Journal Of Industrial Engineering International
Volume:16 Issue: 2, Spring 2020

In this paper, a bi-level game-theoretic model is proposed to investigate the effects of governmental financial intervention on green supply chain. This problem is formulated as a bi-level program for a green supply chain that produces various products with different environmental pollution levels. The problem is also regard uncertainties in market demand and sale price of raw materials and products. The model is further transformed into a single-level nonlinear programming problem by replacing the lower-level optimization problem with its Karush–Kuhn–Tucker optimality conditions. Genetic algorithm is applied as a solution methodology to solve nonlinear programming model. Finally, to investigate the validity of the proposed method, the computational results obtained through genetic algorithm are compared with global optimal solution attained by enumerative method. Analytical results indicate that the proposed GA offers better solutions in large size problems. Also, we conclude that financial intervention by government consists of green taxation and subsidization is an effective method to stabilize green supply chain members’ performance.

Undergraduate thesis examination in Industrial Engineering Department of Universitas Sebelas Maret conducted through two stages, namely intermediate and final examination. Currently, the scheduling process of such examinations is done by the undergraduate thesis coordinator manually without certain systematic method or approach. In this paper, we develop an optimization model for the examinations scheduling considering several factors, namely the number of lecturers that must attend the examinations, the availability of rooms for examinations, the availability of each lecturer, and the assignment distributions. The model uses integer programming approach. Two performance criteria are used in the model, namely the difference between the number of each lecturer’s assignment with the average number of lecturer assignments and the number of penalties from the assignment of lecturers on certain time slot. The developed model is able to solve the scheduling problem more efficiently than manual scheduling done by thesis coordinator. The optimal solutions from the optimization model show a total difference in the assignment of lecturer with an average of 29.6 and a penalty of 0.

In this paper, we consider the problem of scheduling on two-machine permutation flowshop with minimal time lags between consecutive operations of each job. The aim is to find a feasible schedule that minimizes the total tardiness. This problem is known to be NP-hard in the strong sense. We propose two mixed-integer linear programming (MILP) models and two types of valid inequalities which aim to tighten the models’ representations. One of them is based on dominance rules from the literature. Then, we provide the results of extensive computational experiments used to measure the performance of the proposed MILP models. They are shown to be able to solve optimally instances until the size 40-job and even several larger problem classes, with up to 60 jobs. Furthermore, we can distinguish the effect of the minimal time lags and the inclusion of the valid inequalities in the basic MILP model on the results.

In this study, the implications of the government’s tariffs on optimal pricing decisions in a dual-channel SC with one manufacturer and one retailer by taking into account the retailer services are examined. First, the best response strategies of retailer and manufacturer have obtained following the government’s tariffs by using a Stackelberg game model. Then, the government problem has modeled in six scenarios in a competitive mode about service level, social welfare, and government’s revenue-seeking policies. It can be concluded that retailer services affect the optimal manufacturer and retailer’s decisions. Moreover, with the sensitivities analysis that was studied on government models, it was shown that an integrated SC could better serve the government to achieve its goals. Also, the optimal strategies of the manufacturer and retailer of a dual-channel supply chain have been reached to the government’s social and economic goals. It can be found that the government with proper tariffs could coordinate social, economic, and service objectives.

Emergency departments (EDs) have been becoming increasingly congested due to the combined impacts of growing demand, access block and increased clinical capability of the EDs. This congestion has known to have adverse impacts on the performance of the healthcare services. Attempts to overcome with this challenge have focussed largely on the demand management and the application of system wide process targets such as the “four-hour rule” intended to deal with access blocks. In addition, EDs have introduced various strategies such as “fast tracking”, “enhanced triage” and new models of care such as introducing nurse practitioners aimed at improving throughput. However, most of these practices require additional resources. Some researchers attempted to optimise the resources using various optimisation models to ensure best utilisation of resources to improve patient flow. However, not all modelling approaches are suitable for all situations and there is no critical review of optimisation models used in hospital EDs. The aim of this article is to review various analytical models utilised to optimise ED resources for improved patient flow and highlight benefits and limitations of these models. A range of modelling techniques including agent-based modelling and simulation, discrete-event simulation, queuing models, simulation optimisation and mathematical modelling have been reviewed. The analysis revealed that every modelling approach and optimisation technique has some advantages and disadvantages and their application is also guided by the objectives. The complexity, interrelationships and variability of ED-related variables make the application of standard modelling techniques difficult. However, these models can be used to identify sources of flow obstruction and to identify areas where investments in additional resources are likely to have most benefit.

The supplier selection and order allocation are two key strategic decisions in purchasing problem. The review presented in this paper focuses on the supplier selection problems (SSP) and order allocation from year 2000 to 2017 in which a new structure and classification of the existing research streams and the different MCDM methods and mathematical models used for SSP will be presented. The review was examined in three aspects: the summaries of the existing evidence concerning the problems, the identification of gaps in the current research to help determine where further investigation might be needed and positioning new research activities.

The government organizations grant incentives to promote green product consumption, improve green product quality, boost remanufacturing activities, etc. through various policies. The objective of this study is to highlight pros and cons of two incentive policies, namely (1) incentive on manufacturer’s R&D investment and (2) direct incentive to consumer based on greening level of the product on the optimal pricing and investment decisions in improving used product return and greening level decisions in a closed-loop supply chain (CLSC). Optimal decisions are derived under manufacturer and retailer-Stackelberg games, and results are compared to explore characteristics of optimal decisions, consumer surplus, and environmental improvement under two marketing strategies of a manufacturer. It is found that the greening level and used product return rate in a CLSC are always higher under retailer-Stackelberg game. If the manufacturer sets a target for greening level, the CLSC members may receive higher profits if consumer receives incentive because of higher consumer surplus. However, environmental improvement may be lower. If the manufacturer sets a product return goal, then CLSC members may compromise with consumer surplus or environmental improvement for receiving higher profits. In the presence of direct incentive to consumers, CLSC members can trade with product at lower greening level for higher profits. Moreover, investment in improving used product return is always less compared to the investment in improving greening level.

Water pH and active ingredient concentration are two of the most important variables to consider in the manufacturing process of fungicides. If these variables do not meet the required standards, the quality of the product may be compromised and lead to poor fungicide performance when water is used as the application carrier, which is in most cases. Given the correlation between the variables, these kinds of manufacturing processes must be analyzed in multivariate settings. Thus, this paper analyzes the variables involved in the process using the multivariate control chart S introduced by J. A. Vargas. In the original chart, the arithmetic mean is used as the mean vector estimator. However, in this investigation the arithmetic mean was replaced by the Winsorized Mean for the purpose of evaluating the chart performance with a robust estimator. The results show that using the new estimator, the control chart is able to detect shifts in the variation of the mean vector that the traditional estimator did not. Furthermore, different subgroup sizes for the data were studied in order to examine the performance of the chart in each case. It was found that the proposed control chart is more sensible to changes when the subgroups consist of less observations, since it is able to better identify the outliers in the sample.