Journal of Industrial and Systems Engineering
Volume:12 Issue: 3, Summer 2019

Claim is a big challenge for the contractors and the owners in construction projects. Claims are considered to be one of the most disruptive events of a project. A suitable claim resolution strategy can prevent the damages to the project and the involved parties. In this research, a mathematical model using game theory is presented to find the optimum strategy for resolving cost-related claims in Design-Bid-Build (DBB) projects. The model investigates the strategies of the contractor and the owner in a consecutive four-step process including: negotiation, mediation, arbitration and litigation. It helps the involved parties to have deeper comprehension of the problem, have a better evaluation of their situation and analyze possible strategies in facing with such circumstances. Considering different scenarios, the points which both parties can agree rationally are proposed with an analytical solution. Finally, two cases of real-world problems are presented and analyzed using the proposed approach and the optimum strategy is determined for each case. Based on the results, some strategies for the owners and contractors are presented in order to be more successful in the claim resolution process.

This paper is considering the competition between two multi-echelon supply-chains on price and service under balance and imbalance of market power between the chains which are analyzing through Nash and Stackelberg game approach. The problem is categorized as the centralized or decentralized structure of each chain, which means a few different possible scenarios are developing based on the Nash and Stackelberg games. The aim of the paper is to investigate the simultaneous effect of the chains’ structure and market power on the decision variables. As a surprise result, we show that in the Stackelberg game, the chain will not always have the second-mover advantage. Furthermore, the results demonstrate that the leader''s presence in the market may have different impacts on the situation depending on the structure of the chains. Also, when the chains take their decisions sequentially, the service and the price jointly play a strategic role in earning profits.

Measuring the efficiency of real businesses is not a simple task, because a real business may involve several processes and sub-processes, forming a very complicated dynamic network of interactions. In this paper, a customized dynamic network data envelopment analysis (NDEA) model is proposed to measure the efficiency of the sub-processes in a real business. The proposed dynamic NDEA model is fully designed and customized for IMI which is a leading institute in providing consulting management, publication, and educational services. First, we have identified the network of the Industrial Management Institute (IMI) which includes educational, consulting, and publication sub-processes. Then, the most important sub-processes and the associated dynamic interactions are determined. Afterward, a dynamic NDEA model is proposed to measure the efficiency of sub-processes. The main theoretical properties of the proposed dynamic NDEA model are also discussed through theorems. Assessing the performance of IMI's sub-processes is not a trivial task due to the complexity of sub-processes in IMI. The proposed dynamic NDEA model is applied using real operational data of the IMI gathered through a sixty-month planning horizon. An attempt has been accomplished to form a relationship between the total efficiency of the process and the efficiency of each sub-process by regression analysis. The managers of IMI can monitor the efficiency score of the main process and sub-processes during the planning horizon which can help to improve inefficient sub-process.

Swarm intelligence-based algorithms are soft computing techniques, which have already been applied to solve a broad range of optimization problems. Generally, clustering is the most common technique, which, balances the energy consumption among all sensor nodes and minimizes traffic and overhead during data transmission phases of Wireless Sensor Networks. The performance scope of the existing clustering protocols is fixed and hence, cannot adapt to all possible areas of applications. In this paper, a multi-objective swarm intelligence algorithm – which is based on Shuffled Frog-leaping and Firefly Algorithms (SFFA) – is presented as a clustering-based protocol for WSNs. The multi-objective fitness function of SFFA considers different criteria such as cluster heads’ distances from the sink, residual energy of nodes, inter- and intra-cluster distances and finally overlap and load of clusters to select the most proper cluster heads at each round. The parameters of SFFA in clustering phase can be adapted and tuned to achieve the best performance based on the network requirements. The simulation outcomes demonstrated an average lifetime improvement of up to 49.1%, 38.3%, 7.1%, and 11.3% compared to LEACH, ERA, SIF, and FSFLA in different network scenarios, respectively.

