Warehouse management is one of the main factors that affect logistics operations. Due to the rapid growth of e-commerce, the importance of efficient warehousing has immensely increased in recent years. The main activities in every warehouse are receiving, stocking, order picking, and shipment. However, order picking contributes to the highest share of expenses with more than 50% of the total warehouse costs. Hence, most of the studies related to warehousing are focused on order picking.This study extends the joint order batching and picker routing problem to consider pre-determined levels of sturdiness to the collected items based on their physical characteristics such as weight, fragility, and shape. For this purpose, mathematical models are formulated with an objective of minimizing the traveled distance. To solve the problem, we implemented different batching strategies including First Fit Decreasing (FFD) from the bin-packing problem, an improved time-saving algorithm, and an aisle-saving algorithm. We also applied reduced variable neighborhood search (RVNS) to improve the solution quality for FFD and for the aisle-saving algorithm where a heuristic solution was developed to solve the routing problem for a batch of orders with 3 levels of sturdiness. The results show that the Improved Time-Saving algorithm provides much better solutions, achieving an average distance reduction of approximately 11.3% compared to the next-best heuristic, although it required over 264% more time on average. FFD and Aisle Saving algorithms obtained the results in a relatively short period, however Aisle Saving outperformed FFD. RVNS could notably improve the solutions obtained from the saving Algorithm by 7.8%, and while it showed numerical improvements for FFD by an average of 5.43%, these were not statistically significant.

The dynamic and time-sensitive nature of online food delivery, along with real-world factors like sudden changes in order volumes and the availability of couriers, distinguishes it from traditional vehicle routing scenarios. Apart from the many studies in the literature that handle this problem from specific angles, our solution proposes a new approach that provides real-time routing with the awareness of the expectations of multiple stakeholders in the ecosystem. For this purpose, we develop a Mixed Integer Programming (MIP) model that minimizes unmet demand and workforce requirements simultaneously to meet platform and courier expectations while maintaining the timeliness of the operation to meet restaurant and customer expectations. Since the model requires more time to provide good results for even small-size problems, we develop a multi-step algorithmic approach supported by strategies that hold or dissolve a part of the solutions to create opportunities for better results. A framework for agent-based simulation was created to implement the strategies and the algorithmic steps, accurately mimicking the operations and movements of couriers. The effectiveness of this solution was evaluated through experiments based on a real-world case study. The results indicate that our solution can generate high-quality results in a short time across various configurations, which are defined by different demand and supply patterns and varying problem sizes.

In this study, first, we improved an existing variant of the Nearest Centroid algorithm. In this new version, the predictive power of features and within-class variances are used as weights in distance calculation. This version is called the Enhanced Nearest Centroid (ENC). Second, we proposed a new model tree algorithm for binary classification. It is named as the Enhanced Nearest Centroid Model Tree (ENCMT). The model tree is built using ENC at each leaf node of the decision tree. To evaluate the performance of the new model tree, we used an independent test platform and ran the algorithm on 30 binary datasets available therein. Results showed that ENCMT improves the performance of the decision tree algorithm. We also compared ENCMT with the Logistic Model Tree (LMT) algorithm and showed that it outperforms LMT as well. We also designed a bagging algorithm where ENCMT is used to build a random forest. Our comparison results show that its performance is significantly better than the Random Forest (RF) algorithm.

In this study, the ordered flow shop scheduling problem, which is in the class of NP-hard optimization problems, is considered. This problem is used especially to increase the efficiency and prevent delays in the production process. The problem was first identified in the literature during the 1970s. The main objective of this study is to develop an efficient and fast method to overcome the complexity of this problem. For this purpose, the ordered flow shop scheduling problem is explained in detail and a robust meta-heuristic method is proposed. First of all, a genetic algorithm is developed by considering Smith’s convexity criterion. While performing operations such as crossover and mutation in the genetic algorithm, the pyramid structure is integrated to ensure that the solution has certain symmetry. The developed method is compared with other methods, such as the Nawaz–Enscore–Ham (NEH), pair insert, and iterated local search (ILS) methods. In order to increase the reliability of the results, the Pyramid Structure Adapted Tabu Search (PSA-TS) algorithm is also developed. The results are validated by statistical analysis using the Wilcoxon signed-rank test and Friedman test. The proposed genetic algorithm outperforms the methods with which it is compared. To the best of the authors’ knowledge, there is no other method in the literature that preserves the pyramid structure in the ordered flow shop scheduling problem. Therefore, this study is expected to make a significant contribution to the literature in this respect.

Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company’s (STAR) current “number of days” method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company’s current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology’s effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency.

