Journal Of Industrial Engineering International
Volume:15 Issue: 4, Autumn 2019

Certain inventory items are living organisms, for example livestock, and are therefore capable of growing during the replenishment cycle. These items often serve as various saleable food items downstream in supply chains. The purpose of this paper is to develop a lot sizing model for growing items if the supplier of the items offers incremental quantity discounts. A mathematical model is derived to determine the optimal inventory policy which minimises the total inventory cost in both the owned and rented facilities. A solution procedure for solving the model is developed and illustrated through a numerical example. Sensitivity analysis is performed to demonstrate the response of the order quantity and total costs to some key input parameters. Incremental quantity discounts result in reduced purchasing costs; however, ordering very large quantities has downsides as well. The biggest downsides include the increased holding costs, the risks of running out of storage capacity and item deterioration since the cycle time increases if larger quantities are purchased. Owing to the importance of growing items in the food supply chains, the model presented in this article can be used by procurement and inventory mangers when making purchasing decisions.

In some applications of statistical process monitoring, a quality characteristic can be characterized by linear regressionrelationships between several response variables and one explanatory variable, which is referred to as a “multivariate simplelinear profile.” It is usually assumed that the process parameters are known in Phase II. However, in most applications,this assumption is violated; the parameters are unknown and should be estimated based on historical data sets in Phase I.This study aims to compare the effect of parameter estimation on the performance of three Phase II approaches for monitoringmultivariate simple linear profiles, designated as MEWMA, MEWMA_3 and MEWMA∕2 . Three metrics are usedto accomplish this objective AARL, SDARL and CVARL. The superior method may be different in terms of the AARL andSDARL metrics. Using the CVARL metric helps practitioners make reliable decisions. The comparisons are carried outunder both in-control and out-of-control conditions for all competing approaches. The corrected limits are also obtained bya Monte Carlo simulation in order to decrease the required number of Phase I samples for parameter estimation. The resultsreveal that parameter estimation strongly affects the in-control and out-of-control performance of monitoring approaches,and a large number of Phase I samples are needed to achieve a parameter estimation that is close to the known parameters.The simulation results show that the MEWMA and MEWMA∕2 methods perform better than the MEWMA_3 method interms of the CVARL metric. However, the superior approach is different in terms of AARL and SDARL.

In the traditional economic order quantity/economic production quantity model, most of the items considered are of perfecttype. But this situation rarely takes place in practice. Thus, in this paper, an economic order quantity model with imperfectqualityitems is developed. 100% screening process is performed, and the items of imperfect quality are sold as a single batch.A proportionate rate of discount for the items of imperfect quality has also been studied. Moreover, a case study has beenincorporated to comprehend the model. To nullify the issues of non-random uncertainties of demand rate in business scenario,cloudy fuzzy method has been utilized here. Numerical study reveals that cloud model along with its new defuzzificationmethods can give maximum profit of the model all the time instead of deterministic ones. Finally, sensitivity analysis andgraphical illustrations are made to justify the novelty of the model.

In transportation via containers, unbalanced movement of loaded containers forces shipping companies to reposition empty containers. This study addresses the problem of empty container repositioning (ECR) in the distribution network of a European logistics company, where some restrictions impose decision making in an uncertain environment. The problem involves dispatching empty containers of multiple types and various conditions (dirty and clean) to meet the on-time delivery requirements and repositioning the other containers to terminals, depots, and cleaning stations. A multi-period optimization model is developed to help make tactical decisions under uncertainty and data shortage for flow management of empty containers over a predetermined planning horizon. Employing the operational law of uncertainty programming, a new auxiliary chance-constrained programming is established for the ECR problem, and we prove the existence of an equivalence relation between the ECR plans in the uncertain network and those in an auxiliary deterministic network. Exploiting this new problem, we give the uncertainty distribution of the overall optimal ECR operational cost. The computational experiments show that the model generates good-quality repositioning plans and demonstrate that cost and modality improvement can be achieved in the network.

