Logistics and Supply Chain Management

To meet the diversification of customer's preferences, mixed-model assembly lines are installed in many manufacturing plants. In some of them, a large variation exists in assembly times among different product types. The large variation reduces production efficiency and may cause a line stoppage. These variations can be reduced by installing a bypass subline which processes a portion of assembly operations of products with relatively longer assembly times. In spite of its significance, sequencing problem on bypass subline rarely has been discussed in the literature. This paper addresses a sequencing problem with a bypass subline with the goals of leveling the part usage rates and reducing line stoppages. A novel hybrid algorithm incorporating genetic algorithm and event-based procedure is developed to solve the problem. Efficiency of the proposed algorithm is demonstrated through solving several test problems and comparing the resulted solutions with optimal solutions obtained from an exhaustive enumeration method. Keywords Sequencing . Mixed model assembly line . Bypass subline . Hybrid genetic algorithm Rahimi-Vahed and Mirzaei [21] present a multi-objective model for mixed-model assembly line sequencing problem and solve the model using a novel frog-leaping algorithm. Kim and Jeong [22] introduce a new model for a mixedmodel assembly line sequencing problem to minimize unfinished works. Rahimi-Vahed et al. [4] develop a multi-objective mixed-model assembly line sequencing

Risk is inherent in most economic activities. This is especially true of production activities where results of decisions made today may have many possible different outcomes depending on future events. Since companies cannot usually protect themselves completely against risk, they have to manage it. In this paper, we present a multi-objective model to deal with a multiperiod multi-product multi-site aggregate production planning problem for a medium-term planning horizon under uncertainty. The first objective function attempts to minimize sum of the expected value and the variability of total costs with reference to inventory levels, regular, overtime and subcontracting levels, backordering levels, and labor, machine and warehouse capacities. The second objective function highlighted the concept of customer service level through minimizing the expected value of maximum shortages among all customers' zones from which the variability of that is conducted. The last objective function aims to maximize workers productivity, a weighted average of productivity levels in all factories and in all periods which is weighted by the number of k-level labors. Then, we use an efficient algorithm that is a combination of an augmented ε-constraint method and genetic algorithm to solve our proposed model. The results demonstrate the practicability of the proposed multi-objective stochastic model as well as the proposed algorithm.

In this paper a new model is developed to deal with a simultaneous dynamic cell formation and worker assignment problem (SDCWP). Part routing flexibility and machine flexibility and also promotion of workers from one skill level to another are considered. The proposed model is formulated as a single objective nonlinear integer programming which is converted to a linear one. The objective function consists of two separate components. The first part of the objective function is related to machine-based costs such as production cost, intercell material handling cost, machine costs in the planning horizon. The second part is related to human issues and consists of hiring cost, firing cost, training cost and salary. It is the first time that worker assignment and dynamic cell formation are considered simultaneously. To verify the performance of the proposed model, some numerical examples are presented. Computational and sensitivity analysis results imply the significance of SDCWP.

Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient production planning in a supply chain.

In this paper we are to develop a new model to solve a multi-period multi-product multi-site aggregate production planning problem in a green supply chain for a medium-term planning horizon. Some of the features of the proposed model are as follows: (i) Considering quantity discounts to encourage the producer to order more quantities to the suppliers in one period instead of splitting the order to periodical small quantities; (ii) Considering the interrelationship between lead time and transportation cost; as well as that of between lead time and greenhouse gas emission level, that is, the lower lead time, the more expensive transportation cost, the more greenhouse gas emission; (iii) Backorders are penalized by a general multiple breakpoint function, to persuade producers to reduce the backorders as much as possible; (iv) Some indicators of green supply chain, such as greenhouse gasses emission and the concept of waste management is also incorporated in to the model.

A multi-objective two stage stochastic programming model is proposed to deal with a multiperiod multi-product multi-site production-distribution planning problem for a midterm planning horizon. The presented model involves majority of supply chain cost parameters such as transportation cost, inventory holding cost, shortage cost, production cost. Moreover some respects as lead time, outsourcing, employment, dismissal, workers productivity and training are considered. Due to the uncertain nature of the supply chain, it is assumed that cost parameters and demand fluctuations are random variables and follow from a pre-defined probability distribution. To develop a robust stochastic model, an additional objective functions is added to the traditional production-distribution-planning problem. So, our multi-objective model includes i the minimization of the expected total cost of supply chain, ii the minimization of the variance of the total cost of supply chain and iii the maximization of the workers productivity through training courses that could be held during the planning horizon. Then, the proposed model is solved applying a hybrid algorithm that is a combination of Monte Carlo sampling method, modified ε-constraint method and L-shaped method. Finally, a numerical example is solved to demonstrate the validity of the model as well as the efficiency of the hybrid algorithm.