The subject of the study is ordered flow shop scheduling problems which were first seen in the literature in the 1970s. The main objective is to get a fast and good solution. For the purpose of the study, firstly, the ordered flow shop scheduling problems were defined. Then, a heuristic method was suggested for the ordered flow shop scheduling problems and a sample problem was solved and discussed. A genetic algorithm (GA) based on the complexity property was developed. Full enumeration was applied to determine the optimal makespan values based on Smith’s rule because Smith had specified the conditions under which circumstances the permutation can be optimum. This study is one of the few studies in the literature to obtain optimum solutions up to 15 jobs ordered flow shop problems in a very short amount of time. The developed GA heuristic can also solve large size ordered flow shop scheduling problems very fast. The significant advantage of the proposed method is that, while Smith’s method rule not does not work with large size problems since it requires full enumeration to identify best solution to follow a convexity property where GA algorithm can find a good solution very fast.

The aim of this paper is to introduce a novel classification algorithm based on distance to class centroids with weighted Euclidean distance metric. Features are weighted by their predictive powers and in-class homogeneities. For predictive power, information value metric is used. For in-class homogeneity different measures are used. The algorithm is memory based but only the centroid information needs to be stored. The experimentations are carried at 45 benchmark datasets and 5 randomly generated datasets. The results are compared against Nearest Centroid, Logistic Regression, K-Nearest Neighbors and Decision Tree algorithms. The parameters of the new algorithm and of these traditional classification algorithms are tuned before comparison. The results are promising and has potential to trigger further research.

The Three-Index Assignment Problem (3-AP) is well-known combinatorial optimization problem which has been shown to be NP-hard. Since it is very difficult to find the best result in polynomial time, many heuristic methods have been proposed to obtain near optimal solutions in reasonable time. In this paper, a modified Bacterial Foraging Optimization Algorithm (BFOA) is proposed to solve 3-AP. BFOA is inspired by the social foraging behaviour of Escherichia coli (Ecoli). Algorithm imitates the behaviour of the foraging bacteria Ecoli and aims to eliminate those bacteria that have weak foraging methods and maintaining those bacteria that have strong foraging methods. The Hungarian method (most known method for solving the classical linear two-dimensional assignment problem) is integrated to BFOA algorithm at repositioning phase to swim farther and faster to find the best solution. Proposed algorithm has been tested and benchmarked with other algorithms in literature and results show that the new algorithm outperforms other heuristics in literature in terms of solution quality.

Digitalization is a trendy issue in the business development world.It has experienced how vital the need for digitalization is during the Covid-19 pandemic process.Today's world needs digital transformation both to create a systematic data flow and to compete with competitors.Although the main reason behind digital transformation is to be able to compete because it helps businesses respond more effectively to the customer needs.Software product development differs from the conventional product development, so does the management of the project.In the last century, software development became a complex problem due to the change in customer needs.Thus, it requires different project management approach than traditional project management.This paper seeks to understand large-scale software development issue in terms of project management manners by reviewing literature.The paper will highlight the studies performed starting from 1992 until 2022.In doing so, the reader will observe the change in this issue over time.The papers are categorized into 5 groups according to their research questions.These groups are (1) project management and monitoring papers that focused on the governance of a project, (2) case study papers that they do not provide universal solutions but assess a specific case, (3) problem solving papers as they try to develop an algorithm to solve a problem during management processes, (4) explanatory/taxonomy papers which explains the terms and conditions of project management concept, and finally (5) literature review papers.To the best of authors' knowledge, the literature lacks a review study which provides an overview of project management of software product development concept that contains all project management frameworks in the literature.

The challenging idea of using drones in last-mile delivery systems of logistics addresses a new routing problem referred to as the traveling salesman problem with drone (TSP-D). TSP-D aims to construct a route to deliver parcels to a set of customers by either a truck or a drone, thereby minimizing operational costs. Since TSP-D is considered NP-hard, using metaheuristics is one of the most promising solutions. This paper presents a hybrid metaheuristic solution method of TSP-D based on two state-of-the-art algorithms: the genetic algorithm and ant colony optimization algorithm. Heuristics in TSP-D literature are based on two consequent decisions: truck routing and drone assignment. Unlike those in the existing literature, the proposed metaheuristic constructs both truck and drone routes simultaneously. Additionally, to the best of our knowledge, we introduce for the first time a solution method on the basis of an ant colony optimization approach to TSP-D. Additionally, we propose a binary pheromone framework for both drone and truck, diverging from the traditional pheromone structure. Computational experiments indicate that the proposed hybrid metaheuristic algorithm is able to generate optimal routes for provided instances of TSP-D benchmarking. In addition, the algorithm improves the best-known solutions of some instances found by rival heuristics.

