (RD) options should be applied. On a tactical management level, this involves anticipation of problems such as meeting legislation, limited volumes of secondary end markets, bad quality of return products, and facility investments in recycling infrastructure. In this paper, a model is presented that can be used to determine a recovery strategy for multiple-type consumer products. The objective function incorporates technical, ecological, and commercial decision criteria and optimization occurs using a two-level optimization procedure. First, a set of potential product recovery and disposal (PRD) strategies is generated for each separate product type. Secondly, optimal PRD strategies are assigned to the products within a coherent multiproduct or product group policy. The aim is to find an optimal balance between maximizing net profit and meeting constraints like recovery targets, limited market volumes, and processing capacities. A TV case is worked out to illustrate the working of the model. Also, the managerial use of the model is discussed in view of establishing an economically and ecologically sound base for achieving ICM

Mixed policies for recovery and disposal of multiple type assembly products : commercial exploitation of compulsory return flows

New government policies aim at the closure of material flows as part of Integrated Chain Management (ICM). One of the main implementation instruments is extended producer responsibility, which makes Original Equipment Manufacturers (OEMs) formally responsible for take-back, recovery and reuse of discarded products. One of the key problems for OEMs is to determine to what extent return products must be disassembled and which Recovery and Disposal (RD-) options should be applied. On a tactical management level, this involves anticipation to problems like meeting legislation, limited volumes of secondary end markets, bad quality of return products and facility investments in recycling infrastructure. In this paper a model is described that can be used to find such a Recovery and Disposal Policy for multiple product types. The objective function incorporates technical, ecological and commercial decision criteria and optimisation occurs using a rwo-level optimisation procedure. First, a set of potential Product Recovery and Disposal Strategies is generated for each separate product type. Secondly, optimal PRD-strategies are assigned to the products within the context of a coherent product group. The aim is to find an optimal balance berween maximising net profit and meeting constraints like recovery targets, limited market volumes and processing capacities, A TV-case is worked out to illustrate the working of the model

Myofibrillar Protein Status of the Gastrocnemius in Male Rats: Effect of Mild Undernutrition

The aim of this work was the determination of the myofibrillar protein profiles in the fed and the mildly underfed rat. Sixteen male rats were divided into 2 groups: CR (control) fed ad libitum and MR (mildly undernourished) fed 75% of energetic maintenance needs. The animals were sacrificed at day 23 and the gastrocnemius muscle was taken for myofibrillar protein characterisation. The myofibrillar protein profiles were found to be very similar in the two groups revealing the lack of preferred catabolism of myofibrillar proteins and consequently that the muscle structure is maintained even in situations of mild undernutrition