Achieving a high-resolution approximation and hitting the Pareto optimal set with some if not all members of the population is the goal for multi- and many-objective optimization problems, and more so in real-world applications where there is also the desire to extract knowledge about the problem from this set. The task requires not only to reach the Pareto optimal set but also to be able to continue discovering new solutions, even if the population is filled with them. Particularly in many-objective problems where the population may not be able to accommodate the full Pareto optimal set. In this work, our goal is to investigate some tools to understand the behavior of algorithms once they converge and how their population size and particularities of their selection mechanism aid or hinder their ability to keep finding optimal solutions. Through the use of features that look into the population composition during the search process, we will look into the algorithm&#x2019;s behavior and dynamics and extract some insights. Features are defined in terms of dominance status, membership to the Pareto optimal set, recentness of discovery, and replacement of optimal solutions. Complementing the study with features, we also look at the approximation through the accumulated number of Pareto optimal solutions found and its relationship to a common metric, the hypervolume. To generate the data for analysis, the chosen problem is MNK-landscapes with settings that make it easy to converge, enumerable for instances with 3 to 6 objectives. Studied algorithms were selected from representative multi- and many-objective optimization approaches such as Pareto dominance, relaxation of Pareto dominance, indicator-based, and decomposition.

Hugo Monz&#xf3;n Maldonado

Hern&#xe1;n Aguirre

S&#xe9;bastien Verel

Arnaud Liefooghe

Bilel Derbel

Kiyoshi Tanaka

Understanding Population Dynamics in Multi- and Many-Objective Evolutionary Algorithms for High-Resolution Approximations

Oil industry in India has entered the competitive world, and each organization used probing strategies to reduce cost. India is a non-oil-producing country, and the scope for this lies in reducing supply chain cost in downstream logistics. This research provides an integrated model of key enablers for transporter&#x2019;s performance in downstream logistics excellence of Indian oil sector to provide oil marketing companies&#x2019; a direction for design of future strategies to reduce downstream logistics cost. The sequential mixed-methods design is adopted. It identifies the enablers through literature review and interviews with transporters, working managers, and logistics experts (qualitative), and then, interpretive structural modeling (ISM) and MICMAC analysis (quantitative) are used to develop the diagraph and matrix to establish the contextual relationship and find their role and influence on each other. This readymade, unique, and unified model provides enablers for transporters&#x2019; performance in different individual categories, namely, dependent, independent, and autonomous enablers, and link them based on their driving power and dependence power along with their influencing behavior to enable transporters, working managers, and top management to focus on for reducing the logistics cost and shall add value for the ultimate customers. The academicians shall be benefited by appreciating practical aspects of this business.

Raj Singh Malik

Manoj Kumar Srivastava

Imlak Shaikh

Modeling Enablers of Transporter&#x2019;s Performance in Downstream Logistics of the Indian Oil Sector

Overcrowding of emergency departments (EDs) is a problem that affected many hospitals especially during the response to emergency situations such as pandemics or disasters. Transferring nonemergency patients is one approach that can be utilized to address ED overcrowding. We propose a novel mixed-integer nonlinear programming (MINLP) model that explicitly considers queueing effects to address overcrowding in a network of EDs, via a combination of two decisions: modifying service capacity to EDs and transferring patients between EDs. Computational testing is performed using a Design of Experiments to determine the sensitivity of the MINLP solutions to changes in the various input parameters. Additional computational testing examines the effect of ED size on the number of transfers occurring in the system, identifying an efficient frontier for the tradeoff between system cost (measured as a function of the service capacity and the number of patient transfers) and the systemwide average expected waiting time. Taken together, these results suggest that our optimization model can identify a range of efficient alternatives for healthcare systems designing a network of EDs across multiple hospitals.

Zeynab Oveysi

Ronald G. McGarvey

Kangwon Seo

An Optimization Model to Address Overcrowding in Emergency Departments Using Patient Transfer

The multiple objective simplex algorithm and its variants work in the decision variable space to find the set of all efficient extreme points of multiple objective linear programming (MOLP). Other approaches to the problem find either the entire set of all efficient solutions or a subset of them and also return the corresponding objective values (nondominated points). This paper presents an extension of the multiobjective simplex algorithm (MSA) to generate the set of all nondominated points and no redundant ones. This extended version is compared to Benson&#x2019;s outer approximation (BOA) algorithm that also computes the set of all nondominated points of the problem. Numerical results on nontrivial MOLP problems show that the total number of nondominated points returned by the extended MSA is the same as that returned by BOA for most of the problems considered.

