Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified

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Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Manufacturing scheduling is an optimization process that allocates limited manufacturing resources over time among parallel and sequential manufacturing activities. This allocation must obey a set of rules or constraints that reflect the temporal relationships between manufacturing activities and the capacity limitations of a set of shared resources. The allocation also affects a schedule's optimality with respect to criteria such as cost, lateness, or throughput. The globalization of manufacturing makes such optimization increasingly important. To survive in this competitive market, manufacturing enterprises must increase their productivity and profitability through greater shop floor agility. Agent-based manufacturing scheduling systems are a promising way to provide this optimization.

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Distributed manufacturing scheduling using intelligent agents

The ever fast changes of customers’ needs and demands ask for reconfigurable and adaptive production systems, which can provide companies with the proper level of agility and effectiveness, without disregarding at the same time cost factors. In the last decade, a large amount of research works on the adoption of multi-agent systems (MAS) in several industrial environments has flourished. This approach, unlike traditional centralized or multilevel hierarchical approaches, assumes the presence of several decision-making entities, distributed inside the manufacturing system, interacting and cooperating each other in order to achieve optimal global performance. Aim of this paper is at first to provide readers, which are not experienced with the multi-agent approach, with some definitions and categorizations of this paradigm. Secondarily, by making use of an extensive database of more than 100 contributions on this field, authors intend to evaluate how multi-agents systems have really impacted on the industria...

Multi-agent systems in production planning and control: an overview

The sections in this article are


1
Introduction

2
Mathematical Techniques

3
Dispatching Rules

4
Artificial Intelligence (AI) Techniques

5
Artificial Neural Networks

6
Neighborhood Search Methods

7
Fuzzy Logic

8
Swarm

9
Reactive Scheduling

10
Learning in Scheduling

11
Theory of Constraints

12
Summary and Conclusions

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Survey of Job Shop Scheduling Techniques

Describes an intelligent real time cell control architecture for flexible manufacturing systems. This architecture is based on the multi-agent concept of the distributed artificial intelligence. We propose an actor architecture. This multi-agent prototype system involves the following functions: scheduling, dispatching, monitoring and error handling. A new contract protocol for dynamic scheduling is presented. The purpose of this protocol is to dynamically assign operations to the resources of the manufacturing system in order to accomplish the proposed tasks. This protocol is able to deal with exceptions.

Multi-agent system for dynamic scheduling and control in manufacturing cells

Introduction: Nutrition and health are of great importance throughout life, in particular in adulthood due to active population is included among the adults. Therefore, it is essential to assess the quality of the diet and the possible conditioning factors. Objectives: The objectives of this study were to characterize food habits and assess the quality of the diet of university students from the Virtual University of Tunisia, a North African country in nutritional transition. Methods: This is a cross-sectional study performed with data collected from a sample of 54 students from this University. For each individual a questionnaire involving socio-economic and demographic data was self-reported. Food consumption was gathered by a 24 hours recall. The assessment of diet quality was conducted by Healthy Eating Index and Mediterranean Diet Score. Results and discussion: The study revealed that the diet of this population is hypocaloric. The percentage of total energy from proteins was 18% and the percentage of total energy from carbohydrates was about 40%. The diet was high in simple sugars, saturated fat and cholesterol. Apart from oils and fat, the main source of lipids had an animal origin from meat (19%), and the fish group only provided 3% of this macronutrient. According to Healthy Eating Index classification more than 50% of students scored “poor” and more than 40% “needs improvement” about the quality of their diet. The study also showed low adherence rates to the Mediterranean diet.

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Hábitos alimentarios y evaluación nutricional en una población universitaria tunecina

For the purpose of improving the quality in Elearning process and overcoming the limitations of the current online educational environments, we propose to take into consideration the emotional stat...

Emotions Recognition in an Intelligent Elearning Environment.