In this paper a model is developed to deal with Safe Skill-based Job Rotation Scheduling (SSJRS). A multi objective integer programming model is presented to determine job rotation schedules which considers four objective functions simultaneously. The first objective function aims to minimize maximum occupational noise exposure injuries. The second one is designed to minimize potential of worker's low back injuries and the third objective is developed to consider worker skill level's priorities in assignments and fmally fourth objective is added to minimize workers' idleness during planning horizon. The potential of worker low back injuries and the noise exposure levels of each job are assessed by Job Severity Index (JSI) and Daily Noise Dosage (DND) respectively. Thresholds for JSI and DND, is also considered in the presented model. Finally this model is solved as a single objective model, using LP-Metric method. To verify the efficiency of SSJRS, test problems are solved for each objective of the model separately and the results are compared with solutions of SSJRS model. It is found that, the Results from SSJRS model, dominates the Results from single objective models.

A common assumption in the literature on mixed model assembly line balancing problem is that the task duration is known and deterministic but may differ among various models. In this paper, we present a robust optimization formulation for dealing with task duration uncertainty in a mixed model assembly line balancing problem (RMALB-P) in which task duration can vary in a specific range. RMALB-P is aim to minimize the sum of costs of the stations and the task duplication. Task duplication means that a task which is common to multiple tasks can be assigned to different stations for different models. Finally, RMALB-P is solved optimally and implemented in mixed model assembly lines of IRAN KHODRO Company. The results are compared with the previous existing balance to show the effects of the data uncertainties on the performance of assembly line outputs. The results indicate that the robust balancing approach can be a relatively more reliable method for balancing the mixed model assembly lines.

Quantity discount is considered to encourage the producer to order more quantities.Interrelationship between lead time, transportation cost, and GHGs is considered.Demand uncertainty is assumed to follows a pre-specified distribution function.Shortages are penalized by a generic nonlinear multiple breakpoint function.The concepts of GHG emission and waste management are incorporated into the model.In this paper we develop a stochastic programming approach to solve a multi-period multi-product multi-site aggregate production planning problem in a green supply chain for a medium-term planning horizon under the assumption of demand uncertainty. The proposed model has the following features: (i) the majority of supply chain cost parameters are considered; (ii) quantity discounts to encourage the producer to order more from the suppliers in one period, instead of splitting the order into periodical small quantities, are considered; (iii) the interrelationship between lead time and transportation cost is considered, as well as that between lead time and greenhouse gas emission level; (iv) demand uncertainty is assumed to follow a pre-specified distribution function; (v) shortages are penalized by a general multiple breakpoint function, to persuade producers to reduce backorders as much as possible; (vi) some indicators of a green supply chain, such as greenhouse gas emissions and waste management are also incorporated into the model. The proposed model is first a nonlinear mixed integer programming which is converted into a linear one by applying some theoretical and numerical techniques. Due to the convexity of the model, the local solution obtained from linear programming solvers is also the global solution. Finally, a numerical example is presented to demonstrate the validity of the proposed model.

This paper addresses a multi-product multi-period Inventory Routing Problem (IRP) where multiple capacitated vehicles distribute products from multiple suppliers to a single plant to meet the given demand of each product over a finite planning horizon.
The demand associated with each product is assumed to be deterministic and time varying. In this supply chain, the products are assumed to be ready for collection at the supplier site when the vehicle arrives. A transshipment option is considered as a possible solution to increase the performance of the supply chain and shows the impact of this solution on the environment. A green logistic issue is also incorporated into the model by considering the interrelationship between the transportation cost and the greenhouse gas emission level. The proposed model is a mixed-integer linear program and solved by CPLEX. We provide a numerical study showing the applicability of the model and underlining the impact of the transshipment option on improved supply chain performance.

In recent years consumers and legislation have been pushing companies to design their activities in such a way as to reduce negative environmental impacts more and more. It is therefore important to examine the optimization of total supply chain costs and environmental impacts together. However, because of the recycling of deteriorated items, the environmental impacts of deteriorating items are more significant than those of non-deteriorating ones.

The objective of this paper is to develop a stochastic mathematical model and to propose a new replenishment policy in a centralized supply chain for deteriorating items. In this model, we consider inventory and transportation costs, as well as the environmental impacts under uncertain demand. Several transportation vehicles producing various greenhouse gas (GHG) levels are considered. The best transportation vehicles and inventory policy are determined by finding a balance between financial and environmental criteria. In this way, we develop a linear mathematical model and present a numerical example from the real world to demonstrate its applicability and effectiveness. We then perform a sensitivity analysis and provide some managerial insights. Finally, more promising directions are suggested for future research.

