A genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem

This chapter presents a memetic framework for solving the Synchronized and Integrated Two-level Lot Sizing and Scheduling Problem (SITLSP). A set of algorithms from this framework is thoroughly evaluated. The SITLSP is a real-world problem typically found in soft drink plants, but its presence can also be seen in many other multi-level production processes. The SITLSP involves a two-level production process where lot sizing and scheduling decisions have to be made for raw material storage in tanks and soft drink bottling in various production lines. The work presented here extends a previously proposed memetic computing approach that combines a multi-population genetic algorithm with a threshold accepting heuristic. The novelty and its main contribution is the use of tabu search combined with the multi-population genetic algorithm as a method to solve the SITLSP. Two real-world problem sets, both provided by a leading market soft drink company, have been used for the computational experiments. The results show that the memetic algorithms proposed significantly outperform the previously reported solutions used for comparison.

This chapter studies a two-level production planning problem where, on each level, a lot sizing and scheduling problem with parallel machines, capacity constraints and sequence-dependent setup costs and times must be solved. The problem can be found in soft drink companies where the production process involves two interdependent levels with decisions concerning raw material storage and soft drink bottling. Models and solution approaches proposed so far are surveyed and conceptually compared. Two different approaches have been selected to perform a series of computational comparisons: an evolutionary technique comprising a genetic algorithm and its memetic version, and a decomposition and relaxation approach.

This paper proposes two evolutionary approaches as procedures to solve the Synchronized and Integrated Two-Level Lot-Sizing and Scheduling Problem (SITLSP). This problem can be found in some industrial settings, mainly soft drink companies, where the production process involves two interdependent levels with decisions concerning raw material storage and soft drink bottling. The first approach to solve the SITLSP is a Multi-Population Genetic Algorithm (GA) with a hierarchical ternary tree structure for populations. The second approach is a Memetic Algorithm (MA) that extends the GA approach through the inclusion of a local search procedure. The computational study reported reveals that those methods are an effective alternative to solve real-world instances of the SITLSP.

The method developed previously for the determination of bisphenol A (BPA) in liquid infant formula was adapted and validated for determination of BPA in soft drink products. This method was based on solid phase extraction and derivatization with acetic anhydride followed by gas chromatography-mass spectrometry in selected-ion monitoring mode. The average method detection limit was 0.045 microg/L for a 10 mL sample. The average extraction recoveries were 101 and 99.9% obtained with seven different soft drink products spiked with BPA at 0.5 and 2.5 microg/L, respectively. Good repeatability of the method was observed with replicate analyses of seven different soft drinks; relative standard deviations ranged from 1.3 to 6.6%. This method was used to analyze samples of 72 canned soft drink products for BPA. Except for three products from which BPA-d16 could not be recovered at all due to interference of product compositions (e.g., quinine hydrochloride in tonic water), BPA was detected in samples of all the other products at levels ranging from 0.032 to 4.5 microg/L. About 75% of the products had BPA levels of <0.5 microg/L, and 85% of the products had BPA levels of <1 microg/L. Exposure to BPA through consumption of canned soft drink products is low; dietary intake of BPA was estimated at 0.027 microg/kg of body weight/day on the basis of the consumption of one canned soft drink with the highest BPA level (4.5 microg/L) for an adult with a 60 kg body weight, well below the provisional tolerable daily intake of 25 microg/kg of body weight/day established by Health Canada.

Recovery of Retinol in Soft-Drink Beverages Made with Fortified Unrefined and Refined Sugar: Implications for National Fortification Programs

Product Differentiation and Mergers in the Carbonated Soft Drink Industry

I simulate the competitive impact of several soft drink mergers from the 1980s on equilibrium prices and quantities. An unusual feature of soft drink demand is that, at the individual purchase level, households regularly select a variety of soft drink products. Specifically, on a given trip households may select multiple soft drink products and multiple units of each. A concern is that using a standard discrete choice model that assumes single unit purchases may understate the price elasticity of demand. To model the sophisticated choice behavior generating this multiple discreteness, I use a household‐level scanner data set. Market demand is then computed by aggregating the household estimates. Combining the aggregate demand estimates with a model of static oligopoly, I then run the merger simulations. Despite moderate price increases, I find substantial welfare losses from the proposed merger between Coca‐Cola and Dr. Pepper. I also find large price increases and corresponding welfare losses from the proposed merger between Pepsi and 7 UP and, more notably, between Coca‐Cola and Pepsi.

SUMMARY Traditionally, upon solution, independent demand inventory models result in the determination of a closed form for the economic lot size. Generally, this is obtained from the result that holding costs and setup costs are constant and equal at the optimum. However, the experience of the Japanese indicates that this need not be the case. Specifically, setup cost may be reduced by investing in reduced setup times resulting in smaller lot sizes and increased flexibility. Various authors have investigated the impact of such investment on classical lot sizing formulas which has resulted in the derivation of modified relationships. A common assumption of this research has been that demand and lead time are deterministic. This paper extends this previous work by considering the more realistic case of investing in decreasing setup costs where lead time is stochastic. Closed form relationships for optimal lot size, optimal setup cost, optimal total cost, etc. are derived. Numerical results are presented for...

