In this paper, a new period-based approach is proposed for modeling a capacitated inventory system, operating under an (S,T) policy with backlog. The system experiences stochastic discrete demand and lead time. By using the proposed method, a mathematical model is developed. The model can accurately estimate the inventory system measures of performance: the expected inventory on-hands and over-storage amount. Through a simulation experiment, the new model is compared with two other models, which are developed by using a widely used mean-based approach. The comparison is conducted based on a case study data set. The results demonstrate that the period-based model is superior to the mean-based models with respect to capturing the behaviors of the inventory system. Therefore, better inventory policy parameters can be obtained by employing the new model.

This paper presents a mathematical model for a single depot, time-dependent vehicle routing problem with backhaul considering the first in first out (FIFO) assumption. As the nature of the problem is NP-hard, variable neighborhood search (VNS) meta-heuristic and mat-heuristic algorithms have been designed. For test problems with large scales, obtained results highlight the superior performance of the mat-heuristic algorithm compared with that of the other algorithm. Finally a case study at the post office of Khomeini-Shahr town, Iran, was considered. Study results show a reduction of roughly 19% (almost 45 min) in the travel time of the vehicle.

Quality profiles representing the quality of a process or product as the functional relationship between one or more dependent variables and one or more explanatory variables are nowadays widely recognized in statistical process control (SPC) applications by both researchers and practitioners. On the other hand, in many real-world cases, evaluation of process or product characteristics is carried out with ambiguity or conducted using linguistic values. The theory of fuzzy sets provides an appropriate approach to deal with uncertainty due to ambiguity in human subjective evaluations or vagueness in linguistic variables. The purpose of this study is to introduce two novel methods based on fuzzy regression modeling for monitoring fuzzy linear profiles in phase II of SPC. To accomplish this, fuzzified Hoteling’s T2 statistic and fuzzy hypothesis testing are used. Moreover, a simulation study is used to compare the performance of the proposed methods compared with previous methods, based on the average run length (ARL) criterion in order to assess the detectability of charts with regard to the step shifts in profile parameters. Finally, the results of a real-world example in the tile and ceramic industry are presented.

The Jaya algorithm is a recently developed novel population-based algorithm. The proposed work presents the modifications in the existing many-objective Jaya (MaOJaya) algorithm by integrating the chaotic sequence to improve the performance to optimize many-objective benchmark optimization problems. The MaOJaya algorithm has exploitation more dominating, due to which it traps in local optima. The proposed work aims to reduce these limitations by modifying the solution update equation of the MaOJaya algorithm. The purpose of the modification is to balance the exploration and exploitation, improve the divergence and avoid premature convergence. The well-known chaotic sequence - a logistic map integrated into the solution update equation. This modification keeps the MaOJaya algorithm simple as well as, preserves its parameterless feature. The other component of the existing MaOJaya algorithm, such as non-dominated sorting, reference vector and tournament selection scheme of NSGA-II is preserved. The decomposition approach used in the proposed approach simplifies the complex many-objective optimization problems. The performance of the proposed chaotic based many-objective Jaya (C-MaOJaya) algorithm is tested on DTLZ benchmark functions for three to ten objectives. The IGD and Hypervolume performance metrics evaluate the performance of the proposed C-MaOJaya algorithm. The statistical tests are used to compare the performance of the proposed C-MaOJaya algorithm with the MaOJaya algorithm and other algorithms from the literature. The C-MaOJaya algorithm improved the balance between exploration and exploitation and avoids premature convergence significantly. The comparison shows that the proposed C-MaOJaya algorithm is a promising approach to solve many-objective optimization problems.

