Industrial Management Journal
Year:2017 | Volume:9 | Issue:1 |Papers Count:11

Nowadays assignment issues, as one of the optimization problems in the field of Operations Research is studied by many researchers. Assignment issue is known as a type of NP-Hard issues. However, in real applications, various inputs and outputs are usually concerned in an assignment problem. This paper, based on some of Electrical Engineering concepts that can be considered equivalent to the concept of efficiency in data envelopment analysis, provides a new linear programming model to resolve assignment problems with multiple diverse inputs and outputs for each possible assignment. The objective function in this model is maximum of comparative efficiency rather than cost or profit.The main advantages of this new mathematical model include faster convergence to the optimum solution, focusing on a single mathematical model at the same time not several models, stability in the number of variables and constraints of the proposed model considering any increase in the number of inputs or outputs of problem and also less computational time compared to the other conventional approaches. The proposed model was described along with an applied example, then the results were compared with that of the model proposed by Chen and Lu.

An integer linear programming model for university courses timetabling is proposed here. In order to reduce the number of decisive variables, a combination of a course, a professor schedule and the students ‘group was defined as an activity. In this context, the two integer programming models namely the activity-based model and a two-phase activity-based model were proposed. In the first model, all activities were scheduled based on the number of required weekly sessions in the weekdays intervals; however, in the second model, classes and training courses were determined according to the planned sessions considering their special restrictions. These models were formulated based on the process of assigning the university courses within specific intervals throughout the week considering fierce constraints for a given semester in the department of Economics at University of Isfahan. All regulation concerning the courses timetable of a semester were formulated in GAMS software. Then, 239 courses were successfully scheduled using the two-phase activity-based model in only 9 minutes and 16 seconds.

In recent years, coping with a phenomenon known as bullwhip effect, has been among the most important issues in supply chain management. Bullwhip dramatically affects companies’ financial performance stating that the swing of changes in demands increases when we move from the end of the chain to the beginning of that chain. Many studies have been conducted on reducing the impact of supply chain bullwhip, each focusing on specific aspects. For this purpose in the present study, a linear four-level supply chain including store, retailer, wholesaler and factory was proposed, and the moving average method was used to predict the demand. To do so, nine different scenarios including demand changes (low, medium, high) and precautionary (low, moderate, high) were considered and the lag effect was calculated with a 95% confidence interval and throughout a one-year period. The findings showed that if we use average moving method to predict the demands, any increase in the customers’ demand change swing will result in the decrease of the whip effect on the whole chain. In addition, if the demand changes are considered as constant and fixed, any increase in precautionary saving in each part of the supply chain will increase the whip effect on the whole chain.

In order to manage the expenditures in a road transportation network in which the transport demands between cities are less than a truckload capacity, one needs to determine the location of hubs at first, and then collect the cargo from the cities in some routes which are assigned to the appropriate hubs. In this paper, a special case of hub location-routing issue was considered that is suitable for the particular conditions of Iran as cities are located in the sparse and distant places. A mixed integer mathematical programming model was proposed. As the model is NP-hard in nature, a two-phase hybrid method including genetic algorithms and simulated annealing was designed to solve the model. The results of the comparison between the model and the outputs demonstrated the accuracy and speed of the proposed solution method.Finally, a real case including all 31 capital cities of Iran provinces was solved to illustrate the appropriate performance of the solution method.

There are numerous complexities in supply chain nowadays, and these complexities are evolving due to globalization, customization, innovation, flexibility, sustainability and uncertainties. The increase in supply chain complexity yields negative effects on cost, customer service and reputation. Hence, the present research target was to prioritize supply chain complexity drivers in home appliance companies. After reviewing the related literature, supply chain complexity drivers involved in the process of prioritization have been identified. Then, using Fuzzy Hierarchical Analytic Process, these indicators were weighted up based on pairwise comparisons (obtained from a number of experts in the home appliance companies). The results showed that among the 46 Complexity Drivers examined in this study, “the mechanisms to return raw materials to suppliers" was of highest importance.

