Fuzzy multi-objective optimization for multi-site integrated production and distribution planning in two echelon supply chain

The aim of every manufacturing organization is to fulfil the demand of their customers at minimum total cost or at maximum profit. To fulfil the demand of geographically dispersed customers, manufacturing organizations need to produce products at multiple manufacturing sites located close to the customer. Although planning function in such a scenario becomes very complex, this multi-site environment has been found to improve efficiency and provide better services to customers. It involves interlinked decisions involving procurement, production and distribution at different plants and distribution sites. Production and distribution planning are very important aspects of planning in multi-site manufacturing. To compete in this globalized market, enterprises should focus on optimizing and integrating production and distribution functions simultaneously. The authors in the research work present an integrated production and distribution planning problem for a two-level supply chain consisting of multiple manufacturing sites serving multiple selling locations. The problem is formulated as a multi-objective mixed-integer programming model considering three important aspects of production and distribution planning, viz. set-up, backorder and transportation capacity. Total cost, delivery time and backlog level are the three conflicting objectives that need to be minimized. The proposed multi-objective mathematical problem is solved using pre-emptive goal programming method. The performance of the proposed model is illustrated through an example problem instance. Further analysis is conducted to visualize the effect of changing priority level on objective function and deviation variable values.

Increasing demand for containerization compels container terminals to improve their performance. Uncoordinated scheduling of operations is one of the main factors accounting for poor performance at automated container terminals (ACTs). To increase land utilization efficiency and lower operational times, a new storage system called the split-platform automated storage/retrieval system (SP-AS/RS) has been introduced for temporary storage of containers. This paper describes a multi-objective mixed-integer programming (MIP) model that is based on a combination of multiple interacting sub-tasks. It is aimed at optimizing the integrated scheduling of handling and storage operations in ACTs. The MIP model objective function is to minimize delays in the loading/unloading tasks of the cranes and the travel time of vehicles and platforms in the SP-AS/RS. At the same time, a simulated annealing algorithm (SAA) that provides near-optimal solutions for the problem in a reasonable computation time is appraised. The results of this study show that the objective function of the MIP model is, on average, 58 % lower than that of the non-integrated scheduling method. On the other hand, the best objective function values obtained by the SAA indicate only a 3.7 % disadvantage in comparison with optimal values determined by the MIP model, demonstrating that the SAA is able to provide near-optimal solutions for the integrated scheduling of handling and storage operations.

The vendor selection and order quantity allocation problem with fuzzy demand and price discount is formulated as a multi-objective mixed-integer fuzzy programming model. The proposed model has the following characteristics: deterministic and fuzz constraints, and the assumption of fuzzy demand and price discount are expressed by using constraint equations. According to the special features of the model, appropriate solution strategy is proposed. It involves three steps: (1) membership function for every fuzzy objective and constraint is set up; (2) by means of max-min operator, the multi-objective mixed-integer fuzzy programming model is converted into several equivalent single-objective linear programming model; (3) the optimal solution for order quantity allocation problem is derived based on two-phase approach. An application example was also given for testing the feasibility and effectiveness of the proposed method

Sustainable supply chain networks have attracted considerable attention in recent years as a means of dealing with a broad range of environmental and social issues. This paper reports a multi-objective mixed-integer linear programming (MILP) model for use in the design of a sustainable closed loop supply chain network under uncertain conditions. The proposed model aims to minimize total cost, optimize environmental impacts of establishment of facilities, processing and transportation between each level as well as social impacts including customer satisfaction. Due to changes in business environment the uncertainty existed in the research problem, in this paper the chance constrained fuzzy programming approach applied to cope with uncertainties in parameter of the proposed model. Then the proposed multi-objective model solves as single-objective model using LP-metric method.

