A genetic algorithm for a bicriteria supplier selection problem

Abstract In this paper, we discuss the problem of selecting suppliers for an organisation, where a number of suppliers have made price offers for supply of items, but have limited capacity. Selecting the cheapest combination of suppliers is a straightforward matter, but purchasers often have a dual goal of lowering the number of suppliers they deal with. This second goal makes this issue a bicriteria problem -minimisation of cost and minimisation of the number of suppliers. We present a mixed integer programming (MIP) model for this scenario. Quality and delivery performance are modelled as constraints. Smaller instances of this model may be solved using an MIP solver, but large instances will require a heuristic. We present a multipopulation genetic algorithm for generating Pareto-optimal solutions of the problem. The performance of this algorithm is compared against MIP solutions and Monte Carlo solutions

Multiobjective optimization as a decision aid for managing build-to-order supply chains

This paper provides an overview of multiobjective optimization (MOO) as a decision aid in build-to-order supply chains (BTO-SC). The main features of BTO-SCs are discussed along with capabilities of MOO to enhance decision making at different points along the chain. Key decision points across a typical BTO-SC are identified and potential applications of MOO are discussed. A sample application is presented and future avenues for further research highlighted

An Integration of Rank Order Centroid, Modified Analytical Hierarchy Process and 0-1 Integer Programming in Solving A Facility Location Problem

Hadhramout province is the major producer of dates in The Republic of Yemen. Despite producing substantial quantity and quality of dates, the business losses are still high. The situation worsens with the widespread of the black market activities. Recently, the Yemeni government has issued an agreement stating the importance of building a date palm packaging factory as a resolution to the problems. Hence, this study aims to identify the best location for a date palm packaging factory among the seven districts which produce most of the date palm supplies in Hadhramout. The selection was based on eleven criteria identified by several representatives from the farmers and the local councils. These criteria were market growth, proximity to the markets, proximity to the raw materials, labor, labor climate, suppliers, community, transportation cost, environmental factors, production cost, and factory set up cost. The level of importance and the respective weight of each criterion were calculated using two different approaches, namely, Analytic Hierarchy Process (AHP) and Rank Order Centroid (ROC). In applying AHP, a slight modification was made in the pairwise comparison exercises that eliminated the inconsistency problem faced by the standard AHP pairwise comparison procedure. Likewise, in applying ROC, a normalization technique was proposed to tackle the problem of assigning weights to criteria having the same priority level, which was neither clarified nor available in the standard ROC. Both proposed techniques revealed that suppliers were the most important criterion, while community was regarded to be the least important criterion in deciding the final location for the date palm factory. Combining the criteria weights together with several hard and soft constraints that were required to be satisfied by the location, the final location was determined using three different mathematical models, namely, the ROC combined with 0-1 integer programming model, the AHP combined with 0-1 integer programming model, and the mean of ROC and AHP combined with 0-1 integer programming model. The three models produced the same result; Doean was the best location. The result of this study, if implemented, would hopefully help the Yemeni government in their effort to improve the production as well as the management of the date palm tree in Hadhramout

Bi-objective optimization of the tactical allocation of job types to machines: mathematical modeling, theoretical analysis, and numerical tests

We introduce a tactical resource allocation model for a large aerospace engine system manufacturer aimed at long-term production planning. Our model identifies the routings a product takes through the factory, and which machines should be qualified for a balanced resource loading, to reduce product lead times. We prove some important mathematical properties of the model that are used to develop a heuristic providing a good initial feasible solution. We propose a tailored approach for our class of problems combining two well-known criterion space search algorithms, the bi-directional ε-constraint method and the augmented weighted Tchebycheff method. A computational investigation comparing solution times for several solution methods is presented for 60 numerical\ua0instances

Integrated model for Multi-criteria Supplier Selection and Order Allocation Problem

Abstract. This paper discusses an integrated model of selecting suppliers and order allocation for a company that wishes to decide the quantity to be ordered from each supplier on the basis of some qualitative criteria. Since each supplier may have a different performance with respect to these criteria, an integration of analytical hierarchy process and linear program model is proposed to solve the problem in two stages. In the first stage, suppliers are evaluated based on qualitative criteria to consider both tangible and intangible factors in choosing the best suppliers. The output of this stage is the final score of each supplier. In the second stage, a linear programming model is proposed to placing the optimum order quantities among them such that the total final scores of suppliers become maximum. The mathematical programming model is validated through numerical analysis, and the computation result shows that the model is effective and applicable. Keywords: supplier selection, order allocation, multiple criteria decision making, analytical hierarchy process, linear programming

Multi-objective inventory routing with uncertain demand using population-based metaheuristics

