Lionel Dupont

Minimizing the makespan on a batch machine with non-identical job sizes: an exact procedure

Abstract A batch processing machine can simultaneously process several jobs forming a batch. This paper considers the problem of scheduling jobs with non-identical capacity requirements, on a single-batch processing machine of a given capacity, to minimize the makespan. The processing time of a batch is equal to the largest processing time of any job in the batch. We present some dominance properties for a general enumeration scheme and for the makespan criterion, and provide a branch and bound method. For large-scale problems, we use this enumeration scheme as a heuristic method. Scope and purpose Usually in classical scheduling problems, a machine can perform only one job at a time. Although, one can find machines that can process several jobs simultaneously as a batch. All jobs of a same batch have common starting and ending times. Batch processing machines are encountered in many different environments, such as burn-in operations in semiconductor industries or heat treatment operations in metalworking industries. In the first case, the capacity of the machine is defined by the number of jobs it can hold. In the second case, each job has a certain capacity requirement and the total size of a batch cannot exceed the capacity of the machine. Hence, the number of jobs contained in each batch may be different. In this paper, we consider this second case (which is more difficult) and we provide an exact method for the makespan criterion (minimizing the last ending time).

Scheduling rules to minimize total tardiness in a parallel machine problem with setup and calendar constraints

Quality control lead times are one of most significant causes of loss of time in the pharmaceutical and cosmetics industries. This is partly due to the organization of laboratories that feature parallel multipurpose machines for chromatographic analyses. The testing process requires long setup times and operators are needed to launch the process. The various controls are non-preemptive and are characterized by a release date, a due date and available routings. These quality processes lead to significant delays, and we therefore evaluate the total tardiness criterion. Previous heuristics were defined for the total tardiness criterion, parallel machines, and setup such as apparent tardiness cost (ATC) and ATC with setups (ATCS). We propose new rules and a simulated annealing procedure in order to minimize total tardiness.

A decision support system for optimising the order fulfilment process

Many authors have highlighted gaps at the interfaces between supply chains (SCs) and demand chains. Generally, the latter tends primarily to be ‘agile’ by maximising effectiveness and responsiveness while the former tends to be ‘lean’ by maximising efficiency. When, in the SC, disruptions (that lead to stock-out situations) occur after customer orders have been accepted, managers are faced with the problem of maximising customer satisfaction while taking into consideration the conflicting objectives of the supply and demand sides of the order fulfilment process. This article proposes a cross-functional multi-criteria decision-making (advanced available-to-promise) tool that provides different strategic options from which a solution can be chosen. It also proposes a performance measurement system to support the decision-making and improvement process. The results of some experimental tests show that the model enables to make strategic decisions on the degree of flexibility required to achieve the desired level of customer service.

A decision support system for robust humanitarian facility location

Each year, more than 400 natural disasters hit the world. To be more responsive, humanitarians organize stocks of relief items. It is an issue to know the quantity of items to be stored and where they should be positioned. Many authors have tried to address this issue both in industrial and humanitarian environments. However, humanitarian supply chains today do not perform correctly, particularly as regards resilience and efficiency. This is mainly due to the fact that when a disaster occurs, some hazards can strongly impact the network by destroying some resources or collapsing infrastructure. The expected performance of the relief response is consequently strongly decreased. The problem statement of our research work consists in proposing a decision-making support model in artificial intelligence dedicated to the humanitarian world and capable of designing a coherent network that is still able to adequately manage the response to a disaster despite failures or inadequacies of infrastructure and potential resources. This contribution is defined through a Stochastic Multi-Scenarios Program as a core and a set of extensions. A real-life application case based on the design of a humanitarian supply chain in Peru is developed in order to highlight the benefits and limits of the proposition.

Optimization of a supply portfolio in the context of supply chain risk management: literature review

The aim of this paper is to review the literature in the field of supplier selection under supply chain risk management. Collected papers from 2003 to 2014 are analyzed and classified, first, according to the characteristics of the problem they deal with, secondly, according to the approach they propose, and thirdly, according to the techniques they use. The papers have been grouped into five categories: the first group relates to quantitative approaches to supplier selection, the second concerns qualitative approaches, the third consists of hybrid approaches that blend two or more different approaches together, the fourth relates to simulation approaches and the last group to artificial intelligence. The techniques used in each category are outlined. The different approaches and their associated techniques are analyzed and some recommendations are made on improving their efficiency and performance. This paper is thus a systematic scope review of journal articles and conference papers issued during this period. It brings together a collection of 124 papers on the topic of supplier selection under supply chain risk management.