Variety, quality, and rapid response are becoming a trend in customer requirements in the contemporary competitive markets. Thus, an increasing number of manufacturers are frequently seeking alternatives such as redesigning their fabrication scheme and outsourcing strategy to meet the client’s expectations effectively with minimum operating costs and limited in-house capacity. Inspired by the potential benefits of delay differentiation, outsourcing, and quality assurance policies in the multi-item production planning, this study explores a single-machine two-stage multi-item batch fabrication problem considering the abovementioned features. Stage one is the fabrication of all the required common parts, and stage two is manufacturing the end products. A predetermined portion of common parts is supplied by an external contractor to reduce the uptime of stage one. Both stages have imperfect in-house production processes. The defective items produced are identified, and they are either reworked or removed to ensure the quality of the finished batch. We develop a model to depict the problem explicitly. Modeling, formulation, derivation, and optimization methods assist us in deriving a cost-minimized cycle time solution. Moreover, the proposed model can analyze and expose the diverse features of the problem to help managerial decision-making. An example of this is the individual/ collective influence of postponement, outsourcing, and quality reassurance policies on the optimal cycle time solution, utilization, uptime of each stage, total system cost, and individual cost contributors.

In this research, a maximal covering location problem (MCLP) with real-world constraints such as multiple types of facilities and vehicles with different setup costs is taken into account. An original mixed integer linear programming (MILP) model is constructed in order to find the optimal solution. Since the problem at hand is shown to be NP-hard, a constructive heuristic method and a meta-heuristic approach based on genetic algorithm (GA) are developed to solve the problem. To find the most effective solution technique, a set of problems of different sizes is randomly generated and solved by the proposed solution methods. Computational results demonstrate that the heuristic method is capable of producing optimal or near-optimal solutions in a rational execution time.

An inventory control system having multiple items in stock is developed in this paper to optimize total cost of inventory and space requirement. Inventory modeling for both the raw material storage and work in process (WIP) is designed considering independent demand rate of items and no volume discount. To make the model environmentally aware, the equivalent carbon emission cost is also incorporated as a cost function in the formulation. The purpose of this study is to minimize the cost of inventories and minimize the storage space needed. The inventory models are shown here as a multi-objective programming problem with a few nonlinear constraints which has been solved by proposing a meta-heuristic algorithm called multi-objective particle swarm optimization (MOPSO). A further meta-heuristic algorithm called multi-objective bat algorithm (MOBA) is used to determine the efficacy of the result obtained from MOPSO. Taguchi method is followed to tune necessary response variables and compare both algorithm's output. At the end, several test problems are generated to evaluate the performances of both algorithms in terms of six performance metrics and analyze them statistically and graphically.

A multi-objective methodology was proposed for solving the green vehicle routing problem with a private fleet and common carrier considering workload equity. The iterated local search metaheuristic, which is adapted to the solution of the problem with three objectives, was proposed as a solution method. A solution algorithm was divided into three stages. In the first, initial solutions were identified based on the savings heuristic. The second and third act together using the random variable neighbourhood search algorithm, which allows performing an intensification process and perturbance processes, giving the possibility of exploring new regions in the search space, which are proposed within the framework of optimizing the three objectives. According to the previous review of the state of the art, there is little related literature; through discussions with the productive sector, this problem is frequent due to increases in demand in certain seasons or a part of the maintenance vehicle fleet departing from service. The proposed methodology was verified using case studies from the literature, which were adapted to the problem of three objectives, obtaining consistent solutions. Where cases were not reported in the literature, these could be used as a reference in future research.

In this paper, we consider a two-echelon supply chain in which one warehouse provides a single product to N retailers, using integer-ratio policies. Deterministic version of the problem has been widely studied. However, this assumption can lead to inaccurate and ineffective decisions. In this research, we tackle the stochastic version of two-echelon inventory system by designing an extension of a well-known heuristic. This research considers customer demands as following a normal density function. A set of 240 random instances was generated and used in evaluating both the deterministic and stochastic solution approaches. Due to the nature of the objective function, evaluation was carried out via Monte Carlo simulation. For variable demand settings, computational experiments shows that: i) the use of average demand to define the inventory policy implies an underestimation of the total cost and ii) the newly proposed method offers cost savings.