Integrating two interconnected processes and controlling conflicting goals of a production and distribution problem is necessary for better planning. The aim of this integration is to allocate the limited available resources to produce the products over the time periods and ensure economical dispatching to distribution centres. In a responsive and competitive market, production and distribution planning modules is considered here within the framework of satisfaction level of decision maker. Triangular fuzzy numbers and the concept of minimum accepted level are employed to formulate the problem. Minimum operator and weighted average operator method is used to aggregate all fuzzy set and solved by three algorithms and goal programming method. The proposed approach aims to formulate a fuzzy multi-objective linear programming model to simultaneously minimise the total production cost, distribution cost and delivery time for multi-product and multi-time period under multiple uncertainty. AHP is used to rank multiple objectives and solved with MATLAB.

A multi-objective optimization approach to balancing cost and traceability in bulk grain handling

Efficient human resource deployment is one of the key aspects of road traffic management for maintaining the lifelines of any metropolitan city. The problem becomes relevant when collaboration between human resources with different skills in day-to-day operations is necessary to maintain public and commercial transport, manage various social events and emergency situations, and hence reduce congestion, injuries, emissions, etc. This study proposes a two-phase fuzzy multi-objective binary programming model for optimal allocation of five different categories of human resources to minimize the overall operational cost, maximize the allocation to accident-prone road segments, minimize the number of volunteer personnel and maximize the direct contact to reduce emissions and road traffic violations, simultaneously. A binary programming model is formulated to provide an efficient individual manpower allocation schedule for multiple road segments at different shifts. A case study is proposed for model evaluation and to derive managerial implications. The proposed model can be used to draw insights into human resource allocation planning in traffic management to reduce road traffic congestion, injuries and vehicular emissions.

Automobile service enterprise location determination is an interesting and important issue in the logistic research field. To deal with this uncertainty, some researchers addressed the fuzzy time and cost issues for locating an automobile service enterprise. However, a decision maker hopes to minimize the travel time of customers meanwhile minimizing their costs when locating a facility. They prefer arriving at the destination within the specific time period and cost. To handle this issue in a more practical manner, by taking the vehicle inspection station as a typical automobile service enterprise example, the authors proposed a fuzzy change-constrained multi-objective optimization approach to handle it. Moreover, some region-wide constraints can greatly influence FLA and travel time is also has indeterminate due to the influence of some unpredictable factors. To handle them, they developed a fuzzy chance-constrained multi-objective programming model to determine its location with regional constraints, fuzzy inspection demands and varying velocities. A hybrid algorithm integrating fuzzy simulation, neural networks (NN), and genetic algorithms (GA), namely a random weight based multi-objective NN-GA, is proposed to solve the proposed models. A numerical example is provided to illustrate the effectiveness of the proposed model and the proposed algorithm.

A fuzzy multi-objective programming model for the delivery and distribution of humanitarian relief materials

Toward sustainable microgrids with blockchain technology-based peer-to-peer energy trading mechanism: A fuzzy meta-heuristic approach

Purpose. Radiation delivered over ultra-short timescales (‘FLASH’ radiotherapy) leads to a reduction in normal tissue toxicities for a range of tissues in the preclinical setting. Experiments have shown this reduction occurs for total delivery times less than a ‘critical’ time that varies by two orders of magnitude between brain (∼0.3 s) and skin (⪆10 s), and three orders of magnitude across different bowel experiments, from ∼0.01 to ⪆(1–10) s. Understanding the factors responsible for this broad variation may be important for translation of FLASH into the clinic and understanding the mechanisms behind FLASH. Methods. Assuming radiolytic oxygen depletion (ROD) to be the primary driver of FLASH effects, oxygen diffusion, consumption, and ROD were evaluated numerically for simulated tissues with pseudorandom vasculatures for a range of radiation delivery times, capillary densities, and oxygen consumption rates (OCR’s). The resulting time-dependent oxygen partial pressure distribution histograms were used to estimate cell survival in these tissues using the linear quadratic model, modified to incorporate oxygen-enhancement ratio effects. Results. Independent of the capillary density, there was a substantial increase in predicted cell survival when the total delivery time was less than the capillary oxygen tension (mmHg) divided by the OCR (expressed in units of mmHg/s), setting the critical delivery time for FLASH in simulated tissues. Using literature OCR values for different normal tissues, the predicted range of critical delivery times agreed well with experimental values for skin and brain and, modifying our model to allow for fluctuating perfusion, bowel. Conclusions. The broad three-orders-of-magnitude variation in critical irradiation delivery times observed in in vivo preclinical experiments can be accounted for by the ROD hypothesis and differences in the OCR amongst simulated normal tissues. Characterization of these may help guide future experiments and open the door to optimized tissue-specific clinical protocols.

