JOURNAL OF OPTIMIZATION IN INDUSTRIAL ENGINEERING (JOURNAL OF INDUSTRIAL ENGINEERING)
Year:2018 | Volume:11 | Issue:1 (23)|Papers Count:15

The emergence of sustainability paradigm has influenced many research disciplines including supply chain management. It has drawn the attention of manufacturing companies’ CEOs to incorporate sustainability in their supply chain and manufacturing activities. Supplier selection problem, as one of the main problems in supply chain activities, is also combined with sustainable development where traditional procedures are now transformed to sustainable initiatives. Moreover, allocating optimal order quantities to sustainable suppliers has also attracted attention of many scholars and industrial practitioners, which has not been comprehensively addressed. Therefore, a practical model of supplier selection and order allocation based on the sustainability Triple Bottom Line (TBL) approach is presented in this research article. The proposed approach utilizes Fuzzy Analytical Hierarchy Process combined with Quality Function Deployment (FAHP-QFD) for reflecting buyer’s sustainability requirements into the preference weights that are then exerted by an efficient Fuzzy Assessment Method (FAM) to assess the suppliers to obtain their sustainability scores. Thereupon, these scores are utilized in a fuzzy multi-objective mixinteger non-linear programming model (MINLP) for allocating orders to suppliers based on the manufacturer’s sustainability preference. A real-world application of food industry is presented to show the practicality of the proposed approach.

In service sector, there are challenges in keeping an optimum balance between customers' demand and availability of resources. This problem is going to be more intense in the health sector due to the fact that both arrival and service times are random. Therefore, designing the service environment by keeping the optimum balance between customers’ demand and available resources is becoming a series problem in Teklehaymanot General Hospital. This paper tries to develop a model that investigates the performances of Teklehaymanot General Hospital and determines the optimum number of specialist doctors based on their respective workload. To address this objective, the study develops a model using Arena Simulation Software that considers the real working environment and scenario of Teklehaymanot General Hospital. For the purpose of this research, three years’ secondary data that include the type of services and number of specialized doctors under each service channel are collected from the hospital records and fitted to the model. The findings of the study show that there are unbalanced distributions on the daily workload among specialist doctors and extended long waiting time of patients in Teklehaymanot General Hospital. It reveals that specialist doctors who are working in pre-breast center, Hematology oncology imaging, neurology, obstetrics & gynecology, ophthalmology, pulmonology, urgent care, urology and women’s imaging are relatively overloaded, whereas those who are working in ENT Allergy Audiology, gastroenterology, Nuclear Medicine, orthopedics, physical therapy, and surgery are relatively underloaded. Moreover, from the scenario analysis, the result shows that additional specialized doctors in the fifteen areas are required so as to reduce the waiting time of patients by 54.41%. Therefore, the hospital is recommended to have a balanced workload distribution among specialist doctors and increase the number of specialist doctors by one or two in the fifteen service areas.

This paper studies the parallel machine scheduling problem subject to machine and job deterioration in a batched delivery system. By the machine deterioration effect, we mean that each machine deteriorates over time, at a different rate. Moreover, job processing times are increasing functions of their starting times and follow a simple linear deterioration. The objective functions are minimizing total tardiness, delivery, holding and machine deteriorating costs. The problem of total tardiness on identical parallel machines is NP-hard, thus the under investigation problem, which is more complicated, is NP-hard too. In this study, a mixed-integer programming (MILP) model is presented and an efficient hybrid genetic algorithm (HGA) is proposed to solve the concerned problem. A new crossover and mutation operator and a heuristic algorithm have also been proposed depending on the type of problem. In order to evaluate the performance of the proposed model and solution procedure, a set of small to large test problems are generated and results are discussed. The related results show the effectiveness of the proposed model and GA for test problems.