This paper outlines the development of an improved approach to the use of lean tools and techniques to improve the performance of manufacturing enterprises. Several research studies attempt to measure the overall leanness score of the manufacturing process; however, they failed to consider the interdependent relationships between lean performance metrics and considered all performance measures to be equally important during analysis. This paper proposes the weighted leanness assessment methodology to further extend the most recent developed leanness assessment model. The developed methodology in this research provides an integrated leanness score of the production process which considers the interrelationships between different performance metrics due to competing for business and operational strategies. The fuzzy-based analytic network process approach is used to measure and allocate relative importance weightings to each performance metric. The result from the proposed methodology in this research provides a more accurate overall leanness score by prioritising different performance measures according to the manufacturer’s needs. A case study was conducted to illustrate the effectiveness and validity of the proposed model and methodologies.

Preventive maintenance is the essential part of many maintenance plans. From the production point of view, the flexibility of the maintenance intervals enhances the manufacturing efficiency. On the contrary, the maintenance departments tend to know the timing of the long term maintenance plans as certain as possible. In a single-machine production environment, this paper proposes a simulation–optimization approach which establishes periodic flexible maintenance plans by determining the time between the maintenance intervals and the flexibility (i.e., length) of each interval. The objective is the minimization of the estimated total costs of the corrective and preventive maintenance, the undesirability of the flexibility (i.e., uncertainty) in maintenance timing, and the tardiness and long due date costs of jobs. Two mixed continuous-discrete variations of the ant colony optimization algorithm and the particle swarm optimization algorithm are developed as the solution approaches. Numerical studies are used to compare the performance of these algorithms. Further, the average reduction of the total costs gained from the flexibility of maintenance intervals on a wide range of parameters is reported.

The aim of this paper is to develop an optimization model for quality improvement by considering quality investment in rework policies and supply chain profit sharing. To improve product’s quality, the decision of process target and its tolerance is important since it directly affects the defective rate, manufacturing cost, and loss to customer due to the deviation of product from its specification. In this research, two rework policies are considered. In the first policy, the rework is done by using the same manufacturing facility, while in the second policy a new process facility was added for rework. Quality improvement in the supply chain environment is also necessary. Hence, profit sharing system is added in the model to strengthen the commitment of the suppliers in improving component quality. In the system, the manufacturer shares the profits to the supplier if the supplier can meet or exceed the quality target specified by the manufacturer. A comparison is given to determine the best quality improvement policy between those two policies considering profit sharing system. From the results of the optimization, the managers can make economic investment decision economically to correct a defective product through cost optimization model and to choose the best option toward the goal of least unit production cost. By using this model, the decision-maker can evaluate any quality investment in order to achieve significant financial return.

Acyclic phase-type distributions form a versatile model, serving as approximations to many probability distributions in various circumstances. They exhibit special properties and characteristics that usually make their applications attractive. Compared to acyclic continuous phase-type (ACPH) distributions, acyclic discrete phase-type (ADPH) distributions and their subclasses (ADPH family) have received less attention in the literature. In this paper, we present the definition, properties, characteristics and PH representations of ADPH distributions and their subclasses with finite state space. Based on the definitions of geometric and shifted geometric distributions, we propose a distinct classification for the ADPH subclasses analogous to ACPH family. We develop the PH representation for each ADPH subclass and prove them through their closure properties. The advantage of our proposed classifications is in applying precise representations of each subclass and preventing miscalculation of the probability mass function, by computing the ADPH family based on geometric and shifted geometric distributions.

