Romain Guillaume

Modelling of ill-known requirements and integration in production planning

Making decisions on the basis of uncertain forecasts is one of the key challenges for efficient supply chain management. This article suggests the use of the theory of possibility for building a procurement plan on the basis of ill-known requirements. These requirements, expressed in quantities by date, may come from various sources: forecasts or orders for instance. The possible types of imperfection pervading requirement are analysed, and a unified representation model is suggested. A method is then described for calculating a plausible demand per period without loss of information; it is illustrated with an example in the last section.

A robust lot sizing problem with ill-known demands

The paper deals with a lot sizing problem with ill-known demands modeled by fuzzy intervals whose membership functions are possibility distributions for the values of the uncertain demands. Optimization criteria, in the setting of possibility theory, that lead to choose robust production plans under fuzzy demands are given. Some algorithms for determining optimal robust production plans with respect to the proposed criteria, and for evaluating production plans are provided. Some computational experiments are presented.

A Methodology to Anticipate the Activity Level of Collaborative Networks: The Case of Urban Consolidation

This article presents a methodology relative to the assessment of a particular measure in city logistics: Urban Consolidation Centers (UCCs). We identify that this kind of collaborative network is, often, poorly evaluated and thus operates in a way that is neither sustainable nor efficient. By mobilizing several fields of knowledge, such as operational research, game theory, and transportation studies on real cases, we propose a solution to anticipate the activity level of a UCC and determine the condition under which it generates benefits for carriers. The aim is to provide a suitable aid to public decision makers in territorial management. The study concludes by an application of the method on the test case of the city of Saint-Etienne, France.

Towards a transparent deliberation protocol inspired from supply chain collaborative planning

In this paper we propose a new deliberation process based on argumentation and bipolar decision making in a context of agreed common knowledge and priorities together with private preferences. This work is inspired from the supply chain management domain and more precisely by the “Collaborative Planning, Forecasting and Replenishment” model which aims at selecting a procurement plan in collaborative supply chains.

MRP with imprecise demand and uncertain lead time

Taking into account the uncertainty of real data in the planning process is a real challenge for nowadays companies. It is suggested in this communication to take into account the demand uncertainty, but also the uncertainty on the lead times, for deciding which quantities of components should be released, and when. In that purpose, the various situations that may happen are summarized in a graph which design is detailed here.

Analyzing a Bipolar Decision Structure Through Qualitative Decision Theory

In this paper we study the link between a bipolar decision structure called bipolar leveled framework (BLF) and the qualitative decision theory based on possibility theory. A BLF defines the set of possible decision principles that may be used in order to evaluate the admissibility of a given candidate. A decision principle is a rule that relates some observations about the candidate to a given goal that the selection of this candidate may achieve or miss. The decision principles are ordered according to the importance of the goal they support. Oppositions to decision principles are also described in the BLF under the form of observations that contradict the realization of the decision principles. In order to show that this rich and visual framework is well founded we show how the notions defined in the BLF can be translated in terms of qualitative decision theory.

Robust material requirement planning with cumulative demand under uncertainty

Abstract In this paper, we deal with the problem of tactical capacitated production planning with the demand under uncertainty modelled by closed intervals. We propose a single-item with backordering model under small uncertainty in the cumulative demand for the Master Production Scheduling (MPS) problem with different rules, namely the Lot For Lot rule and the Periodic Order Quantity rule. Then we study a general multilevel, multi-item, multi-resource model with backordering and the external demand on components for the Material Requirement Planning (MRP) problem under uncertainty in the cumulative demand. In order to choose robust production plans for the above problems that hedge against uncertainty, we adopt the well-known minmax criterion. We propose polynomial methods for evaluating the impact of uncertainty on a given production plan in terms of its cost and for computing optimal robust production plans for both problems (MPS/MRP) under the assumed interval uncertainty representation. We show in this way that the robust problems (MPS/MRP) under this uncertainty representation are not much computationally harder than their deterministic counterparts.

Deciding under Ignorance: In Search of Meaningful Extensions of the Hurwicz Criterion to Decision Trees

The major paradigm for sequential decision under uncertainty is expected utility. This approach has many good features that qualify it for posing and solving decision problems, especially dynamic consistency and computational efficiency via dynamic programming. However, when uncertainty is due to sheer lack of information, and expected utility is no longer a realistic criterion, the approach collapses because dynamic consistency becomes counterintuitive and the global non-expected utility criteria are no longer amenable to dynamic programming. In this paper we argue against Resolute Choice strategies, following the path opened by Jaffray, and suggest that the dynamic programming methodology may lead to more intuitive solutions respecting the Consequentialism axiom, while a global evaluation of strategies relying on lottery reduction is questionable.

Production planning with uncertain demands

The paper deals with a single-item production planning problem with uncertain demands modeled by fuzzy intervals whose membership functions are possibility distributions for the values of the uncertain demands. Optimization criteria, in the setting of possibility theory, that lead to choose robust production plans under fuzzy demands are given. Algorithms for determining optimal robust production plans with respect to the proposed criteria are provided and some computational experiments are presented.

Group Decision Making in a Bipolar Leveled Framework

We study the use of a bipolar decision structure called BLF (bipolar leveled framework) in the context of collective decision making where the vote consists in giving factual information about a candidate which the group should accept or reject. A BLF defines the set of possible decision principles that may be used in order to evaluate the admissibility of a given candidate. A decision principle is a rule that relates some observations about the candidate to a given goal that the selection of this candidate may achieve or miss. The decision principles are ordered accordingly to the importance of the goal they support. Oppositions to decision principles are also described in the BLF under the form of observations that contradict the realization of the decision principles. We show how the use of a common BLF may reduce the impact of manipulation strategies in the context of group decision making.