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New stricter environmental regulations and consumer rising issues are making greenhouse gases (GHG) emission an increasing and urgent concern for manufacturing companies. Companies and researchers are seeking appropriate methods to reduce GHG emission of the manufactured products. Previous studies on low-carbon product design mainly concern on a single product. Currently, it is common to design a product family instead of a single product for increasing varieties to satisfy customers’ requirements. Owing to the difference in design methods, the low-carbon design method for a single product cannot handle a product family. In addition, nowadays, the sourcing strategy is widely adopted by companies. A key problem of the procurement is supplier selection. The supplier selection affects not only profit but also GHG emission. However, it has not been simultaneously considered in low-carbon product design. In this article, an optimization model for coordinating low-carbon design of product family and supplier selection is proposed. In the model, the profit and the GHG emission of a product family are taken into consideration at the same time. Moreover, a genetic algorithm is developed to solve the established model. Finally, a case study is performed to verify the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality. [ABSTRACT FROM AUTHOR]

Many real-world problems in Artificial Intelligence (AI) as well as in other areas of computer science and engineering can be efficiently modeled and solved using constraint programming techniques. In many real-world scenarios the problem is partially known, imprecise and dynamic such that some effects of actions are undesired and/or several unforeseen incidences or changes can occur. Whereas expressivity, efficiency, and optimality have been the typical goals in the area, there are several issues regarding robustness that have a clear relevance in dynamic Constraint Satisfaction Problems (CSPs). However, there is still no clear and common definition of robustness-related concepts in CSPs. In this paper, we propose two clearly differentiated definitions for robustness and stability in CSP solutions. We also introduce the concepts of recoverability and reliability, which arise in temporal CSPs. All these definitions are based on related well-known concepts, which are addressed in engineering and other related areas. [ABSTRACT FROM AUTHOR]

Constraint programming is a paradigm wherein relations between variables are stated in the form of constraints. Many real life problems come from uncertain and dynamic environments, where the initial constraints and domains may change during its execution. Thus, the solution found for the problem may become invalid. The search for robust solutions for constraint satisfaction problems (CSPs) has become an important issue in the field of constraint programming. In some cases, there exists knowledge about the uncertain and dynamic environment. In other cases, this information is unknown or hard to obtain. In this paper, we consider CSPs with discrete and ordered domains where changes only involve restrictions or expansions of domains or constraints. To this end, we model CSPs as weighted CSPs (WCSPs) by assigning weights to each valid tuple of the problem constraints and domains. The weight of each valid tuple is based on its distance from the borders of the space of valid tuples in the corresponding constraint/domain. This distance is estimated by a new concept introduced in this paper: coverings. Thus, the best solution for the modeled WCSP can be considered as a most robust solution for the original CSP according to these assumptions. [ABSTRACT FROM AUTHOR]

Container terminals are open systems that generally serve as a transshipment zone between vessels and land vehicles. These terminals carry out a large number of planning and scheduling tasks. In this paper, we consider the problem of scheduling a number of incoming vessels by assigning a berthing position, a berthing time, and a number of Quay Cranes to each vessel. This problem is known as the Berth Allocation Problem and the Quay Crane Assignment Problem. Holds of vessels are also managed in order to obtain a more realistic approach. Our aim is to minimize the total waiting time elapsed to serve all these vessels. In this paper, we deal with the above problems and propose an innovative metaheuristic approach. The results are compared against other allocation methods. [ABSTRACT FROM AUTHOR]

The areas of planning and scheduling (from the Artificial Intelligence point of view) have seen important advances thanks to application of constraint satisfaction techniques. Currently, many important real-world problems require efficient constraint handling for planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Solutions to these problems require integration of resource allocation and plan synthesis capabilities. Hence to manage such complex problems planning, scheduling and constraint satisfaction must be interrelated. This special issue on Constraint Satisfaction for Planning and Scheduling Problems compiles a selection of papers dealing with various aspects of applying constraint satisfaction techniques in planning and scheduling. The core of submitted papers was formed by the extended versions of papers presented at COPLAS'2009: ICAPS 2009 Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems. This issue presents novel advances on planning, scheduling, constraint programming/constraint satisfaction problems (CSPs) and many other common areas that exist among them. On the whole, this issue mainly focus on managing complex problems where planning, scheduling, constraint satisfaction and search must be combined and/or interrelated, which entails an enormous potential for practical applications and future research. [ABSTRACT FROM AUTHOR]

Over the last few years constraint satisfaction, planning, and scheduling have received increased attention, and substantial effort has been invested in exploiting constraint satisfaction techniques when solving real life planning and scheduling problems. Constraint satisfaction is the process of finding a solution to a set of constraints. Planning is the process of finding a sequence of actions that transfer the world from some initial state to a desired state. Scheduling is the problem of assigning a set of tasks to a set of resources subject to a set of constraints. In this paper, we introduce the main definitions and techniques of constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view. [ABSTRACT FROM AUTHOR]

In recent years the development of automated traffic systems has been gaining increasing attention, and a substantial amount of effort has been invested in trying to find a solution to problems associated to road transport. Among these problems are road accidents caused by human-related factors, such as tiredness, loss of control, a slow reaction time, limited field of view, etc. Another transport-related problem is that of loss of time which may be caused by a slow driving speed due to weather conditions, road conditions, visibility, traffic congestion, etc. In this paper, we present a distributed architecture and the underline distributed algorithm for solving the global road transportation system, which is being developed by several European Universities. [ABSTRACT FROM AUTHOR]

Planning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI). Many real-world problems are known as AI planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. Therefore, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Constraint satisfaction plays also an important role to solve real-life problems, so that integrated techniques that manage planning and scheduling with constraint satisfaction remains necessary. This special issue on Planning, Scheduling and Constraint Satisfaction compiles a selection of papers of CAEPIA’2007 workshop on Planning, Scheduling and Constraint Satisfaction and COPLAS’2007: CP/ICAPS 2007 Joint Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems. This issue presents novel advances on planning, scheduling, constraint programming/constraint satisfaction problems (CSPs) and many other common areas that exist among them. On the whole, this issue mainly focus on managing complex problems where planning, scheduling, constraint satisfaction and search must be combined and/or interrelated, which entails an enormous potential for practical applications and future research. Furthermore, this issue also includes a complete survey about constraint satisfaction, planning, scheduling and integration among these areas. [ABSTRACT FROM AUTHOR]

Many real problems can be naturally modelled as constraint satisfaction problems (CSPs). However, some of these problems are of a distributed nature, which requires problems of this kind to be modelled as distributed constraint satisfaction problems (DCSPs). In this work, we present a distributed model for solving CSPs. Our technique carries out a partition over the constraint network using a graph partitioning software; after partitioning, each sub-CSP is arranged into a DFS-tree CSP structure that is used as a hierarchy of communication by our distributed algorithm. We show that our distributed algorithm outperforms well-known centralized algorithms solving partitionable CSPs. [ABSTRACT FROM AUTHOR]