Search results

The purpose of this paper is to develop a model‐based methodology for the repetitive testing of multiple products with limited capacity, when the testing process is imperfect.

In a repetitive testing process, items that are classified as non‐conforming may be conforming, resulting in excessive scrapping of good items. Failed items are commonly retested to reduce scrapping costs. This paper develops a stochastic optimization formulation and its solution to determine the numbers of repetitive tests for multiple products that minimize the sum of the expected scrapping costs and variable testing costs, subject to a testing equipment capacity constraint. It also develops a procedure to estimate the parameter values that are used in the optimization formulation.

Computational experiments are conducted to evaluate the estimation and solution procedure and to understand the effect of testing machine capacity on the optimal total cost. These results demonstrate the viability of the proposed approach and the criticality of accurate parameter estimation.

This research shows the usefulness of the proposed optimization/statistical estimation approach to a real‐life complex inspection problem. However, the proposed model has to be modified when the characteristics of the testing equipment are changed.

The authors capture the idiosyncrasies in semiconductor manufacturing such as the high outgoing quality level, the repetitive testing environment, the high coefficient of variation in the number of failure products, and the testing capacity constraint. Conducting extensive computational experiments, the authors demonstrate that the proposed approach is viable.

The paper describes a complex, real‐life inspection management situation, develops a rigorous model‐based solution approach, and carefully demonstrates its viability.

The purpose of this paper is twofold: first to carry out a comprehensive literature review for state of the art regarding airline schedule planning and second to identify some new research directions that might help academic researchers and practitioners.

The authors mainly focus on the research work appeared in the last three decades. The search process was conducted in database searches using four keywords: “Flight scheduling,” “Fleet assignment,” “Aircraft maintenance routing” (AMR), and “Crew scheduling”. Moreover, the combination of the keywords was used to find the integrated models. Any duplications due to database variety and the articles that were written in non-English language were discarded.

The authors studied 106 research papers and categorized them into five categories. In addition, according to the model features, subcategories were further identified. Moreover, after discussing up-to-date research work, the authors suggested some future directions in order to contribute to the existing literature.

The presented categories and subcategories were based on the model characteristics rather than the model formulation and solution methodology that are commonly used in the literature. One advantage of this classification is that it might help scholars to deeply understand the main variation between the models. On the other hand, identifying future research opportunities should help academic researchers and practitioners to develop new models and improve the performance of the existing models.

This study proposed some considerations in order to enhance the efficiency of the schedule planning process practically, for example, using the dynamic Stackelberg game strategy for market competition in flight scheduling, considering re-fleeting mechanism under heterogeneous fleet for fleet assignment, and considering the stochastic departure and arrival times for AMR.

In the literature, all the review papers focused only on one category of the five categories. Then, this category was classified according to the model formulation and solution methodology. However, in this work, the authors attempted to propose a comprehensive review for all categories for the first time and develop new classifications for each category. The proposed classifications are hence novel and significant.

The purpose of the paper is to solve hydrothermal scheduling (HTS) problem for energy-efficient management by allocating the optimal real power outputs for thermal and hydroelectric generators.

HTS can be formulated as a complex and non-linear optimization problem which minimizes the total fuel cost and emissions of thermal generators subject to various physical and operational constraints. As the artificial bee colony algorithm has proven its ability to solve various engineering optimization problems, it has been used as a main optimization tool to solve the fixed-head HTS problem.

A meta-heuristic search technique-based algorithm has been implemented for hydrothermal energy management, and the simulation results show that this approach can provide trade-off between conflict objectives and keep a rapid convergence speed.

The proposed methodology is implemented on the standard test system, and the numerical results comparison indicates a considerable saving in total fuel cost and reduction in emission.

The purpose of this paper is to evaluate the health‐states of unit under test (UUT) in aerospace systems by means of unreliable test outcomes, and the evaluation results can provide a guide for engineers to carry out proper maintenance prior to total failure.