Loss-averse behavior makes the newsvendors avoid the losses more than seeking the probable gains as the losses have more psychological impact on the newsvendor than the gains. In economics and decision theory, the classical newsvendor models treat losses and gains equally likely, by disregarding the expected utility when the newsvendor is loss-averse. Moreover, the use of unbounded utility to model risk attitudes fails to explain some decision-making paradoxes. In contrast, this paper deals with the utility maximization of the newsvendor using a class of bounded utility functions to study the effect of loss aversion on the newsvendor certainty equivalents and risk premiums. New formulas are introduced to find the utility-optimal order quantity of the normal distribution. The results show that when an exponential loss aversion exists, the classical newsvendor optimal quantity serves as a lower bound when the overage costs are high and as an upper bound when the underage costs are high. In addition, we show that high loss aversion entails higher risk premiums. Similar conclusion holds when the overage/underage costs increase. Higher standard deviations, on the other hand, mean lower utility-optimal quantities and higher risk premiums. The presented formulas are advantageous in finding the optimal order quantities and risk premiums of a stochastic short-shelf life inventory when the loss is a key factor in the decision-making process.

One of the problems tourism faces is how to make itineraries more effective and efficient. This research has solved the routing problem with the objective of maximizing the score and minimizing the time needed for the tourist’s itinerary. Maximizing the score means collecting a maximum of various kinds of score from each destination that is visited. The profits differ according to whether those destinations are the favorite ones for the tourists or not. Minimizing time means traveling time and visiting time in the itinerary being kept to a minimum. Those are small case with 16 tourism destinations in East Java, and large case with 56 instances consists of 100 destinations each from previous research. The existing model is the Team Orienteering Problem with Time Window (TOPTW), and the development has been conducted by adding another objective, minimum time, become Flexible TOPTW. This model guarantees that an effective itinerary with efficient timing to implement will be produced. Modification of Iterated Local Search (ILS) into Adjustment ILS (AILS) has been done by replacing random construction in the early phase with heuristic construction, continue with Permutation, Reserved and Perturbation. This metaheuristic method will address this NP-hard problem faster than the heuristic method because it has better preparation and process. Contributing to this research is a multi-objective model that combines maximum score and minimum time, and a metaheuristics method to solve the problem faster and effectively. There are calibration parameter with 17 instances of 100 destinations each, small case test using Mixed Integer Linear Programming, and large case test comparing AILS with Multi-Start Simulated Annealing (MSA), Simulated Annealing (SA), Artificial Bee Colony (ABC), and Iterated Local Search. The result shows that the proposed model will provide itinerary with less number of visited destination 4.752% but has higher total score 8.774%, and 3836.877% faster, comparing with MSA, SA, and ABC. While AILS is compared with ILS, it has less visited destination 5.656%, less total score 56.291%, and faster 375.961%. Even though AILS has more efficient running time than other methods, it needs improvement in algorithm to create better result.

Original data envelopment analysis models treat decision-making units as independent entities. This feature of data envelopment analysis results in significant diversity in input and output weights, which is irrelevant and problematic from the managerial point of view. In this regard, several methodologies have been developed to measure the efficiency scores based on common weights. Specifically, Ruiz and Sirvant (Omega 65:1–9, 2016) formulated an aggregated DEA model to minimize the gap between actual performances and best practices and identify a common best practice frontier. Their model is capable of determining target units for all units under evaluation, simultaneously, with the property that all of them are located on a common best practice frontier. However, in practice it is difficult for some units to achieve that specified target in a single step. Consequently, developing a methodology for assisting units to reach their corresponding targets, through a path of intermediate improving targets, is useful. This problem is investigated in this paper, and we propose a stepwise target setting approach which provides a path of intermediate targets for each unit. We study efficient and inefficient units separately and provide two distinct models for each category, although both of them are intrinsically similar. A simple numerical example and an application are also provided to illustrate our approach.

Sustainability in the supply chain means pushing the supply chain to focus on social, economic and environmental aspects, and addressing the existing problems in the traditional supply chain. Considering the importance of evaluating supply chain networks, especially in the field of perishable commodities, this paper aimed to design a mathematical model for the reverse supply chain of perishable goods, taking into account the sustainable production system. In this research, four objective functions were considered to maximize profitability and the level of satisfaction with the use of technology, minimize costs and measure environmental impacts. The results of the implementation of the proposed model for a manufacturing company show that objective functions are sensitive to demand, so the change in demand changes the objective functions, in particular the profitability function.