This article studies a tri-objective formulation of the inventory routing problem, extending the recently studied bi-objective formulation. As compared to distance cost and inventory cost, which were discussed in previous work, it also considers stockout cost as a third objective. Demand is modeled as a Poisson random variable. State-of-the-art evolutionary multi-objective optimization algorithms and a new method based on swarm intelligence are used to compute approximation of the 3-D Pareto front. A benchmark previously used in bi-objective inventory routing is extended by incorporating a stochastic demand model with an expected value that equals the average demand of the original benchmark. The results provide insights into the shape of the optimal trade-off surface. Based on this the dependences between different objectives are clarified and discussed. Moreover, the performances of the four different algorithmic methods are compared and due to the consistency in the results, it can be concluded that a near optimal approximation to the Pareto front can be found for problems of practically relevant size.Algorithms and the Foundations of Software technolog

Comparative Study Between Mixed Model Assembly Line And Flexible Assembly Line Based On Cost Minimization Approach [TS167. F278 2008 f rb].

Mixed Model Assembly Line are widely used to produce different models as per customer's demands. Barisan Penggabungan Model Campuran digunakan secara meluas untuk menghasilkan model-model yang berbeza mengikut kehendak pelanggan

Solving the Fixed Charge Transportation Problem by New Heuristic Approach

The fixed charge transportation problem (FCTP) is a deployment of the classical transportation problem in which a fixed cost is incurred, independent of the amount transported, along with a variable cost that is proportional to the amount shipped. Since the problem is considered as an NP-hard, the computational time grows exponentially as the size of the problem increases. In this paper, we propose a new heuristic along with well-known metaheuristic like Genetic algorithm (GA), simulated annealing (SA) and recently developed one, Keshtel algorithm (KA) to solve the FCTP. Contrary to previous works, we develop a simple and strong heuristic according to the nature of the problem and compare the result with metaheuristics. In addition, since the researchers recently used the priority-based representation to encode the transportation graphs and achieved very good results, we consider this representation in metaheuristics and compare the results with the proposed heuristic. Furthermore, we apply the Taguchi experimental design method to set the proper values of algorithms in order to improve their performances. Finally, computational results of heuristic and metaheuristics with different encoding approaches, both in terms of the solution quality and computation time, are studied in different problem sizes

SIMULATION AND OPTIMIZATION OF A CROSSDOCKING OPERATION IN A JUST-IN-TIME ENVIRONMENT

In an ideal Just-in-Time (JIT) production environment, parts should be delivered to the workstationsat the exact time they are needed and in the exact quantity required. In reality, formost components/subassemblies this is neither practical nor economical. In this study, thematerial flow of the crossdocking operation at the Toyota Motor Manufacturing plant inGeorgetown, KY (TMMK) is simulated and analyzed.At the Georgetown plant between 80 and 120 trucks are unloaded every day, with approximately1300 different parts being handled in the crossdocking area. The crossdocking areaconsists of 12 lanes, each lane corresponding to one section of the assembly line. Whereassome pallets contain parts designated for only one lane, other parts are delivered in such smallquantities that they arrive as mixed pallets. These pallets have to be sorted/crossdocked intothe proper lanes before they can be delivered to the workstations at the assembly line. Thisprocedure is both time consuming and costly.In this study, the present layout of the crossdocking area at Toyota and a layout proposed byToyota are compared via simulation with three newly designed layouts. The simulation modelswill test the influence of two different volumes of incoming quantities, the actual volumeas it is now and one of 50% reduced volume. The models will also examine the effects ofcrossdocking on the performance of the system, simulating three different percentage levelsof pallets that have to be crossdocked.The objectives of the initial study are twofold. First, simulations of the current system,based on data provided by Toyota, will give insight into the dynamic behavior and the materialflow of the existing arrangement. These simulations will simultaneously serve to validateour modeling techniques. The second objective is to reduce the travel distances in the crossdockingarea; this will reduce the workload of the team members and decrease the lead timefrom unloading of the truck to delivery to the assembly line. In the second phase of theproject, the design will be further optimized. Starting with the best layouts from the simulationresults, the lanes will be rearranged using a genetic algorithm to allow the lanes withthe most crossdocking traffic to be closest together.The different crossdocking quantities and percentages of crossdocking pallets in the simulationsallow a generalization of the study and the development of guidelines for layouts ofother types of crossdocking operations. The simulation and optimization can be used as abasis for further studies of material flow in JIT and/or crossdocking environments

Comparative Study Between Mixed Model Assembly Line And Flexible Assembly Line Based On Cost Minimization Approach [TS167. F278 2008 f rb].

Mixed Model Assembly Line are widely used to produce different models as per customer's demands. Barisan Penggabungan Model Campuran digunakan secara meluas untuk menghasilkan model-model yang berbeza mengikut kehendak pelanggan