This paper studies multiple cross-dockings where the loads are transferred from origins (suppliers) to destinations (customers) through cross docking facilities. Products are no longer stored in intermediate depots and incoming shipments are consolidated based on customer demands and immediately delivered to them to their destinations. In this paper, each cross-docking has a covering radius that customers can be served by at least one cross-docking provided. In addition, this paper considers the breakdown of trucks. We present a two-stage model for the location of cross-docking centers and scheduling inbound and outbound trucks in multiple cross-dockings. We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and route them to the customers via cross-docking facilities. The objective, in the first stage, is to minimize transportation cost of delivering products from suppliers to open cross-docks and cross-docks to the customers; in the second-stage, the objective is to minimize the make spans of open cross-dockings and the total weighted summation of completion time. Due to the difficulty of obtaining the optimum solution to medium- and large-scale problems, we propose four types of metaheuristic algorithms, i.e., genetic, simulated annealing, differential evolution, and hybrid algorithms. The result showed that simulated annealing is the best algorithm between the four algorithms.

Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper, a stochastic optimization problem is transformed into an equivalent deterministic problem, which can be solved by any known classical methods (interior penalty method is applied here).The paper mainly focuses on investigating the effect of applying various probability functions distributions (normal, gamma, and exponential) for design variables. The following basic required equations to solve nonlinear stochastic problems with various probability functions for random variables are derived and sensitivity analyses to study the effects of distribution function types and input parameters on the optimum solution are presented as graphs and in tables by studying two considered test problems. It is concluded that the difference between probabilistic and deterministic solutions to a problem, when the normal distribution of random variables issued, is very different from the results when gamma and exponential distribution functions are used. Finally, it is shown that the rate of solution convergence tithe normal distribution is faster than the other distributions.

Optimizing gate scheduling at airports is an old, but also a broad problem. The main purpose of this problem is to find an assignment for the flights arriving at and departing from an airport, while satisfying a set of constraints. A closer look at the literature in this research line shows that in almost all studies airport gate processing time has been considered as a fix parameter. In this research, however, we investigate a more realistic situation in which airport gate processing time is a controllable. It is also assumed that the possible compression/expansion processing time of a flight can be continuously controlled, i.e. it can be any number in a given interval. Doingsohas some positive effects which lead to increasing the total performance at airports’ terminals. Depending on the situation, different objectives become important.. Therefore, a model which simultaneously (1) minimize the total cost of tardiness, earliness, delay and the compression as well as the expansion costs of job processing time, and (2) minimize passengers overcrowding on gate is presented. In this study, we first propose a mixed-integer programming model for the formulated problem. Due to complexity of problem, two multi-objective meta-heuristic algorithms, i.e. multi-objective harmony search algorithm (MOHSA) and nondominated sorting genetic algorithm II (NSGA-II) are applied in order to generate Pareto solutions. For calibrating the parameter of the algorithms, Taguchi method is used and three optimal levels of the algorithm’s performance are selected. The algorithms are tested with real-life data from Mehrabad International Airport for nine medium size test problems. The experimental results show that NSGA-II has better convergence near the true Pareto-optimal front as compared to MOHSA; however, MOHSA finds a better spread in the entire Pareto-optimal region. Finally, it is possible to apply some practical constraints into the model and also test them with even large real-life problems instances.

The multi-level programming problems are attractive for many researchers because of their application in several areas such as economic, traffic, finance, management, transportation, information technology, engineering and so on. It has been proven that even the general bi-level programming problem is an NP-hard problem, so the multi-level problems are practical and complicated problems therefore solving these problems would be significant. The literature shows several algorithms to solve different forms of the bi-level programming problems (BLPP).Not only there is no any algorithm for solving quad-level programming problem, but also it has not been studied by any researcher. The most important part of this paper is presentation and studying of a new model of non-linear multi-level problems. Then we attempt to develop an effective approach based on Taylor theorem for solving the non-linear quad-level programming problem. In this approach, by using a proposed smoothing method the quad-level programming problem is converted to a linear single problem. Finally, the single level problem is solved using the algorithm based on Taylor algorithm. The presented approach achieves an efficient and feasible solution in an appropriate time which has been evaluated by solving test problems.