In this paper, the no-wait flow shop problem with earliness and tardiness objectives is considered. The problem is proven to be NP-hard. Recent no-wait flow shop problem studies focused on familiar objectives, such as makespan, total flow time, and total completion time. However, the problem has limited studies with solution approaches covering the concomitant use of earliness and tardiness objectives. A novel methodology for the parallel simulated annealing algorithm is proposed to solve this problem in order to overcome the runtime drawback of classical simulated annealing and enhance its robustness. The well-known flow shop problem datasets in the literature are utilized for benchmarking the proposed algorithm, along with the classical simulated annealing, variants of tabu search, and particle swarm optimization algorithms. Statistical analyses were performed to compare the runtime and robustness of the algorithms. The results revealed the enhancement of the classical simulated annealing algorithm in terms of time consumption and solution robustness via parallelization. It is also concluded that the proposed algorithm could outperform the benchmark metaheuristics even when run in parallel. The proposed algorithm has a generic structure that can be easily adapted to many combinatorial optimization problems.

The purpose of this study is to provide program managers and systems engineers with a novel algorithm in determining the design refresh time (DRT) of sustainment‐dominated systems due to COTS obsolescence. Most of the research done so far has focused on cost optimization. The main contribution of the study is two‐fold. First, besides cost optimization, we have introduced efficiency optimization within a balanced approach to determine the DRT, under multiple objectives. Second, we used a set‐based approach over the hypervolume quality values of solutions rather than population‐based Pareto solutions. We proposed a discrete‐time simulation model by using Multi‐Objective Evolutionary Algorithms where the deterioration over the quality values of Pareto solution sets is used as an indicator for a DRT. We supported the proposed mathematical model in theory with empirical findings from a case study for a sustainment‐dominated Naval Command and Control System that was designed in 2004 and deployed in 2007. We ran the simulation as for 2007 and conducted an analysis over the cost and operational efficiency objectives to compare the situation experienced in real life against the simulation outputs of the proposed model. The results revealed that not only the total life cycle cost but also efficient operational sustainability of a system would be increased significantly if the system had gone through design refreshes as proposed by the model. We showed that the deterioration of the Pareto optimal solutions’ hypervolume quality values over time is an effective marker to decide the optimal DRT under conflicting multiple objectives.

Polycarbonate (PC) resins are widely preferred by manufacturers to produce a large portfolio of products.It is in the group of thermoplastic polymers.PCs are durable, flame-retardant, and have efficient electricity insulation performance.They can be used to produce many different types of products like capacitors, dome lights, roofing sheets, compact disks, water bottles, decorative bezels, etc.It is a crucial issue to select rational PC resin specific to the application area.The manufacturers can optimize their processes, as well as finances by selecting the most appropriate resin.Many methods may be applied to optimize the selection process.In this paper, a Multi-Criteria Decision-Making (MCDM) Model is proposed that considers both technical criteria and operational criteria.The Intuitionistic Fuzzy Analytical Hierarchy Process (IF-AHP) method is integrated into the Intuitionistic Fuzzy Goal Programming (IF-GP) method to weight criteria, assess the alternatives, and suggest ranking of the alternatives.A sensitivity analysis completes the methodology to determine the boundaries of the solution.The results reveal that they are very sensitive to weighting preferences since PC resin alternatives show very similar properties considering defined criteria.

This study addresses the bi-objective flexible job shop problem (BOFJSP) with respect to minimization of the maximum completion time (makespan) and total tardiness. This study aims to propose an algorithm called Biobjective Hybrid Genetic Algorithm - hypervolume contribution measure (BOHGA-HCM) that integrates GA with a multi-search algorithm and uses hypervolume contribution measure (Delta s) in its two-level selection strategy. The initial population is created by randomly assigning operations to the available machines via dispatching rules to find better areas in the search space and enhance diversity to avoid premature convergence. The algorithm handles the objective functions simultaneously with the Pareto Optimality approach. The effectiveness and performance of the proposed algorithm are benchmarked and compared with other algorithms by using well-known data sets presented in the literature.

As more and more Commercial Off The Shelf (COTS) parts are being used in sustainment-dominated systems where average product lifecycle is above 25 years, obsolescence management becomes a greater concern for program managers. The lack of management and poor planning for obsolescence cause companies, governments, and military organizations to spend progressively more to deal with aging systems. For a successful obsolescence management, program managers should consider both the cost-effectiveness and system availability issues simultaneously and should implement appropriate solution approaches. In this chapter, the authors first define the obsolescence management in sustainment-dominated systems and then give a brief summary of the related literature. They finally discuss and propose multiple criteria decision-making methodologies and evolutionary algorithms to tackle the management problem.

