Jorge Pinho de Sousa

A time indexed formulation of non-preemptive single machine scheduling problems

We consider the formulation of non-preemptive single machine scheduling problems using time-indexed variables. This approach leads to very large models, but gives better lower bounds than other mixed integer programming formulations. We derive a variety of valid inequalities, and show the role of constraint aggregation and the knapsack problem with generalised upper bound constraints as a way of generating such inequalities. A cutting plane/branch-and-bound algorithm based on these inequalities has been implemented. Computational experience on small problems with 20/30 jobs and various constraints and objective functions is presented.

Using metaheuristics in multiobjective resource constrained project scheduling

Although single objective metaheuristics are widely spread and applied in many combinatorial optimisation problems, only very recently have multiobjective metaheuristics (MOMH) been designed and used in practice. They aim at obtaining good approximations of the set of nondominated solutions of a problem, in an efficient way. In this work, we have applied multiobjective versions of simulated annealing and taboo search to the resource constrained project scheduling problem (RCPSP), in order to minimise the makespan, the “weighted” lateness of activities and the violation of resource constraints. Computational experience performed on randomly generated instances shows that this general approach is flexible, effective and able to deal with multiple objectives and with variations in the problem structure.

Recyclable waste collection planning--a case study

Abstract This paper describes a study of planning vehicle routes for the collection of urban recyclable waste. The aim is to create collection routes for every day of the month, to be repeated every month, minimizing the operation cost. Two important features of the problem are the planning of a relatively long period of time and the separate collection of three types of waste. The collection operation was modelled in accordance to the practice of the company that manages the collection system. Heuristic techniques were developed to solve the model in three phases: definition of the geographic zones served by the vehicles, definition of the waste type to collect on each day of the month, definition of the collection routes. Preliminary results suggest that significant economies in collection costs are possible.

Partner selection in virtual enterprises

A virtual enterprise (VE) is a temporary organisation that pools member enterprises core competencies and exploits fast changing market opportunities. VEs offer new opportunities to companies operating with a growing number of participants (consumers, vendors, partners and others) in a global business environment. The success of such an organisation is strongly dependent on its composition, and the selection of partners therefore becomes a crucial issue. Partner selection can be viewed as a multi-criteria decision making problem that involves assessing trade-offs between conflicting tangible and intangible criteria, and stating preferences based on incomplete or non-available information. In general, this is a very complex problem due to the large number of alternatives and criteria of different types (quantitative, qualitative and stochastic). In this paper we propose an integrated approach to rank alternative VE configurations using an extension of TOPSIS (a technique for ordering preferences by similarity to an ideal solution) for fuzzy data, improved through the use of a tabu search meta-heuristic. A sensitivity analysis is also presented. Preliminary computational results clearly demonstrate the potential of the approach for practical application.

Distributed planning and control systems for the virtual enterprise: organizational requirements and development life-cycle

This paper describes the requirements analysis and system specification of an Order Promise module to be used as part of a broader Decision Support System for production and operations planning of a Virtual Enterprise. This work is part of a broader project with a particular focus on the microelectronics industry which is a good example of Virtual Enterprise, and where a quick response to the customers needs and to unpredictable changes in production conditions is considered a major factor for success. First, the analysis and specification are presented within a development framework that involves the study of organizational issues of semiconductor enterprises. The use of ontological engineering for supporting the communication and shared understanding of the system concepts is explained and a virtual enterprise ontology is outlined. Following the clarification of the concept of virtual enterprise, the generic techno-organizational requirements for the information system are derived. Finally, a specification of the global planning module and a more detailed one regarding the order promise module is presented.