Decision-making concerning the location of critical resource on the geographical network is important in many industries. In the healthcare system, these decisions include location of emergency and preventive care. The decisions of location play a crucial role due to determining the travel time between supply and demand points and response time in emergencies. Organs are considered as highly perishable products, whose variety of each product has a specific perish time. Despite the importance of this field, only a small proportion of healthcare sector is dedicated to this field. Matching and finding the best recipient for a donated organ is one of the major problems in this field, which is also crucial for the overall organ transplantation process. Balancing the demand and supply in a transplant organ supply chain in order to decrease the waiting list needs certain scheduling and management. The main contribution of this paper consists of considering recipient regions as another component of the supply chain; in addition, importance of transportation time and waiting lists has led us to consider a bi-objective model. In addition, uncertainty of input data has led us to consider a stochastic approach.

Hub networks have always been acritical issue in locating health facilities. Recently, a study has been investigated by Cocking et al. (2006) in Nouna health district in Burkina Faso, Africa, with a population of approximately 275, 000 people living in 290 villages served by 23 health facilities. The travel times of the population to health services become extremely high during the rainy season, since many roads are unusable. In this regard, for many people, travelling to a health facility is a deterrent to seeking proper medical care. Furthermore, in real applications of hub networks, the travel times may vary due to traffic, climate conditions, and land or road type. To handle this challenge this paper considers the travel times are assumed to be characterized by trapezoidal fuzzy variables in order to present a fuzzy green capacitated single allocationp -hub center system (FGCSA p HCP) with uncertain information. The proposed FGCSA p HCP is redefined into its equivalent parametric integer nonlinear programming problem using credibility constraints. The aim is to determine the location of pc apacitated hubs and the allocation of center nodes to them in order to minimize the maximum travel time in a hub-and-center network in such uncertain environment. As the FGCSAp HCP is NP-hard, a novel algorithm called opposition biogeography based optimization is developed to solve that. This algorithm utilizes a binary opposition based learning mechanism to generate a diversity mechanism. At the end, both the applicability of the proposed approach and the solution methodologies are demonstrated using GAMS/BARON Software under several kind of problems. Sensitivity analyses on the number of hubs and center nodes are conducted to provide more insights as well.

The aim of lot sizing problems is to determine the periods where production takes place and the quantities to be produced in order to satisfy the customer demand while minimizing the total cost. Due to its importance on the efficiency of the production and inventory systems, Lot sizing problems are one of the most challenging production planning problems and have been studied for many years with different modeling features. In this paper, we propose a fuzzy mathematical model for the single-item capacitated lot-sizing problem in closed-loop supply chain. The possibility approach is chosen to convert the fuzzy mathematical model to crisp mathematical model. The obtained crisp model is in the form of mixed integer linear programming (MILP), which can be solved by existing solver in crisp environment to find optimal solution. Due to the complexity of the problems harmony search (HS) algorithm and genetic algorithm (GA) have been used to solve the model for fifteen problem. To verify the performance of the algorithm, we computationally compared the results obtained by the algorithms with the results of the branch-and-bound method. Additionally, Taguchi method was used to calibrate the parameters of the meta-heuristic algorithms. The computational results show that, the objective values obtained by HS are better from GA results for large dimensions test problems, also CPU time obtained by HS are better than GA for Large dimensions.

In this paper, a hub covering location problem is considered. Hubs, which are the most congested part of a network, are modeled as M/M/C queuing system and located in places where the entrance flows are more than a predetermined value. A fuzzy constraint is considered in order to limit the transportation time between all origin-destination pairs in the network. On modeling, a nonlinear mathematical program is presented. Then, the nonlinear constraints are converted to linear ones. Due to the computational complexity of the problem, genetic algorithm (GA), particle swarm optimization (PSO) based heuristics, and improved hybrid PSO are developed to solve the problem. Since the performance of the given heuristics is affected by the corresponding parameters of each, Taguchi method is applied in order to tune the parameters. Finally, the efficiency of the proposed heuristics is studied while designing a number of test problems with different sizes. The computational results indicated the greater efficiency of the heuristic GA compared to the other methods for solving the problem.