Nowadays, </strong>scholars do their best to study more practical aspects of classical problems. Job shop Scheduling Problem (JSSP) is an important and interesting problem in scheduling literature which has been studied from different aspects so far. Considering assumptions like learning effects, flexible maintenance activities and transportation times can make this problem more close to the real life, however these assumptions have rarely been studied in this problem. This paper aims to provide a mathematical model of JSSP which covers these assumptions. MILP model is suggested, Three different sizes of instances are generated randomly,  and this model has been solved for small-sized problems exactly by GAMS software and the effects of learning on reducing the value of objective function is shown. Due to the complexity of the problem, in order to obtain near optimal solutions, medium and large instances are solve by applying Ant Colony Optimization for continuous domains(ACOR) and Invasive weed Optimization (IWO) algorithm, finally results are compared.

Pricing and controlling the inventory of perishableproducts have key roles in determining the level of profit for those involved in the supply chains. Chain profit can be increased by increasing sales during the product life via the application of pricing strategies, avoiding the loss of value of perishable products over time. In this research, sales profit was maximized by presenting a mathematical model to determine the price change points (using the Hsien function) and the optimal price and order quantity for perishable products with an exponential and price- and time-dependent distributed demand. Due to the complexity of the problem, the solution method used in this study was the genetic algorithm. The analysis of the effect of different parameters and optimal solution results showed that a 2% increment in decay rate would lead to a 10% reduction in profits, and other analyses recommended for managers at the end of the article.

This paper proposes a mathematical model for ride-sharing vehicles with a common destination. A number of cars should assign to individuals by a company to pick up other participants in their way to the common destination. Traveling time as an important parameter is considered an uncertain parameter to enhance the applicability of the model which is formulated using fuzzy programming and necessity concept. Moreover, to have a better solution with better productivity, maximizing the earliest departure time of the individuals is considered beside of minimizing total traveling time. This helps to make justice among individuals for departure time. Goal programming is employed to work with objective functions and solve the model. Furthermore, a numerical example is implemented on the model to evaluate the applicability of the model which indicates the efficiency of employing fuzzy programming and considering both of the objective functions using goal programming. Results of the numerical example indicate the importance of considering both of the objective functions together in which ignoring each of them leads to inefficient solutions.

Natural disasters such as earthquakes have a destructive impact on urban infrastructures and their performance. Due to the existence of inherent uncertainty in natural disasters, related organizations are not able to optimally use the critical infrastructures to reduce destructive effects. Also, estimating the demand for relief commodities according to various scenarios has always been a concern for decision makers and relief organizations. The correct estimate of demand can reduce the time of relief operations and can greatly reduce human casualties. In this paper, the demand forecast for relief commodities will be as the output of the fuzzy inference system. The proposed mechanism has been tested in a case study of Tehran city. The results of this research can be useful for many decision making centers, including the fire department, the Red Cross, hospitals and so on. Estimation of demand for relief commodities using the fuzzy inference system for different scenarios and considering a case study for a possible earthquake in Tehran are the contributions of this research.

In this paper, a supply chain, including a manufacturer, a distributor and some retailers, is considered. The manufacturer produces a single product and outsources distribution operations to a distributor in order to deliver products demanded by retailers. Each retailer has a time window to receive the products and faces the Newsvendor problem with stochastic demand. The manufacturer aims to serve retailers providing that the maximum lateness doesn’t exceed a predetermined value. All players in the supply chain are willing to maximize their own profit. The model simultaneously includes pricing, order quantity and routing decisions. First, the manufacturer announces the whole sale price, then the distributor declares the unit transportation cost to the retailers, and finally each retailer decides on the amount of his order quantity. The profit functions of the players are formulated and linearized; then the solution is determined in three stages using game theory. Finally, a numerical example is presented and the equilibrium decisions of the players are determined using GAMS software.

Earned value management (EVM) is a well-known tool in the project control phase. Upon running the projects, it is critical to control the project to determine the amount of deviation from the plan. Most employers expect the project to be completed according to their requirements and at the expected cost and time. In traditional earned value management, the employer does not present his/her plan, but in the proposed approach the employer gives his/her plan and asks the project manager to offer their time, cost, and quality plan of project based on this plan. The proposed method, called earned incentive metric (EIM), is an extension of the EVM approach that is introduced with triangular intuitionistic fuzzy sets. The plan of project team is compared to the employer’s plan, and the project results are finally compared to the employer's plan. The difference between the two comparisons indicates project performance. In the conventional approaches of EVM, the project is controlled in terms of time and cost, but in the presented approach, the quality criterion is controlled along with time and cost criteria. For the quality values of each activity in each work period, a new group decision-making process is provided. Finally, an application example is given, in which the cost, quality, and progress percentages of each activity in each period are regarded as triangular intuitionistic fuzzy numbers and accordingly performance of time, cost, and project quality are calculated.