This paper derives a mathematical integrated production inventory model of single vendor and buyer to investigate the effects of capital investment in setup cost of the vendor and order processing cost of the buyer on the decision variables of the model. Main purpose of this study is to minimize the integrated total cost of the vendor and buyer by optimizing the delivery lot size, lead-time, setup and order processing cost and number of deliveries per order simultaneously while lead-time demand follows normal distribution and unknown distribution, i.e., distribution free case. This model is analyzed by calculus method and justified with appropriate numerical illustrations. Numerical results show that significant savings can be achieved at optimal investments in reducing lead time, ordering cost and setup cost. Finally, sensitivity analysis of the key parameters is carried out and some managerial implications are also included.

A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality

The petroleum industry's extensive use of LP-based operations planning models is well known and often cited as a major triumph of applied operations research. However, few people outside of the petroleum industry realize the degree to which mathematical programming concepts and techniques have penetrated all facets of the oil business from strategic planning through process control. Mathematical programming techniques have been developed and applied, decade by decade, from the 1940s to the present and hold even greater potential for the 1990s.

The objective of this study was to establish the influence of customer perception on the relationship between green marketing practices and Customer Satisfaction in the soft drink industry in Kenya. This study was anchored on consumer behavior theory and was guided by positivistic philosophy. The study adopted a descriptive cross-sectional research design to determine how green marketing practices affect customer satisfaction in soft drink companies in Nairobi County, Kenya and how the relationship is influenced by customer perception. The study targeted a sample of 180 trade customers but, the researcher managed to successfully collect data from 130 of the trade customers. The study adopted Cronbach’s Alpha. Descriptive statistics (mean scores and measures of dispersion) and inferential statistics (correlation, analysis of variance and regression analysis) was conducted to determine the expected relationships between green marketing practices and customer satisfaction. R 2 increased from .121 to .223 when customer perceptions were included (.223-.121=.102). The results imply that customer perception explain an additional 10.2% of the variation in customer satisfaction. The results indicate that the effect of green marketing practices on customer satisfaction in the final step of the analysis (path c) is significant at 0.05 significance level. The regression coefficient reduced from β=.389 in path “a” to β=.349 in path c’ and was statistically significant at 0.05 level of significance. Based on the overall study results obtained from the tests of the study hypotheses, it is concluded that customer perception had a statistically insignificant mediating effect on the relationship between green marketing practices and customer satisfaction Keywords: Customer Perception, Green Marketing Practices, Customer Satisfaction, Soft Drink Industry, Nairobi Kenya DOI : 10.7176/EJBM/11-3-17

Minimum mileage and maximization of utilities will help the product distribution process to be optimal. Soft drink companies in the city of Medan have problems in distributing products to consumers who are not on time. This is related to the lack of regulation of product distribution channels by the company. Therefore this research was conducted to optimize product distribution routes in order to minimize distribution costs. With the Traveling Salesman Problem (TSP) method, it will combine several closest distribution routes and improve the route sequence. Then the development of sub-routes that are formed from the travel time and utility of transportation equipment used. Formation of sub-routes is still done manually while increasing the order of sub-routes is done by using the Software Quant System (QS) version 3.0. and produce minimum mileage on each sub-route. In planning product distribution, 6 proposed sub-routes are formed in one week and are served by 1 transportation car with a capacity of 220 crates. The proposed sub-route is 171 km shorter than the available sub-routes, thus shortening the travel time of 302 minutes to transport the product.

School Food Policies and Practices: A State-Wide Survey of Secondary School Principals

In the last 20 years, evolutionary algorithms (EAs) have shown to be an effective method to solve multiobjective optimization problems (MOPs). Due to their population-based nature, multiobjective EAs (MOEAs) are able to generate a set of tradeoff solutions (called nondominated solutions) in a single algorithmic execution instead of having to perform a series of independent executions, as normally done with mathematical programming techniques. Additionally, MOEAs can be successfully applied to problems with difficult features such as multifrontality, discontinuity and disjoint feasible regions, among others. On the other hand, coevolutionary algorithms (CAs) are extensions of traditional EAs which have become subject of numerous studies in the last few years, particularly for dealing with large-scale global optimization problems. CAs have also been applied to the solution of MOPs, motivating the development of new algorithmic and analytical formulations that have advanced the state-of-the-art in CAs research, while simultaneously opening a new research path within MOEAs. This paper presents a critical review of the most representative coevolutionary MOEAs (CMOEAs) that have been reported in the specialized literature. This survey includes a taxonomy of approaches together with a brief description of their main features. In the final part of this paper, we also identify what we believe to be promising areas of future research in the field of CMOEAs.