Understanding and Managing Shared Projects in Smes Networks

This research work presents a management framework applied to a network of SMEs which organizes its production like a shared project Starting from a set of network definitions and considering the collective nature of their activities and the concentration on the own core competences, the study tries to specify what is expected from the management. Then, it proposes a framework to plan and manage the collaborative work using concepts coming from project and risk management theories and practices based on the results of an empirical study in a twelve SMEs network from the aeronautical sector located in Toulouse, France. A specific typology of risks that affect the operation of networks is proposed. Additionally, work planning recommendations are presented.

Supplier selection under risk of delivery failure: a decision-support model considering managers’ risk sensitivity

This paper studies the problem of supplier selection and order allocation in a retail supply chain (comprising suppliers, a central purchasing unit and outlets) under disruption risk. The final demand is deterministic. Suppliers are located in different geographic areas, and supplies are subject to a positive probability of disruption. Different capacity and failure probabilities for each supplier are considered. Our analysis focuses on the insurance versus profitability trade-off faced by a supply manager who buys from suppliers for the outlets. Instead of determining optimal decisions given an objective function and the risk sensitivity of the decision-maker, we use a mixed integer linear programming approach to provide decision-making support that shows a supply manager the ‘elasticity of (expected) losses versus (expected) profits’. Under this model, and depending on the profit-and-loss targets, a supply manager of known risk sensitivity (i.e. risk aversion and loss aversion) can make better decisions when choosing suppliers. Moreover, taking into account, the impact of the share of fixed costs that must be covered by the operation, we consider the net values of expected profit and loss. We discuss the potential influence of the level of the firm’s fixed costs on the supply strategy. In particular, we show how the minimum value of the gross margin needed for the strategy’s profitability affects that strategy. A numerical application is conducted to illustrate the contribution of our decision-making support mechanism, and several managerial insights are obtained.

Towards a demand forecast methodology for recurrent disasters

Humanitarian supply chains have received a lot of attention over the last fifteen years, and can now be considered a new research area. But a gap exists between the research work proposals and their applications in the field. One of the main issues is that the demand, in the case of disaster, is hard to assess because of the high-level of uncertainty. Gathering knowledge about future demand is of prime importance to be able to propose models, which are relevant to implement for a real problem. This paper tackles this problematic proposing a four-step methodology for forecast disaster impact, and in this way, the future demand, such as cyclones in the Caribbean or earthquakes along the Pacific Ring of Fire. This approach uses data analysis techniques such as Principal Component Analysis and Multivariate Regression Analysis. An application case on Peruvian earthquake demand is proposed to illustrate the benefits of our approach.

A Location-Allocation Model for More Consistent Humanitarian Supply Chains

During the preparedness phase, humanitarians plan their relief response by studying the potential disasters, their consequences and the existing infrastructures and available resources. However, when the disaster occurs, some hazards can impact strongly the network by destroying some resources or collapsing infrastructures. Consequently, the performance of the relief network could be strongly decreased. The problem statement of our research work can be defined as the capability to design a consistent network that would be able to manage adequately the disaster response despite of potential failures or deficiencies of infrastructures and resources. Basically, our research work consists in proposing an innovative location-allocation model in order to improve the humanitarian response efficiency (cost minimization) and effectiveness (non-served beneficiaries minimization) regarding the foreseeable network weaknesses. A Stochastic Mixed Integer Program is proposed to reach this goal. A numerical application regarding the management of the Peruvian earthquake’s relief network is proposed to illustrate the benefits of our proposition.

How to Maintain the Network Resilience and Effectiveness in Case of Resources Reduction? A Covering Set Location Approach

The objective of covering set location models consists in designing or modifying a network (commercial shops, antennas, drugstores, etc.) in order to cover totally or partially the demand related to a given area. When an area is covered by several activities (multi coverage), this can improve the responsiveness of the network (in case of congestion traffic for example) but this can also have negative effects such as customers’ cannibalization. In this paper, we propose an innovative covering set location model able to support decision makers to design networks that have a high resiliency level (i.e. ability to maintain service level despite hazards or failures) in a context of resources reduction (i.e. closing-down of network installations). Basically, the proposal allows studying the benefits and limits of multi coverage on each part of the network. Heuristic and exact methods are suggested to solve this problem.