In recent years consumers and legislation have been pushing companies to design their activities in such a way as to reduce negative environmental impacts more and more. It is therefore important to examine the optimization of total supply chain costs and environmental impacts together. However, because of the recycling of deteriorated items, the environmental impacts of deteriorating items are more significant than those of non-deteriorating ones.

The objective of this paper is to develop a stochastic mathematical model and to propose a new replenishment policy in a centralized supply chain for deteriorating items. In this model, we consider inventory and transportation costs, as well as the environmental impacts under uncertain demand. Several transportation vehicles producing various greenhouse gas (GHG) levels are considered. The best transportation vehicles and inventory policy are determined by finding a balance between financial and environmental criteria. In this way, we develop a linear mathematical model and present a numerical example from the real world to demonstrate its applicability and effectiveness. We then perform a sensitivity analysis and provide some managerial insights. Finally, more promising directions are suggested for future research.

This paper presents a multi-objective cell formation problem considering alternative process routes and machine utilization with fuzzy demand. Two conflicting objectives include the total cell load variation and sum of the other costs consisting machine cost, inter-cell material handling cost, parts purchasing, operation, maintenance, and reconfiguration of machines cost are to be minimized simultaneously. Moreover, we consider demand in fuzzy condition, because it is more realistic to take into account the inexact and uncertain nature of demand. Due to the complexity of this problem, we develop a scatter search algorithm. Also by using the Taguchi as a robust parameter design method, we tune the effective factors of the developed algorithm on two sizes of benchmark problems that are generated randomly. NSGAII and Scatter Search evaluated and the related results confirm the efficiency and the effectiveness of our proposed Scatter Search provides good output according to some quality...

In this study, we address an Inventory Routing Problem for hazardous and deteriorating pharmaceutical items in a healthcare network. Each hospital's demand is assumed to be deterministic but time varying over a finite planning horizon. Demand can be satisfied through a supply network, either from the main central pharmacy, or from other related hospitals' drugstores by allowing the transshipment option. The medicines deteriorate under a constant rate during the storage period. The proposed model is a bi-objective mixed integer mathematical programming. The first objective aims to minimize the total cost of logistics including ordering, transportation, delivery, pickup, shortage and inventory holding costs. The second objective function attempts to minimize the maximum accident loss during distribution among all periods. To consider the accident loss, for each route, two essential parameters are estimated based on historical data; the occurrence probability and severity index of accident. The model therefore attempts to utilize the best configuration of the routes and transshipment option to satisfy the demand while minimizing the costs and accident loss simultaneously. Several numerical examples are generated and solved by CPLEX and compared with the solutions of an efficient Hybrid Genetic Algorithm (HGA). The results show that the transshipment option not only can be used as a lever to increase the economic supply network performance through saving the routes, but also it can help the system to avoid risky routes.

Recently, 'greenness' has become a very much needed condition in the transportation industry. In this study we develop a 'green', transshipment-enabled model for the Inventory Routing Problem (IRP), in a many-to-one distribution network where demand for each product is realistically assumed to be uncertain. The proposed framework is a bi-objective stochastic programming model. The first objective function aims to minimize the expected value of the supply chain costs including inevitable shortage costs. The second objective function aims to minimize the total quantity of the greenhouse gas (GHG) emission produced by the vehicles and disposed products. We introduce a very practical innovative application of transshipment option to control transportation cost, reduce GHG emissions and absorb the uncertainty. In order to solve the proposed model an efficient hybrid algorithm combining L-shaped method (a sort of decomposition approach for stochastic optimization) and compromise programming (a well-known approach for multi-objective optimization) is proposed. The results show that how companies can make a reasonable tradeoff between the cost and environmental concerns and emphasize the role of transshipment option as a lever to improve both economic and environmental performance and absorb the demand fluctuations.

In this paper, a generic multi-tiered reverse logistic model for waste management is proposed in the presence of stochastic waste generation ratio. Various aspects of a comprehensive waste management network such as recycling and remanufacturing are considered. The carbon footprint's social cost and Green House Gas (GHG) emission are also incorporated in the model. In addition, several separation ratios for each facility are taken into account and unnecessary decision variables are avoided accordingly. The proposed model is applied in a real case study, and several sensitivity analyses are performed which allow the extraction of the managerial insights for optimizing the operational and tactical performance of waste management.