In this paper, the authors formulate the health‐state evaluation (HSE) problem with unreliable test outcomes based on Bayes rule, and develop the Lagrangian relaxation and adaptive genetic algorithm (LRAGA) to solve it. The solution scheme can be viewed as a two‐level coordinated solution framework for the HSE problem. At the top level, the Lagrange multipliers are updated by using AGA. At the bottom level, each of the sub‐problems is solved by using AGA.

The experimental results show that the HSE model appears promising and the LRAGA can obtain the higher quality solution and converge to it at a faster rate than conventional methods (i.e. Lagrangian relaxation (LR), genetic algorithm (GA), simulated annealing (SA) and Lagrangian relaxation and genetic algorithm (LRGA).

The proposed method for the HSE problem of large‐scale systems which include thousands of faults and tests needs to be verified further.

The HSE results for aerospace systems can help engineers to carry out a schedule for prompt maintenance prior to UUTs' failure, to avoid the consequences of total failure. It is important to improve aerospace systems' safety, reliability, maintainability, affordability, and reduce life cycle cost.

This paper constructs the HSE model with unreliable test outcomes based on the Bayes rule and proposes a method based on LRAGA to solve the HSE problem.

The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks.

This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time.

Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window.

The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies.

For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

This paper proposed a bi-level mathematical model for location, routing and allocation of medical centers to distribution depots during the COVID-19 pandemic outbreak. The developed model has two players including interdictor (COVID-19) and fortifier (government). Accordingly, the aim of the first player (COVID-19) is to maximize system costs and causing further damage to the system. The goal of the second player (government) is to minimize the costs of location, routing and allocation due to budget limitations.

The approach of evolutionary games with environmental feedbacks was used to develop the proposed model. Moreover, the game continues until the desired demand is satisfied. The Lagrangian relaxation method was applied to solve the proposed model.

Empirical results illustrate that with increasing demand, the values of the objective functions of the interdictor and fortifier models have increased. Also, with the raising fixed cost of the established depot, the values of the objective functions of the interdictor and fortifier models have raised. In this regard, the number of established depots in the second scenario (COVID-19 wave) is more than the first scenario (normal COVID-19 conditions).

The results of the current research can be useful for hospitals, governments, Disaster Relief Organization, Red Crescent, the Ministry of Health, etc. One of the limitations of the research is the lack of access to accurate information about transportation costs. Moreover, in this study, only the information of drivers and experts about transportation costs has been considered. In order to implement the presented solution approach for the real case study, high RAM and CPU hardware facilities and software facilities are required, which are the limitations of the proposed paper.

The main contributions of the current research are considering evolutionary games with environmental feedbacks during the COVID-19 pandemic outbreak and location, routing and allocation of the medical centers to the distribution depots during the COVID-19 outbreak. A real case study is illustrated, where the Lagrangian relaxation method is employed to solve the problem.

In this paper, the authors aim to investigate the relationship between buyback policy and the potential number of used products that could be collected by developing a robust fuzzy reverse logistics network.

In this approach, the authors seek to determine the amount of buyback based on the condition of used products at the time of return. In this process, the authors also take into account that apart from the condition of used products, other factors exist that the actual return rate could be dependent on them. This matter propelled us to make a novel distinction between the probability of return estimated from appropriate buybacks offered to consumers, and the actual return rate of used products using fuzzy mathematical methods. Besides that, a compatible robust fuzzy optimization method has been implemented on the model to deal with uncertain properties of it and simultaneously fortifying its responses against any possible effect of return rate fluctuation.

To analyze and evaluate the model performance, the authors decided to apply a series of exhaustive randomly generated experiments onto it. Also, the authors introduced a Lagrangian relaxation solution methodology to facilitate and improve the solving process of the model. Then, the evaluation of the results enabled us to demonstrate the model validity, and underscore its utility to deal with problems with more sophisticated used product collection process that practitioners tend to encounter in the real-world circumstances.