Paschal B. Nyiam

Abdellah Salhi

A Comparison of Benson&#x2019;s Outer Approximation Algorithm with an Extended Version of Multiobjective Simplex Algorithm

The main objective of this study was to assess customers&#x2019; relationship management practices of Oromia Credit and Saving Share Company, Bule Hora city branch in Bule Hora, Ethiopia. Customer relationship management (CRM) as a strategy has gained tremendous interest among researchers and practitioners in recent times. Thus, this study tried to assess the status and ways CRM has been put in for practice by Oromia Credit and Saving Share Company (OCSSCO). In addition, this study considers different CRM dimensions such as empathy, bonding and satisfaction, and responsiveness. To achieve the objective of the study, primary data were collected through a questionnaire from a sample of 246 Oromia Credit and Saving Share Company customers of Bule Hora city branch, Bule Hora, Ethiopia, by using simple random sampling technique. The data collected through the questionnaire were analyzed using descriptive statistical analysis method and inferential statistics by using SPSS version 20 as a tool of data analysis. The study clearly revealed that the four CRM dimensions are strongly related. Thus, from the perspective of customers as well as management bodies of the Oromia Credit and Saving Share Company, CRM has a significant influence on customer retention and loyalty of the organization. Generally speaking, microfinance institutions are in need of doing a lot of CRM-based customer-focused practices.

Teshita Uke Chikako

Gemedi Tesfaye Hamu

Assessment of Customers&#x2019; Relationship Management Practices on Customer Retention and Loyalty of Oromia Credit and Saving Share Company: Bule Hora City Branch

In the classical inventory systems, the retailer had to settle the accounts of the purchased items at the time they were received. But in practice, the supplier applies some strategic tools, such as trade credit contract, to enhance his sales channel and offers delay period to his customers to settle the account. Any member of the supply chain may offer full or partial trade credit contract to his downstream level. Full trade credit is the case that the latter is allowed to defer the whole payment to the end of the credit period. In partial trade credit, however, the downstream supply chain member must pay for a proportion of the purchased goods at first and can delay paying for the rest until the end of the credit period. This paper considers a two-level trade credit, where the supplier offers order-quantity-dependent partial trade credit to a retailer, who suggests full trade credit to his customers. An economic order quantity (EOQ) inventory model of a deteriorating item is formulated here, and the Branch and Reduce Optimization Navigator is applied to find the optimal replenishment policy. The sensitivity of the variables on different parameters has been analyzed by applying some numerical examples. The data reveal that increasing the credit periods of the retailer and the customers can decrease and increase the retailer&#x2019;s total cost, respectively.

Zohreh Molamohamadi

Abolfazl Mirzazadeh

Ordering Policies of a Deteriorating Item in an EOQ Model under Upstream Partial Order-Quantity-Dependent Trade Credit and Downstream Full Trade Credit

Workforce scheduling process consists of three major phases: workload prediction, shift generation, and staff rostering. Shift generation is the process of transforming the determined workload into shifts as accurately as possible. The Shift Minimization Personnel Task Scheduling Problem (SMPTSP) is a problem in which a set of tasks with fixed start and finish times must be allocated to a heterogeneous workforce. We show that the presented three-phase metaheuristic can successfully solve the most challenging SMPTSP benchmark instances. The metaheuristic was able to solve 44 of the 47 instances to optimality. The metaheuristic produced the best overall results compared to the previously published methods. The results were generated as a by-product when solving a more complicated General Task-based Shift Generation Problem. The metaheuristic generated comparable results to the methods using commercial MILP solvers as part of the solution process. The presented method is suitable for application in large real-world scenarios. Application areas include cleaning, home care, guarding, manufacturing, and delivery of goods.

Kimmo Nurmi

Nico Kyng&#xe4;s

A Successful Three-Phase Metaheuristic for the Shift Minimization Personal Task Scheduling Problem

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Advances in Operations Research publishes original research and review articles contributing to the theory and methodology of operational research.

Advances in Operations Research