Emphasize on cost-cutting, increasing customers' satisfaction, and trying to manage and reduce the risks are among the key strategies of decision-makers in the design of supply chain networks. This study provides a stochastic bi-objective multi-product optimization model for designing a resilient supply chain network under disruption risks. The objectives of the proposed model are minimizing the total cost of the supply chain, as well as, minimizing the non-resiliency of the network. In addition, a ε-constraint method is used to convert the bi-objective model into a single-objective formulation. The model decisions include locating manufacturers, warehouses, and distribution centers and determining the amount of production of different products in each manufacturer, the amount of product transport between the different nodes of the network, and the amount of lost sales for different products in each market. The validity of the proposed model is investigated through random examples and the results of the model implementation on these examples are presented.

The present research introduces a multi-objective robust optimization model to design emergency medical services network for uncertain costs and demands. The proposed model determines the location and the optimum capacity of relief medical service centers. In addition, the model determines the number and the type of ambulances that should be placed in each of the centers and allocated to demand zones. The multi-objective model attempts to maximize the coverage of demand zones, the availability of ambulances and minimizing the total costs simultaneously. A robust model is applied to our real word case study in an urban district.

A Cellular Manufacturing System (CMS) is the practical use of Group Technology (GP) in a production environment, which has received attention from researchers in recent years. In this paper, a mathematical model for the design of a cell production system is presented with consideration of Production Planning (PP). Consideration of environmental factors such as energy consumption and waste generated by machines in the proposed model is considered. Also, the problem of scheduling component processing in the presented model has been considered. Due to the complexity of the model presented in this paper, a hierarchical approach is proposed for solving the model. At first, the proposed model is analyzed without considering the scheduling topic using the GAMS software and the results are analyzed. Then an Imperialist Competitive Algorithm (ICA) was used to solve the scheduling problem. To evaluate the performance of the proposed model, numerical examples are used in small, medium, and large dimensions. In addition, the ICA presented in this paper is compared with the methods available in the literature as well as the genetic algorithm and its quality is confirmed.

Planning the freight flow from the plants to the customer zones is one of the most challenging problems in the field of supply chain management. Because of many traffic regulations, oversize/overweight vehicles often are not permitted to enter city boundaries. Therefore, intermediate facilities (city distribution centers) play a very important role in distribution networks. Accordingly, in this paper, transportation of goods from the plants to the customers is considered an integrated process containing two phases, namely, transportation from plant to distribution centers and distribution from city distribution centers to customers using small and environmentally-friendly vehicles. The Transportation Location Routing Problem (TLRP) studied can be considered as an extension of the two-echelon location routing problem. Minimizing the operational costs, and the workload balancing of the heterogeneous fleet in the distribution phase are considered as the two objective functions. A Mixed Integer Programming (MIP) model, as well as two solution approaches, based on Multi-objective Particle Swarm Optimization Algorithm, and Non-dominated Sorting Genetic Algorithm, is presented for the problem. In order to illustrate the efficacy of the proposed methods, they have been implemented on test problems of different sizes. The results show the methods are able to produce efficient solutions in a reasonable amount of time.

Pharmaceutical manufacturers have a vital role in the healthcare system insofar as any disruption in their production processes jeopardizes people’s health and the environment they inhabit. Resilience engineering (RE) could shift such systems from an abnormal to a normal state. In addition, macro-ergonomics (ME) can enhance all system factors in a pharmaceutical plant. It is argued that integrating RE with ME could result in the overall optimization of a pharmaceutical unit. This study presents an integrated approach based on RE and ME factors which could be used to optimize the performance of pharmaceutical plants. A standard questionnaire was designed and distributed among experts. Data envelopment analysis (DEA) and fuzzy DEA were used to conduct optimization. Then. Pearson correlation test and paired t-test were used to evaluate the best DEA or fuzzy DEA model. Next, the best model was tested in terms of RE, ME, and combined factors. The results showed that reporting culture, flexibility, and formalization are the most important factors in the pharmaceutical industry. This is the first study to carry out an integrated optimization of RE and ME in a pharmaceutical unit and, thus, provides a practical approach for the pharmaceutical industry.