Reducing fuel consumption by transportation fleet in a supply chain, reduces transportation costs and consequently, the product final cost. Moreover, it reduces environmental pollution, and in some cases, it helps governments constitute less subsidies for fuels. In this paper, a supply chain scheduling is studied, with the two objective functions of minimizing the total fuel consumption, and the total order delivery time. After presenting the mathematical model of the problem, a genetic algorithm, named Social Genetic Algorithm (SGA) is proposed to solve it. The proposed algorithm helps decision makers determine the allocation of orders to the suppliers and vehicles and production and transportation scheduling to minimize total order delivery time and fuel consumption. In order for SGA performance evaluation, its results are compared with another genetic algorithm in the literature and optimal solution. Finally, a sensitivity analysis is performed on SGA. The results of comparisons also show the high performance of SGA. Moreover, by increasing the number of suppliers and vehicles and decreasing the number of orders, the value of the objective function is reduced.

In pragmatic project management cases, many complex resources and large-scale related activities are involved. Moreover, when situations are under the conflict and incommensurate of time and cost, it becomes more difficult for managers to make their decisions. Hence, we construct fuzzy multi-objective programming model from CPM technique. Through emphasizing the selectable flexibility among the feasible projects, we describe the decision problem brought from uncertainty and complex in project. In our research, taking the conflict and incommensurate of time and cost as two major factors, we measure weight and priority to denote the importance degree between the objectives. Adopting Tiwari et al., we also develop the result when trading off the decision vs. the vague environment of time and cost and the past' single objective' method has become the exceptional case of multi-objective models. Secondly, we broaden the consumed time and cost of activity events when assumptions are certain to merge the practical situation. We applied fuzzy number to express estimate time and cost. It based on Lee and Li method to solve this problem. Finally, we employ numerical example to explain it with LINGO package to calculate.

Acupuncture treatment in the final weeks of pregnancy has been claimed to shorten the duration of labor in primiparous women. In the present study, the length of the various phases of labor was calculated for 56 primiparous women who were repeatedly treated with manual acupuncture during the month prior to parturition. In vaginally delivering women, the average lengths of the latent and active phase and the second stage of labor were 4.1, 3.4 and 1.4 h, respectively. In a nontreated control group of 112 primiparous women, the corresponding durations were 4.4, 3.5 and 1.1 h. Acupuncture treatment before delivery did not shorten the delivery time. Instead, acupuncture seemed to lengthen pregnancy and to prolong labor, for there was a positive correlation between the number of acupuncture treatments given and the length of gestation, second-stage labor and total delivery time. Based on the results of the present study, it appears possible that the effect of acupuncture is the opposite to that suggested by others, i.e. it lengthens the pregnancy as well as delivery time and does not reduce the duration of labor.

In order to improve the distribution system for the Nordic countries the BASF AG considered 13 alternative scenarios to the existing system. These involved the construction of warehouses at various locations. For every scenario the transportation, storage, and handling cost incurred was to be as low as possible, where restrictions on the delivery time were given. The scenarios were evaluated according to (minimal) total cost and weighted average delivery time. The results led to a restriction to only three cases involving only one new warehouse each. For these, a more accurate model for the cost was developed and evaluated, yielding results similar to a simple linear model. Since there were no clear preferences between cost and delivery time, the final decision was chosen to represent a compromise between the two criteria. Copyright © 1999 John Wiley & Sons, Ltd.