Multiprocessor open shop makes a generalization to classical open shop by allowing parallel machines for the same task. Scheduling of this shop environment to minimize the makespan is a strongly NP-Hard problem. Despite its wide application areas in industry, the research in the field is still limited. In this paper, the proportionate case is considered where a task requires a fixed processing time independent of the job identity. A novel highly efficient solution representation is developed for the problem. An ant colony optimization model based on this representation is proposed with makespan minimization objective. It carries out a random exploration of the solution space and allows to search for good solution characteristics in a less time-consuming way. The algorithm performs full exploitation of search knowledge, and it successfully incorporates problem knowledge. To increase solution quality, a local exploration approach analogous to a local search, is further employed on the solution constructed. The proposed algorithm is tested over 100 benchmark instances from the literature. It outperforms the current state-of-the-art algorithm both in terms of solution quality and computational time.

Hub location problems are of those main issues which are focused on by researchers from different aspects for the last three decades especially along with the growth of transportation networks in the world. However, several methods developed for addressing hub location problems do not perform well for large-scale networks because of their computational complexity. This study presents heuristic methodologies based on characteristic features which affect the design of incomplete hub networks. The main idea of this methodology is predicated upon the analysis of characteristics of hub locations. In this respect, the focus was placed on centrality measures, which are frequently used in social networks. Capacity constraints were not addressed, and single-allocation hub location problems were analyzed in this study focused on p-median problems which are the most common problems in the literature. Such characteristics of hub locations as the allocation across the distribution network distance and demand quantities were assessed based on centrality measures, and simple heuristic methods were developed. These methods evaluate nodes across distribution networks from diverse aspects such as centrality, distance, flow, and specify the level of importance of nodes to the networks. Candidate hub nodes are divided into sub-sets in terms of the level of importance and are inserted into the original model as constraints, and then the model is solved under these constraints, which are produced based on certain specifications. In order to test the performance of the proposed methods, data sets such as CAB, AP, URAND and TR which were frequently used in the literature were employed. It was ascertained that the proposed methodology provided good quality solutions in a short solution time. Moreover, the proposed method enables the detailed analysis of hub locations across the network and reduce the problem size. That being the case, it offers valuable opportunities in terms of both the quality of solutions and the solution time for p-hub median problems.

In this study, a new coordinated scheduling problem is proposed for the multi-stage supply chain network. A multi-product and multi-period supply chain structure has been developed, including a factory, warehouses, and customers. Furthermore, the flexible job shop scheduling problem is integrated into the manufacturing part of the supply chain network to make the structure more comprehensive. In the proposed problem, each product includes a sequence of operations and is processed on a set of multi-functional machines at the factory to produce the final product. Final products are delivered to the warehouses to meet customers’ demands. If the demands of customers are not fulfilled, the shortage in the form of backorder may occur at any period. The problem is expressed as a bi-objective mixed-integer linear programming (MILP) model. The first objective function is to minimize the total supply chain costs. On the other hand, the second objective function aims to minimize the makespan in all periods. A numerical example is presented to evaluate the performance of the proposed MILP model. Five multi-objective decision-making (MODM) methods, namely weighted sum, goal programming, goal attainment, LP metric, and max–min, are used to provide different alternative solutions to the decision-makers. The performance of the methods is evaluated according to both objective function values and CPU time criteria. In order to select the best solution technique, the displaced ideal solution method is applied. The results reveal that the weighted sum method is the best among all MODM methods.

Water and energy are significant resources for industrial processes. While energy is the main source for the heating and cooling of water to be used in manufacturing processes, water is used to produce energy and as a cleaning agent in the production process, a pollution diluent or as part of the final product. This situation indicates that water and energy are intertwined in industrial processes and they should be considered simultaneously. Mathematical programming methods and pinch analysis are used for water and energy minimization in industrial processes. This paper depends on a comprehensive literature search of the databases of Google Scholar and the Web of Science for publications relating to water and energy minimization, heat integrated water network, and mathematical programming. This study presents a detailed overview on mathematical programming methods used in the literature regarding water network synthesis problems, their proposed approaches, and the improvements achieved through mathematical programming in sectoral case studies published from 2014 to 2019. In addition, heat integrated water network problems are examined according to isothermal and nonisothermal mixing. Several research gaps are also noted, with regards to the assumptions of the mathematical models, the sectoral applications of water network synthesis problems and sensitivity analysis of case studies. The paper ends with a critical analysis outlining the current situation and the need for future research for the integration of water and heat losses into the existing mathematical models. (C) 2020 Elsevier Ltd. All rights reserved.

This study seeks to integrate Random Key Genetic Algorithm (RKGA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to compute makespan and solve the Flow Shop Scheduling Problem (FSSP). FSSP is considered as a Multi Criteria Decision Making Problem (MCDM) by setting machines as criteria and jobs as alternatives. RKGA is employed to determine the best weights for the criteria that directly affect the robustness of the solution. The proposed methodology is presented with illustrative example and applied to benchmark problems. The solutions are compared to well-known construction heuristics. The proposed methodology provides the best or reasonable solutions in acceptable computational times.