Instances for the paper "Infrastructure Planning, Fleet Sizing, and Scheduling for E-public Transports"

Instances for the paper "Infrastructure Planning, Fleet Sizing, and Scheduling for E-public Transports"

Cloud Manufacturing is an emerging concept that enables the orchestration, matching, and sharing of services or resources among collaboration partners or intra-plant facilities. In this context, we introduce the capacitated multi-level lot sizing problem with transshipments and set up carry-over. We consider components that can only be produced by one specific agent as well as components, that can be provided by more than one producer. Since capacities are limited, agents might have to share resources and cover required demands jointly. In this case, finished components are trans- shipped between agents. As an agent can be in charge of producing more than one component, we include the concept of set up carry-over into our modeling. We address a centralized planning approach, where the objective is to find a globally optimized lot sizing plan for all participating agents. Thus, we cover both horizontal and vertical collaboration between agents. The new prob- lem class is formulated mathematically. The used dataset extends publicly available instances (http://www.dmlulsp.com/) and provides production capacities for 2 (first block) and 5 (second block) agents.

Cloud Manufacturing is an emerging concept that enables the orchestration, matching, and sharing of services or resources among collaboration partners or intra-plant facilities. In this context, we introduce the capacitated multi-level lot sizing problem with transshipments and set up carry-over. We consider components that can only be produced by one specific agent as well as components, that can be provided by more than one producer. Since capacities are limited, agents might have to share resources and cover required demands jointly. In this case, finished components are trans- shipped between agents. As an agent can be in charge of producing more than one component, we include the concept of set up carry-over into our modeling. We address a centralized planning approach, where the objective is to find a globally optimized lot sizing plan for all participating agents. Thus, we cover both horizontal and vertical collaboration between agents. The new prob- lem class is formulated mathematically. The used dataset extends publicly available instances (http://www.dmlulsp.com/) and provides production capacities for 2 (first block) and 5 (second block) agents.

This data is generated in order to investigate the multi-depot vehicle routing problem with profit fairness (MDVRP-PF), a bi-objective optimization problem that adds a fairness objective function to the classical cost minimization function. By studying the MDVRP-PF , we explore the effects of integrating fairness in the optimization process.In order to perform the desired experiments, artifcial MDVRP-PF instances are generated. These instances represent different configurations that could be suitable for carrier coalitions, especially with respect to customer locations and stand-alone revenue share of each carrier. In this sense, we differentiate between two types of customer locations (clustered vs. uniform) and two types of initial revenue share distribution (balanced vs. unbalanced). In clustered instances customers are placed closer to the depots of the carriers, while being randomly located in the uniform type. Both types represent possible realistic situations, where partners are located in different distant industrial/commercial regions or within the same urban area. Regarding revenue share, in balanced instances, all carriers contribute a similar amount of revenue. Contrarily, in unbalanced instances, notable differences exist in the initial revenues contributed by each carrier. For each pair of location-revenue share configurations (from now on coded as C B, C U, U B and U U), we generate a set of instances. Each set contains three instances of different sizes: 2 depots and 100 customers (2D 100C), three depots and 150 customers (3D 150C), and four depots and 200 customers (4D 200C). To keep simplicity of the experiments, all customers have the same demand (10) and the same revenue (100).

This data is generated in order to investigate the multi-depot vehicle routing problem with profit fairness (MDVRP-PF), a bi-objective optimization problem that adds a fairness objective function to the classical cost minimization function. By studying the MDVRP-PF , we explore the effects of integrating fairness in the optimization process.In order to perform the desired experiments, artifcial MDVRP-PF instances are generated. These instances represent different configurations that could be suitable for carrier coalitions, especially with respect to customer locations and stand-alone revenue share of each carrier. In this sense, we differentiate between two types of customer locations (clustered vs. uniform) and two types of initial revenue share distribution (balanced vs. unbalanced). In clustered instances customers are placed closer to the depots of the carriers, while being randomly located in the uniform type. Both types represent possible realistic situations, where partners are located in different distant industrial/commercial regions or within the same urban area. Regarding revenue share, in balanced instances, all carriers contribute a similar amount of revenue. Contrarily, in unbalanced instances, notable differences exist in the initial revenues contributed by each carrier. For each pair of location-revenue share configurations (from now on coded as C B, C U, U B and U U), we generate a set of instances. Each set contains three instances of different sizes: 2 depots and 100 customers (2D 100C), three depots and 150 customers (3D 150C), and four depots and 200 customers (4D 200C). To keep simplicity of the experiments, all customers have the same demand (10) and the same revenue (100).

This data is generated in order to investigate the multi-depot vehicle routing problem with profit fairness (MDVRP-PF), a bi-objective optimization problem that adds a fairness objective function to the classical cost minimization function. By studying the MDVRP-PF , we explore the effects of integrating fairness in the optimization process.In order to perform the desired experiments, artifcial MDVRP-PF instances are generated. These instances represent different configurations that could be suitable for carrier coalitions, especially with respect to customer locations and stand-alone revenue share of each carrier. In this sense, we differentiate between two types of customer locations (clustered vs. uniform) and two types of initial revenue share distribution (balanced vs. unbalanced). In clustered instances customers are placed closer to the depots of the carriers, while being randomly located in the uniform type. Both types represent possible realistic situations, where partners are located in different distant industrial/commercial regions or within the same urban area. Regarding revenue share, in balanced instances, all carriers contribute a similar amount of revenue. Contrarily, in unbalanced instances, notable differences exist in the initial revenues contributed by each carrier. For each pair of location-revenue share configurations (from now on coded as C B, C U, U B and U U), we generate a set of instances. Each set contains three instances of different sizes: 2 depots and 100 customers (2D 100C), three depots and 150 customers (3D 150C), and four depots and 200 customers (4D 200C). To keep simplicity of the experiments, all customers have the same demand (10) and the same revenue (100).