An advanced agent-based order planning system for dynamic networked enterprises

There is an increasing interest in exploring the opportunities for competitive advantage that arise from reinforcing core competencies and innovative capabilities of the individual companies, and by forming integrated supply networks. In complex and dynamic environments such as the automotive and semiconductor industries, managing and co-ordinating the procurement of materials, their transformation into intermediate and finished products, and the distribution to the final customers, are very demanding tasks in terms of information systems. In general, current available software packages do not provide the full support needed for networked and distributed organizations, and are clearly insufficient in what concerns the planning and coordination activities needed in these heterogeneous environments. In order to adequately tackle these problems, this work proposes a multi-agent system architecture for real-time customer-order planning in distributed manufacturing enterprises, addressing the requirements of a make-to-order environment.  The present research work resulted from the Co-OPERATE European Project. Enhanced visibility of information, early warning of disturbances, synchronized production and collaborative planning in the supply chain are the general goals of the project. In particular, for aggregate planning and order promising, a distributed and decentralized information system, based on an architecture of agents and extensively using the internet, was designed and implemented. This system aims at responding to the basic requirements of cooperativeness, integration and configurability, and at providing new and more powerful decision support.

A component-based approach to support order planning in a distributed manufacturing enterprise

Abstract Traditional Production Planning and Control systems do not successfully deal with new organisational forms of manufacturing, like production “islands”, product oriented or customer driven production. These current trends lead in practice to a strong decentralisation of production management tasks and to an “object oriented structuring” of the manufacturing process. Planning and operation in this global environment requires new skills and new approaches, namely, the co-ordination of global networks of manufacturing units and of large complex supply chains. In this paper, we present a decentralised information system designed to address the tasks of production planning that result from sales orders, originated in customers located anywhere in the world, and accomplished through a distributed manufacturing network. The system addresses the requirements of a make-to-order environment and is hopefully able to produce realistic satisfactory delivery dates. The information infrastructure designed and implemented using distributed object-oriented technology with a component based architecture has proven to be efficient and powerful, satisfying all the major tight requirements of information systems in an environment of distributed manufacturing.

Using GRASP to Solve the Unit Commitment Problem

In this paper, the Unit Commitment (UC) problem is presented and solved, following an innovative approach based on a metaheuristic procedure. The problem consists on deciding which electric generators must be committed, over a given planning horizon, and on defining the production levels that are required for each generator, so that load and spinning reserve requirements are verified, at minimum production costs. Due to its complexity, exact methods proved to be inefficient when real size problems were considered. Therefore, heuristic methods have for long been developed and, in recent years, metaheuristics have also been applied with some success to the problem. Methods like Simulated Annealing, Tabu Search and Evolutionary Programming can be found in several papers, presenting results that are sufficiently interesting to justify further research in the area. In this paper, a resolution framework based on GRASP – Greedy Randomized Adaptive Search Procedure – is presented. To obtain a general optimisation tool, capable of solving different problem variants and of including several objectives, the operations involved in the optimisation process do not consider any particular characteristics of the classical UC problem. Even so, when applied to instances with very particular structures, the computational results show the potential of this approach.

Partner selection in virtual enterprises: a multi-criteria decision support approach

Partner selection in virtual enterprises (VE) can be viewed as a multi-criteria decision making problem that involves assessing trade-offs between conflicting tangible and intangible criteria. In general, this is a very complex problem due to the dynamic topology of the network, the large number of alternatives and the different types of criteria. In this paper we propose an exploratory process to help the decision-maker obtain knowledge about the network in order to identify the criteria and the companies that best suit the needs of each particular project. This process involves a multi-objective tabu search metaheuristic designed to find a good approximation of the Pareto front, and a fuzzy TOPSIS algorithm to rank the alternative VE configurations. In the exploratory phase we apply clustering analysis to confine the search according to the decision-maker beliefs, and case base reasoning, an artificial intelligence approach, to totally or partially construct VEs by reusing past experiences. Preliminary computational results clearly demonstrate the potential of the approach for practical application.

Hierarchical production planning in a make-to-order company: A case study

In this paper, we analyse a complex production planning problem in a Make-To-Order company, involving quoting due dates, along with production scheduling, some plant layout decisions and line balancing issues. A general framework has been developed that identifies the main levels for decision making, and tackles these problems in a hierarchical fashion, the resulting interactions being carefully taken into consideration. This framework, based on considering discretised planning horizons, is implemented on a Decision Support System developed around an interface specially designed to involve the various participants in the planning process. In practice, the system is composed by a package of software products used as add-ons to the main management information system of the company. A comprehensive and detailed description of the approach, techniques used and developed algorithms is presented. The experience in using a first prototype of the system shows this may become a highly valuable computer tool in production planning, layout design and assignment of orders.