The Toyota production system (TPS) or lean has long been regarded as a powerful approach for managing manufacturing factories. However, in the early 1990s, the TPS was found not to work when it was applied to Japanese electronics companies. TPS is fit for a stable, but not volatile, business environment such as that which the electronics industry belongs. This volatile environment can be described as one with short product life cycles, uncertain product types, and fluctuating production volumes (sometimes mass, sometimes batch, and sometimes very small volumes). Seru, a new production organization, was developed to cope with this environment. Many leading global companies such as Samsung, Sony, Canon, Panasonic, LG, and Fujitsu have adopted seru. Seru overcame a lot of disadvantages inherent in TPS and brought amazing benefits to seru users. For example: 1) Seru requires a much smaller workforce, 2) It can greatly reduce space requirements, and 3) It can reduce lead time, setup time, WIP inventories, finished-product inventories, and cost. This article introduces seruâ€™s history and defines various seru types. A seru pyramid is constructed to compare seru with the TPS. A JIT organization system is introduced. The authors show why applying it can bring great productivity, efficiency, and flexibility to a production organization.

Seru: The Organizational Extension of JIT for a Super-Talent Factory

Multi-choice stochastic transportation problem involving extreme value distribution

This paper explores the study of multi-choice multi-objective transportation problem (MCMTP) under the light of conic scalarizing function. MCMTP is a multi-objective transportation problem (MOTP) where the parameters such as cost, demand and supply are treated as multi-choice parameters. A general transformation procedure using binary variables is illustrated to reduce MCMTP into MOTP. Most of the MOTPs are solved by goal programming (GP) approach, but the solution of MOTP may not be satisfied all times by the decision maker when the objective functions of the proposed problem contains interval-valued aspiration levels. To overcome this difficulty, here we propose the approaches of revised multi-choice goal programming (RMCGP) and conic scalarizing function into the MOTP, and then we compare among the solutions. Two numerical examples are presented to show the feasibility and usefulness of our paper. The paper ends with a conclusion and an outlook on future studies.

Conic scalarization approach to solve multi-choice multi-objective transportation problem with interval goal

Uncertain goal programming models for bicriteria solid transportation problem

Multi-Objective Goal Programming is applied to solve problems in many application areas of real-life decision making problems. We formulate the mathematical model of Two-Stage Multi-Objective Transportation Problem (MOTP) where we design the feasibility space based on the selection of goal values. Considering the uncertainty in real-life situations, we incorporate grey parameters for supply and demands into the Two-Stage MOTP, and a procedure is applied to reduce the grey numbers into real numbers. Thereafter, we present a solution procedure to the proposed problem by introducing an algorithm and using the approach of Revised Multi-Choice Goal Programming. In the proposed algorithm, we introduce a utility function for selecting the goals of the objective functions. A numerical example is encountered to justify the reality and feasibility of our proposed study. Finally, the paper ends with a conclusion and an outlook to future investigations of the study.

Multi-objective two-stage grey transportation problem using utility function with goals

This paper proposes a new approach to analyze the solid transportation problem (STP). This new approach considers the multi-choice programming into the cost coefficients of objective function and stochastic programming which is incorporated in three constraints namely sources, destinations and capacities constraints followed by Cauchy's distribution for solid transportation problem. The multi-choice programming and stochastic programming are combined into solid transportation problem and this new problem is called multi-choice stochastic solid transportation problem (MCSSTP). The solution concepts behind the MCSSTP are based on a new transformation technique which will select an appropriate choice from a set of multi-choice which optimizes the objective function. The stochastic constraints of STP convert into deterministic constraints by stochastic programming approach. Finally, the authors have constructed a non-linear programming problem for MCSSTP and have derived an optimal solution of the specified problem. A realistic example on STP is considered to illustrate the methodology.

Solving Solid Transportation Problem with Multi-Choice Cost and Stochastic Supply and Demand

This paper is concerned with the use of fuzzy programming technique to the objec- tive function, and the stochastic method have been applied for the randomness of sources and destination parameters in inequality type of constraints of multi-objective stochastic unbalanced transportation problem. In this paper, we focus on the solution procedure of the specified prob- lems where the objective functions are minimization type (i.e. non-commensurable and conflict- ing in nature) and the supplies and demands are replaced by the random variables. By using the chance constrained technique we first converted the multi-objective stochastic transportation problem multi-objective into equivalent deterministic transportation problem. By introducing the concept of linear membership function of fuzzy programming, multi-objective deterministic transportation problem is to convert into single objective deterministic transportation problem

/pdf/stochastic-based-on-multi-objective-transportation-problems-5e75bflgad.pdf

Stochastic Based on Multi-objective Transportation Problems Involving Normal Randomness

This paper presents an application to the multi-objective stochastic transportation problem in fuzzy environment. In this paper, we focus on our attention to multiobjective stochastic transportation problem involving an inequality type of constraints in which all parameters ( supply and demand ) are log-normal random variable and the objectives are non-commensurable and conflicting in nature. At first we convert the proposed multi-objective linear stochastic transportation problem into an equivalent deterministic problem under chance constrained programming technique framework. Then fuzzy programming technique is applied to solve this problem and we obtained the compromise solution. Lastly a numerical example is provided for the sake of illustrate the methodology.

http://inet.vidyasagar.ac.in:8080/jspui/bitstream/123456789/803/2/Art7.pdf

Deshabrata Roy Mahapatra

Papers

Multi-choice stochastic transportation problem involving extreme value distribution

Solving Solid Transportation Problem with Multi-Choice Cost and Stochastic Supply and Demand

Stochastic Based on Multi-objective Transportation Problems Involving Normal Randomness

Multi-objective Stochastic Transportation Problem Involving Log-normal