In today’s competitive market, those producers who can quickly adapt themselves to diverse demands of customers are successful. Therefore, in order to satisfy these demands of market, Mixed-model assembly line (MMAL) has an increasing growth in industry. A mixed-model assembly line (MMAL) is a type of production line in which varieties of products with common base characteristics are assembled on. This paper focuses on this type of production line in a stochastic environment with three objective functions: 1) total utility work cost, 2) total idle cost, and 3) total production rate variation cost that are simultaneously considered. In real life, especially in manual assembly lines, because of some inevitable human mistakes, breakdown of machines, lack of motivation in workers and the things alike, events are not deterministic, so we consider operation time as a stochastic variable independently distributed with normal distributions; for dealing with it, chance constraint optimization is used to model the problem. At first, because of NP-hard nature of the problem, multi-objective harmony search (MOHS) algorithm is proposed to solve it. Then, for evaluating the performance of the proposed algorithm, it is compared with NSGA-II that is a powerful and famous algorithm in this area. At last, numerical examples for comparing these two algorithms with some comparing metrics are presented. The results have shown that MOHS algorithm has a good performance in our proposed model.

In this paper, a preemptive multi-objective multi-mode project scheduling model for resource investment problem is proposed. The first objective function is to minimize the completion time of project (makespan); the second objective function is to minimize the cost of using renewable resources. Non-renewable resources are also considered as parameters in this model. The preemption of activities is allowed at any integer time units, and for each activity, the best execution mode is selected according to the duration and resource. Since this bi-objective problem is the extension of the resource-constrained project scheduling problem (RCPSP), it is NP-hard problem, and therefore, heuristic and metaheuristic methods are required to solve it. In this study, Non-dominated Sorting Genetic AlgorithmII (NSGAII) and Non-dominated Ranking Genetic Algorithm (NRGA) are used based on results of Pareto solution set. We also present a heuristic method for two approaches of serial schedule generation scheme (S-SGS) and parallel schedule generation scheme (P-SGS) in the developed algorithm in order to optimize the scheduling of the activities. The input parameters of the algorithm are tuned with Response Surface Methodology (RSM). Finally, the algorithms are implemented on some numerical test problems, and their effectiveness is evaluated.

This paper proposed a parallel automated assembly line system to produce multiple products having multiple autonomous guided vehicles (AGVs). Several assembly lines are configured to produce multiple products in which the technologies of machines are shared among the assembly lines when required. The transportation between the stations in an assembly line (intra-assembly line) and that among stations in different assembly lines (inter assembly line) are performed using AGVs. Scheduling of AGVs to service the assembly lines and the corresponding stations are proposed. In the proposed problem, the assignment of multiple AGVs to different assembly lines and stations is performed using minimum-cost network flow (MCF). It optimizes weighted completion time of tasks for each short-term window by formulating the task and resource assignment problem as MCF problem during each short-term scheduling window. The novelties of the paper are as follows: to configure an autonomous assembly line, to model a minimum cost network flow, and to develop a heuristic solution approach. The results and comparisons show the effectiveness and efficiency of the model and solution algorithm.

Conventional data envelopment analysis (DEA) models are used to measure efficiency score of production systems when they are considered as black boxes and their internal relationship is ignored. This paper deals with a common special case of network systems called multi-stage production system and can be generalized to many organizations. A multi-stage production system has some stages in which the outputs of each stage are used as the inputs of the next stage to produce the final outputs of the system. Most of the approaches handling multi-stage systems in DEA evaluate efficiency measure of a production system considering the interrelationship between its stages; however, they do not present their ranking or impact of each stage on ranking of a special multi-stage system through comparison with the others. In this paper, considering the series internal structure of the multi-stage systems and their efficiency measures, we propose some new ratio-based DEA models to determine the best and worst ranks of the multi-stage systems over all sets of feasible weights. In order to improve the performance of the whole system, the proposed models are used to recognize the stages with the most important role in the system’s inefficiency. Some numerical examples are presented to illustrate the approach.