Nowadays, considering the importance of the energy issue, it has become a major concern of economic topic. According to the energy balance sheet data, more than one third of the total energy consumption in Iran is consumed in the building sector. With regard to the massive waste of energy in the existing buildings, as well as the low efficiency of heating and cooling systems, seeking the right solution to reduce energy consumption in this sector is of utmost important. In this study, based on mathematical modeling, a solution has been presented to help us choose a proper combination of primary building materials and active or inactive air conditioning systems for residential buildings. It aimed to minimize both costs and thermal energy consumption of building materials. The proposed model has been utilized for a residential building in Tehran. Then, the optimal combinations of materials have been determined based on national building regulations (code 19).

Determining the optimal sequence of jobs in job shop scheduling for small and medium enterprises, affect the machine productivity, earliness and tardiness costs of delivery. The deterministic variant of the problem is well-known to be NP-Hard. If random elements are introduced into the problem, the level of complexity goes higher. Hence, many priority rules have been developed to tackle stochastic job shop scheduling problem. However, to devise a better solution approach, simulation-optimization approach might be used. In this study, a mathematical model was developed for job shop scheduling with random process times and possible machine breakdowns. Then, a simulation-optimization model was applied to choose among a list of priority rules using Rockwell Arena 14. Finally, a numerical example was used to evaluate the quality of the model. Results showed that the rule Longest Processing Time (LPT) yields the lowest total earliness and tardiness costs.However, the total costs of the following rules are also acceptable: First in First out (FIFO), Last in First out (LIFO), Earliest Due Date (EDD) and Slack time (Slack).

Producing various types of waste and related environmental problems, has faced urban management with many problems in the areas like: collection, transportation and waste disposal. Applying a good way to reduce the costs of collecting waste seems necessary, because collection and transportation of the waste needs allocation of a significant part of garbage management budget. In the present study, a mathematical model is presented for waste collection by which we can reduce the costs of collecting the waste by minimizing the distance for the trucks. The model is flexible enough to manage collecting the waste of a node in different separate times. Then, some small-scale issues were solved using CPLEX software and meta-heuristic algorithm and the results were compared. The data for the case study were collected and the meta-heuristic algorithm was used to determine the vehicle schedule. Finally, a mathematical model was used to decide about the number of vehicles required.

One of the effective ways in reducing time is eliminating or reducing time wastes. Wastes are activities and events that are prolonging the process of producing but would not provide any value-added. For identification, after literature review, interview and thematic analysis were used and according to three factors presence in executing, controllability and direct effect on time, 8 causes were chosen among 35 ones. After that, by distributing questionnaire and using LASSO method, causes ranking and the formula of time waste prediction were obtained. Performing most tasks in site, rework and contractor delay were found out as the most important causes in wasting time. The reason of uniqueness of this paper is using LASSO method for ranking and providing a formula for time waste prediction that not provided in any internal or external articles before now. This prediction is useable 5 to 7 floor residential building projects. According to formula if any 8 causes exist in project process, the amount of waste will be 38% of executing time. By quantifying the amount of time waste, decision making about how to deal with causes is simpler.

This paper proposes a multi-period resilient supply network model for a two stage supply chain in which one of the main raw materials should be bought from different suppliers and various products should be produced for customers in several manufacturer production sites. We used two-stage stochastic programming provided that the customers have uncertain demands and the suppliers propose uncertain prices from time to time. In contrast with previous researches, we considered disruption risks of supply, mitigation strategies and contingency plans against risks to improve the resilience of our supply network. Also, to increase the authenticity of our model, we consider the production planning in each production site simultaneously. The supply network design was analyzed from a risk-neutral and a risk-averse decision maker point of view. To illustrate the applicability of our proposed approach, computational results of using this model in a real-life case was presented. Finally, to investigate the influence of cost parameter changes on the results of the model, we performed a sensitive analysis.