This study suggests a novel way to design the return rate of used products in a reverse logistics network with buyback offers through a complete set of factors affecting it. Furthermore, the procedure of developing the model encompasses several important aspects that significantly decrease its complexity and improve its applicability.

Effective inventory management is very critical to market success. The purpose of this paper is to formulate an integrated model for the location of warehouse, the allocation of retailers to the opened warehouses, and finding the perfect policy for inventory control to managing order quantity and safety stock level. The goal is to select the optimum numbers, locations, capacities of the opening warehouses and inventory policy so that all stochastic customer demands can be satisfied.

It is assumed that the location of plant has already been determined and the paper answers the following questions: what are the location decisions over the planning horizon? How retailers are allocated to the warehouses? What are the optimum capacities for the opened warehouses? What is the best inventory policy for this supply chain? What are the total minimum costs?

The model was developed as a non‐linear mixed integer programming and solved using Lagrange relaxation and sub‐gradient search for the location/allocation module and a procedure for the capacity planning module. The results for the randomly selected problems show that the average duality gap ranges are between 0.51 and 1.58 percent. Also, from the CPU time point of view, the performance of the proposed algorithm was very good.

The paper addresses an integrated location, allocation, and inventory decisions in the design of a supply chain distribution network. In addition sensitivity analyses are conducted to evaluate the effects of the multi‐capacity levels on some performance measures.

The purpose of this paper is to present the benefits of using the Lagrangian relaxation (LR) and subgradient methods in scenario studies for wavelength division multiplexing (WDM) network planning. The problem of WDM network planning for a given set of lightpath demands in a mesh topology network is to select lightpath routes and then allocate wavelength channels to the lightpaths. In WDM network planning, a scenario study is to find out the network performance under different lightpath demands and/or different network resource configurations.

A scenario study must solve a series of related static WDM network planning problems. Each static WDM network planning problem is an optimization problem, and may be formulated as an integer linear programming problem, which can be solved by the proposed Lagrangian relaxation and subgradient methods. This paper uses the Lagrange multipliers that are obtained from previous scenarios as initial Lagrange multiplier values for other related scenarios.

This approach dramatically reduces the computation time for related scenarios. For small to medium variations of scenarios, the method reduces the computation time by several folds. The proposed method is the first method that effectively considers the relations between related scenarios, and uses such relations to improve the computation efficiency of scenario studies in WDM network planning.

The method improves the efficiency of a scenario study in WDM network planning. By using it, many “what‐if” type of scenario study questions can be answered quickly.

Unlike other existing methods that treat each scenario individually, this method effectively uses the information of the relation between different scenarios to improve the overall computation efficiency.

This study aims to design a reliable multi-level, multi-product and multi-period location-inventory-routing three-echelon supply chain network, which considers disruption risks and uncertainty in the inventory system.

A robust optimization approach is used to deal with the effects of uncertainty, and a mixed-integer nonlinear programming multi-objective model is proposed. The first objective function seeks to minimize inventory costs, such as ordering costs, holding costs and carrying costs. It also helps to choose one of the two modes of bearing the expenses of shortage or using the excess capacity to produce at the expense of each. The second objective function seeks to minimize the risk of disruption in distribution centers and suppliers, thereby increasing supply chain reliability. As the proposed model is an non-deterministic polynomial-time-hard model, the Lagrangian relaxation algorithm is used to solve it.

The proposed model is applied to a real supply chain in the aftermarket automotive service industry. The results of the model and the current status of the company under study are compared, and suggestions are made to improve the supply chain performance. Using the proposed model, companies are expected to manage the risk of supply chain disruptions and pay the lowest possible costs in the event of a shortage. They can also use reverse logistics to minimize environmental damage and use recycled goods.

In this paper, the problem definition is based on a real case; it is about the deficiencies in the after-sale services in the automobile industry. It considers the disruption risk at the first level of the supply chain, selects the supplier considering the parameters of price and disruption risk and examines surplus capacity over distributors’ nominal capacity.