Operational Research

Leyva López, Juan Carlos; Solano Noriega, Jesús Jaime; Ahumada Valenzuela, Omar; Romero Serrano, Alma Montserrat

doi: 10.1007/s12351-021-00666-xpmid: N/A

Abstract The design of new products is a matter of great importance that can directly affect profitability and competitiveness in modern companies. For this reason, the selection of a final product design needs to consider at least four factors of importance: anticipated market demand for the product design, preference heterogeneity among consumers, decision-maker preferences, and fuzzy preference information in the design criteria. This paper proposes a frequency-based preference choice model that considers all the above factors and can be used with algorithms that solve the optimal product design problem using the share of preference frequency criterion. The choice model introduced in this paper is based on the multicriteria outranking approach, and its predictive accuracy is optimized with genetic algorithms. The proposed genetic algorithm is compared with Interior Point OPTimizer, a software package for large-scale non-linear optimization. The experiment results demonstrate that the proposed method achieves near-optimal solutions in reasonable computational time and significantly outperforms the runtime compared algorithm.

Bera, Raj Kumar; Mondal, Shyamal Kumar

doi: 10.1007/s12351-022-00691-4pmid: N/A

In the recent competitive market of transportation business, it is becoming increasingly difficult to improve the facilities for retailer services. Recently, several kinds of policies are being adopted by different distributors to provide their best services in favor of retailers. One of these is the advantage in credit period policy which increases the period between the receipt of items and the payment due date. Because of this policy, due to the late payment, certain interest for the payable amount is credited to whom the credit period is offered. From this perspective, a multi-objective transportation problem has been formulated under cost-dependent credit period policy. Here, the items are transported from a production house to the retailers by distributors who act as mediators. For adequate uncertainty in the cost parameter, it is considered as Gaussian fuzzy number. The key objective of this research is to maximize the profit of the distributors and to minimize the cost of the retailers simultaneously under a credit period policy in Gaussian fuzzy environment. Considering all the aspects, four models are constructed and illustrated mathematically and numerically. The effect of fuzziness on the model solution has been analyzed in detail; how distributors can improve their profit structure and retailers can reduce their cost structure under the used cost dependent credit period policy is also discussed. Finally, we have figured out some managerial insights and mentioned future research scope.

Belkhiri, Hadjer; Chergui, Mohamed El-Amine; Ouaïl, Fatma Zohra

doi: 10.1007/s12351-021-00664-zpmid: N/A

Abstract In this work, we deal with a global optimization problem (P) for which we look for the most preferred extreme point (vertex) of the convex polyhedron according to a new linear criterion, among all efficient vertices of a multi-objective linear programming problem. This problem has been studied for decades and a lot has been done since the 70’s. Our purpose is to propose a new and effective methodology for solving (P) using a branch and bound based technique, in which, at each node of the search tree, new customized bounds are established to delete uninteresting areas from the decision space. In addition, an efficiency test is performed considering the last simplex tableau corresponding to the current visited vertex. A comparative study shows that the proposed method outperforms the most recent and performing method dedicated to solve (P).

Ghahtarani, Alireza; Saif, Ahmed; Ghasemi, Alireza

doi: 10.1007/s12351-022-00690-5pmid: N/A

This paper reviews recent advances in robust portfolio selection problems and their extensions, from both operational research and financial perspectives. A multi-dimensional classification of the models and methods proposed in the literature is presented, based on the types of financial problems, uncertainty sets, robust optimization approaches, and mathematical formulations. Several open questions and potential future research directions are identified.

Hernandez, Liseth Contreras; Jiménez G., Hanser S.; Dantas, Priscilla P. L.; Cavalcante, Cristiano A. V.

doi: 10.1007/s12351-020-00603-4pmid: N/A

Choosing an order picking strategy is one of the most important decisions related to warehouse management. Making this decision properly can lead to high standards of efficiency, since order picking represents more than a half of a wholesale and retail organization’s operational costs and consumes a huge amount of the resources allocated to warehouse labor. Moreover, some productivity and service-oriented objectives related to order picking are sometimes conflicting, and require managers’ preferences to be considered, thus making the decision problem multi-objective and complex. We put forward a multicriteria decision model based on the ELECTRE III method that supports how to choose an order picking strategy. It takes managers’ preferences into consideration and integrates all the core elements for assessing how picking is being performed. Results showed that the model is able to identify the strategy that yields the best compromise between the objectives of productivity and the service-oriented ones, and that this strategy also represents the organization’s aims.

