Combining Fix and Relax Heuristic and LP-Metric Method to Solve the Multi-Objective Integrated Production-Routing Problem

Production routing problem is one of the problems of the integrated planning that interests in optimizing simultaneously production, inventory, and distribution planning. This chapter has the purpose of developing two mono-objective models for the production-routing problem: one of them minimizes the total costs which is the classical objective while the other one minimizes the energy consumed by the production system. A bi-objective model is then proposed to combine the two objectives mentioned previously using LP-metric method. To solve big instances of the problem in reasonable time, an approximate approach is proposed using the rolling horizon-based fix and relax heuristic. Finally, computational results are presented to compare the solutions obtained by both approaches.

Modified ridge and other regularization criteria: A brief review on meaningful regression models

The work describes a series of techniques designed to obtain regression models resistant to multicollinearity and having some other features needed for meaningful results. These models include enhanced ridge-regressions with several regularization parameters, regressions by data segments and by levels of the dependent variable, latent class models, unitary response, models, orthogonal and equidistant regressions, minimization in Lp-metric, and other criteria and models. All the approaches have been practically implemented in various projects and found useful for decision making in economics, management, marketing research, and other fields requiring data modeling and analysis.

A robust fuzzy multi-objective programming model to design a sustainable hospital waste management network considering resiliency and uncertainty: A case study

With the increase in the number of patients and activity of hospitals, the issue of hospital waste management (HWM) is becoming more and more challenging and worrying. In addition to financial losses, there will be irreparable damage to the ecosystem and environment which will create many problems for people (because the job of some people in the area is livestock and agriculture and they have a lot to do with their surroundings). It also doubles the need to pay attention to the issue of sustainable development (simultaneous attention to social, economic and environmental dimensions) in waste management. Moreover, the climatic and geographical conditions and lack of proper waste management in this area lead to major problems. Therefore, in this research, by developing a novel multi-objective mixed integer linear programming model, HWM is addressed in the hospitals of Sari, Iran. The aim is to design an HWM network considering sustainability, resiliency and uncertainty. In order to deal with uncertainty, a robust fuzzy programming approach is employed, and then an improved goal programming technique and Lp-metric method is proposed to solve the model. It was revealed that goal programming outperforms the Lp-metric method in terms of all objectives. Furthermore, the obtained results demonstrate the applicability and efficiency of the proposed methodology to design an efficient sustainable HWM network.

Optimal location of electronic charity boxes in charity NGOs by proposing a combined mathematical model

The topic of collecting public donations has always attracted the attention of charity Non-Governmental Organizations as their basic policy; nevertheless, despite its importance, not much scientific research has been conducted on the issue. The optimal location of electronic charity boxes is effective in optimizing the obtained benefits. In this paper, for the first time in the literature, a location problem is defined with the aim of maximizing people's motivation for money donation. We propose a new model to find the optimal location of e-charity boxes across the city with an aim to maximize the total amount of received benefits, as well as the total amount of donation motivation. To reach this aim, the effective criteria on the amount of gathered donation in each district and in each location type are investigated separately. Then, considering some constraints of the problem, a new mathematical model is proposed in order to determine the optimal location of e-charity boxes. We solve the model using LP-Metric, SAW, and TOPSIS methods, as a combination of Multi-Objective and Multi-Attributes Decision Making methods. Besides, we run the model in a real case study and 80 final locations are specified as the optimal locations in the studied city.

New GRFM Approach and Fuzzy LPMetric weighting score

Nowadays a thorough understanding of the business processes and the organization`s customers is the essential point to survive in the market competition. In this study, a new idea was applied to import customer purchased basket data into data mining computations. First, the products were divided into families, and we assigned a numerical code for each product in the family. The sum of these assigned numbers indicates the status of the basket. After this step, the transactions were clustered based on their basket values. Customers are then clustered in each cluster using the RFM method. Using a new fuzzy LP-metric approach and pairwise comparisons, RFM indices were weighted and we obtain customer value per cluster. Then we will proceed by averaging the customer value according to the presence of each customer in different clusters. Then we clustered customers based on customer lifetime value.

An Efficient Approach for Solving Integrated Production and Distribution Planning Problems

The increasing customer expectations for customized products of high quality in short delays and the worldwide competition in terms of quality and costs have pushed industries to implement new strategies to manage their supply chain decisions. In this context, the integrated planning is becoming the most dominant over the operational research field because of its efficiency and its ability to cover the different aspects of the problem. Production routing problem is one of the problems of the integrated planning that is of interest in optimizing simultaneously production, inventory, and distribution planning. This paper has the purpose of developing two mono-objective models for the production-routing problem; one of them minimizes the total costs, while the other one minimizes the energy consumed by the production system. Finally, a bi-objective model is proposed to combine the two objectives mentioned previously using the LP-metric method in the context of a sustainable supply chain. Experimental results are also presented and discussed through the different scenarios.

Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness

We turn the definition of individual fairness on its head - rather than ascertaining the fairness of a model given a predetermined metric, we find a metric for a given model that satisfies individual fairness. This can facilitate the discussion on the fairness of a model, addressing the issue that it may be difficult to specify a priori a suitable metric. Our contributions are twofold: First, we introduce the definition of a minimal metric and characterize the behavior of models in terms of minimal metrics. Second, for more complicated models, we apply the mechanism of randomized smoothing from adversarial robustness to make them individually fair under a given weighted Lp metric. Our experiments show that adapting the minimal metrics of linear models to more complicated neural networks can lead to meaningful and interpretable fairness guarantees at little cost to utility.

A multi-objective mixed-integer linear model for sustainable fruit closed-loop supply chain network

PurposeThis paper aims to present a closed-loop supply chain (CLSC) optimization problem for a perishable agricultural product to achieve three pillars of sustainability, including minimizing total network costs and carbon dioxide emissions from different network activities and maximizing responsiveness to demands simultaneously.Design/methodology/approachThe research problem is formulated as a multi-objective mixed-integer linear programming model, and classical approaches, including the LP-Metric and weighted Tchebycheff method, have been applied to solve the optimization model. A set of test problems has been proposed to validate the model, and the results are presented.FindingsComputational time to find Pareto optimal solutions by using the weighted Tchebycheff method was twice as much as that of the LP-Metric method. Also, the result of the study is a mathematical model that can be applied to other products that are close to the fruit, such as vegetables.Research limitations/implicationsThe present study is limited to fruits supply chains and the inventory is considered at the distribution centers only. The study also considers only one type of transport.Practical implicationsThe paper can assist supply chain managers to define strategies to achieve a sustainable CLSC network configuration for the fruits.Originality/valueThis study is one of the early studies to consider environmental indicators in fruits supply chain design along with two other indicators of sustainability, namely, economic and social indicators. Therefore, this can help supply chain managers to achieve sustainability by optimizing location decisions, inventory quantities and flow between facilities.

A Hybrid Approach Based on LP Metric Method and Genetic Algorithm for the Vehicle-Routing Problem with Time Windows, Driver-Specific Times, and Vehicles-Specific Capacities

Due to increasing population, increasing number of vehicles as well as environmental pollution, planning vehicles efficiently one of important problems nowadays. This article proposes a Multi-Objective Mixed Integer Programming (MOMIP) model for the vehicle-routing problem with time windows, driver-specific times and vehicles-specific capacities (VRPTDV), a variant of the classical VRPT that uses driver-specific travel and service times and vehicles-specific capacity to model the familiarity of the different drivers with the customers to visit. The first objective function aims to minimize traveled distance and the second objective function minimizing working duration. Since the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, the hybrid approach based on LP metric method and genetic algorithm is proposed to solve the given problem.