The flow shop scheduling problem consists, according to a certain number of criteria, in finding the best possible allocation of n jobs on m resources, so that operations of every job must be processed on all resources in a unique order. Because of its highly combinatorial aspect, this scheduling procedure has been widely studied in the literature by exact and mostly heuristic methods. The approach, we adopt here to deal with this problem, combines a Multi-Agent system with a stochastic combinatorial optimization tool, the simulated annealing. This paper stresses the efficiency and the optimality of a distributed implementation of this tool compared to a classical one.

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How To Schedule A Flow Shop Plant By Agents

Objective This study aimed to characterise food consumption among students at the University of Carthage (Tunisia), assessing quality of diet and main dietary patterns, and their association with potential conditioning factors. Design Cross-sectional study. Participants self-reported food consumption in two 24-h recalls and information about sociodemographic, anthropometric and lifestyle characteristics such as BMI, birthplace, physical activity, eating places or kitchen appliances. Mediterranean Diet Score (MDS) and the Nutrient Rich Foods (NRF) index (NRF9.3) were used to assess diet quality. Exploratory factor analysis was conducted to identify eating patterns. Linear regression models were used to test the association of dietary patterns with the diet quality markers. Setting Students at the University of Carthage, Tunisia. Participants 132 students. Results Almost 96 % of participants need shifts towards healthier food. Four main food patterns were identified 'Traditional food', 'Transitional food', 'European breakfast' and 'Western food'. 'Traditional food' was the first dietary model, positively associated with MDS and NRF9.3. Women showed higher adherence to 'European breakfast' and higher quality of diet by NRF9.3. Students eating out of home showed higher adherence to 'Western food', those who never kept a regular schedule of meals consumed lower amount of vegetables and tubers, and sedentary or low active students had a higher intake of ready-to-eat products. Conclusions Diet quality of the participating Tunisian students is inadequate, but traditional Mediterranean diet remains the main pattern in spite of the advanced transitional nutrition. Some lifestyle risk behaviours affecting quality of diet were identified in this work, which supports the youth's particular vulnerability.

Food consumption in Tunisian university students and its association with sociodemographic characteristics and lifestyle behaviours.

Because of its highly combinatorial aspect, its dynamic nature and its practical interest for manufacturing systems, the scheduling problem has been widely studied in the literature by various methods: heuristics, constraint propagation techniques, Constraint Satisfaction Problem formalism, simulated annealing, Taboo search, genetic algorithms, neural networks, etc. This paper deals with the Flow Shop Scheduling Problem (FSSF) by a Multi-Agent_Simulated Annealing Model (MASAM). FSSP consists, according to a certain number of criteria, in finding the best possible allocation of n jobs on m resources, so that operations of every job must be processed on all resources in a unique order. MASM involves two types of agents: jobs and resources. Each job requests the resources by sending its operations one by one, according to the precedence relations existing between these operations (process plan). When a resource requested by a job J, its behavior, based on simulated annealing (SA), consists of two phases: generating a new state comprising necessarily the new operation just received and deciding on whether to accept this new state or not. The generation phase comprises the following subphases: determining a position to J, computing the start and finish dates of J, satisfying the deadline of J and reacting to side effects. The objective (cost function) is minimized setup durations between successive operations on the same resource and to maximize the slack time of each job. It is well known that the convergence of simulated annealing requires an infinite run-time. Consequently, we propose two alternatives to put simulated annealing in practice. The first one consists in implementing a unique centralized SA whilst the second one (Distributed SA) consists in assigning to each resource on the one hand a local cost function representing locally the above objective and on the other hand a simulated annealing whose parameters are locally controlled. The obtained experimental results show that the distributed aspect outperforms the centralized one in terms of quality (cost function) and complexity (run-time). Despite the first promising experimental results, stronger experiments have to be performed, and further comparisons with other known methods have to be done.

Thouraya Daouas

Papers

Hábitos alimentarios y evaluación nutricional en una población universitaria tunecina

Emotions Recognition in an Intelligent Elearning Environment.

How To Schedule A Flow Shop Plant By Agents

Food consumption in Tunisian university students and its association with sociodemographic characteristics and lifestyle behaviours.

Distributed Flow Shop Scheduling Problem - Global versus Local Optimization.