Mahapatra, Maheswar Singh; Pradhan, Pravash Chandra; Jha, J. K.

doi: 10.1007/s12351-021-00661-2pmid: N/A

Abstract This paper addresses sourcing decisions with order allocation in the presence of supplier disruption risks in a two-echelon supply chain considering both quantitative and qualitative aspects. A mixed-integer linear program is proposed for the optimal supplier selection and order allocation considering finite and expandable production capacity, failure probability, all-unit price discount, and spot-market cost. Due to the time complexity of the problem to get an optimal solution, we develop a heuristic which is found highly efficient in time complexity and highly competitive in solution quality. A multi-objective model is formulated to capture the qualitative aspect of suppliers by maximizing the total purchase value along with minimizing the expected total cost. We have applied NSGA-II and MOPSO, two widely used evolutionary algorithms, to solve the multi-objective model. A numerical illustration is presented along with sensitivity analysis considering a supplier base of twenty suppliers and a sourcing strategy up to six suppliers. It has been found that dual-sourcing and triple-sourcing are mainly part of the non-dominated Pareto front. Also, increasing demand would lead to a higher level of sourcing strategy, which also depends on the maximum capacity of suppliers and the minimum order to be allocated to the selected suppliers.

de Morais Correia, Lucas Miguel Alencar; da Silva, Jonhatan Magno Norte; dos Santos Leite, Wilza Karla; Lucas, Ruan Eduardo Carneiro; Colaço, Geraldo Alves

doi: 10.1007/s12351-021-00671-0pmid: N/A

Abstract This study proposes a decision model to solve a workstation problem in the footwear industry by considering multiple conflicting objectives that influence the decision-making process. Following the appearance of employee reports of pain symptoms and due to limited resources and the need to protect health, companies strive to improve working conditions. Ranking workstations for ergonomics interventions may help if an appropriate decision is made through a well-structured decision-making process. Accordingly, the association of two Problem Structuring Methods (PSMs), Value Focused-thinking (VFT), and Strategic Options Development and Analysis (SODA) were used to structure a hierarchy of key objectives for evaluating the decision model. Preference modeling with the Decision Maker was conducted in a flexible and interactive manner, aided by the Flexible and Interactive Tradeoff Method (FITradeoff). Partial information was gathered about the preferences of the Decision Maker that lead to finding a recommendation that less cognitive effort is required. Decision Maker answered 10 questions and the method found six levels of ordering, indicating FITradeoff method is effective for rank workstations for ergonomic interventions. It was concluded that the combination of the structuring methods VFT and SODA, and the multicriteria method FITradeoff facilitates ergonomic decision-making.

Gerami, Javad; Mozaffari, Mohammad Reza; Wanke, P. F.; Correa, Henrique

doi: 10.1007/s12351-021-00679-6pmid: N/A

In this paper we present a new model combining the major underlying ideas behind the Slacks-Based Measure (SBM) and the Data Envelopment Analysis-Ratio based (DEA-R) models. This new proposed model computes efficiency and super-efficiency scores based on the corresponding slack values for both input/output and output/input ratios, depending upon the orientation of the production frontier. A comprehensive step-by-step derivation of the SBM-DEA-R model in light of previous approaches is offered to readers. Besides, in order to show the validity of the approach proposed in light of the current body of literature, we revisit a case study of twenty-one medical centers in Taiwan. Limitations and directions for future research are discussed.

Ngo, Thanh; Tsui, Kan Wai Hong

doi: 10.1007/s12351-021-00667-wpmid: N/A

Data envelopment analysis (DEA) is a popular non-parametric approach to examine performance and productivity of airlines; however, it could not provide statistical information such as confidence intervals on the estimated efficiency scores. We combined stochastic frontier analysis and DEA into a single framework to disentangle noise and ‘pure’ inefficiency from the DEA inefficiency scores and accordingly provide confidence intervals for the estimated efficiency scores. Monte-Carlo simulation verified that our novel model is a good alternative for the conventional DEA as well as the bootstrap DEA. Empirical application using Asia-Pacific airlines’ data (2008‒2015) shows that after accounting for the ‘pure’ random errors, the sampled Asia-Pacific airlines performed well during the study period but their ‘pure’ efficiency was declining, hence, there is still room for improvement.

Moghaddas, Zohreh; Amirteimoori, Alireza; Kazemi Matin, Reza

doi: 10.1007/s12351-022-00692-3pmid: N/A

Conventional data envelopment analysis (DEA) models are often extended for constant or variable returns to scale assumptions based on the under-investigated technology. It is assumed that all inputs and outputs are real-valued data. However, in many practical applications, proportionality or convexity axioms require to be modified. This study attempts to further expand upon the hybrid returns to scale DEA models in the presence of integer-valued input and output data. We refine the previous axioms to introduce a new minimal extrapolation technology set. Moreover, we formulate a couple of mixed-integer linear programming models for efficiency evaluation and target setting. An empirical application on 30 high schools in Iran is provided to validate the proposed approach. The data analysis, including efficiency evaluations along with providing benchmark units, is also performed.